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Prioritizing Species for Conservation Planning
CONVENORS: Caroline Lees, Onnie Byers, Phil Miller
AIM: To explore the need for and potential solutions to, prioritizing species for conservation planning
within the SSC.
BACKGROUND: The IUCN’s Species Survival Commission recently launched a new initiative aimed at
increasing its participation and effectiveness in species conservation through species conservation
planning. Of the 85,604 species assessed through the IUCN Global Red List, 24,307 are considered
threatened with extinction. Resources are finite and decisions will need to be taken about which of
these species to focus on, or at least which to focus on first. A number of initiatives, including some led
by IUCN member countries and organizations, have considered the issue of species prioritization for
conservation attention and have developed their own approaches. A sample is described in Table 1. We
can learn much from these initiatives. In particular:
1) That there is no universally “right” outcome of species prioritization for conservation.
Different prioritization goals and contexts will necessarily give rise to different priorities – that
is, different priorities can be “right” for different circumstances.
2) That despite the necessary subjectivity embedded within specific prioritization schemes, it is
possible to design systematic approaches that are transparent about where this subjectivity lies.
3) That those prioritizing species for conservation attention, though working in different
contexts and towards different goals, will often cover some of the same ground and draw the
same conclusions about what is important.
4) That where prioritization criteria require the de novo assembly or analysis of large amounts
of data, the use of those criteria is likely to be limited to well-understood taxa.
5) That developing from scratch a prioritization scheme acceptable to a large group of
stakeholders can take much time, energy and resources.
In this workshop we will explore the species prioritization needs of the SSC community in the area of
conservation planning. We will invite participants to share their experiences of developing or
implementing prioritization schemes and use this as a basis for discussing how we might move forward
with one or more tools to assist prioritization for use within the SSC, and potentially beyond.
PROCESS:
Presentations:
• Summary of different approaches to prioritizing species for conservation attention.
Discussion:
• Prioritization needs: what kinds of species planning prioritization problems are there, or
might there be within the SSC?
• Pros and cons of different schemes: from participants’ experiences of developing or
implementing species prioritization schemes, what has or is working well and why? What
has not and why? What were the biggest challenges? What else can we learn?
• For the prioritization needs identified, are existing tools adequate? If not, what kinds of new
tools might be most useful? Is an expert system an option? Would written guidelines be a
better approach?
Some of the results of this session will be used to inform the subsequent workshop on multi-species
planning.
PREPARATION: Participants are asked to familiarize themselves with Mace et al., 2007 and to come
equipped with details and insights into any species prioritization schemes or tools with which they are
familiar.
Table 1. Example schemes for prioritizing species for conservation attention
Note that all of the schemes exemplified here use the IUCN Red List or equivalent as a criterion, which
automatically excludes the many species not yet assessed.
Overarching goal List of alternatives Criteria for selection/scoring Resulting priorities
Initiative: Alliance for Zero Extinction (AZE) (88 NGOs. National Alliances now also exist.) See
http://www.zeroextinction.org To defend against the most predictable species losses.
All species for which endangerment and distribution are known.
Endangered or Critically Endangered (IUCN) AND restricted to a single remaining site
920 species prioritised to date - mammals, birds, amphibians, reptiles, conifers, and reef-building corals.
Asian Species Action Partnership (ASAP) http://www.speciesonthebrink.org/
Reversing declines in the wild of Asian species on the brink of extinction.
Southeast Asian species
Critically Endangered, freshwater and terrestrial vertebrates, occurring regularly in Southeast Asia
174 species
Initiative: Evolutionarily Distinct and Globally Endangered (EDGE) (Zoological Society of London) (see Isaac et al., 2007)
To maximise conservation of phylogenetic diversity.
All species for which phylogenetic uniqueness has been assessed: mammals, amphibians, corals and birds.
Score = Evolutionary Distinctiveness (ED) X Global Endangerment (IUCN). EDGE species have a greater than average score (Isaac et al., 2007)
Portfolio approach includes the top 100 scoring species in each of the major taxonomic groups considered
Initiative: Method for the Assessment of Priorities for International Species Conservation (MAPISCo) To identify species for which targeted conservation action would have the broadest co-benefits for other species, habitats, wider ecosystems, and ecosystem
Species in the IUCN Red List database for which sufficient data exist to allow assessment against
Ability to contribute to: (1) habitat and area conservation (2) sustainable harvesting of
?
Overarching goal List of alternatives Criteria for selection/scoring Resulting priorities
services. the criteria (?). fish, invertebrates and aquatic plants, (3) conservation of genetic diversity of wild relatives of cultivated plants and domesticated animals, (4) protection of the provisioning of ecosystem services (5) the prevention of species extinctions.
Initiative: National prioritisation scheme for conservation action planning (New Zealand Dept. of Conservation) (NZ DOC) (see Joseph et al., 2009)
To optimise allocation of conservation planning resources towards the goal of ensuring the persistence of all New Zealand species somewhere.
All species native to New Zealand.
Assessed (using NZ RL-equivalent) as conservation dependent OR as threatened and declining, with threats understood and conservation action considered feasible.
≈700 species prioritised for management planning out of ≈10,000 assessed.
READING:
Isaac N.J., Turvey S.T., Collen B., Waterman C., Baillie J.E. 2007. Mammals on the EDGE: Conservation
Priorities Based on Threat and Phylogeny. PLoS ONE 2(3): e296.
https://doi.org/10.1371/journal.pone.0000296
Joseph, L. N., R. F. Maloney, J. E. M. Watson, and H. P. Possingham. 2011. Securing nonflagship species
from extinction. Conservation Letters 4:324-325.
Mace, G.M., Possingham, H.P., and Leader-Williams, N. 2007. Prioritizing Choices in Conservation. In:
MacDonald, D.W. and Service, K. (Eds) Key Topics in Conservation Biology. Blackwell Publishing Ltd.
MAPISCo Project Team (2013) Method for the assessment of priorities for international species
conservation. Newcastle University, Newcastle upon Tyne, UK.
http://www.zeroextinction.org
Mammals on the EDGE: Conservation Priorities Based onThreat and PhylogenyNick J. B. Isaac*, Samuel T. Turvey, Ben Collen, Carly Waterman, Jonathan E. M. Baillie
Institute of Zoology, Zoological Society of London, London, United Kingdom
Conservation priority setting based on phylogenetic diversity has frequently been proposed but rarely implemented. Here, wedefine a simple index that measures the contribution made by different species to phylogenetic diversity and show how theindex might contribute towards species-based conservation priorities. We describe procedures to control for missing species,incomplete phylogenetic resolution and uncertainty in node ages that make it possible to apply the method in poorly knownclades. We also show that the index is independent of clade size in phylogenies of more than 100 species, indicating thatscores from unrelated taxonomic groups are likely to be comparable. Similar scores are returned under two different speciesconcepts, suggesting that the index is robust to taxonomic changes. The approach is applied to a near-complete species-levelphylogeny of the Mammalia to generate a global priority list incorporating both phylogenetic diversity and extinction risk. The100 highest-ranking species represent a high proportion of total mammalian diversity and include many species not usuallyrecognised as conservation priorities. Many species that are both evolutionarily distinct and globally endangered (EDGEspecies) do not benefit from existing conservation projects or protected areas. The results suggest that global conservationpriorities may have to be reassessed in order to prevent a disproportionately large amount of mammalian evolutionary historybecoming extinct in the near future.
Citation: Isaac NJB, Turvey ST, Collen B, Waterman C, Baillie JEM (2007) Mammals on the EDGE: Conservation Priorities Based on Threat andPhylogeny. PLoS ONE 2(3): e296. doi:10.1371/journal.pone.0000296
INTRODUCTIONOur planet is currently experiencing a severe anthropogenically
driven extinction event, comparable in magnitude to prehistoric
mass extinctions. Global extinction rates are now elevated up to
a thousand times higher than the background extinction rates
shown by the fossil record, and may climb another order of
magnitude in the near future [1–3]. The resources currently
available for conservation are, unfortunately, insufficient to
prevent the loss of much of the world’s threatened biodiversity
during this crisis, and conservation planners have been forced into
the unenviable situation of having to prioritise which species
should receive the most protection–this is ‘the agony of choice’ [4]
or the ‘Noah’s Ark problem’ [5].
A range of methods for setting species-based conservation
priorities have been advocated by different researchers or
organisations, focusing variously on threatened species, restrict-
ed-range endemics, ‘flagship’, ‘umbrella’, ‘keystone’, ‘landscape’ or
‘indicator’ species, or species with significant economic, ecological,
scientific or cultural value [6–8]. To date, global priority-setting
exercises have tended to focus on endemic (or restricted range)
species [6,9,10], presumably because endemism is easier to
measure than competing methods. However, recent data show
that endemism is a poor predictor of total species richness or the
number of threatened species [11].
It has also been argued that maximising Phylogenetic Diversity
(PD) should be a key component of conservation priority setting
[4,12–14]. Species represent different amounts of evolutionary
history, reflecting the tempo and mode of divergence across the
Tree of Life. The extinction of a species in an old, monotypic or
species-poor clade would therefore result in a greater loss of
biodiversity than that of a young species with many close relatives
[15,16]. However, conserving such lineages may be difficult, since
there is some evidence that they are more likely to be threatened
with extinction than expected by chance [17]. This clumping of
extinction risk in species-poor clades greatly increases the loss of
PD compared with a null model of random extinction [18] and
suggests that entire vertebrate orders may be lost within centuries
[19]. Among mammals alone, at least 14 genera and three families
have gone extinct since AD 1500 [20], and all members of a further
19 families and three orders are considered to be in imminent
danger of extinction [2]. Many academic papers have suggested
ways to maximise the conservation of PD [e.g. 12,13,21–23] and
measure species’ contributions to PD [e.g. 4,23–25], but these
have rarely been incorporated into conservation strategies.
Therefore, it is possible that evolutionary history is being rapidly
lost, yet the most distinct species are not being identified as high
priorities in existing conservation frameworks.
There are several reasons why PD has not gained wider accept-
ance in the conservation community. First, although evolutionary
history consists of two distinct components (the branching pattern
of a phylogenetic tree and the length of its branches), complete
dated species-level phylogenies for large taxonomic groups have
only recently become available [26]. Early implementations of PD-
based approaches were therefore unable to incorporate branch
length data, and focused solely on measurements of branching
pattern [4]. Second, PD removes the focus from species and so
may lack wider tangible appeal to the public; conserving PD may
be seen as less important than the protection of endemic or
Academic Editor: Walt Reid, The David and Lucile Packard Foundation,Conservation and Science Program, United States of America
Received January 16, 2007; Accepted February 19, 2007; Published March 14,2007
Copyright: � 2007 Isaac et al. This is an open-access article distributed underthe terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided theoriginal author and source are credited.
Funding: The authors did not receive any funding to conduct the researchdescribed in this paper.
Competing Interests: The authors have declared that no competing interestsexist.
* To whom correspondence should be addressed. E-mail: [email protected]
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threatened species [16]. However, the current instability in species
taxonomy [27] means that decisions based on PD might be more
objective than those based on different species concepts
[13,16,27]. Combining species’ conservation status with a measure
of their contribution to PD is therefore desirable, because species
can be retained as units but weighted appropriately [5,22]. This
would generate a useful and transparent means for setting global
priorities for species-based conservation [25].
This paper describes a new method for measuring species’
relative contributions to phylogenetic diversity [the ‘originality’ of
species: ref 24]. We explore the statistical properties of the
resulting measure, which we call Evolutionary Distinctiveness
(ED), and test its robustness to changing species concepts. ED
scores are calculated for the Class Mammalia, and combined with
values for species’ extinction risk to generate a list of species that
are both evolutionarily distinct and globally endangered (‘EDGE
species’). The resultant list provides a set of priorities for
mammalian conservation based not only on the likelihood that
a species will be lost, but also on its irreplaceability.
Evolutionary Distinctiveness and its use in
priority-settingIn order to calculate ED scores for each species, we divide the total
phylogenetic diversity of a clade amongst its members. This is
achieved by applying a value to each branch equal to its length
divided by the number of species subtending the branch. The ED
of a species is simply the sum of these values for all branches from
which the species is descended, to the root of the phylogeny. For
the examples in this paper, we have measured ED in units of time,
such that each million years of evolution receives equal weighting
and the branches terminate at the same point (i.e. the phylogeny is
ultrametric). The method could be applied to non-ultrametric
phylogenies if the conservation of other units [e.g. character
diversity 28,29] was prioritised [although see ref 30].
The basic procedure for calculating ED scores is illustrated in
figure 1, which describes a clade of seven species (A–G). The ED
score of species A is given by the sum of the ED scores for each of
the four branches between A and the root. The terminal branch
contains just one species (A) and is 1 million years (MY) long, so
receives a score of 1 MY. The next two branches are both 1 MY
long and contain two and three species, so each daughter species
(A, B and C) receives 1/2 and 1/3 MY respectively. The deepest
branch that is ancestral to species A is 2 MY long and is shared
among five species (A to E), so the total ED score for species A is
given by (1/1+1/2+1/3+2/5) = 2.23 MY. Species B is the sister
taxon of A, so receives the same score. By the same arithmetic, C
has a score of (2/1+1/3+2/5) = 2.73 MY, both D and E receive
(1/1+2/2+2/5) = 2.4 MY, and both F and G receive (0.5/1+4.5/
2) = 2.75 MY. The example illustrates that ED is not solely
determined by a species’ unique PD (i.e. the length of the terminal
branch). Species F and G are the top-ranked species based on their
ED scores, even though each represents just a small amount of
unique evolutionary history (0.5 MY). This suggests that the
conservation of both F and G should be prioritised, because the
extinction of either would leave a single descendant of the oldest
and most unusual lineage in the phylogeny [c.f. 15,24]. The ED
calculation is similar to the Equal Splits measure [25], which
apportions branch length equally among daughter clades, rather
than among descendent species.
In order to represent a useful tool in priority setting, ED scores
must be applicable in real phylogenies of large taxonomic groups.
To do this, we modified the basic procedure described above to
control for missing species, incomplete phylogenetic resolution and
uncertainty in node ages (see Materials and Methods). The
approach is implemented using a dated phylogeny of the Class
Mammalia that is nearly complete (.99%) at the species level
[31]. We then combined ED and extinction risk to identify species
that are both evolutionarily distinct and globally endangered
(‘EDGE species’). We measured extinction risk using the
quantitative and objective framework provided by the World
Conservation Union (IUCN) Red List Categories [2]. We follow
previous researchers in treating the Red List categories as intervals
of extinction risk and by assuming equivalence among criteria
[32,33, but see 34]. The resulting list of conservation priorities
(‘EDGE scores’) was calculated as follows:
EDGE~ln(1zED)zGE � ln(2) ð1Þ
where GE is the Red List category weight [Least Concern = 0,
Near Threatened and Conservation Dependent = 1, Vulnera-
ble = 2, Endangered = 3, Critically Endangered = 4, ref 32], here
representing extinction risk on a log scale. EDGE scores are
therefore equivalent to a loge-transformation of the species-specific
expected loss of evolutionary history [5,25] in which each
increment of Red List category represents a doubling (eln(2)) of
extinction risk. For the purposes of these analyses, we did not
calculate EDGE scores for species listed as Extinct in the Wild
(n = 4), domesticated populations of threatened species and 34
species (mostly of dubious taxonomic status) for which an
evaluation has not been made.
Figure 1. Hypothetical phylogeny of seven species (A–G) withEvolutionary Distinctiveness (ED) scores. Numbers above each branchindicate the length; numbers below show the number of descendentspecies. MYBP, millions of years before present.doi:10.1371/journal.pone.0000296.g001
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RESULTS
Statistical properties of EDWe measured ED in clades of different sizes to test whether ED
scores from different taxonomic groups are likely to be
comparable. We found that most ED is derived from a few
branches near the tips (i.e. those shared with few other species) and
that virtually no ED is gained in clades above ,180 species
(figure 2). Median ED in clades of 60 species is 88% of the total
accumulated using the whole tree (n = 10, figure 2). Moreover, the
rank order of ED scores is unaffected by the size of the clade under
consideration, except in very small clades and among species with
low overall ED (i.e. few of the lines in figure 2 cross one another).
These findings suggest that ED scores of different taxonomic
groups measured on separate phylogenies (i.e. with no nodes in
common) will be comparable, so long as each phylogeny is larger
than a threshold size. Based on the scaling observed in figure 2, we
suggest a minimum species richness of 100 as a useful rule of
thumb to ensure comparability among taxa.
Although most species (90% in figure 2) derive at least two-
thirds of their total ED from the terminal branch (which is not
shared with others), this branch length is a poor predictor of total
ED (r2 = 0.03 on a log-log scale). For species on short branches,
there is an order of magnitude difference between the length of the
terminal branch and ED. For example, the pale-throated and
brown-throated three-toed sloths (Bradypus tridactylus and B.
variegatus) share a common ancestor thought to be just over
a million years old, but the total ED of both species is 20.4 MY
(Table S1) since they have few close living relatives.
ED scores are also robust to taxonomic changes. For example,
ED scores in primates under the biological species concept [35]
are tightly correlated with ED scores under the phylogenetic
species concept [36] (r2 = 0.65 on a log-log scale), in spite of the
fact that there are substantial differences between the two: the
number of primate species differs by 50%. Furthermore, the
highest-ranking species do not change their identity: 45 of 58
biological species in the upper quartile of ED scores are also in the
upper quartile as phylogenetic species. However, species that have
been split into three or more species do tend to lose a large portion
of their ED. For example, the fork-marked lemur (Phaner furcifer) is
the second most distinct biological species of primate, with an ED
score of 38.33. It was split into four phylogenetic species [36] with
an ED score of 10.45 (Table S2), which is just inside the upper
quartile.
ED and EDGE scores in mammalsMammal ED scores range from 0.0582 MY (19 murid rodents) to
97.6 MY (duck-billed platypus, Ornithorhynchus anatinus). Scores are
approximately log-normally distributed, with a median of 7.86
MY and geometric mean of 6.28 MY.
Evolutionary Distinctiveness is not evenly distributed among the
Red List categories. Least Concern species have significantly lower
ED than the other categories (F1,4180 = 26.3, p,0.0001, using loge
transformed scores); there are no significant differences among the
remaining categories. This suggests that species with low ED
scores tend to suffer from low levels of extinction risk, although the
explanatory power of this model is extremely low (r2 = 0.006).
EDGE scores range from 0.0565 (10 murid rodents) to 6.48
(Yangtze River dolphin or baiji, Lipotes vexillifer) and are
approximately normally distributed around a mean of 2.63
(60.017; figure 3). The 100 highest priority (EDGE) species
includes several large-bodied and charismatic mammals, including
the giant and lesser pandas, the orang-utan, African and Asian
Figure 2. Scaling of ED scores with clade size for ten Critically Endangered mammal species. ED scores were calculated at each node between the tipsand root for ten species in different orders. Species chosen are: the baiji (Lipotes vexillifer), sumatran rhino (Dicerorhinus sumatrensis), northern hairy-nosed wombat (Lasiorhinus krefftii), persian mole (Talpa streeti), Omiltemi rabbit (Sylvilagus insonus), Przewalski’s gazelle (Procapra przewalskii), black-faced lion tamarin (Leontopithecus caissara), Livingstone’s flying fox (Pteropus livingstonii), red wolf (Canis rufus) and northern Luzon shrew rat(Crunomys fallax). See Materials and Methods for further details.doi:10.1371/journal.pone.0000296.g002
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elephants, four rhinoceroses, two tapirs, two baleen whales,
a dugong and a manatee. However, many smaller and less
appreciated species also receive high priority, including sixteen
rodents, thirteen eulipotyphlans, twelve bats, four lagomorphs and
an elephant shrew (Table S1). The top 100 also includes at least 37
species that would not qualify for most area-based definitions of
endemism, since they are listed as threatened under Red List
criterion A (reduction in population size) without qualifying for
criteria B–D, which are based on population size or geographical
range. Whilst the highest-ranked species, by definition, are all
highly threatened (44 of the top 100 species are Critically
Endangered, a further 47 are Endangered), threat status alone
does not guarantee a high priority. For example, 10 Critically
Endangered species (in the genera Gerbillus, Peromyscus and
Crocidura), as well as 32 Endangered species, fail to make the top
1000, whilst 130 Near Threatened species do.
DISCUSSIONIt is important that conservation priority-setting approaches are
able to satisfy two conditions: they capture biodiversity and are
robust to uncertainty. The method described herein satisfies the
first condition because EDGE scores incorporate species value (in
terms of originality, or irreplaceability) weighted by urgency of
action (i.e. risk of extinction). Our approach satisfies the second
condition because the scores are also robust to clade size, missing
species and poor phylogenetic resolution. EDGE scores are also
easy to calculate, as all that is required is a set of Red List
assessments and a near-complete phylogeny containing at least
100 species.
In particular, EDGE priorities are much less sensitive to
taxonomic uncertainty than alternate methods. The current trend
towards the adoption of the phylogenetic species concept among
biologists [27] is likely to produce a large number of ‘new’
threatened and endemic species [37], potentially altering the
distribution of hotspots [38] and distorting other biodiversity
patterns [27]. The EDGE approach is robust to such distortion
because any increase in extinction risk due to splitting is balanced
by a decrease in ED. A good example is that of the ruffed lemurs
(Varecia spp.), which consist of one Endangered biological species
(ED = 19.8; EDGE = 5.11) or two phylogenetic species (Endan-
gered and Critically Endangered; ED = 10.3; EDGE = 4.50 and
5.20). Using the same approach, we estimate that the long-beaked
echidna (Zaglossus bruijni) would fall from the second-ranked
priority to the 20th after the addition of two new congeners
[suggested by 39]. Thus, EDGE scores for existing species are
robust to the ongoing discovery of new species.
EDGE priorities are also robust to several other forms of
uncertainty. Like all phylogenetic methods, the precise EDGE
scores are dependent on the topology and branch lengths of the
phylogeny. However, errors in the phylogeny are unlikely to alter
the identity of high-ranking species, particularly for clades of
several hundred species. Topological uncertainty is usually
expressed in supertrees as polytomies, which are accounted for
using simple correction factors. Likewise, branch length un-
certainty has been incorporated into the scoring system to down-
weight the priority of species descended from nodes with
imprecisely estimated ages (see Materials and Methods). These
developments make it possible to estimate robustly the contribu-
tion to phylogenetic diversity of species in poorly known clades.
The other major source of uncertainty is in estimating extinction
risk: most recent changes in Red List category have come about
through improvements in knowledge, rather than genuine changes
in status [32]. EDGE scores will inevitably be affected by future
changes in extinction risk, although no more so than other
approaches using the Red List categories.
A minority of mammal species could not be assigned EDGE
scores. Around 300 species are classified as Data Deficient and
could not be meaningfully included, although in reality they may
have a high risk of extinction [17]. By far the most likely candidate
for high EDGE status following future Red List re-assessment is
the franciscana or La Plata River dolphin Pontoporia blainvillei
(ED = 36.3 MY). In addition, fifty extant species are missing from
the phylogeny. The highest ranked of these are probably a pair of
Critically Endangered shrews (Sorex cansulus and S. kizlovi); median
and maximum ED scores for the genus are 4.55 and 14.6 MY,
giving potential respective EDGE scores of 4.49 and 5.52 for these
species (cf. figure 3). A further 260 species have been described
since the chosen taxonomy was published [40]. Of these, the
recently described Annamite striped rabbit Nesolagus timminsi [41] is
the sister species to the tenth-ranked Sumatran rabbit N. netscheri,
so would be a high priority if similarly threatened.
It has been suggested that species with few close relatives (i.e.
high ED) are ‘relicts’ or ‘living fossils’ that have limited ability to
generate novel diversity. This view implies that conservation
efforts should instead be focused on recent radiations containing
species with low ED scores (e.g. murid rodents), which represent
‘cradles’ rather than ‘museums’ of diversity [e.g. 16,42]. However,
the assumption that we are able to predict future evolutionary
potential is dubious and no general relationships between
phylogeny and diversity over geological time have yet been
established [43,44]. Furthermore, phylogenetic diversity is clearly
related to character diversity [30], and so ED may be a useful
predictor of divergent properties and hence potential utilitarian
value [14]. Moreover, because species with low ED scores tend to
suffer from low levels of extinction risk, phylogenetic cradles of
mammalian diversity are likely to survive the current extinction
crisis even without specific interventions. Focusing on lower risk
species, at the expense of EDGE priorities, would therefore result
in a severe pruning of major branches of the Tree of Life
comparable to that seen in previous mass extinction events
[45,46].
Figure 3. Histogram of EDGE scores for 4182 mammal species, by threatcategory. Colours indicate the Red List category: Least Concern (green),Near Threatened and Conservation Dependent (brown), Vulnerable(yellow), Endangered (orange) and Critically Endangered (red).doi:10.1371/journal.pone.0000296.g003
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The top 100 EDGE species span all the major mammalian
clades [being distributed among 18 orders and 52 families
recognised by ref 35] and display a comparable range of
morphological and ecological disparity, including the largest and
smallest mammals, most of the world’s freshwater cetaceans, an
oviparous mammal and the only species capable of injecting
venom using their teeth. However, around three-quarters of
species-based mammal conservation projects are specifically aimed
at charismatic megafauna [47], so conventional priority-setting
tools may not be sufficient to protect high priority EDGE species.
This concern is supported by two additional lines of evidence.
First, we found that species not found in protected areas [‘gap
species’ defined by ref 48] tended to have higher EDGE scores
than those found inside protected areas (logistic regression:
x21,3994 = 69.46, p,0.0001). Second, an assessment of published
conservation strategies and recommendations (including IUCN
Specialist Group Conservation Action Plans, captive breeding
protocols and the wider scientific literature listed in the 1978–2005
Zoological Record database) reveals that no species-specific
conservation actions have even been suggested for 42 of the top
100 EDGE species. Most of these species are from poorly known
regions or taxonomic groups and until now have rarely been
highlighted as conservation priorities. Little conservation action is
actually being implemented for many other top EDGE species,
despite frequent recommendations in the conservation literature.
Indeed, the top-scoring EDGE species, the Yangtze River dolphin
(Lipotes vexillifer), is now possibly the world’s most threatened
mammal despite two decades of debate over a potential ex situ
breeding programme, and may number fewer than 13 surviving
individuals [49]. The lack of conservation attention for priority
EDGE species is a serious problem for mammalian biodiversity
and suggests that large amounts of evolutionary history are likely
to be lost in the near future. This phenomenon of diversity slipping
quietly towards extinction is likely to be much more severe in less
charismatic groups than mammals.
The approach described in this paper can be used for
conservation in a number of ways. First, conservation managers
with limited resources at their disposal typically need to conserve
populations of several threatened species. If all other factors were
equal, the management of the most evolutionarily distinct species
should be prioritized. Second, a list of high-priority species
requiring urgent conservation action can be generated easily. In
this paper, we have selected the 100 highest-ranking species, but
one might equally choose all threatened (Vulnerable and above)
species with above average ED. This would result in a list of 521
(using median) or 630 (using geometric mean) ‘EDGE species’ that
are both evolutionarily distinct and globally endangered. Third,
EDGE scores could also be used to weight species’ importance in
selecting reserve networks, building on previous studies that have
used phylogenetic diversity [50–52] or threatened species [11] to
identify priority areas for conservation. The statistical properties of
EDGE scores (they are both normally-distributed and bounded at
zero) make them especially suitable for these kinds of analysis. In
this way, the EDGE approach is not an alternative to existing
conservation frameworks [e.g. 6] but complements them.
The EDGE approach identifies the species representing most
evolutionary history from among those in imminent danger of
extinction. Our methods extend the application of PD-based
conservation to a wider range of taxa and situations than previous
approaches [4,5,13,22,24,25]. Future work might incorporate
socioeconomic considerations [5,14] and the fact that a species’
value depends also on the extinction risk of its close relatives [53].
However, our results suggest that large numbers of evolutionarily
distinct species are inadequately served by existing conservation
measures, and that more work is carried out to prevent the
imminent loss of large quantities of our evolutionary heritage. It is
hoped that this approach will serve to highlight their importance
to biodiversity and emphasize the need for urgent conservation
action.
MATERIALS AND METHODS
Implementing ED scores for mammalsWe used a composite ‘supertree’ phylogeny [31] to calculate ED
scores for mammals. The supertree presents several challenges to
the estimation of ED when compared with the (unknown) true
phylogeny: poor resolution, missing species and uncertainty in
node ages. Accordingly, we modified the basic procedure to
control for these problems.
Phylogenetic information is poor in many mammalian clades
(especially bats and rodents, which together make up .60% of
species) and the whole supertree contains only 47% of all possible
nodes, many of which are polytomies (nodes with more than two
daughter branches). Across the whole phylogeny, ,40% of species
are immediately descended from bifurcations, ,20% from small
polytomies (3–5 daughters), ,15% from medium-sized polytomies
(6–10 daughters) and the remainder from large polytomies with
.10 daughters. Polytomies in supertrees result from poor or
conflicting data rather than a true representation of the speciation
process, so the distinctiveness of branches subtending them is
overestimated [54], thus leading to biased ED scores. For example,
the common ancestor of species X, Y and Z is believed to be 1 MY
old, but the branching pattern within the clade is unknown. The
polytomy appears to show that each species represents 1 MY of
unique evolutionary history. In reality, the phylogeny is bi-
furcating, with one species aged 1 MY and the others sharing
a more recent common ancestor. The bias induced by polytomies
can be corrected by estimating the expected ED of descendant
species under an appropriate null model of diversification. We
achieved this by applying a scaling factor based on the empirical
distribution of ED scores in a randomly generated phylogeny of
5000 species grown under constant rates of speciation (0.1) and
extinction (0.08). The mean ED score of species in 819 clades of
three species was 0.81 of the clade age; ED scores for nodes of 2–
20 species scale according to (branch length) * (1.081–0.267 *
ln{d}), where d is the number of descendent branches (n = 2873
clades, r2 = 0.69). Quantitatively similar values were obtained in
bifurcating clades of primates [1.117–0.246 * ln{d}, n = 78, ref 55]
and carnivores [1.139–0.269 * ln{d}, n = 101, ref 56].
The mammal supertree contains 4510 of the 4548 (.99%)
extant species listed in Wilson & Reeder [35]. Although few in
number, the missing species need to be taken into account because
their absence will tend to inflate the ED scores of close relatives.
For example, omitting species A from the phylogeny in figure 1
would elevate B from the joint lowest ranking species (with A) to
the joint highest-ranking (with C), with an ED score of (2/1+1/
2+2/4) = 3.5 MY. The problem is acute in real datasets since
missing species tend not to be a random sample: 22 of the 38
missing mammals are from the genus Sorex. We account for this
problem using a simple correction factor that allocates the missing
species among their presumed closest relatives. For example, we
correct for the omission of the bare-bellied hedgehog (Hemiechinus
nudiventris) by treating the other five Hemiechinus spp. as 6/5 = 1.2
species, and we correct for the omission of both Cryptochloris species
by spreading the two missing species evenly between other
Chrysochloridae.
Variation among morphological and molecular estimates of
divergence times (node ages) can lead to considerable uncertainty
Mammals on the EDGE
PLoS ONE | www.plosone.org 5 March 2007 | Issue 3 | e296
in ED scores. To reduce the effects of this uncertainty, we
estimated ED using three sets of branch lengths. One set was based
on the best (i.e. mean) estimates of node age; the others were
derived from the upper and lower 95% confidence intervals
around these dates. Species values of ED were calculated as the
geometric mean of scores under the three sets of branch lengths.
The geometric mean was preferred since it down-weights species
whose scores are based on nodes with symmetrical but wide
confidence intervals in estimate age, and is therefore more
conservative than the arithmetic mean.
Tests of robustnessTo test whether ED scores are comparable among taxonomic
groups, we examined how species’ ED accumulates as pro-
gressively larger clades are considered. If ED scores are truly
comparable, their rank order will be independent of the size of the
clade considered. We randomly selected one Critically Endan-
gered species from each of ten mammal orders and measured the
cumulative ED score at each node between the species and the
root of the mammal supertree, thus redefining and enlarging the
clade (and so increasing the number of species it contained) at each
step.
Taxonomic changes have the potential to dramatically alter the
ED scores of individual species. Splitting a species in two reduces
the distinctiveness of all branches ancestral to the split, particularly
those near the tips. If ED scores are highly sensitive to taxonomic
changes then it may be meaningless to apply them in setting
conservation priorities. The effects of taxonomic changes on ED
scores were therefore investigated in the primates, which have
recently experienced considerable taxonomic inflation [27]. We
compared primate ED scores under a biological species concept
[35: 233 species] and a phylogenetic species concept [36: 358
species]. We employed a single phylogeny [31], but changed the
number of species represented by each tip. We calculated the
expected ED for multi-species tips by treating them as if they were
descended from a polytomy of {n+r+1} descendent branches,
where n is the actual number of descendent branches and r is the
number of species represented by the tip.
SUPPORTING INFORMATION
Table S1 Evolutionary Distinctiveness and EDGE scores for
mammals. This table shows Evolutionary Distinctiveness (ED) and
EDGE scores for all species included in the mammal supertree
[31] ranked by their EDGE score. Species that could not be
assigned EDGE scores are appended to the bottom of the list,
sorted by status and ED score. Species taxonomy follows Wilson &
Reeder [35]. Red List categories follow the 2006 IUCN Red List
[2]: CR = Critically Endangered, EN = Endangered, VU = Vul-
nerable, NT = Near Threatened, LC = Least Concern, CD = Con-
servation Dependent, DD = Data Deficient, NE = Not Evaluated.
The NE category includes species in Wilson & Reeder [35] that
could not be matched with any species or subspecies names in the
Red List.
Found at: doi:10.1371/journal.pone.0000296.s001 (0.42 MB
PDF)
Table S2 Evolutionary Distinctiveness for primates under two
species concepts. This table lists ED scores for primates under the
biological species concept i[.e. the taxonomy of ref 35], the
number of phylogenetic species into which the biological species
was split [36] and the estimated ED score of each phylogenetic
species. See Materials and Methods for further information. ED
scores are lower for phylogenetic species than biological species,
even for taxa whose taxonomic status is the same under both
concepts (i.e. the number of phylogenetic species is one). This
occurs because the total number of species in the phylogeny is
greater, so each receives a smaller share of the distinctiveness of
ancestral branches. ED scores were calculated using just one set of
branch lengths (the ‘best’ set), so differ from those in table S1.
Found at: doi:10.1371/journal.pone.0000296.s002 (0.05 MB
PDF)
ACKNOWLEDGMENTSWe thank the PanTHERIA consortium for use of the mammal supertree.
In particular, Olaf Bininda-Emonds generously provided us with electronic
copies of the topology and alternate branch lengths. We also thank Ana
Rodrigues for providing a list of species found in protected areas. We are
also grateful to Guy Cowlishaw, Sarah Durant, Dan Faith, John Gittleman,
Rich Grenyer, Kate Jones, Georgina Mace, Arne Mooers, Andy Purvis
and three anonymous referees for useful comments and discussion.
Author Contributions
Conceived and designed the experiments: JB BC NI ST. Performed the
experiments: NI. Analyzed the data: NI. Contributed reagents/materials/
analysis tools: NI ST CW. Wrote the paper: JB BC NI ST.
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CORRESPONDENCE
Securing nonflagship species from extinctionLiana N. Joseph1, Richard F. Maloney2, James E.M. Watson1, & Hugh P. Possingham3
1 Wildlife Conservation Society, 2300 Southern Boulevard, Bronx, New York 10460, USA2 Threatened Species Development, Research and Development Group, Department of Conservation, Christchurch, New Zealand3 ARC Centre of Excellence for Environmental Decisions, The Ecology Centre, University of Queensland, St Lucia 4072, Australia
Received17 December 2010
Accepted6 February 2011
EditorJames Blignaut
doi: 10.1111/j.1755-263X.2011.00174.x
Introduction
A recent article in Conservation Letters by Verissimoand colleagues provides clarity with respect to theconcept of flagship species. As the authors state, theuse of flagship species can offer a powerful tool forenvironmental organizations to raise money and raisepublic awareness generally. Regrettably, in many cases,the money that is raised for flagship species is tied tospending solely on that species. Consequently, other non-flagship threatened species are unlikely to benefit. Theuse of flagship species creates a conundrum for those or-ganizations that aim to secure the greatest number ofthreatened species from extinction. This goal will notbe achievable if the limited conservation budget is con-strained to specific actions that only assist the few flagshipspecies. The authors make a brief reference to this weak-ness of the flagship-species approach and suggest thatsolutions may include using the funds to pay for over-heads that benefit multiple species or declaring upfrontthat funding will be spent on other species. We suggestthat there is another option: a marketing tool that maybe attractive to donors and result in funding that is nottied to a single species.
We believe that it is possible to raise funds by focus-ing on the task of securing large numbers of threatenedspecies rather than a single flagship species. We illustratethe potential power of this type of marketing tool with aspecies prioritization exercise recently undertaken by theNew Zealand Department of Conservation. In this plan-
ning exercise, priority actions, and costs and feasibility forthose actions, were identified for securing each of ∼660of New Zealand’s most threatened species (Joseph et al.2009; O’Conner et al. 2009). The New Zealand govern-ment is now in the position to state how much it will costto secure all or a selection of these species from extinc-tion. With this kind of information, it is possible to cal-culate the exact amount required to secure species andmake statements like: “. . . as little as $x million is neededto secure a given number of the most threatened speciesand $y million would secure a greater number.” Simi-larly, these data can be used to demonstrate the expectedgains of additional funding for threatened species. Thesefigures give the Department of Conservation a powerfultool for seeking wider support for managing threatenedspecies in New Zealand.
The concept of saving large numbers of endangeredspecies is commonly used to “sell” priority landscapes orregions for conservation NGOs (e.g., Conservation Inter-national’s Biodiversity Hotspots, Myers et al. 2000; Al-liance for Zero Extinction sites, Ricketts et al. 2005). Yet,the example that we present here illustrates a methodfor proving clear and fully costed opportunities to raisefunds for priority actions that will result in the recoveryof threatened species specifically. We suggest that mar-keting the ability to secure from extinction of large num-bers of species is an effective complementary tool to theflagship-species approach that can be particularly usefulfor securing threatened species that will never be poten-tial flagship species.
324 Conservation Letters 4 (2011) 324–325 Copyright and Photocopying: c©2011 Wiley Periodicals, Inc.
L. N. Joseph et al. Protecting non-flagship species
References
Joseph, L.N., Maloney R.F., Possingham H.P. (2009)
Optimal allocation of resources among threatened
species: a project prioritization protocol. Conserv Biol 23,
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2
Prioritizing choices inconservation
__________
Georgina M. Mace, Hugh P. Possingham and NigelLeader-Williams
The last word in ignorance is the man who says of an animal or plant: ‘What good is it?’ If the landmechanism as a whole is good, then every part is good, whether we understand it or not. If the biota, in thecourse of aeons, has built something we like but do not understand, then who but a fool would discardseemingly useless parts? To keep every cog and wheel is the first precaution of intelligent tinkering.(Aldo Leopold, Round River, Oxford University Press, New York, 1993, pp. 145–6.)
Introduction
We are in the midst of a mass extinction in
which at least 10%, and may be as much as
50%, of the world’s biodiversity may disappear
over the next few hundred years. Conservation
practitioners face the dilemma that the cost of
maintaining global biodiversity far exceeds the
available financial and human resources. Esti-
mates suggest that in the late twentieth century
only US$6 billion per year was spent globally
on protecting biodiversity (James et al. 1999),
even though an estimated US$33 trillion per
year of direct and indirect benefits were derived
from ecosystem services provided by biodiver-
sity, implying an asset worth US$330 trillion
(Costanza et al. 1997). Together these crude
estimates suggest that there could be a 500-
fold underinvestment in conserving the world’s
biodiversity. However, even if these estimates
are wildly wrong, the imbalance of funding is
seriously inconsistent with best business prac-
tice in other sectors. In business, many com-
panies spend about 10% of the value of their
capital assets each year on maintaining those
assets, although the figure varies depending on
the type of asset. For example, 30% might be
spent for computers compared with 5% for
buildings: contrast that with 0.02% for bio-
diversity! Furthermore, the scale of underin-
vestment in biodiversity may be exaggerated
by the effects of poor governance, sometimes
even corruption, on achieving success in
conservation (Smith et al. 2003). Given such
problems, conservation scientists and non-
government organizations (NGOs) supporting
international conservation efforts are begin-
ning to develop systems to more effectively
target investment in biodiversity conservation
(Johnson 1995; Kershaw et al. 1995; Olson &
Dinerstein 1998; Myers et al. 2000; Possingham
et al. 2001; Wilson et al. in press).
One fundamental resource allocation question
facing conservation scientists and practitioners is
whether conservation goals are best met by man-
aging single species as opposed to whole ecosys-
Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 17 6.5.2006 11:07pm
tems (Simberloff 1998). Efforts in conservation
priority setting have historically concentrated on
ecosystem-based priorities – determining where
and when to acquire protected areas (Ferrier et
al. 2000; Margules & Pressey 2000; Pressey &
Taffs 2001; Meir et al. 2004). There has been
comparatively little work on the question of
how to allocate conservation effort between
species. Despite the tension between ecosystem-
based and species-based conservation, we believe
there is merit in considering the issue of resource
allocation between species because:
1. a ‘fuzzy’ idea such as ecosystem manage-
ment holds little appeal for the general pub-
lic, who prefer to grasp simpler messages
conveyed by charismatic species such as
tigers (Leader-Williams & Dublin 2000);
2. data on species, whether through direct
counts of indicator species (Heywood
1995), or through assessments of threat
(Butchart et al. 2005), provide some of the
most readily available, repeatable and expli-
cit monitoring and analytic systems with
which to assess the success or otherwise of
conservation efforts (Balmford et al. 2005).
3. in practice, almost regardless of their ultim-
ate goal, conservation bodies often end up
directing conservation actions to species
and species communities (see e.g. figure 1
of Redford et al. 2003), probably because
these are tangible and manageable compon-
ents of ecosystems.
The topic of setting priorities for conservation is
immense, so here we restrict ourselves to dif-
ferent methods for setting priorities between
species. We explore the issues that a systematic
approach should consider, and we show how
simple scoring systems may lead to unintended
consequences. We also recommend an explicit
discussion of attributes of the species that make
them desirable targets for conservation effort.
Using a case study, we show how different
perspectives will affect the outcome, and so as
an alternative we present a method based
on economic optimization. Ultimately, any
decisions about ‘what to save first’ should in-
clude judgments that cannot be made by scien-
tists or managers alone. Involving wider
societal and political decision-making processes
is vital to gain local support for, and ensure the
ultimate success of, all conservation planning.
Single species approaches
Species-based conservation management ap-
proaches have, until fairly recently, concentrated
on a single species, such as keystone species,
umbrella species, indicator species or flagship
species (see Leader-Williams & Dublin 2000).
Keystone and umbrella species differ in the im-
portance of their ecological role in an ecosystem:
1. keystone species have a disproportionate
effect on their ecosystem, due to their size or
activity, and any change in their population
will have correspondingly large effects on
their ecosystem (e.g. the sole fruit disperser
of many species of tree);
2. umbrella species have such demanding
habitat and/or area requirements that, if we
can conserve enough land to ensure their
viability, the viability of smaller and more
abundant species is almost guaranteed.
In contrast, ‘flagship species’ encompass
purely strategic objectives:
3. flagship species are chosen strategically to
raise public awareness or financial support
for conservation action.
Furthermore, definitions for indicator species
can encompass both ecological and strategic
roles:
4. indicator species are intended either to
represent community composition or to re-
flect environmental change. With respect to
the latter, indicator species must respond to
the particular environmental change of con-
Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 18 6.5.2006 11:07pm
18 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS
cern and demonstrate that change when
monitored.
One species may be a priority species for
more than one reason, depending on the situ-
ation or context in which the term is used.
However, the terms ‘keystone’ and ‘umbrella’
are likely to remain more of a fixed character-
istic or property of that species. In contrast, the
term ‘flagship’ and, possibly to a lesser extent,
‘indicator’ may be more context-specific.
Promoting the conservation of a specific focal
species may help to identify potential areas for
conservation that satisfy the needs of other spe-
cies and species assemblages (Leader-Williams
& Dublin 2000). For example, the umbrella
species concept (Simberloff 1998) can represent
an efficient first step to protect other species. In
addition, minimizing the number of species
that must be monitored once a protected area
has been created will reduce the time and
money that must be devoted to its maintenance
(Berger 1997).
Alternatively, conservation managers and
international NGOs may choose to focus on
the most charismatic ‘flagship’ species, which
stimulate public support for conservation ac-
tion, and that in turn may have spin-off bene-
fits for other species. For example, use of the
giant panda (Ailuropoda melanoleuca) as a logo
by the World Wildlife Fund (WWF) has been
widely accepted (Dietz et al. 1994) as a success-
ful mechanism for conserving many other spe-
cies across a wide variety of taxonomic groups.
Furthermore, other mammalian and avian
‘flagships’ have been used to promote the con-
servation of large natural ecosystems (i.e. Mit-
termeier 1986; Goldspink et al. 1998; Downer
1996; Johnsingh & Joshua 1994; Western
1987; Dietz et al. 1994).
Nevertheless, the context of what may con-
stitute a charismatic species can differ widely
across stakeholders. For example, the tiger
(Panthera tigris) is among the most popular flag-
ship species in developed countries, but those
in developing countries who suffer loss of life
and livelihood because of tigers or other large
predators have a different view (Leader-
Williams & Dublin 2000). Such dissonance is
best avoided by promoting locally supported
flagship species (Entwistle 2000). For example,
the discovery of a new species of an uncharis-
matic, but virus-resistant, wild maize, with its
possible utilitarian value for human food pro-
duction, highlighted the conservation value of
the Mexican mountains in which it was found
(Iltis 1988). This increase in local public aware-
ness led to the establishment of a protected area
that conserves parrots and jaguars (Panthera
onca), orchids and ocelots (Leopardus pardalis),
species that many consider charismatic. Hence
this species of wild maize served as a strategic-
ally astute local flagship species. Another way
of promoting local flagships is to prioritize those
species that bring significant and obvious local
benefits (Goodwin & Leader-Williams 2000),
such as the Komodo dragon, Varanus komodoen-
sis (Walpole & Leader-Williams 2001), which
generates tourism. Similarly, species that can
be hunted for sport, such as the African ele-
phants (Loxodonta africana), may contribute dir-
ectly to community conservation programmes
(Bond 1994).
Several questions can arise from promoting
conservation through single species (Simberloff
1998). One of these is how should individual
species be prioritized? The common response is
to begin with species that are most at risk of
extinction, the critically endangered species.
Many countries and agencies take this ap-
proach. However, there may be no known
management for some of these species, and if
there is, it may be risky and/or expensive. This
can lead to a large share of limited conservation
resources being expended with negligible or
uncertain benefit (Possingham et al. 2002).
On the other hand, when taking an ecosystem
approach, managers might choose to focus on
the keystone species that play the most signifi-
cant role in the ecosystem. Unfortunately in
many ecosystems we do not know the identity
of keystone species. Often, after intensive
study, they turn out to be invertebrates or
fungi (Paine 1995), groups that are unlikely to
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PRIORITIZING CHOICES IN CONSERVATION 19
attract public or government support unless
ways can be found to make them locally
relevant.
Another problem with single species conser-
vation arises when the management of one focal
species is detrimental to the management of
another focal species. For example, in the Ever-
glades of Florida, management plans for two
charismatic, federally listed birds are in conflict.
One species, the Everglades snail kite (Rostrha-
mus sociabilis plumbeus), has been reduced to
some 600 individuals by wetland degradation
and agricultural and residential development.
It feeds almost exclusively on freshwater snails
of the genus Pomacea and requires high water
levels, which increase snail production. The
snail kite is thus an extreme habitat specialist
(Ehrlich et al. 1992). The other species, the
wood stork Mycteria americana, has been reduced
to about 10,000 pairs by swamp drainage, habi-
tat modification and altered water regimes.
Ironically, the US Fish and Wildlife Service op-
posed a proposal by the Everglades National
Park to modify water flow to improve stork
habitat on the grounds that the change would
be detrimental to the kite (Ehrlich et al. 1992)
(an added thought-provoking detail is that both
species are common in South America).
Another issue is that few studies have been
carried out to assess the effectiveness of one
focal species in adequately protecting viable
populations of other species (Caro et al. 2004).
For example, the umbrella-species concept is
often applied in management yet rarely tested.
The grizzly bear (Ursus arctos) has been recog-
nized as an umbrella species but, had a pro-
posed conservation plan for the grizzly bear in
Idaho been implemented, taxa such as reptiles
would have been underrepresented (Noss et al.
1996). Similarly, in a smaller scale study, the
areas where flagship species, such as jaguar,
tapir (Tapirus terrestris) and white-lipped pec-
cary (Tayassu pecari), were most commonly
seen did not coincide with areas of vertebrate
species richness or abundance (Caro et al. 2004).
Although these results may not hold true for all
other protected areas based around flagship spe-
cies, it does highlight the need for more field-
based studies to determine the most appropriate
approach for conserving the most biodiversity.
As a result of problems associated with single
species management, focus has been turning
towards multiple species approaches.
Multispecies approaches
Methods based on several focal species, or pro-
tecting a specific habitat type, might be a more
appropriate means of prioritizing protected
areas (Lambeck 1997; Fleishman et al. 2000;
Sanderson et al. 2002b). A frequent criticism of
setting conservation priorities based on a single
focal species is that it is improbable that the
requirements of one species would encapsulate
those of all other species (Noss et al. 1996; Basset
et al. 2000; Hess & King 2002; Lindenmayer et al.
2002). Hence, there is a need for multispecies
strategies to broaden the coverage of the pro-
tective umbrella (e.g. Miller et al. 1999; Fleish-
man et al. 2000, 2001; Carroll et al. 2001).
Among the different multispecies ap-
proaches, Lambeck’s (1997) ‘focal species’ ap-
proach seems the most promising because it
provides a systematic procedure for selecting
several focal species (Lambeck 1997; Watson
et al. 2001; Bani et al. 2002; Brooker 2002;
Hess & King 2002). In Lambeck’s (1997) in-
novative approach, a suite of focal species are
identified and used to define the spatial, com-
positional and functional attributes that must
be present in a landscape. The process involves
identifying the main threats to biodiversity and
selecting the species that is most sensitive to
each threat. The requirements of this small
and manageable suite of focal species guide
conservation actions. The approach was
extended by Sanderson et al. (2002a), who
proposed the ‘landscape species approach’.
They defined landscape species by their ‘use of
large, ecologically diverse areas and their im-
pacts on the structure and function of natural
ecosystems . . . their requirements in time and
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20 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS
space make them particularly susceptible to
human alteration and use of wild landscapes’.
Because landscape species require large, wild
areas, they could potentially serve an umbrella
function (sensu Caro & O’Doherty 1999) –
meeting their needs would provide substantial
protection for the species with which they co-
occur. Like other focal-species approaches, this
method of setting priorities carries inherent
biases (Lindenmayer et al. 2002), and may be
constrained by incomplete or inconsistent data.
Ecosystem and habitat-basedapproaches
Some conservation scientists believe that set-
ting conservation priorities at the scale of eco-
systems and habitats is more appropriate for
developing countries with limited resources
for conservation, inadequate information about
single species and pressing threats such as
habitat destruction. Logically, how much effort
we place in conserving a particular ecosystem
should take into account factors such as: how
threatened it is, how well represented that
ecosystem is in that country’s protected area
network, the number of species restricted to
that ecosystem (endemic species), the cost of
conserving the ecosystem and the likelihood
that conservation actions will work. One can
debate the relative importance of each of these
factors – for example, some consider the the
number of endemic species is paramount,
whereas others prefer the notion of ‘equal rep-
resentation’ whereby a fixed percentage of
every habitat type is conserved.
The main goals of an ecosystem approach
are to:
1. maintain viable populations of all native
species in situ;
2. represent, within protected areas, all native
ecosystem types across their natural range
of variation;
3. maintain evolutionaryand ecological processes;
4. manage over periods of time long enough
to maintain the evolutionary potential of
species;
5. accommodate human use and occupancy
within these constraints (Grumbine 1994).
Although the financial efficiencies inherent
in managing an ecosystem rather than several
single species are attractive, this approach is
also not without its problems. First, compared
with a species, ecosystem boundaries are
harder to define, so determining the location,
size, connectivity and spacing of protected areas
to conserve the full range of ecosystems, and
variation within those ecosystems, is more dif-
ficult (Possingham et al. 2005). Second, indi-
vidual species are more interesting to people
and will attract greater emotional and financial
investments than ecosystems. Third, although
ecological services are provided by ecosystems,
individual species often play pivotal roles in the
provision of these services, particularly for dir-
ect uses such as tourism or harvesting. Finally,
the main problem faced by managers wishing
to implement an ecosystem approach is the lack
of data available on how ecosystems function.
This manifests itself in confusion about how
much of each ecosystem needs to be conserved
to protect biodiversity adequately in a region.
In contrast, for the better known single species,
the issue of adequacy can be dealt with using
population viability analysis and/or harvesting
models (Beissinger & Westphal 1998; this vol-
ume, Chapter 15).
Systematic conservation planning
Systematic conservation planning (or gap-an-
alysis in the USA: Scott et al. 1993) focuses on
locating and designing protected areas that
comprehensively represent the biodiversity of
each region. Without a systematic approach,
protected area networks have the tendency to
occur in economically unproductive areas
(Leader-Williams et al. 1990), leaving many
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PRIORITIZING CHOICES IN CONSERVATION 21
habitats or ecosystems with little or no protec-
tion (Pressey 1994). The systematic conserva-
tion planning approach can be divided into six
stages (Margules & Pressey 2000).
1. Compile biodiversity data in the region of
concern. This includes collating existing
data, along with collecting new data if ne-
cessary, and if time and funds permit.
Where biodiversity data, such as habitat
maps and species distributions, are limited
more readily available biophysical data may
be used that reflect variation in biodiversity,
such as mean annual rainfall or soil type.
2. Identify conservation goals for the region,
including setting conservation targets for
species and habitats, and principles for pro-
tected area design, such as maximizing con-
nectivity and minimizing the edge-to-area
ratio.
3. Review existing conservation areas, includ-
ing determining the extent to which they
already meet quantitative targets, and miti-
gate threats.
4. Select additional conservation areas in the
region using systematic conservation plan-
ning software.
5. Implement conservation action, including
decisions on the most appropriate form of
management to be applied.
6. Maintain the required values of the conser-
vation areas. This includes setting conserva-
tion goals for each area, and monitoring key
indicators that will reflect the success of
management (see below).
Ultimately, conservation planning is riddled
with uncertainty, so managers must learn to
deal explicitly with uncertainty in ways that
minimize the chances for major mistakes (Mar-
gules & Pressey 2000; Araujo & Williams 2000,
Wilson et al 2005), and be prepared to modify
their management goals appropriately through
adaptive management.
Systematic conservation planning can com-
plement species-based approaches because it
focuses on removing the threat of development
and it compliments a long tradition of species
recovery plans that concentrate on mitigating
threats. The degree to which different countries
use species-based planning as opposed to sys-
tematic conservation planning depends on his-
torical, cultural and legislative influences. Even
with systematic conservation planning, how-
ever, the better surveyed species or species
groups often feature as the units for assessment.
In other words, the conservation value of dif-
ferent areas is often assessed on the presence or
conservation status of the species within it,
simply because these are the best known elem-
ents of biodiversity. Systematic conservation
planning approaches have become popular
and widespread, partly because they are sup-
ported by several decision-support software
packages (Possingham et al 2000, Pressey et al
1995, Williams et al 2000, Garson et al 2002).
Methods for setting conservationpriorities of species
Prioritizing species, habitats and ecosystems by
their perceived level of endangerment has be-
come a standard practice in the field of conser-
vation biology (Rabinowitz 1981; Master 1991;
Mace & Collar 1995; Carter et al. 2000; Stein
et al. 2000). The need for a priority-setting
process is driven by limited conservation re-
sources that necessitate choices among a subset
of all possible species in any given geographical
area, and distinct differences among species in
their apparent vulnerability to extinction or
need for conservation action. This need has
led to the development of practical systems
for categorizing and assessing the degree of vul-
nerability of various components of biodiver-
sity, particularly vertebrates (e.g. Millsap et al.
1990; Mace & Lande 1991; Master 1991; Reed
1992; Stotz et al. 1996), and more recently
ecoregions (Hoekstra et al. 2005).
Methods used for assessing the conservation
status of species are varied but follow three
general styles (Regan et al. 2004), rule-based,
point scoring and qualitative judgement. Per-
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22 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS
haps the best known system is that developed
by the IUCN (International Union for the Con-
servation of Nature and Natural Resources) –
The World Conservation Union – which uses a
set of five quantitative rules with explicit
thresholds to assign a risk of extinction (Mace
& Lande 1991; IUCN 2001). Other methods
adopt point-scoring approaches where points
are assigned for a number of attributes and
summed to indicate conservation priority (Mill-
sap et al. 1990; Lunney et al. 1996; Carter et al.
2000). Other methods assess conservation sta-
tus using qualitative criteria; judgements about
a species’ status are determined intuitively
based on available information and expert
opinion (Master 1991). One widely applied sys-
tem is the biodiversity status-ranking system
developed and used by the Natural Heritage
Network and The Nature Conservancy (Master
1991; Morse 1993). This ranking system has
been designed to evaluate the biological and
conservation status of plant and animal species
and within-species taxa, as well as of ecological
communities.
Rule-based methods
Quantitative rule-based methods can be used to
estimate the extinction risk of a species and
thus contribute to determining priority areas
for conservation action. For example, the
IUCN Red List places species in one of the fol-
lowing categories: extinct (EX), extinct in the
wild (EW), critically endangered (CR), endan-
gered (EN), vulnerable (V), near threatened
(NT) or least concern (LC), based on quantita-
tive information for known life history, habitat
requirements, abundance, distribution, threats
and any specified management options of that
species, and in a data deficient (DD) category if
there are insufficient data to make an assess-
ment (IUCN 2001). The IUCN system is based
around five criteria (A to E) which reflect dif-
ferent ways in which a species might qualify for
any of the threat categories (CR, EN, VU). A
species is placed in a category if it meets one or
more of the criteria – for example because there
are less than 250 mature individuals of the
Norfolk Island green parrot (Cyanoramphus coo-
kii) in the wild it is immediately listed as endan-
gered under criterion D of the IUCN Red List
protocol. A similar species can meet a higher
category of threat if it meets alternative cri-
teria. For example, the orange-bellied parrot
(Neophema chrysogaster) also has less than 250
mature individuals but it is listed as critically
endangered, under criterion C2b, because the
population is also in decline and all the individ-
uals are in a single subpopulation. One concep-
tual problem with rule-based methods is that a
species that just missed out on being listed as,
say, endangered on several criteria would be
ranked as vulnerable, equal with a species that
may have only just met the criteria for being
vulnerable.
The rule-based methods have the advantage
that they are completely explicit about what
feature of the species led to it being listed as
threatened. In the IUCN system, assessors have
to list the criteria whereby the species qualified
for a particular category of threat, and also have
to provide documentation to support this infor-
mation – usually in the form of scientific sur-
veys or field reports that detail the information
used. As a result, listings may be continually
updated and improved as new data become
available. Normally this will allow a new con-
sensus among experts, but in the exceptional
cases where this is not agreed, the IUCN have a
petitions and appeals process to resolve matters.
For example, in 2001 some of the listings of
marine turtle species were disputed among ex-
perts. On this occasion, IUCN implemented
their appeal procedure and provided a new
assessment (http://www.iucn.org/themes/ssc/
redlists/petitions.html). The wide use of the
IUCN system also means that there is an ever
increasing resource of best-practice documen-
tation and guidelines, which aid consistent and
comparable approaches by different species
assessors (see http://www.iucn.org/themes/ssc/red-
lists.htm).
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PRIORITIZING CHOICES IN CONSERVATION 23
Point scoring method
The point scoring method for assigning conser-
vation priority involves assigning a series of
scores to each species based on different param-
eters relating to their ecology or conservation
status, which together will determine their
relative priority. One method of dealing with
the scores is then to simply sum them to give an
overall conservation priority, although this can
be misleading. Beissinger et al. (2000) suggest
that a categorical approach based on a combin-
ation of scores might be more accurate in
determining overall conservation priority.
An example of a point scoring system is that
developed by Partners in Flight (PIF) in 1995 in
an effort to conserve non-game birds and their
habitats throughout the USA (Carter et al.
2000). The PIF system involves assigning a series
of scores to each species ranging from 1 (low
priority) to 5 (high priority) for seven param-
eters that reflect different degrees of need for
conservation attention. The scores are assigned
within physiographical areas and the seven
parameters are based on global and local infor-
mation. Three of the parameters are strictly
global and are assigned for the entire range of
the bird: breeding distribution (BD), non-breed-
ing distribution (ND) and relative abundance
(AR). Other parameters are threats to breeding
(TB), threats to non-breeding (TN), population
trend (PT) and, locally, area importance (AI).
The scores for each of these seven parameters
are obtained independently (Carter et al. 2000).
The PIF then uses a combination of approaches,
including the summing of scores, to determine
an overall conservation priority (Carter et al.
2000), with species that score highly on several
parameters achieving high priority. Although
this method of defining bird species of high con-
servation priority is thought to be reliable, like
other methodologies, it is hindered by the lack
of data on species distribution, abundance and
populations trends, particularly in areas outside
the USA to which many of these species migrate
(Carter et al. 2000).
A problem with some point-scoring methods
is that there is no explicit link to extinction risk,
the weightings of each criteria, from 1 to 5 in
the example above, are completely arbitrary,
and there is an infinity of ways in which the
scores could be combined: adding, multiplying,
taking the product of the largest three values,
and so forth. A related problem is that point-
scoring methods can generate an artificially
high ranking for a species when criteria are
interrelated. For example, a system that priori-
tized species because they needed large home
ranges, had slow reproductive rates and small
litter sizes might end up allocating unreason-
ably high scores to any large-bodied species.
All three of these traits are associated with
relatively large body size, but they are not
necessarily so much more vulnerable.
Conservation status ranks method
Status ranks are based primarily on objective
factors relating to a species’ rarity, population
trends and threats. Four aspects of rarity are
typically considered: the number of individuals,
number of populations or occurrences, rarity of
habitat, and size of geographic range. Ranking
is based on an approximately logarithmic scale,
ranging from 1 (critically imperiled) to 5 (dem-
onstrably secure). Typically species with ranks
from 1 to 3 would be considered of conserva-
tion concern and broadly overlap with species
that might be considered for review under the
Endangered Species Act or similar state or
international statutes.
The NatureServe system (Master 1991) is one
example of a system that uses status ranks.
Developed initially by The Nature Conservancy
(TNC) and applied throughout North America,
the NatureServe system uses trained experts
who evaluate quantitative data and make in-
tuitive judgements about species vulnerability.
The aim of the NatureServe system is to deter-
mine the relative susceptibility of a species or
ecological community to extinction or extirpa-
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24 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS
tion. To achieve this, assessments consider both
deterministic and stochastic processes that can
lead to extinction. Deterministic factors include
habitat destruction or alteration, non-indigen-
ous predators, competitors, or parasites, over-
harvesting and environmental shifts such as
climate change. Stochastic factors include, en-
vironmental and demographic stochasticity,
natural catastrophes and genetic effects (Shaffer
1981).
NatureServe assessments are performed on a
basic unit called an element. An element can
be any plant or animal species or infraspecific
taxon (subspecies or variety), ecological com-
munity, or other non-taxonomic biological
entity, such as a distinctive population (e.g.
evolutionarily significant unit or distinct popu-
lation segment, as defined by some agencies) or
a consistently occurring mixed species aggrega-
tion of migratory species (e.g. shorebird migra-
tory concentration area) (Regan et al. 2004).
Defining elements in this way ensures that a
broad spectrum of biodiversity and ecological
processes are identified and targeted for conser-
vation (Stein et al. 2000). This approach is be-
lieved to be an efficient and effective approach
to capturing biodiversity in a network of
reserves (e.g. Jenkins 1976, 1996). Assessment
results in a numeric code or rank that reflects
an element’s relative degree of imperilment or
risk of extinction at either the global, national
or subnational scale (Master et al. 2000).
Back to basics – extinction risk versussetting priorities
The discussion above has reviewed methods for
categorizing species according to their conser-
vation priority. Running throughout is a ten-
dency to equate conservation priority with
extinction risk; yet these are clearly not the
same thing (Mace & Lande 1991). Extinction
risk is only one of a range of considerations that
determine priorities for action or for conserva-
tion funding. The threat assessment is really an
assessment of urgency, and an answer to the
question of how quickly action needs to be
taken. Hence, all other things being equal, the
critically endangered species will be most likely
to become extinct first if nothing is done. How-
ever, this is by no means the only consideration
that should be used by a conservation planner.
How then should extinction risk be used for
priority-setting? It may be easier to make the
analogy with a different system altogether. For
example, the priority-setting systems used by
Triage nurses in hospital emergency depart-
ments categorize people according to how ur-
gently they need to be seen; those seen first are
the ones that appear to have the most urgent
and threatening symptoms. The symptoms can
be very diverse, however, and some may turn
out upon inspection and diagnosis to be less
serious than might have been expected. Medical
planning across the board would not use the
triage system to allocate resources. The same is
true for conservation planning. As with ill and
injured people, our first sorting of cases should
be according to urgency, and should also be
precautionary (i.e. take more risks with listing
species that are in fact not threatened than with
failing to list those that really are). However,
once the diagnosis is made, and the manager
is reasonably sure that most critical cases are
now known and diagnosed, a more systematic
planning process should follow.
Variables other than risk
Now we consider a whole range of new variables
other than risk. Table 2.1 shows a range of vari-
ables – grouped under headings of biological
value (i.e. what biologists would consider), eco-
nomic value, social and cultural value, urgency
and practical issues. Under each of these head-
ings are a range of attributes that might contrib-
ute to a species priority. The first three columns
concern values, but the last two are rather dif-
ferent. Urgency is a measure that can be compli-
cated to implement – i.e. high urgency may
indicate that if nothing is done now, then it
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PRIORITIZING CHOICES IN CONSERVATION 25
will be too late. This measure is not a value score
that can easily be added to the others, and a
moderate score has little meaning. Practical
issues are also rather different, and will vary
greatly in their nature and importance depend-
ing on the context. Some species that are con-
sidered urgent cases may be extremely
impractical and/or costly to attend to. This set
of considerations is probably not complete, but it
does illustrate the point that there are more
things to think about than extinction risk.
This initial classification by the value type is
hard to manage in a priority-setting system.
Therefore, in Table 2.2, we classify these into
six criteria reflecting the nature of the attribute
(importance, feasibility, biological benefits,
economic benefits, urgency and chance of suc-
cess). This classification has the advantage that
the different questions are more or less inde-
pendent of one another, and each addresses a
question that public, policy-makers and scien-
tists can all address, and for which they can
provide at least relative scores.
Interestingly, the criteria that biologists com-
monly consider, and which form the basis of
most formal decision-processes, fall under one
heading (biological benefits). Yet in practice,
the other five criteria (Table 2.2) also influence
real decisions. Would it not, therefore, be pref-
erable to incorporate these other criteria expli-
citly in the process of setting priorities?
Turning criterion-based ranksinto priorities
A potential next step would be to add the scores
from Table 2.2. By allocating a score of 1, 2 or 3
to each criterion and then adding the ranks, an
overall priority could be calculated. We advise
against this for several reasons. First, the differ-
ent variables are not equal; we might for ex-
ample wish to weight the biological issues
more highly. Second, they are not additive: as
mentioned earlier both urgency and chance
of success are all or nothing decisions. For
Table 2.1 Classes and kinds of issues that are considered in priority-setting exercises for single-species
recovery
Biological value Economic valueSocial andcultural value Urgency Practical issues
Degree of endemism Cost of management
or recovery
Scientific and
educational benefits
Threat status
¼ extinction risk
Feasibility and logistics
Relictual status Direct economic
benefits
Cultural status
(e.g. ceremonial)
Time limitation,
i.e. opportunities
will be lost later
Recoverability, i.e.
reversibility of threats,
rate of species response
Evolutionary
uniqueness
Indirect economic
benefit
Political status
(e.g. symbolic or
emblematic)
Timeliness, i.e.
likelihood of
success varies
with time
Popularity – will there
be support from the
community?
Collateral benefits to
other species
Ecological services Popularity Responsibility, i.e. how
much is this also someone
else’s responsibility?
Collateral costs to
other species
Local or regional
significance
Land tenure
Ecological uniqueness Governmental/agency
jurisdictions
Keystone species status
Umbrella species status
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26 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS
example, if chance of success is nil we would
not wish to invest in that species at all, so it
would seem more logical to multiply other
scores by the chance of success. Third, although
we have sorted the issues into more-or-less
independent categories, there still are associ-
ations between them. For example, the feasibil-
ity and chance of success are likely to be
positively correlated, as are biological benefits
and importance. Hence, simple scoring can lead
to double-counting, which is not what was in-
tended.
Multicriteria decision-analysis is one decision-
making tool for choosing between priorities that
rate differently for separate criteria. There are
innumerable ways of carrying out a multicriteria
analysis, and the process can be complex and
may lead to ambiguous results. An expedient
process at this stage is to invite a range of experts
representing different perspectives to rate the
priorities explicitly. For example, given the pos-
sible set of scores in Table 2.2, what set would
they most wish to see in the top priorities versus
those lower down? This sounds complicated but
in practice we think it is feasible.
A good example of this approach was devel-
oped for UK birds by the Royal Society for the
Protection of Birds (Avery et al. 1995). Three
criteria were used: global threat, national de-
cline rates and national responsibility, and each
was rated high, medium or low. However, by
simply adding these scores, globally endan-
gered species that are stable, and for which
the UK has medium responsibility, had the
Table 2.2 Criteria for setting prorities. The different kinds of considerations from Table 2.1 are classified into
six criteria (rows), each of which can be qualitatively assessed for a particular species
Criterion Explanation Subcriteria Scores
Importance ‘Does anyone care?’ A
measure of how much
support there is likely to be
Social and cultural importance
(including charisma)
Responsibility –
how much of the species status
depends on this project?
Important (I)
Moderately important (M)
Unimportant (U)
Feasibility ‘How easy is this to achieve?’
An assessment of the difficulty
associated with this project
Logistical and political, source of
funds, community attitudes
Biological
Feasible (F)
Moderately difficult (M)
Difficult (D)
Benefits ‘What good will it do?’ A
measure of how much good
will result from the project.
Reduction in extinction risk,
increase in population size, extent
of occurrence
Collateral biological
benefits, to other species or processes
Highly beneficial (H)
Moderately beneficial (M)
Unclear benefits (U)
Costs ‘What will it cost?’ An assessment
of the relative economic costs
of the project (or gains). In this
criterion there are both postive
and negative aspects which have
to be weighed against each other
Direct and indirect costs of project
Direct and indirect social and
economic costs and benefits that will
flow from the project
Expensive
Moderately costly
Inexpensive
Urgency ‘Can it be delayed?’ A measure
of whether the project is time-
limited, or whether it can be
delayed
Extinction risk, potential for loss of
opportunity if delayed
Urgent
Moderately urgent
Less urgent
Chance of
success
‘Will it work?’ An assessment of
whether or not the project will
work
Will it meet its specified objectives? Achievable
Uncertain
Highly uncertain
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PRIORITIZING CHOICES IN CONSERVATION 27
same priority as globally secure species exhibit-
ing slow decline in the UK. This would not be
most people’s choice; whatever their status in
the UK, a globally endangered species probably
should be in the category of highest priority.
Hence, Avery et al. (1995) set priorities using
their conservation cube (Fig. 2.1). Here they
evaluated each of the 27 possible circumstances
into three categories for priority. In their
system, any globally threatened species and
any species declining at a high rate nationally
are the highest priority.
This approach can be taken more generally
using the six criteria in Table 2.2. By asking
what would be the criteria associated with top
priority species, it is possible to assemble a pro-
file. For example, whereas a species conserva-
tion ‘idealist’ might choose to ignore
importance, feasibility, economic benefits and
chance of success, and to focus just on the most
urgent and most threatened forms, a more ‘pol-
itical’ approach would be to maximize import-
ance and economic benefits and minimize risk
of failure. Hence the two profiles would look
quite different (Fig. 2.2). Figure 2.2 illustrates
the different approaches – see how you would
score the criteria in Table 2.2 to make your own
set!
Here we are effectively creating a complex
rule set that maps any species into one of
three categories without adding or multiplying
the scores for different criteria. The method
suffers from its somewhat arbitrary nature.
Below we suggest that optimal allocation of
funds between species can be achieved more
rigorously if we place the problem within an
explicit framework in which we can apply
decision theory.
A decision theory approach – optimalallocation
A major problem with using scores or ranks for
threatened species to determine funding and
action priorities is that these methods were not
designed for that task – they were designed to
determine the relative level of threat to a suite of
species (Possingham et al. 2002). Hence they
cannot provide the solution to the problem of
optimal resource allocation between species –
this problem should be formulated then solved
properly (Possingham et al. 2001).
Optimal allocation is one simple and attract-
ive approach to prioritization that could inform
decisions about how to allocate resources be-
tween species. It requires information about
National decline
Responsibility
Conservation priority set
1
2 2 22
2233
2
2
1 11
11
1
1
Global threat
Fig. 2.1 The conservation cube. (From Avery et al.
1995.)
Importance
Manager 1
H M L
Manager 2
H M L
Idealist
H M L
Politician
H M L
Feasibility
Biological benefits
Economic benefits
Urgency
Chance of success
Fig. 2.2 Priority sets for four different people. The blocked out cells indicate the conditions under which
assessors would choose to include species in their priority set, according to how they scored on the variables
in Table 2.2 as H, high; M, medium; L, low.
Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 28 6.5.2006 11:07pm
28 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS
the relationship between the resources allo-
cated to the species and the reduction in prob-
ability of extinction. Here we use expert
opinion and/or population models to estimate
the relationship between percentage recovery
(measured, for example, in terms of probability
of not becoming extinct) and the funds allo-
cated to that species.
For poorly known taxa the curves showing
this relationship would very much be a reflec-
tion of expert opinion, garnered by asking
questions about how much it might cost to
give a particular species a 90% chance of not
becoming extinct in the long term. Given a set
amount of money for a set period in the con-
servation budget, the optimal allocation of
100
90
80
70
60
50
40
30
20
10
00 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000
$ spent
Rec
over
y (%
)
Scheme to maximise the total amount of recovery, given $ amount
16
14
12
10
8
6
4
2
00.E+00 1.E+07 2.E+07 3.E+07 4.E+07 5.E+07 6.E+07 7.E+07 8.E+07 9.E+07 1.E+08
$ in budget
Exp
ecte
d sp
ecie
s re
cove
red
Species accumulation
(a)
(b)
Fig. 2.3 Optimal allocation. (a) Three curves show the expected recovery for three different species given
certain amounts of investment. If the manager has a specified budget (in this case $1 million), the optimal
allocation among species that achieves the greatest total amount of recovery will result if funds are allocated
as shown by the vertical dotted lines (see Possingham et al. 2002). (b) Increasing investment leads to
gradually increasing numbers of species recovered.
Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 29 6.5.2006 11:07pm
PRIORITIZING CHOICES IN CONSERVATION 29
funds can be determined between species. This
occurs when the rate of gain of recovery for
each species is equal, such that there is no
advantage in shifting resources from one spe-
cies to another (see Fig. 2.3 and Possingham
et al. 2002). The implicit objective is to maxi-
mize the mean number of recovered species
given a fixed budget and assuming all species
are of equal ‘value’.
Using the set of species plotted in Fig. 2.3, we
estimate the costs of recovery, and then find
the optimal allocation of funds per species.
The species accumulation curve shows the
total expected number of species that can be
recovered given a conservation budget. The al-
gorithm will tend first to select species that
show large recovery for relatively low costs.
Slow responders will be conserved later. Given
an annual budget basis, the more intractable
conservation problems may never be funded
because the selection process will always favour
allocation of resources to the species that pro-
vide the greatest gains for the smallest costs
(the low-hanging fruit).
So how would these two approaches: cri-
teria-based prioritization and optimal allocation
of resources differ in practice? Obviously there
is no general answer to this, other than a priori
we do not expect them to be the same. The
outcome of a small case study, based on real
species and the expertise of two real conserva-
tion managers is shown in Fig. 2.4.
When species are rated highly by the criteria
the two approaches give similar results, but at
low criterion scores there can be much variabil-
ity. Perhaps the only general conclusion here is
that inevitably the optimal allocation approach
will favour some species that, on the basis of the
criteria, would not be given high priority. In
practice, sensible management could use both
approaches – the criteria to select high-priority
sets and the financial algorithm to then maxi-
mize the benefits from the finite resources avail-
able to conservation.
Conclusions
Priority setting needs to consider a range of
variables, and although this undoubtedly oc-
curs, it is not always transparent. Although
much effort has gone into biologically based
systems, in practice other societal value judg-
ments are often included. We suggest that, if
conservation goals are to be achieved, it is vital
to be explicit about what these are, and to
decide upon them in an open and consultative
manner before choices are made.
Different people and organizations, and differ-
ent sectors in society, will make different choices
in their value judgments. Approaches to under-
standing these choices are important so we can
interpret the differences in setting priorities.
4.5
4
3.5
3
2.5
2
1.5
1
0.5
00 1 2 3 4 5
Opt
imis
atio
n ra
nk
Criterion rank
Fig. 2.4 Comparison of priority ranks for18 species using the criteria-based method versus optimal allocation
of funds.
Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 30 6.5.2006 11:07pm
30 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS
We recommend using more than one
method to set priorities, and the comparison
can be informative. We also recommend that
decisions about resource allocation be formu-
lated more explicitly in terms of objectives,
constraints and costs.
For if one link in nature’s chain might be lost, another might be lost, until the whole of things will vanish bypiecemeal.(Thomas Jefferson (1743–1826) in Charles Miller, Jefferson and Nature, 1993.)
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Macdonald/Key Topics in Conservation Biology 1405122498_4_002 Final Proof page 34 6.5.2006 11:07pm
34 G.M. MACE, H.P. POSSINGHAM AND N. LEADER-WILLIAMS
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Project Team
Matthew Grainger (Newcastle University)
Phil McGowan (Newcastle University)
Ailsa McKenzie (Newcastle University)
Jeroen Minderman (Newcastle University)
Jon-Paul Rodriguez (IUCN-SSC)
Alison Rosser (UNEP-WCMC)
Andy South (Freelance)
Selina Stead (Newcastle University)
Mark Whittingham (Newcastle University)
Steering Group
Vin Fleming (JNCC)
Noel McGough (RBG Kew)
Trevor Salmon (Defra)
Michael Sigsworth (Defra)
Andy Stott (Defra)
Dominic Whitmee (Defra)
METHOD FOR THE ASSESSMENT OF PRIORITIES FOR
INTERNATIONAL SPECIES CONSERVATION
(MAPISCo)
Final Report
March 2013
MAPISCo Final report: Table of contents
2
Table of contents
1. EXECUTIVE SUMMARY ........................................................................................................ 4
2. INTRODUCTION .................................................................................................................. 9
2.1. Background ............................................................................................................................................................................... 9
2.2. Why is this method necessary? The link between policy demands and scientific capability ................. 10
2.3. Outline of the proposed project ..................................................................................................................................... 11
3. DEVELOPMENT OF THE METHOD ...................................................................................... 12
3.1. Selection of co-benefits ...................................................................................................................................................... 12
3.2. Focal co-benefits, data sets used and sources & category scoring..................................................................... 13 3.2.1. Habitat and area conservation (Aichi Targets 5 and 7) .................................................................................................... 13 3.2.2. Sustainable harvesting (Aichi Target 6) .................................................................................................................................. 15 3.2.3. Conservation of genetic diversity (Aichi Target 13) .......................................................................................................... 17 3.2.4. Protection of ecosystem services (Aichi Target 14) .......................................................................................................... 18 3.2.5. Preventing species extinction (Aichi Target 12) ................................................................................................................. 20
3.3. Database & prioritisation ................................................................................................................................................. 22 3.3.1. Database building .............................................................................................................................................................................. 22 3.3.2. Co-benefit weighting, re-scaling and priority score calculation ................................................................................... 22
3.4. Inclusion of red list threat classification data ........................................................................................................... 24
4. RESULTS - EXAMPLE PRIORITY LISTS .................................................................................. 27
4.1. Example 1: All species ........................................................................................................................................................ 30 4.1.1. Summary findings ............................................................................................................................................................................. 30 4.1.2. Taxonomic composition ................................................................................................................................................................. 30 4.1.3. Geographic composition ................................................................................................................................................................. 30 4.1.4. IUCN threat categories and classifications ............................................................................................................................. 30 4.1.5. Co-benefits ............................................................................................................................................................................................ 31
4.2. Example 2: Taxonomic case study – birds .................................................................................................................. 36 4.2.1. Summary findings ............................................................................................................................................................................. 36 4.2.2. Orders and Families.......................................................................................................................................................................... 36 4.2.3. Country/region ................................................................................................................................................................................... 36 4.2.4. IUCN Red List threat categories and classifications ........................................................................................................... 36 4.2.5. Co-benefits ............................................................................................................................................................................................ 37 4.2.6. Key findings and how they relate to policy ............................................................................................................................ 37
4.3. Example 3: Geographic case study – SE Asia .............................................................................................................. 44 4.3.1. General findings ................................................................................................................................................................................. 44 4.3.2. Taxonomic composition ................................................................................................................................................................. 44 4.3.3. Geographic composition ................................................................................................................................................................. 44 4.3.4. IUCN Red List threat categories and classifications ........................................................................................................... 44 4.3.5. Co-benefits ............................................................................................................................................................................................ 45 4.3.6. Key findings and how they relate to policy ............................................................................................................................ 45
5. USING THE METHOD ............................................................................................................. 50
MAPISCo Final report: Table of contents
3
5.1. Expandable –How does the does the priority list respond to the inclusion of additional co-benefit data? A plant example. ............................................................................................................................................................... 50
5.2. Adaptable - How does the priority list respond to changes in co-benefit weightings? .............................. 51 5.2.1. Sensitivity analysis ............................................................................................................................................................................ 51 5.2.2. Worked examples – threat status and ecosystem services ............................................................................................. 51
5.3. Usable - Development of Graphic User Interface (GUI) ......................................................................................... 54 5.3.1. GUI Development ............................................................................................................................................................................... 54 5.3.2. Constraints and legacy .................................................................................................................................................................... 55 5.3.3. Future development options ........................................................................................................................................................ 55 5.3.4. Future hosting options .................................................................................................................................................................... 56
6. DISCUSSION ......................................................................................................................... 57
6.1. Fit to original project brief ............................................................................................................................................... 57
6.2. How does the method compare with ‘business as usual’? .................................................................................... 58
6.3 How do co-benefits relate to IUCN threat status? ..................................................................................................... 58
6.4. Operating constraints ........................................................................................................................................................ 59
6.5. Integration of MAPISCo into decision-making – next steps ................................................................................. 60 6.5.1. SCIENCE. Ensuring that the methodology fully accounts for scientific advances .................................................... 61 6.5.2. PRACTICAL - Maintenance of database and incorporation of additional data. Where will the database be housed? ............................................................................................................................................................................................................... 61 6.5.3. POLICY - Integrating MAPISCo into policy and resource allocation decisions .......................................................... 63
6.6. Concluding remarks ............................................................................................................................................................ 64
7. REFERENCES ......................................................................................................................... 65
MAPISCo Final report: 1. Executive summary
4
1. Executive summary
Context. Biodiversity is declining at unprecedented levels globally, and meeting international
targets aimed at halting these declines requires conservation efforts targeted not only at species
but also at other aspects of biodiversity such as habitats, cultural values and ecosystem services.
In spite of this wide range of targets requiring investment, resources available are declining.
Against this backdrop, the UK Department for Environment, Food and Rural Affairs (Defra) sought,
via this project, to develop a methodology for identifying species for which targeted conservation
action would have the broadest consequential benefits (hereafter co-benefits) on other species,
habitats, wider ecosystems, and ecosystem services.
Agreed scope and aims. The objective of the MAPISCo project was to develop a scoring method
that enables species to be ranked based on their combined contribution to a selection of co-
benefits linked to conservation targets (Aichi Targets). Concentration of conservation on these
high-ranking species would, in theory, result in the largest associated biodiversity benefit. This
methodology would be expandable, able to include further datasets should they become available,
adaptable, with the weighting of co-benefits able to be altered in line with varying policy
aspirations, and usable, ultimately able to be used by non-expert practitioners.
Selection of co-benefits. Five co-benefits were selected for inclusion in the methodology- (1)
habitat and area conservation (Aichi Targets 5 and 7), sustainable harvesting of fish, invertebrates
and aquatic plants (Aichi Target 6), (3) conservation of genetic diversity of wild relatives of
cultivated plants and domesticated animals (Aichi Target 13), (4) protection of the provisioning of
ecosystem services (Aichi Target 14) and (5) the prevention of species extinctions (Aichi Target
12). The selection of these co-benefits was based on the Aichi Targets of policy interest to Defra.
They could also be linked in a scientifically defensible way with conservation effort on a species
level AND have adequate data associated with them (from preliminary searches) to be related to
species conservation lists, incurring as few taxonomic and geographic constraints as possible.
However, it should be stressed that this methodological framework can be extended to include
more co-benefits in the future, given, for example, specific policy needs (expandable).
Scoring. The methodology proposed here produces species lists ranked by their expected value in
contributing to each of the five focal co-benefits under consideration. First, reliable data sources
were identified that could be used to quantify the value of a given species to each of the five co-
benefits. Species from these data sources were then added to a database and given a score for
each of the five co-benefits. Details of how this was done are explained briefly in Table S1. The
scores from each database were then combined to obtain an overall value that corresponds to
species rank (see Box S1). Twelve data sources were found to be suitable for inclusion in the
database at this stage. Many more were identified but later disregarded because of issues with
data coverage and compatibility. The weighting, or importance, of each co-benefit can be adjusted
in response to policy aspirations – this makes the methodology highly adaptable.
Results – example priority lists. The results generated by the database in its current format are
constrained by data availability (e.g. only around 3% of all plant species have been categorised on
the IUCN Red List while almost all bird species are included). For this reason in this section we
present three different sets of results 1. All species, 2.Birds only (taxonomic case study) and 3. SE
Asia only (geographic case study). The use of case studies allows us to focus on discrete sets of
data, which, while not eliminating the constraints completely, allows a more meaningful
MAPISCo Final report: 1. Executive summary
5
demonstration of how the database can be used. For each set of lists we have outlined the main
findings from the method followed by a discussion of how some of the key findings could be related
to policy actions.
Generally, the results indicate that “politically interesting” or flagship species often championed by
interest groups do not generally rank highly (e.g. Polar Bear Ursus maritimus 45625th and White
Rhinoceros Ceratotherium simum 51510th), because they are associated with only a small number
of the co-benefits considered here. There are 1064 species which occur in the top 500 regardless
of changes in the co-benefit weightings, these include 502 birds, 161 mammals, 158 amphibians,
151 fish, 54 plants, 20 reptiles and 18 “other” species. This reflects the need for more data on
plants, reptiles and invertebrates. Both the Habitat and Ecosystem Services co-benefits are
significantly negatively correlated to Threat Status, meaning that more traditional approaches to
conservation (based on extinction risk- the IUCN Red List) do not capture more recent concerns
about protecting a range of co-benefits from each species. The MAPISCo methodology
successfully prioritises both extinction risk and contribution to co-benefits.
Using the method. Expandable. We demonstrate how additional species or co-benefit data can
be added to the database, and outline how such changes impact on the ranking of priority lists.
Adaptable. We examine the effect changing individual co-benefit weightings (i.e. making certain
co-benefits “more important” in the calculation of priority lists than others) has on priority list
ranking. Usable. Here we outline the development of a web-based interface, which, using a variety
of tabs and graphics, allows users to fully explore the priority lists created by the methodology
under a number of different scenarios. Importantly, it also enables user to investigate how varying
individual co-benefit weightings impact upon rankings. We view this as a critical feature of the
Graphical User Interface (GUI), as it makes it extremely adaptable to policy aspirations. Further
investment in this project could see this tool becoming available (open source) to interested parties
Discussion and project legacy. We conclude that we have delivered a methodology that can
prioritise species for conservation based on their expected contributions to a selection of co-
benefits. Thus, higher-ranking species should make greater contributions to meeting relevant Aichi
Targets (5-8 and 12-14). This methodology is expandable – additional datasets can be added to it
should they become available, adaptable – co-benefit weightings can be altered to fit with
individual policy aims and usable – the development of a graphic user interface will allow non-
technical users to use the method.
The original ambitious conceptual development of MAPISCo was rooted in the desire to embed
science firmly in international species policy. The core issue was that biodiversity spending can
tend towards projects focussed on charismatic animals with little evidence scientific justification for
such action. The method we present here yields priority lists based on available scientific evidence
but there are major caveats. The most important is the paucity of data available for some taxa
(especially plants). Whilst our analysis based on a well-known taxon (birds) for which all species
are assessed on the Red List does yield potentially usable results other prioritisation results based
on combining taxa are inevitably strongly constrained by data availability.
As a consequence of this we therefore outline a road-map to overcoming the challenges of linking
science and policy effectively in biodiversity governance in a way that will help ensure that
MAPISCo strengthens the UK’s ability to maximise the wider value to biodiversity of its spend on
international species conservation.
MAPISCo Final report: 1. Executive summary
6
Conclusion. We have developed a methodology which provides a broad-brush mechanism for
identifying species conservation priorities based on a selection of co-benefits. These co-benefits
are based on currently available and accessible data that are accepted to be good quality and
have the potential to be expanded as new data emerges. The project has demonstrated that the
choice of co-benefits, the importance given to them and the data sources used has a strong effect
on which species are identified as being higher priorities. Therefore, explicit policy decisions are
required (and need to be documented) throughout the prioritisation process. This finding alone is a
significant contribution to increasing engagement at the science-policy interface, because it shows
how closely intertwined the two spheres are. This feature of MAPISCo is likely to make it more
policy relevant than other prioritisation processes which are less sensitive to the practicalities of
policy-making. Implied in the original project brief is an assumption of a relatively straightforward
and linear science-policy interface, where policy asks a question, science answers it and then
policy decides what action should be taken. In practice, while this assumption has proved broadly
accurate, this must go hand in hand with meaningful dialogue between policy-makers and
scientists so that the best information available is used to inform policy as soundly as possible.
There is clear scope for Defra to build on the progress made in this project to allow scientific
knowledge and practice to better support UK government objectives. Overall, there is significant
potential for the methodology we have developed to become part of an iterative process where
conservation science and policy continually inform each other to produce evidence-based scientific
policy that is more relevant to society.
MAPISCo Final report: 1. Executive summary
7
Table S1. The data sources and scoring format used for each of the five co-benefits currently included in
the methodology. With further development we envisage being able to include a greater number of data
sources.
*These scores were rescaled to between 0 and 1 for the final ranking process and then standardized to give equal weighting between scores.
Co-Benefit Which data source do scores come
from?
How were species scored? *
1. Habitat/area
conservation
1) Important Bird Area (IBA)
2) Alliance for Zero Extinction (AZE)
1) Mean (average) number of co-
occurring species of conservation
concern (e.g. at high risk of
extinction) in all of the IBA’s in which
a species occurs.
2) Total number of species of
conservation concern co-occurring
with the target species in an AZE
2. Sustainable
harvesting
3) “FishBase” data on commercial
value in fisheries
4) IUCN Red List listed as affected
by aquaculture
5) “FishBase” for species used in
aquaculture
3) 1-6 (1=no interest, 6=highly
commercial
4) 1-3 (1=unknown, 3=industrial)
5) 1 or 0 (1 if listed, 0 if not)
3. Conservation
of genetic
diversity
6) Database of crop wild relatives
7) Lists of wild relatives of
domesticated animals
8) Plants listed as of medicinal use
6) 1 or 0 (1 if listed, 0 if not)
7) 1 or 0 (1 if listed, 0 if not)
8) 1-3 (least to most use)
4. Protection of
ecosystem
services
9) Carbon loss through
deforestation (country-level)
10) Freshwater availability (country-
level)
9) Estimate of loss of carbon through
deforestation (tonnes/year)
10) Availability of freshwater per
capita per year (m3/capita/year).
5. The
prevention of
extinctions
11) IUCN Red List (for animals)
12) SRLI (for plants)
11) & 12) 1-9 (1= extinct, 2= least
concern, 9=critically endangered)
MAPISCo Final report: 1. Executive summary
8
2) The mean score calculated in 1) is then standardised by taking away from it
the mean of all the values in the entire co-benefit column, then dividing it by
the standard deviation of that co-benefit mean (calculation of a “z score”).
The resultant score will be positive if the individual species score is greater
than the mean score and negative if the individual species score is smaller
than the mean score.
Box S1: A worked example of the final priority score calculation
The final priority score for a species is the sum of the scores given for the five co-benefits. The method for
calculating co-benefits scores is outlined below.
Species Habitat Harvesting Genetic diversity Ecosystem Service
Provisioning
Threat status Final Score
Francolinus camerunensis
(0.136+0.789)/2
=0.462
0.462-0.07 0.08
= 5.05
5.05*1
(0+0+0)/3
= 0
0-0.25 0.10
=-2.58
-2.58*1
(0+0+3)/3=
0.333
0.333- 0.24 0.11 =0.82
0.82*1
(0.323+0.975)/2
=0.649
0.462-0.55 0.11
=0.77
0.77*1
max(0.778,0)
= 0.778
0.462-0.45 0.25
=1.35
1.35*1
((5.05)+(-2.58)+ (0.82)+(0.77)
+(1.35)=
5.42
1) Mean taken of the scores assigned from original individual datasets (in this example, two different datasets).
4) Final score calculated by adding together the 5 co-benefit scores. This score is then used to rank species in the priority list.
3) The new co-benefit score is then multiplied by a weighting factor (in this case all co-benefits are weighted equally (i.e. weighting set to 1).
2) The mean score calculated in step 1 is then standardised by taking away from it the mean of all the values in the entire co-benefit column, then dividing it by the standard deviation of that co-benefit mean (calculation of a “z score”). The resultant score will be positive if the individual species score is greater than the mean score and negative if the individual species score is smaller than the mean score.
MAPISCo Final report: 2. Introduction
9
2. Introduction
2.1. Background
Biodiversity is declining at unprecedented levels globally: rates of species extinctions are
increasing while natural habitats are declining. This is largely as a result of anthropogenic
pressures (Butchart et al. 2010; Hoffmann et al. 2010). As a consequence, negative impacts on
humans accrue, not only through intrinsic loss of wildlife but also as a result of declines in and loss
of the ecosystem services healthy natural systems underpin and provide (Millennium Ecosystem
Assessment 2005; Cardinale et al. 2012). The choice of where and how to invest biodiversity
conservation effort is becoming increasingly difficult as available resources are shrinking and the
number of targets to which to contribute is growing. Against this backdrop, the UK Department for
Environment, Food and Rural Affairs (Defra) seeks through this project to develop a scientifically
robust and repeatable method to identify species for which targeted conservation action by
the UK Government would have the broadest consequential benefits (hereafter termed co-
benefits) for other species (or taxa), habitats, wider ecosystems, and ecosystem services. Key
conservation action aimed at such species will maximise contributions to international species
conservation treaties such as the Convention on Biological Diversity’s (CBD) Strategic Plan for
Biodiversity 2011-2020, which established twenty international targets to safeguard global
biodiversity. These are known as the Aichi Targets (COP 10 Decision X/2, see
http://www.cbd.int/decision/cop/?id=12268), and aim to safeguard biodiversity in its broadest sense
and at different levels. The targets of interest include preventing extinctions, conserving
habitats, controlling invasive species, sustainable harvesting, and protection of ecosystem
services.
As described in the original project brief (see Appendix 1), this method would involve the
development of a scoring system where individual species are linked, via existing data sources
(e.g. IUCN Red List, FishBase), to their expected contribution to various co-benefits (such as
ecosystem service provision or genetic relatedness to domesticated plants and animals). Species
recorded in the FishBase database, for example, as being important food sources would receive a
high score for a “sustainable harvesting” co-benefit, whereas a species recorded as having little or
no importance in harvesting would receive a low, or zero, score. This would enable individual
species to be ranked within an overall priority list based on their contribution to all the co-benefits
added together (the score for each co-benefit summed). Conservation action aimed at species
ranked at the top of this list would, therefore, be expected to have their greatest co-
Capsule.
With biodiversity declining at unprecedented levels globally, the choice of where and how to
invest biodiversity conservation effort is becoming increasingly difficult.
This project seeks to develop a methodology by which species can be prioritised for
conservation based not only on individual species benefits, but also on the contribution their
conservation may make to other species, habitats, wider ecosystems, and ecosystem
services.
This project aims to help bridge the gap between the contrasting spheres of science and
policy.
MAPISCo Final report: 2. Introduction
10
sequential benefits to the environment (based on the co-benefits selected for inclusion in the
method).
This methodology should be 1) expandable allowing the incorporation of future data, 2) adaptable
to changing policy aims and 3) usable by non-technical practitioners. This methodology would
then be available to and usable by a range of practitioners, and be adaptable to a wide range of
policy and conservation goals.
2.2. Why is this method necessary? The link between policy demands and scientific
capability
One key goal of this project is to link policy goals (i.e. the addressing of Aichi Targets) to real
conservation actions via sound scientific method. The fulfilment of this goal requires a smooth
transition from policy to science and back to policy - that scientifically robust findings can be used
to explore policy aspirations, and that the resulting policy is based on sound science and clear
decisions (Figure 1).
However, as the science and policy “spheres” tend to have very different rationales, time-lines and
objectives, the transition between them is often far from straightforward. Koetz et al. (2008)
synthesise several authors in arriving at their view of the issues at the heart of the science-policy
interface. They suggest that while science objectively deals with the generation of knowledge,
policy tends to be concerned with making subjective choices between different arguments, often
tackling interests and values that ultimately conflict (see also Appendix 5. Rapid Assessment
Report to support development of a Methodology for the Assessment of Priorities for International
Species Conservation, a report commissioned by this project, subcontracted to UNEP-WCMC).
This project aims to help bridge the gap between science and policy spheres by taking an
integrated approach. While the inputs of the methodology will be policy-driven (i.e. the selection
of co-benefits to which individual species conservation will be related and how these co-benefits
are individually weighted), the methodology used to address these policy questions will be based
on the best available scientific evidence. The development of a usable “front end” for this
methodology should enable the scientific finding to be used by policy makers and applied directly
to the policy sphere.
Figure 1. Classic view of the science-policy interface
1) Scope defined by policy aspirations
(e.g. Aichi Targets)
2) Scientific knowledge and data availability used to identify 'priorities'
3) Resources available and
deployed by policy makers
MAPISCo Final report: 2. Introduction
11
2.3. Outline of the proposed project
The project brief underwent considerable development in the early phase of this project (see
Appendix 1 and 2 for full details of the original brief and how it was amended). The final agreed
aims of the project are as follows:
To develop a scoring method which enables species to be ranked based on their
contribution to a selection of conservation targets (or co-benefits), that would be
expandable allowing the incorporation of future data, adaptable to changing policy
aims and usable by non-technical practitioners.
To test the results and usability of this methodology using case studies (taxonomic and
geographic), testing the expandability and adaptability of the database with the
addition of extra data sources and changes to co-benefit weighting.
To develop a web-based tool (a graphical user interface GUI) so that the methodology
can be demonstrated to and used by non-technical practitioners - usability.
To consider the broader science-policy context in which MAPISCo sits and propose
how it may become fully integrated in the future (the project legacy).
MAPISCo Final report: 3. Development of the method
12
3. Development of the method
3.1. Selection of co-benefits
The original aim of this project was to link the conservation of individual species to a large suite of
ecosystem co-benefits that could be related directly to the relevant Aichi Targets. The targets of
interest included preventing extinctions, conserving habitats, controlling invasive species,
sustainable harvesting, and protection of ecosystem services. However, preliminary work indicated
that for many species groups, sufficient data linking their conservation to many of the suggested
co-benefits are either not available or not easily accessible. Moreover, the full range of co-benefits
set out in the original brief was likely too large for the project timeframe.
For these reasons, a subset of five co-benefits was selected for inclusion in the development of the
methodology (see Box 1). This selection was made based on the contribution these co-benefits
made to Aichi Targets of policy interest to Defra, their links with conservation effort on a species
level and adequate data being available (from preliminary searches) to link them to species
conservation lists.
However, it should be stressed that further co-benefits could be included in the future to
incorporate specific policy needs (expandable).
Capsule.
Co-benefits selected (by the steering group) for inclusion in the method: (1) Habitat and
area conservation, (2) Sustainable harvesting of fish, invertebrates and aquatic plants, (3)
Conservation of genetic diversity of wild relatives of cultivated plants and domesticated
animals, (4) Protection of the provisioning of ecosystem services, and (5) Prevention of
species extinctions.
These co-benefits can be changed or added to - this makes the method expandable.
Scoring method developed which produces lists in which species are ranked by their
expected value in contributing to each of the five co-benefits above.
The weighting, or importance, of each co-benefit can be adjusted in response to policy.
aspirations. This makes the method adaptable.
Box 1. The five co-benefits selected for inclusion in the development of the methodology, and the
Aichi Targets to which they contribute
1. Habitat and area conservation (Targets 5 and 7; hereafter termed “Habitat co-benefit”)
2. Sustainable harvesting of fish, invertebrates and aquatic plants (Target 6; hereafter “Harvesting co-benefit”)
3. Conservation of genetic diversity, in particular of wild relatives of cultivated plants and domesticated animals (Target 13; hereafter “Genetic Diversity co-benefit”)
4. Conservation of the provisioning of ecosystem services (Target 14; hereafter “ES co-benefit”)
5. Prevention of species extinctions (Target 12; hereafter “species extinction co-benefit”).
MAPISCo Final report: 3. Development of the method
13
3.2. Focal co-benefits, data sets used and sources & category scoring
In the following sections, for each of the five co-benefits selected for inclusion we discuss (i) the
rationale for links to conservation effort on a species level, (ii) data sets chosen to make this link
and (iii) quantitative scoring used to integrate each data source in the prioritisation methodology.
A note on data sources
The majority of data sources were identified through discussions with experts at the project
workshop (see Appendix 4). Many suggested data sources were unsuitable for use in the final
methodology due to taxonomic or geographic biases in datasets, or general data accessibility
issues. For example, for the habitat co-benefit, Biodiversity Hotpots (Myers et al. 2000) and the
Global “200” Ecoregions project data (Olsen & Dinerstein 2002) could not be used as species
associations made in the lists were taxonomically biased and the data were not easily available.
For the harvest co-benefit, the Seas Around Us project (www.seaaroundus.org) was also rejected
due to the data not being publicly accessible. For the genetic diversity co-benefit, the SEPASAL
database (www.kew.org/ceb/sepasal) was rejected as data were both taxonomically and regionally
biased as well as being difficult to access. Further examples of investigated but unsuitable
databases are listed in Appendix 8, Table A8-1.
3.2.1. Habitat and area conservation (Aichi Targets 5 and 7)
Rationale
Target 5: “By 2020, the rate of loss of all natural habitats, including forests, is at least halved and
where feasible brought close to zero, and degradation and fragmentation is significantly reduced.”
Target 7: “By 2020 areas under agriculture, aquaculture and forestry are managed sustainably,
ensuring conservation of biodiversity.”
Both Aichi Target 5 and 7 relate to the conservation or sustainable management of natural and
semi-natural habitats. Although conservation effort directed at species usually involves a degree of
protection of the habitat(s) (Mace & Collar 2002), such contribution to habitat conservation is likely
to be greater for some species than for others.
One way to link species-level conservation to habitat conservation is to focus on those species that
are thought to be of disproportionate importance to their habitats or co-occurring species (i.e.
“surrogate species” such as umbrella, keystone or indicator species; (Caro & O’Doherty 1999;
Caro & Girling 2010). However, the effectiveness of surrogate species for conservation is widely
criticised (e.g. Lindenmayer et al. 2002; Saetersdal & Gjerde 2011). More importantly, concrete
evidence for the effectiveness of species as surrogates for habitats is limited and often highly
context-dependent (Andelman & Fagan 2000), which means globally applicable lists of appropriate
habitat surrogate species are not available.
Instead, in the current project we have chosen to link species to habitats by focussing on
species that have previously been associated with, or used as “triggers” for the
designation of Key Biodiversity Areas (KBAs) (Eken et al. 2004). Defined as “sites of global
significance for biodiversity conservation “(Eken et al. 2004), KBAs are designated based on the
conservation of habitat within them being important or even vital for the persistence of one or more
MAPISCo Final report: 3. Development of the method
14
target species (Eken et al. 2004). However, conservation effort directed at these target species is
likely to benefit the wider habitat, making KBAs important areas for a wide and varied number of
species. There is also a general consensus that the conservation of KBA sites has many wider
benefits (e.g. in terms of cultural value or provisioning of ES (e.g. Butchart et al. 2012; Larsen,
Turner & Brooks 2012).
Theoretically, conservation effort directed at a species recorded in a KBA will also benefit non-
target species co-occurring in that KBA, as well as having other localised benefits (e.g. ecosystem
service provision). Therefore, conservation effort directed at species recorded in species-rich KBAs
is likely to produce higher levels of habitat co-benefits than conservation of a species that occurs in
a species poor KBA.
A “habitat score” for each individual species is calculated as follows: for each species associated
with a KBA, we use the mean number of other species known to co-occur in all KBAs in which that
individual species occurs. This is, in effect, a proxy measure of the expected contribution a given
species may make to habitat conservation overall.
Data sources
The linking of species and habitats necessary in this approach relies upon the availability of
species inventories for individual KBAs. However, these inventories are not always available for all
types of KBA, or for all species groups. For this reason we have decided to include data from the
two types of KBA for which most data is readily available: Important Bird Areas and Alliance for
Zero Extinction sites.
Important Bird Areas (IBAs) are sites identified as being globally important for the
persistence of one or more populations of endangered bird species. Identified using
standardised criteria (http://www.birdlife.org/datazone/info/bacritglob [Accessed 22 August
2012]), a site qualifies for IBA status if it holds or is thought to hold significant populations
(or parts of populations) of bird species (1) listed as endangered (Critically Endangered,
Endangered or Vulnerable) on the IUCN Red List, (2) with a restricted range (e.g.
endemics), (3) that are (largely) restricted to a single biome, or (4) that are migratory or
congregatory and for which the site is important during particular parts of the year (BirdLife
International 2008, http://www.birdlife.org/datazone/info/ibacriteria [Accessed 22 August
2012]). To date, over 10,000 IBAs have been identified globally
(http://www.birdlife.org/datazone/site [Accessed 22 August 2012]), in which 4847 species
are listed to occur. The number of bird species listed per IBA site ranges from 1 to 247, with
an average of 9 per site.
Alliance for Zero Extinction (AZE) sites are sites which hold the last remaining population(s)
of highly threatened species of mammals, birds and/or selected reptiles, amphibians and
conifers. To qualify as an AZE site, a site must (1) hold at least one species listed as
Critically Endangered or Endangered on the IUCN Red List, (2) hold all or the majority
(>95%) of the known population of the species, and (3) must be geographically and
politically discrete (Ricketts et al. 2005). To date, 587 AZE sites containing 920 species
have been identified. The number of species listed per AZE site ranges from 1 to 22, with
an average 1.6 species per site.
MAPISCo Final report: 3. Development of the method
15
Species-level score
All IBA and AZE inventories were sourced and the species included on them listed in a database in
preparation for the assignment of a score.
For species listed as occurring in an IBA (or listed as a “trigger” species used to delineate an
IBA), individual species scores were equal to the mean number of species recorded as co-
occurring with that individual species across all IBAs for which there was a record. For example, if
species A was recorded on three IBA inventories, and co-occurred with five species on one
inventory, ten on another and twenty on the third, the score species A received would be mean of
5, 10 and 20 = 11.67. This resulted in each species receiving a value between 1 and 246, with a
mean of 34.3.
Species listed as occurring in an AZE were attributed a score equal to the total number of
species co-occurring with that species at the site in which it was recorded (not an average because,
by definition, a given species occurs in only a single AZE site). This resulted in a score ranging
from 1 to 22 (22 being the maximum number of species listed for one site) with a mean of 3.5.
Species listed as occurring in both an IBA and an AZE were given the mean score. Those not
listed as occurring in either an IBA or an AZE site were not allocated any score.1
For both scores, higher values indicate that, on average, a species occurs in AZE or IBA sites that
hold larger numbers of other species. Conservation effort directed at species with such higher
scores is therefore likely to both benefit their wider habitat as well as a larger number of co-
occurring species.
3.2.2. Sustainable harvesting (Aichi Target 6)
Rationale
“By 2020 all fish and invertebrate stocks and aquatic plants are managed and harvested
sustainably, legally and applying ecosystem based approaches, so that overfishing is avoided,
recovery plans and measures are in place for all depleted species, fisheries have no significant
adverse impacts on threatened species and vulnerable ecosystems and the impacts of fisheries on
stocks, species and ecosystems are within safe ecological limits.”
It was assumed (in discussion and agreement with the steering group) that conservation effort
directed at harvested fish species or species involved in aquaculture production would contribute
to this target. Moreover, we assumed that such contributions would be stronger for species that are
seen as having greater economic value.
Data sources
Thus, we used three data sources to identify species relevant to this target:
1) Commercial value of a species to fisheries. FishBase (Froese & Pauly 2012) provides a
qualitative assessment of the economic value of 3111 fish species across countries in
which they are harvested. The overall economic value or importance of each species is
categorised in one of six categories, ranging from “no interest” to “highly commercial”. The
definitions of the categories are given in Table 1.
MAPISCo Final report: 3. Development of the method
16
2) Species listed as affected by aquaculture on the IUCN Red List (IUCN 2012). As well as
conservation status and extinction risk, the IUCN Red List (2012.1) records types of threats
faced by species. One of these threat classifications is aquaculture. We identified 269
species listed under Threat Classification (v.3.1) 2.4 (Marine & freshwater aquaculture),
which distinguishes between species impacted by “Industrial” (Classification 2.4.2),
“Subsistence/artisanal” (Classification 2.4.1.) and “Unknown” levels of aquaculture
(Classification 2.4.3).
3) Species listed as used in aquaculture production on FishBase (204 species, C. McOwen
pers. comm.).
Table 1. FishBase commercial harvesting categories and scores attributed.
Category No. spp. % spp.
Category
score Definition
Highly commercial 207 6.7 6 The species is very important to the
capture fisheries (or aquaculture) of
a country
Commercial 1416 45.5 5 The species is regularly taken in the
capture fisheries or regularly found
in aquaculture activities of a country
Minor commercial 1233 39.6 4 The species is of comparatively less
importance in capture fisheries or
aquaculture in a given country
Subsistence fisheries 210 6.8 3 The species is consumed locally only,
mostly by the fishers themselves
Of potential interest 2 0.1 2
Of no interest 43 1.4 1
Species-level scores
All species were attributed a score for each of the three data sources listed above. First, species
listed as commercially harvested in FishBase were attributed a score between 1 (for “no interest”)
to 6 (for “highly commercial”), reflecting increasing conservation priority for species of greater
economic interest (Table 1). Second, species listed as threatened by aquaculture on the IUCN Red
List were attributed a score between 1 (for “unknown scale”) and 3 (for “industrial”) reflecting the
increasing intensity of aquaculture threat and therefore the increasing potential for conservation
effort directed at such species to benefit the target in question (Table 2). Third, species listed as
used in aquaculture production on FishBase were attributed a score of 1, whereas other species
were not attributed any value.
MAPISCo Final report: 3. Development of the method
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Table 2. Species and classifications listed under Threat Classification (v.3.1) 2.4 (Marine & freshwater aquaculture) on the IUCN Red List (2012.1), and category scores attributed.
Category No. spp. % of spp.
Category
score
Industrial 27 10 3
Subsistence/artisanal 16 5.9 2
Scale unknown 226 84 1
3.2.3. Conservation of genetic diversity (Aichi Target 13)
Rationale
“By 2020, the genetic diversity of cultivated plants and farmed and domesticated animals and of
wild relatives, including other socio-economically as well as culturally valuable species, is
maintained, and strategies have been developed and implemented for minimising genetic erosion
and safeguarding their genetic diversity.”
It was assumed (in discussion and agreement with the steering group) that conservation effort
directed at species extant in the wild will contribute to the preservation of (unique) genetic diversity
of the targeted species. Following the central tenet of this target, we chose to focus on wild
relatives of crops and domestic animals, and further expanded our consideration into plant species
with a known medicinal use.
Data sources
We used the following data sources to identify species relevant to this target:
1) A database of wild relatives of plant crop species. This database (Vincent et al. in prep.)
lists 1385 high priority crop wild relative (CWR) species. CWR are wild species closely
related to crop species which have the potential to contribute valuable traits (e.g. disease
resistance) to crops in the future. Vincent et al. define CWR as those species which are
sufficiently similar genetically to allow crossing (either naturally or in the laboratory; the
“gene pool concept”) or in some cases those species belonging to the same genus (the
“taxon concept”).
2) Lists of wild relatives of domesticated animal species, compiled from the FAO World Watch
List for Domestic Animal Diversity (FAO 2000) and from (McGowan 2010). The former
document identified avian and mammal species representing domestic animal genetic
resources at risk of loss, based on a range of survey- and monitoring- efforts. From these,
we identified those species extant in the wild and/or listed as at risk from hybridisation on
the IUCN Red List (IUCN 2012, in total 210 species). Among birds, Galliformes are
particularly important economically and we therefore added species from family and genera
identified in McGowan 2010 and listed on the IUCN Red List (IUCN 2012) as relatives of
domesticated animals (in total 323 species). It should be noted that in light of a recent
review (Owens & McGowan in prep), neither Cracidae (chachalacas, guans and curassows
MAPISCo Final report: 3. Development of the method
18
from Central and South America) nor Megapodidae (mound-builders from the Australasian
region) receive scores for this co-benefit. This is because no successful hybrids between
species from these families and domesticated poultry have been recorded (McCarthy 2006).
3) Plants species listed as used for medicinal purposes in the BGCI PlantSearch database
(http://www.bgci.org/plant_search.php/ [Accessed 22 August 2012]) and Hawkins (2008).
The BGCI PlantSearch database is compiled from information supplied by botanical
gardens worldwide, including whether a given species has a medicinal use. The database
holds 1788 species records listed as having medicinal use. Hawkins (2008) compiled
information on many medicinal plant species from a range of sources and expert opinion
questionnaires, and lists 429 medicinal plant priorities.
Species-level scores
All species were attributed a numerical score for each of the data sources outlined above. First,
reflecting their status as CWR, species occurring in the CWR database were attributed a score of 1.
Second, species occurring on our compiled list of relatives of domesticated animals were also
attributed a score of one. Species not on either list were not attributed any score. The resulting
binary scores reflect our limited ability using these data sources to distinguish further between the
relevant species in terms of priority (e.g. a species either is a CWR or not). Third, species listed in
the top 35 of Annex 5 of Hawkins (2008) were attributed a score of 3, remaining species in the
same list were scored as 2 and other species with a known medical use (listed in the PlantSearch
database) were scored as 1.
3.2.4. Protection of ecosystem services (Aichi Target 14)
Rationale
“By 2020, ecosystems that provide essential services, including services related to water, and
contribute to health, livelihoods and well-being, are restored and safeguarded, taking into account
the needs of women, indigenous and local communities, and the poor and vulnerable.”
To maximise contributions to the protection of ecosystem services (ES) by conservation effort
directed at a species level, evidence is required that shows the relative importance of species to
the provisioning of ES. While it is widely recognised that ecosystems provide a range of services
and benefits to humans, and indeed that groups of species can be associated with broad service
provision (Millenium Ecosystem Assessment 2005; UK NEA 2011), the evidence base linking
individual species to particular services is limited and evidence showing the relative value of
different species for a given service even more so. Where such evidence is available, it is often
limited to a single species in a particular context (e.g. Vira & Adams 2009; MAPISCo Project Team
2012). Such context-dependent examples do not constitute the solid evidence base necessary for
the taxonomic and geographic scope required for the present methodology. Moreover, examples of
broad species groups providing essential services are prevalent and often cited. However, in such
cases, often large numbers of species are involved, and their relative importance to the provision
of the service in different contexts is unclear. For example, although it is well known that many
insects are vital as pollinators of economically important crop species, the value of individual
pollinating species has, in the vast majority of cases, not been quantified. This inability to
MAPISCo Final report: 3. Development of the method
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distinguish between such species in terms of their relative value to the service in question limits the
use of such data in a species prioritisation methodology.
By contrast, there is a growing consensus that priorities for biodiversity conservation and for ES
can be reconciled using area-based (as opposed to species-based) approaches, for example
freshwater provisioning or carbon sequestration along with levels of biodiversity (Lamoreux et al.
2005; Goldman et al. 2008; Naidoo et al. 2008; Egoh et al. 2011; Fisher et al. 2011; Butchart et al.
2012). Thus, here we chose to make the link between species and the provisioning of a selection
of ES by focusing on a larger, habitat- and country scale. The advantage of focusing on a country
level is that broad measures of the provisioning of some ES are available at country level, and
species occurrence data within broad habitats in countries is more readily available (and potentially
more reliable) than finer-scale measures of distribution.
Data sources
We focus on two example ES – 1) estimated carbon loss through deforestation and 2) freshwater
availability. We used the following data sets to link country-level measures of these two services
to species level:
1) Carbon loss through deforestation (tonnes/year). To estimate this, we multiplied country-
level estimates of (1) the stock of carbon in living forest biomass in 2010 (tonnes/hectare)
with (2) the trend of the extent of primary forest between 2005-2010 (change in hectares),
as available in the FAO Global Forest Resources Assessment 2010 (FAO 2010) database
(http://countrystat.org/index.asp?ctry=for&HomeFor=for [Accessed 22 August 2012]).
2) Freshwater availability. As a measure of the availability of freshwater to people, we used
the country-level estimated total renewable per capita freshwater supply in 2010, as
obtained from the FAO AQUASTAT database
(http://www.fao.org/nr/water/aquastat/data/query/index.html [Accessed 22 August 2012]).
Lower values (<1000 m3/capita) indicate water scarcity (UNEP Vital Water Graphics:
www.unep.org/geo/geo4/report/Glossary.pdf [Accessed 22 August 2012]).
3) Habitat- and country- occurrence. We used the data in the IUCN Red List (IUCN 2012)
augmented by the Sampled Red List Index (SRLI) for plants
(http://threatenedplants.myspecies.info/ [Accessed 22 August 2012], and S. Bachman pers.
comm.) (to increase representation of plant species) to identify species occurrence in
countries, and in (1) forest habitats (Habitat Classification 1) and wetland (inland) habitats
(Habitat Classification 5). See section 3.2.5 (page 20) for more information on the data
used from the Red List and SRLI for plants.
Species-level scores
We assumed that conservation effort directed at forest species occurring in countries with higher
estimated rates of carbon loss through deforestation is more likely to make contributions to targets
to reduce carbon loss or increase carbon sequestration. Similarly, because wetland and forest
habitats are particularly important in controlling both the supply and quality of freshwater (Millenium
Ecosystem Assessment 2005; Larsen, Turner, & Brooks 2012), we assumed that conservation
effort directed at forest or wetland species occurring in countries with lower levels of per capita
MAPISCo Final report: 3. Development of the method
20
freshwater supply is likely to make greater contributions to targets aiming to alleviate water scarcity
or stress.
Accordingly, species occurring in forest habitats (IUCN Red List Habitat Classification 1) were
attributed a score for the estimated carbon loss through deforestation, calculated as the average
estimated carbon loss through deforestation across all countries in which the species occurs.
Lower values indicate an association with higher rates of loss and therefore higher conservation
priority. Similarly, species occurring in forest- or wetland habitats (IUCN Red List Habitat
Classifications 1 and 5) were attributed a score for freshwater supply calculated as the average
estimated supply across countries in which it occurs. Lower values indicate a greater association
with higher levels of water scarcity, and therefore higher conservation priority.
3.2.5. Preventing species extinction (Aichi Target 12)
Rationale
“By 2020 the extinction of known threatened species has been prevented and their conservation
status, particularly of those most in decline, has been improved and sustained.”
As the present prioritisation methodology aims to maximise co-benefits of the conservation of
species, we considered Aichi Target 12 to be our “focal” target and assumed that conservation
effort directed at more highly threatened species would contribute most to it.
Since revisions from the IUCN Red Data Books (Mace & Lande 1991), the IUCN Red List has
grown to become not only the most comprehensive data set on the extinction risk of a wide range
of species from various taxonomic groups, but also represents an effective data source for species
occurrence and habitat classifications (IUCN 2012). Red List threat status assessments are made
by experts according to well-documented standards (IUCN 2001)
(http://www.iucnredlist.org/technical-documents/categories-and-criteria/2001-categories-criteria
[Accessed 22 August 2012]). Species are placed into one of nine threat categories representing
increasing extinction risk, based on a range of criteria including (1) declines in population size, (2)
restrictions in geographic range, (3) small absolute population size or (4) analytical evidence of
high extinction risk.
Data sources and species-level scores
For each species listed on the IUCN Red List v. 2012.1 (IUCN 2012) and/or the SRLI for plants
(http://threatenedplants.myspecies.info/ [Accessed 22 August 2012], and S. Bachman pers.
comm.), the most recent threat status assessment was obtained. Each category was attributed a
default numerical score on a linear scale, from 1 for the lowest category (Extinct) to 9 for Critically
Endangered (this scale was set by the larger number of categories in the Red List data which has
a “Lower Risk” category not present in the SRLI plant data), so that higher scores represent a
greater risk of extinction. Species not occurring on either list were not attributed any score. The
SRLI data had a “Not Evaluated” category, which was attributed a score of zero. Extinct in the Wild
was treated as a higher priority category by attributing the second-to-highest score in both cases,
with the view that the key goal of the prioritisation methodology is to achieve in situ conservation
and species currently only persisting ex situ therefore require substantial conservation effort. For
precautionary reasons, Data Deficient species were scored between Near Threatened and Least
MAPISCo Final report: 3. Development of the method
21
Concern, which in both the Red List and SRLI scoring is near the middle of the score distributions.
See Tables 3 and 4 for the default numerical scoring for the Red List and SRLI for plants,
respectively.
Table 3. Threat categories and number of species in the IUCN Red List (2012.1), and scores attributed. Species “Not Evaluated”(NE) were scored 0.
Table 4. Threat categories and number of species in the Sampled Red List Index (SRLI) for Plants, and scores attributed.
Although both the relative scores among species and the scale used (e.g. linear, exponential) are
inevitably largely subjective, the translation of threat categories into numerical scores used here is
similar to that used in previous prioritisation studies (e.g. Rodriguez et al. 2004, Butchart et al.
2012). Because of the qualitative nature of these scores, we suggest that in the final version of the
present prioritisation methodology these scores can be altered to suit changing expert opinion or
policy aspirations.
In addition to conservation status assessments, from the RL and SRLI for plants we obtained lists
of countries in which each species occurs, and lists of species occurring in forest habitats (IUCN
Red List Habitat Classification 1) and wetlands (Classification 5).
Category code Category No. spp. % of spp. Category score
CR Critically Endangered 3947 6.412 9
EW Extinct in the Wild 63 0.102 8
EN Endangered 5766 9.368 7
VU Vulnerable 10105 16.417 6
NT Near Threatened 3452 5.608 5
DD Data Deficient 10497 17.054 4
LC Least Concern 26922 43.738 2
EX Extinct 801 1.301 1
Category code Category No. spp. % of spp.
Category
score
CR Critically Endangered 1813 10.930 9
EW Extinct in the Wild 31 0.187 8
EN Endangered 2688 16.204 7
VU Vulnerable 4998 30.130 6
NT Near Threatened 752 4.533 5
DD Data Deficient 1244 7.499 4
LR/cd Lower Risk 223 1.344 3
LR/lc Lower Risk 909 5.480 3
LR/nt Lower Risk 677 4.081 3
LC Least Concern 3162 19.062 2
EX Extinct 91 0.549 1
MAPISCo Final report: 3. Development of the method
22
3.3. Database & prioritisation
3.3.1. Database building
For a full explanation of the database structure see Appendix 6. Briefly, data from the sources
described in section 3.2, page 13, were cleaned (errors removed) and compiled using R (v. 2.15.0,
R Development Core Team 2012) and resulting tables stored in a SQLite relational database (v.
3.7.11). The resulting main output table contained one row per species, with either a relevant value
for each data source, or a “blank” indicating the species does not occur in that data set.
3.3.2. Co-benefit weighting, re-scaling and priority score calculation
A worked example of how the final priority score for each species was calculated is provided in
Box 2). Broadly, the score was calculated by following these steps
1) Individual species scores from each data set were rescaled to allow them to be compared
on the same scale. First, each individual dataset score was divided by the maximum value
for scores from that particular dataset. This allowed all scores to be assigned a value
between 0 and 1. For example, a species receiving a score of 7 for the species extinction
co-benefit (i.e. a threatened species) was rescored to 0.778 (raw score of 7 divided by the
maximum score for that database of 9 [the score given to Critically Endangered species]),
while a species scoring 2 for extinction risk (Least Concern) was rescored to 0.222 (raw
score of 2 divided by the maximum score of 9). For database scores listed only as 0 or 1
(binary scores, such as those for “Aquaculture use” obtained from FishBase and species
listed as “Crop Wild Relatives”) this transformation had no effect. For both ES data sources
(estimated carbon loss through deforestation and freshwater availability) lower scores were
associated with higher priorities, so their scales were inverted as well as rescaled.
2) Scores attaining to each co-benefit were then combined to create a “score per co-benefit”.
For the prevention of species extinction (section 3.2.5, page 20), this co-benefit score
equalled either the Red List conservation status score or the plant SRLI score, whichever
was greater. All other co-benefits scores (Habitat, Harvesting, Genetic Diversity and ES
Provisioning) were calculated by taking the mean of the individual dataset scores
contributing to the co-benefit.
3) The overall co-benefit score for each species was then standardised. This was necessary
because while the individual database scores were “rescaled” to between zero and one as
described in 1), their position along this 0-1 scale was arbitrary. For example, for a species
in receipt of a co-benefit score of 1 for harvesting (of which there could only be a score of 1
or 0 due to limitations in the data) and a score of 0.56 for extinction list, the harvesting
score is not “twice” as important as the habitat extinction score – it is only twice as large as
a result of the scoring method of the individual datasets. For scores to be standardised a z-
score calculation was made – this is an accepted standardising technique. The quantity z
represents the distance between the raw score and the population mean in units of the
standard deviation. z is negative when the raw score is below the mean, positive when
above. This means that the co-benefit scores will be related in terms of the overall mean
score.
MAPISCo Final report: 3. Development of the method
23
a. First, the mean and standard deviation of all the scores given to individual species
was calculated for each co-benefit. It is important to note that this mean is
calculated with empty cells being treated as missing data rather than as zero data
(i.e. not included in the calculation of the mean). This is important, as the database
contain a large percentage of “missing data” given few species receive scores in all
databases. Treating them as “zeros” biases the database towards species that
have scores for more co-benefits. By treating them as missing data this is a more
accurate representation of what is known – i.e. it is not known if there is a
relationship, rather than there is no relationship (p 45).
b. Each co-benefit score was then standardised by taking this mean score away from
it, and dividing it by the standard deviation of this mean. The “z-score” was negative
when the raw score was below the mean and positive when it was above.
4) The new score per co-benefit was then able to be modulated further by multiplying each by
a weighting factor between 0 (no contribution) and 1 (maximum contribution). These
weights can be modified based on policy decisions regarding the importance of each co-
benefit. The default (as set in the database) is that all are equally important.
5) The final composite priority score per species equalled the sum of the weighted co-benefit
scores.
The resultant list was sorted by decreasing final priority score. We used seven broad taxonomic
groups to present the results below: amphibians (class Amphibia), birds (class Aves), fish (classes
Actinopterygii, Cephalaspidomorphi, Chondrichthyes, Myxini and Sarcopterygii), mammals (class
Mammalia), plants (kingdom Plantae), and reptiles (class Reptilia). These groups were chosen
because they represent a wide range of taxonomic groups and are relatively well represented in
the combined data sources used (e.g. as opposed to insects). The Red List was used as the
primary source of taxonomic data, although for species not included on the Red List additional
taxonomic data was used from the other data sets, where available, or inferred from the type of
data. Species not belonging to any of the above groups were grouped as “Other” (mainly
invertebrates).
MAPISCo Final report: 3. Development of the method
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3.4. Inclusion of red list threat classification data
While Red List “threat category” (e.g. Critically Endangered, Near Threatened etc.) was included
as a co-benefit in the methodology (from the Red List for animals and from the SRLI list for plants),
the nature of that threat was not. The Red List “Threats Classification scheme” assigns a threat
type to each of the species listed within it. The threats take a hierarchical format, with main threat
categories subdivided into a number of subcategories, some of which are subdivided further (Table
5 and Appendix 8, Table A8-2 for full explanation of categories). The addition of this data to the
overall database would allow the production of a) lists containing major threat classifications for
particular species groups or geographical regions, and/or b) lists of species per threat classification.
2) The mean score calculated in 1) is then standardised by taking away from it
the mean of all the values in the entire co-benefit column, then dividing it by
the standard deviation of that co-benefit mean (calculation of a “z score”).
The resultant score will be positive if the individual species score is greater
than the mean score and negative if the individual species score is smaller
than the mean score.
Box 2: A worked example of the final priority score calculation
The final priority score for a species is the sum of the scores given for the five co-benefits. The method for
calculating co-benefits scores is outlined below.
Species Habitat Harvesting Genetic diversity Ecosystem Service
Provisioning
Threat status Final Score
Francolinus camerunensis
(0.136+0.789)/2
=0.462
0.462-0.07 0.08
= 5.05
5.05*1
(0+0+0)/3
= 0
0-0.25 0.10
=-2.58
-2.58*1
(0+0+3)/3=
0.333
0.333- 0.24 0.11 =0.82
0.82*1
(0.323+0.975)/2
=0.649
0.462-0.55 0.11
=0.77
0.77*1
max(0.778,0)
= 0.778
0.462-0.45 0.25
=1.35
1.35*1
((5.05)+(-2.58)+ (0.82)+(0.77)
+(1.35)=
5.42
1) Mean taken of the scores assigned from original individual datasets (in this example, two different datasets)
4) Final score calculated by adding together the five co-benefit scores. This score is then used to rank species in the priority list.
3) The new co-benefit score is then multiplied by a weighting factor (in this case all co-benefits are weighted equally (i.e. weighting set to 1)
2) The mean score calculated in step 1 is then standardised by taking away from it the mean of all the values in the entire co-benefit column, then dividing it by the standard deviation of that co-benefit mean (calculation of a “z score”). The resultant score will be positive if the individual species score is greater than the mean score and negative if the individual species score is smaller than the mean score.
MAPISCo Final report: 3. Development of the method
25
Species data were downloaded from the IUCN Red List website for each major threat category
(categories 1-12 Table 5). Only “first tier” threat classifications were used, rather than sub-
categories, as these data were more robust. These data were then integrated into the main
MAPISCo database using the unique binomial species name and or species identification number
as a link between data tables. This enabled us to produce lists of threat data as shown in Table 6.
Threat data was not scored in the current incarnation of the database because of uncertainties in
the IUCN threat classification process for each focal taxon. Where data are better standardised
(such as for the birds) there is the potential for these threats to be ranked and scored in
accordance with policy aspirations (expandable).
Table 5. The IUCN threat classification scheme categories (see Appendix Table A8-2 for full list and
definitions)
Main threat category Sub-category (number of further sub-categories)
1 Residential & commercial development
1.1 Housing & urban areas 1.2 Commercial & industrial areas 1.3 Tourism & recreation area
2 Agriculture & aquaculture
2.1 Annual & perennial non-timber crops (4) 2.2 Wood & pulp plantations (3*) 2.3 Livestock farming & ranching (4) 2.4 Marine & freshwater aquaculture (3)
3 Energy production & mining
3.1 Oil & gas drilling 3.2 Mining & quarrying 3.3 Renewable energy
4 Transportation & service corridors
4.1 Roads & railroads 4.2 Utility & service lines 4.3 Shipping lanes 4.4 Flight paths
5 Biological resource use
5.1 Hunting & collecting terrestrial animals (4) 5.2 Gathering terrestrial plants (4) 5.3 Logging & wood harvesting (5) 5.4 Fishing & harvesting aquatic resources (6)
6 Human intrusions & disturbance
6.1 Recreational activities 6.2 War, civil unrest & military exercises 6.3 Work & other activities
7 Natural system modifications
7.1 Fire & fire suppression (3) 7.2 Dams & water management/use (11) 7.3 Other ecosystem modifications
8 Invasive & other problematic species, genes & diseases
8.1 Invasive non-native/alien species/diseases (2) 8.2 Problematic native species/diseases (2) 8.3 Introduced genetic material 8.4 Problematic species/diseases of unknown origin (2) 8.5 Viral/prion-induced diseases (2) 8.6 Diseases of unknown cause
9 Pollution
9.1 Domestic & urban waste water (3) 9.2 Industrial & military effluents (3) 9.3 Agricultural & forestry effluents (4) 9.4 Garbage & solid waste 9.5 Air-borne pollutants (4) 9.6 Excess energy (4)
10 Geological events
10.1 Volcanoes 10.2 Earthquakes/tsunamis 10.3 Avalanches/landslides
11 Climate change & severe weather
11.1 Habitat shifting & alteration 11.2 Droughts 11.3 Temperature extremes 11.4 Storms & flooding 11.5 Other impacts
12 Other options 12.1 Other threat
MAPISCo Final report: 3. Development of the method
26
Table 6. Example output of the MAPISCo database with threat classifications added
Species Taxonomic
group
1. R
esid
en
tial
2. A
gric
ult
ure
3.E
ner
gy
4. T
ran
spo
rt
5. R
eso
urc
e u
se
6. D
istu
rban
ce
7. S
yste
m m
od
ific
atio
ns
8. I
nva
sive
sp
ecie
s
9. P
ollu
tio
n
10
. Geo
logi
cal e
ven
ts
11
. Clim
ate
chan
ge
12
. Oth
er
Thre
at s
tatu
s
Hab
itat
Har
vest
ing
Gen
. div
ersi
ty
ES p
rovi
sio
nin
g
Sco
re
Ran
k
Francolinus
camerunensis birds 1 0 0 0 1 0 0 0 1 0 1 0 0.78 0.46 0.33 0.65 5.42 1
Caprimulgus
prigoginei birds 0 0 0 0 0 0 0 0 0 0 1 0 0.78 0.57 0.66 3.88 2
Afropavo
congensis birds 1 0 0 0 1 0 0 1 0 0 1 0 0.67 0.36 0.33 0.66 3.73 3
Craugastor
polymniae amphibians 1 0 0 0 0 1 0 0 0 0 1 0 1 0.50 0.62 3.58 4
Ecnomiohyla
echinata amphibians 1 0 0 0 0 1 0 1 0 0 1 0 1 0.50 0.62 3.58 4
MAPISCo Final report: 4. Results - Example priority lists
27
4. Results - Example priority lists
In this section we present species priority lists generated using the method outlined in section 3.
However, we do so with an important caveat - priority lists generated by the current version of
the method are limited by the data used to calculate species scores.
As described in section 3.2 (page 13), only 12 data sources were deemed suitable for inclusion in
the method. This has resulted in constraints on the database, both in terms of which species
have been able to be included on the lists, and in terms of the numbers of co-benefits on which
species on the lists can be scored. There are also geographical biases, with many more species
being recorded from some regions than others.
If this method is to become fully integrated into conservation policy in the UK, these constraints
must be addressed. The focus of further development should be on sourcing data for taxa and
from regions which are under-represented by the current version of the database, and also on
identifying those species groups within the lists which have small numbers of co-benefit scores
(see Box 3, page 28, for full description of constraints in the database). This topic is further
discussed in both sections 5 (page 50) and 6.4 (page 59).
For these reasons, in this section we have presented three different priority lists.
1. A list for all species included on this list
2. A list containing only bird species (taxonomic case study)
3. A list containing only data from SE Asia (geographic case study)
The use of case studies allows us to focus on discrete sets of data, which, while not eliminating
constraints completely, allows a more meaningful demonstration of how the database can be used.
Capsule.
The results generated by the database in its current format are constrained by data (e.g. only
around 3% of all plant species have been categorised on the IUCN Red List while almost all bird
species are included).
For this reason in this section we present three different sets of results
1. All species
2. Birds only (taxonomic case study)
3. SE Asia only (geographic case study)
These case studies allow us to focus on discrete sets of data, which, while not eliminating
constraints completely, allows a more meaningful demonstration of how the database can be used.
These results are then linked to policy actions.
MAPISCo Final report: 4. Results - Example priority lists
28
The case studies were selected, based on a range of different criteria, outlined below -
Taxonomic case study. Birds were selected for number of reasons: 1) birds are a very
well-studied and understood group of species; 2) unlike for other taxa, there is only one
Red List authority responsible for the prioritisation of all bird species (BirdLife International),
which means the classification of species for the Red List is more uniform and more robust
than for other taxa; 3) for birds, unlike for other taxa, all species are evaluated on the Red
List. This is not the case for other taxonomic groups.
Geographic case study. SE Asia. Given the constraints in data availability between taxa it
is important to note that any region will be biased in its taxonomic coverage. However, we
Box 3. Description of constraints incurred in the “all species” database
The coverage of the database (the number of species on the database as a percentage of all the species
currently described globally) varies greatly between taxa. It is very good for birds (100.64%4), mammals
(100.22%4) and amphibians (94.10%; Table B3-1), but poor for reptiles (38.39%) and fish (37.82%) and poorer
still for plants (6.30%) and “other” species (1.02%; Table B3-1).
Table B3-1 (from RL Stats Table 2 2012 IUCN Red List website)
Taxonomic group Estimated number of
described species
Number of species on the
database
Coverage of the
database (%)
Amphibians 6771 6371 94.10
Birds 10064 10128 100.644
Fish 32400 12252 37.82
Mammals 5501 5513 100.224
Other 1305250 13298 1.02
Plants 307674 19398 6.30
Reptiles 9547 3665 38.39
* note, the coverage for the database is greater than 100% for mammals and birds because some species listed on the database are not
considered full species by all authorities, particularly those species that have been domesticated.
The database is also constrained by the number of co-benefits for which individual species have received
scores (mean 2.29 for birds and 1.25 for plants; Table B3-2). This is significant because missing scores do not
result from a “zero impact”, but from missing data. There are also large differences for the individual co-
benefits, with birds receiving by far the most scores for habitat, and fish for harvesting (Table B3-2).
Taxonomic group
Mean number of
co-benefits scored
(max = 5)
Proportion of species in each taxon scored on each co-benefit (%)
Threat status Habitat Harvesting Genetic Diversity
Ecosystem
Services
Birds 2.29 99.37 48.36 0.32 2.38 78.65
Amphibians 1.98 99.98 7.97 0.09 0 90.03
Mammals 1.77 99.78 3.01 0.33 3.66 69.73
Reptiles 1.63 99.97 0.46 0.08 0.49 61.47
Fish 1.57 84.57 0 26.03 0 46.89
Other 1.57 100.00 0.02 0.44 0 56.64
Plants 1.25 85.66 0.13 0.42 15.83 22.71
MAPISCo Final report: 4. Results - Example priority lists
29
present an example based on one region as an example of the applicability of the method.
We selected SE Asia as the case study region because large numbers of species on the
“all species” database were recorded from this region. There was also good taxonomic
coverage for these species (Figure 2). A further case study using UKOTs was also carried
out (as described in the original contract specification), which is detailed in Appendix 7.
For each set of lists we have outlined the main findings from the method. This is followed, for the
case studies (sections 4.2, page 36 and 4.3, page 44), by a discussion of how some of the key
findings could be related to policy actions. This section has not been included for the “all species”
section (section 4.1, page 30), as we believe there to be too much uncertainty in these results for
them to be related directly to policy actions.
Figure 2. The percentage contribution of each taxon (amphibians, birds, fish, mammals, plants, reptiles
and other) to the MAPISCo database for each region (not including European regions). Regions are ordered
by the number of species on the database they contain (shown in brackets). See Appendix 8, Table A8-3 for
how countries were assigned to regions.
MAPISCo Final report: 4. Results - Example priority lists
30
4.1. Example 1: All species 4.1.1. Summary findings
Combining all data sources, and with all co-benefit weighting set to 1, the final output list consisted
of 70625 species. The top 500 species from the list is shown in Appendix 9, Table A9-1.The top
ten species in the list are Francolinus camerunensis, Caprimulgus prigoginei, Afropavo congensis
(bird species), Craugastor polymniae, Ecnomiohyla echinata, Megastomatohyla mixe, Plectrohyla
calvicollina, Plectrohyla celata, Plectrohyla cyanomma, Plectrohyla sabrina (amphibians) with
priority scores ranging from 5.43 to 3.59 (Table 7). The score resolution1 for the full list is 18.55%
(13255 unique ranks for 70625 species).
4.1.2. Taxonomic composition
The overall list is made up of 14.34% birds, 27.47% plants, 7.81% mammals, 5.19% reptiles, 9.02%
amphibians, 17.35% fish and 18.83% “other” species (Table 7). The highest scoring species for
each taxon are shown in Table 7. This top 500 list consists of 116 (23.2%) amphibians, 242
(48.4%) birds, 36 (7.2%) fish, 79 (15.8%) mammals, 10 (2%) plants, 13 (2.6%) reptiles and 4
(0.8%) other species (Table 8).
4.1.3. Geographic composition
In the overall list 241 countries are represented, and 80 in the top 500. The ten countries for which
the largest percentage of bird species on the list have been recorded are shown in Table 9. In the
overall list, Indonesia has the largest percentage of species on the global list (2.05%) and Brazil
has the largest percentage of species (19.12%) in the top 500 list.
4.1.4. IUCN threat categories and classifications
The threat status of species in the overall and top 500 lists are shown in Table 10. The makeup of
the overall list mirrors the Red List, apart for species that have not been assessed by it (6.72%).
The majority of species on the list are classed as Least Concern (40.01%), followed by Data
Deficient (15.22%), Vulnerable (14.50), Endangered (8.33%) Critically Endangered (5.68%) and
Near Threatened (5.14%). The remaining categories make up less than 6%. In the top 500,
species classed as Critically Endangered contribute the biggest proportion (30.00%), followed by
species classed as Endangered (26.20%), Vulnerable (21.20%), Least Concern and Near
Threatened (10.40% each). Species in the remaining categories make up less than 2%. When the
numbers of species falling in each Red List major threat category were compared with the top 500
list, 3.74% of all the bird species listed as Critically Endangered, 3.17% of species listed as Extinct
in the Wild, 2.23% of species listed Endangered, 1.03% of species listed as Vulnerable and 1.43%
of species listed as Near Threatened were also in the top 500.
1 “Score resolution” refers to the ability of a given output list (priority list) to distinguish between species in terms of priorities: for example, some species receive the same final priority score, which results in the same rank (and therefore equal priority) for all these species. The score resolution is calculated as the number of unique ranks divided by the total number of species on a given list.
MAPISCo Final report: 4. Results - Example priority lists
31
The threat classification which occurred most frequently in both the overall and top 500 lists was
Biological resource use (23.94% and 46.0% of species respectively). This category includes
hunting, fishing and logging activities. Agriculture and aquaculture was the threat category next
most frequently recorded (17.02% and 40.8%), followed by Natural system modifications (10.86%
and 19.4%), Residential and commercial development (10.43% and 16.2%), Pollution (10.41% and
10.2 %) and Invasive species (8.38% and 16.2%).The remaining six classifications made up less
than 7% each (Table 11 and Figure 3). Looking at each species groups individually, the patterns
are remarkably similar (Table 11). The top two threats across all species groups are Biological
resource use and Agriculture and aquaculture. Beyond the top two, there are individual taxa
variations. Birds, for example, are more threatened by Climate change than any other taxa in the
list (9.36% vs. 7.16% and below); fish are more threatened by Pollution than any other taxa (13.49%
vs. 11.81% and below) and amphibians by Residential and commercial developments (13.22% vs.
11.79% and below).
4.1.5. Co-benefits
In the overall list, 56.84% are scored for threat status, 32.34% for ES Provisioning, 4.84% for
Habitat and Area Conservation, 2.92% for Sustainable Harvesting and 3.04% for Genetic Diversity.
In the top 500 list, 33.53% of species are scored for conservation status and ES Provisioning,
24.28% for Habitat and Area Conservation, 2.62% for Sustainable Harvesting and 6.04% for
Genetic Diversity.
MAPISCo Final report: 4. Results - Example priority lists
32
Table 7. Top 20 species listed in the database
Species name Taxonomic Group Thre
at s
tatu
s
Hab
itat
Har
vest
ing
Gen
. div
ersi
ty
ES p
rovi
sio
nin
g
Sco
re
Ran
k
Francolinus camerunensis birds 0.78 0.46 0.33 0.65 5.43 1
Caprimulgus prigoginei birds 0.78 0.57 0.66 3.89 2
Afropavo congensis birds 0.67 0.36 0.33 0.66 3.74 3
Craugastor polymniae amphibians 1.00 0.50 0.62 3.59 4
Ecnomiohyla echinata amphibians 1.00 0.50 0.62 3.59 4
Megastomatohyla mixe amphibians 1.00 0.50 0.62 3.59 4
Plectrohyla calvicollina amphibians 1.00 0.50 0.62 3.59 4
Plectrohyla celata amphibians 1.00 0.50 0.62 3.59 4
Plectrohyla cyanomma amphibians 1.00 0.50 0.62 3.59 4
Plectrohyla sabrina amphibians 1.00 0.50 0.62 3.59 4
Pseudoeurycea saltator amphibians 1.00 0.50 0.62 3.59 4
Pseudoeurycea smithi amphibians 1.00 0.50 0.62 3.59 4
Pseudoeurycea unguidentis amphibians 1.00 0.50 0.62 3.59 4
Thorius aureus amphibians 1.00 0.50 0.62 3.59 4
Thorius smithi amphibians 1.00 0.50 0.62 3.59 4
Habromys chinanteco mammals 1.00 0.50 0.62 3.59 4
Habromys ixtlani mammals 1.00 0.50 0.62 3.59 4
Habromys lepturus mammals 1.00 0.50 0.62 3.59 4
Calyptura cristata birds 1.00 0.24 0.96 3.14 19
Duellmanohyla ignicolor amphibians 0.78 0.50 0.62 2.68 20
Table 8. Number, percentage, highest ranks and scores and highest-ranking species in the complete and top 500 species lists.
Taxonomic
group No. spp. % spp.
No. spp. in
top 500
% spp. In
top 500
Highest
rank
Highest
score Highest scoring species
Amphibians 6371 9.02 116 23.2 4 3.59
Craugastor polymniae, Ecnomiohyla echinata,
Megastomatohyla mixe, Plectrohyla calvicollina, Plectrohyla
celata, Plectrohyla cyanomma, Plectrohyla sabrina,
Pseudoeurycea saltator, P. Smithi, P. Unguidentis, Thorius
aureus, T. smithi
Birds 10128 14.34 242 48.4 1 5.43 Francolinus camerunensis
Fish 12252 17.35 36 7.2 30 2.05 Acipenser sturio
Mammals 5513 7.81 79 15.8 4 3.59 Habromys chinanteco
Other 13298 18.83 4 0.8 149 0.08 Elga newtonsantosi
Plants 19398 27.47 10 2 149 0.08 Devillea flagelliformis
Reptiles 3665 5.19 13 2.6 89 0.69 Ctenosaura oaxacana
MAPISCo Final report: 4. Results - Example priority lists
33
Table 9. The top 10 countries in the overall and top 500 lists and the number and percentage of species which have been recorded as occurring within them.
Overall list Top 500 list
Country rank
Country No. spp.
% spp.
Country No. spp.
% spp.
1 Indonesia 6298 2.05 Brazil 528 19.12
2 Ecuador 6116 1.99 Indonesia 99 3.59
3 India 5340 1.74 Mexico 85 3.08
4 United States 5245 1.71 India 62 2.25
5 China 5059 1.65 Congo, The Democratic Republic of the 55 1.99
6 Brazil 4867 1.59 China 54 1.96
7 Malaysia 4842 1.58 Cameroon 53 1.92
8 Mexico 4782 1.56 Thailand 44 1.59
9 Colombia 4651 1.52 Myanmar 43 1.56
10 Thailand 4522 1.47 Malaysia and Argentina 39 1.41
Table 10. The proportion of species in the overall and top 500 lists and the IUCN threat category in which they are listed. The end column shows the proportion of bird species listed from each Red List category which occur in the top 500 list.
Overall list Top 500
Threat Status No. spp. % spp. No. spp. % spp.
% of all spp. in threat category on red list in top 500
CR 4009 5.68 150 30.00 3.74
DD 10672 15.11 6 1.20 0.06
EN 5882 8.33 131 26.20 2.23
EW 63 0.09 2 0.40 3.17
EX 801 1.13 0 0 0
LC 28258 40.01 52 10.40 0.18
LR/cd 255 0.36 0 0 0
LR/lc 1018 1.44 1 0.20 0.10
LR/nt 1015 1.44 0 0 0
NE 29 0.04 0 0 0
NT 3631 5.14 52 10.40 1.43
VU 10243 14.50 106 21.20 1.03
Not Assessed 4749 6.72 0 0 0
MAPISCo Final report: 4. Results - Example priority lists
34
Table 11. Percentage of species classified as threatened by each of the 12 IUCN threat classification categories, for the overall list and for each species
group individually.
Percentage of species classified as threatened by each of the 12 categories
overall list top 500 mammals plants birds fish reptiles other amphibians
Biological resource use 23.94 46.00 26.21 23.52 22.88 28.71 23.91 23.38 23.71
Agriculture /aquaculture 17.02 40.80 18.84 17.89 18.69 14.23 19.16 14.86 21.33
Natural system modification 10.86 19.40 10.51 12.28 12.60 11.29 9.90 10.73 8.56
Residential/commercial development 10.43 16.20 10.69 11.62 8.03 9.08 11.79 11.07 13.22
Pollution 10.18 10.20 8.90 9.91 7.44 13.49 10.01 11.81 10.03
Invasive species 8.39 16.20 7.87 8.31 8.98 8.12 8.58 9.17 8.55
Climate change 6.25 10.40 4.81 5.36 9.36 5.85 5.66 7.16 5.10
Human intrusions/disturbance 4.21 5.40 4.23 4.20 4.11 3.58 4.08 5.35 4.05
Energy production/mining 3.68 6.60 4.63 3.83 4.34 3.59 3.88 3.52 2.88
Transportation/service corridors 2.27 4.20 2.63 2.37 3.03 1.68 2.38 2.32 1.95
Geological events 0.48 1.20 0.59 0.57 0.51 0.27 0.49 0.51 0.55
Other threats 0.10 0.60 0.11 0.14 0.03 0.13 0.16 0.11 0.07
MAPISCo Final report: 4. Results - Example priority lists
35
Figure 3. Percentage of species classified as threatened by each of the 12 IUCN threat classification
categories a) the overall list; b) the top 500. Full names for each threat category are given in Table 11.
MAPISCo Final report: 4. Results - Example priority lists
36
4.2. Example 2: Taxonomic case study – birds
4.2.1. Summary findings
The bird list contained 10128 species, 14.34% of the overall database (for the top 500 list see
Appendix 9, Table A9-2). The score resolution for the overall list was 61.57% (6326 unique ranks
for species) and for the top 500 list 89% (445 unique ranks). The top five species on the list are all
Galliformes; Cameroon Francolin Francolinus camerunensis (scoring 6.53), Congo Peacock
Afropavo congensis (4.62), Nahan's Francolin Francolinus nahani (3.39), White-breasted
Guineafowl Agelastes meleagrides (2.30) and Swierstra’s Francolin Francolinus swierstra (1.44)
(for top 20 see Table 12).
4.2.2. Orders and Families
The overall list consisted of birds from 25 orders. Passeriformes were the most represented in the
list at 57.73%, followed by Apodiformes (4.96%), Piciformes (4.63%), Psittaciformes (4.13%), and
Galliformes (3.49%) (Table 13).
The top 500 list consisted of species from 19 orders. Of these, two made up almost three-quarters
of the species listed – Galliformes (44.8%) and Passeriformes (33.6%), (Table 13). Sixty-eight
individual families made up the top 500, with Phasianidae, making up the largest percentage
(36.2%). The highest ranking species in each order is also shown in Table 13.
4.2.3. Country/region
Two hundred and thirty four countries are represented in the overall list, and 80 in the top 500. The
ten countries for which the largest percentage of bird species on the list have been recorded are
shown in Table 14. In the overall list, Colombia has the largest percentage of species on the global
list (1.71%) and Brazil has the largest percentage of species (29.40%) in the top 500 list. The
number and proportion of species recorded in each country, for both the overall and top 500 lists,
is detailed in Appendix 9, Table A9-3.
4.2.4. IUCN Red List threat categories and classifications
The threat status of species in the overall and top 500 lists is shown in Table 15. The makeup of
the overall list mirrors the Red List, apart for species that have not been assessed by it. The
majority of species are classed as Least Concern (75.8%), followed by Near Threatened (8.69%),
Vulnerable (7.18%), Endangered (3.84%) and Critically Endangered (1.95%). The remaining
categories make up less than 3%. In the top 500, again species classed as Least Concern
contribute the biggest proportion (36.6%), followed by species classed as Vulnerable (20.2%),
Near Threatened (19.2%), Endangered (11.8%) and Critically Endangered (10.8%) followed by
species in the remaining categories making up less than 2%. When the numbers in each category
on the Red List were compared with the top 500 list, 27.41% of all the bird species listed as
Critically Endangered by the Red List were in that top 500, 25% of species listed as Extinct in the
Wild, 15.17% of species listed Endangered, 13.89% of species listed as Vulnerable and 10.91% of
species listed as Near Threatened (Table 15).
MAPISCo Final report: 4. Results - Example priority lists
37
In the overall list the threat classification against which the majority of species are listed is
Biological resource use (22.33%). Agriculture and aquaculture (21.56%), Natural system
modifications (14.28%), Climate change (12.42%), Invasive species (9.45%) and
Residential/commercial development (4.79%) make up the next largest proportions. The remaining
6 categories make up the remaining 15% (see Table 16 for full list). For the top 500 list, the threat
classification against which the majority of species are listed is also Biological resource use
(23.90%) followed by Agriculture and aquaculture (22.54%), Natural system modifications
(12.10%), Invasive species (9.53), Climate change (9.23%), Energy production and mining (5.59%),
Residential and commercial development (4.99%) and Transportation and service corridors
(4.54%). The remaining four categories made up less than 8% of the threats listed Table 16).
4.2.5. Co-benefits
In the overall list, 43.29% are scored for Threat Status, 33.67% for ES Provisioning, 20.62% for
Habitat and Area Conservation, 0.68% for Sustainable Harvesting and 1.73% for Genetic Diversity.
In the top 500 list, 31.83% of species are scored for Conservation Status and ES Provisioning,
29.79% for Habitat and Area Conservation, 0.57% for Sustainable Harvesting and 5.98% for
Genetic Diversity.
4.2.6. Key findings and how they relate to policy
Species from the order Galliformes made up the majority of the top 500 bird species on the priority
list. This is not surprising considering that the majority of Galliformes are scored on three co-
benefits more (mean number per species = 3.10). As one of the most threatened group of birds,
with over 25% of species in the group being classified as Vulnerable, Endangered or Critically
Endangered, Galliformes score highly on Threat Status. Over two-thirds of the Galliformes in the
top 500 have a score for Genetic Diversity; this reflects their close genetic relationship to the
domesticated chicken, guineafowl, pheasant and quail. Over two-thirds have a score for
Ecosystem Services and over half for Habitat, which reflects forest being the predominant habitat
of Galliformes on the top 500 list. These characteristics are scored highly by the current co-benefit
scoring system.
In order to maximise conservation benefit for Galliformes species (if we accept the inherent
constraints in the MAPISCo prioritisation) policy-makers could target resources to countries with a
high number of Galliformes species from the top 500 list. These countries (shown in Figure 4)
include Indonesia, Brazil, The Democratic Republic of the Congo and Malaysia.
MAPISCo Final report: 4. Results - Example priority lists
38
Table 12. The top 20 highest scoring species, all in the order Galliformes, the country(ies) in which they have been recorded, scores each of the five co-benefits as well as resultant priority score and rank. Note that all values have been rounded to three decimal points. All co-benefit weighting factors set to 1 (default).
Rank Species name English name Country
Thre
at
stat
us
Hab
itat
Har
vest
i
ng
Gen
.
div
ersi
ty
ES
pro
visi
o
nin
g
Sco
re
1 Francolinus camerunensis Cameroon
Francolin Cameroon 0.778 0.462 0 0.333 0.649 6.539
2 Afropavo congensis Congo Peacock Democratic Republic of Congo 0.667 0.361 0 0.333 0.659 4.625
3 Francolinus nahani Nahan's Francolin Uganda & Democratic Republic of Congo 0.778 0.245 0 0.333 0.640 3.389
4 Agelastes meleagrides White-breasted
Guineafowl Cote d' Ivoire, Ghana, Liberia & Sierra Leone 0.667 0.230 0 0.333 0.606 2.301
5 Francolinus swierstrai Swierstra's
Francolin Angola 0.778 0.116 0 0.333 0.626 1.436
6 Guttera plumifera Plumed
Guineafowl
Angola, Cameroon, Central African Republic, Congo, Democratic Republic of Congo,
Equatorial Guinea & Gabon 0.222 0.340 0 0.333 0.584 1.400
7 Agelastes niger Black Guineafowl Angola, Cameroon, Central African Republic, Congo, Democratic Republic Congo, Equatorial
Guinea, Gabon, Nigeria 0.222 0.330 0 0.333 0.597 1.380
8 Tragopan satyra Crimson Horned-
pheasant Bhutan, China, India, Nepal 0.556 0.231 0 0.333 0.561 1.347
9 Francolinus ochropectus Djibouti Francolin Djibouti 1 0.026 0 0.333 0.619 1.233
10 Lophura edwardsi Edwards's
Pheasant Vietnam 1 0.007 0 0.333 0.595 0.754
11 Odontophorus capueira Spot-winged
Wood-quail Argentina, Brazil, Paraguay 0.222 0.151 0 0.333 0.795 0.642
12 Lophura hoogerwerfi Aceh Pheasant Indonesia 0.667 0.016 0 0.333 0.747 0.564
MAPISCo Final report: 4. Results - Example priority lists
39
13 Polyplectron schleiermacheri Bornean Peacock-
pheasant Indonesia, Malaysia 0.778 0.013 0 0.333 0.684 0.514
14 Lophura inornata Salvadori's
Pheasant Indonesia 0.667 0.012 0 0.333 0.747 0.501
15 Arborophila orientalis Grey-breasted
Partridge Indonesia 0.667 0.007 0 0.333 0.747 0.434
16 Odontophorus melanonotus Dark-backed
Wood-quail Colombia, Ecuador 0.667 0.098 0 0.333 0.603 0.411
17 Francolinus lathami Forest Francolin
Angola, Cameroon, Central African Republic, Congo, The Democratic Republic of Congo, Cote
d' Ivoire, Equatorial Guinea, Gabon, Ghana, Guinea, Liberia, Nigeria, Sierra Leone, Sudan,
Tanzania, Togo, Uganda
0.222 0.249 0 0.333 0.608 0.329
18 Francolinus nobilis Handsome
Francolin Burundi, Democratic Republic of Congo, Rwanda, Uganda 0.222 0.225 0 0.333 0.629 0.177
19 Cyrtonyx ocellatus Ocellated Quail El Salvador, Guatemala, Honduras, Mexico, Nicaragua 0.667 0.069 0 0.333 0.617 0.124
20 Odontophorus dialeucos Tacarcuna Wood-
quail Colombia, Panama 0.667 0.079 0 0.333 0.590 0.027
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Table 13. Proportion of species in the overall and top 500 species lists by Order, along with the highest rank and score per order and highest scoring species.
Order Overall list Top 500 Highest rank Highest score Highest scoring species Red List status
No. spp. % spp. No. spp. % spp.
PASSERIFORMES 4763 57.73 168 33.60 207 -8.230 Calyptura cristata CR
APODIFORMES 409 4.96 11 2.20 242 -8.960 Schoutedenapus schoutedeni VU
PICIFORMES 382 4.63 8 1.60 306 -11.920 Indicator pumilio NT
PSITTACIFORMES 341 4.13 30 6.00 249 -10.210 Touit melanonotus EN
GALLIFORMES 288 3.49 224 44.80 1 6.530 Francolinus camerunensis EN
FALCONIFORMES 275 3.33 5 1.00 261 -10.923 Leptodon forbesi CR
COLUMBIFORMES 270 3.27 3 0.60 415 -12.990 Columba albinucha NT
CHARADRIIFORMES 214 2.59 1 0.20 498 -13.560 Charadrius thoracicus VU
CORACIIFORMES 207 2.51 3 0.60 447 -13.220 Bycanistes cylindricus VU
STRIGIFORMES 181 2.19 10 2.00 245 -9.660 Phodilus prigoginei EN
GRUIFORMES 177 2.15 3 0.60 295 -11.740 Psophia viridis EN
ANSERIFORMES 159 1.93 18 3.60 21 0.001 Cairina scutulata EN
CUCULIFORMES 152 1.84 2 0.40 422 -13.060 Neomorphus squamiger VU
CICONIIFORMES 111 1.35 1 0.20 450 -13.212 Bostrychia bocagei CR
CAPRIMULGIFORMES 100 1.21 2 0.40 201 -7.435 Caprimulgus prigoginei EN
PROCELLARIIFORMES 55 0.67 3 0.60 340 -12.374 Pterodroma magentae CR
TROGONIFORMES 44 0.53 0 0.00 587 -14.253 Apaloderma aequatoriale LC
PELECANIFORMES 35 0.42 1 0.20 458 -13.300 Fregata andrewsi CR
TINAMIFORMES 32 0.39 1 0.20 401 -12.895 Crypturellus noctivagus NT
PODICIPEDIFORMES 22 0.27 0 0.00 821 -14.956 Podiceps taczanowskii CR
STRUTHIONIFORMES 12 0.15 6 1.20 29 -0.238 Casuarius casuarius VU
SPHENISCIFORMES 10 0.12 0 0.00 1548 -16.062 Eudyptes robustus VU
PHOENICOPTERIFORMES 5 0.06 0 0.00 2663 -17.468 Phoeniconaias minor NT
GAVIIFORMES 5 0.06 0 0.00 3509 -17.928 Gavia adamsii NT
COLIIFORMES 2 0.02 0 0.00 3105 -17.689 Colius castanotus LC
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Table 14. The top 10 countries in the overall and top 500 lists and the number and percentage of species that have been recorded as occurring within them.
Overall list Top 500 list
Country
rank Country No. spp. % spp. Country No. spp. % spp.
1 Colombia 1835 1.71 Brazil 147 29.40
2 Peru 1814 1.69 DR Congo 27 5.40
3 Brazil 1766 1.65 Cameroon 18 3.60
4 Ecuador 1647 1.54 Indonesia 15 3.00
5 Indonesia 1600 1.49 India, Uganda 11 2.20
6 China 1269 1.19 Argentina, Liberia, Malaysia, Nigeria
10 2.00
7 India 1225 1.14 Colombia, Cote d' Ivoire, Ghana, Paraguay
9 1.80
8 DR Congo 1129 1.05 Gabon, Myanmar, Sierra Leone
8 1.60
9 Mexico 1103 1.03
Central African Republic, China, Congo, Equatorial Guinea
7 1.40
10 Kenya 1098 1.03 Angola, Guinea, Nepal, Thailand, Viet Nam
6 1.20
MAPISCo Final report: 4. Results - Example priority lists
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Table 15. The proportion of species in the overall and top 500 lists and the IUCN threat category in which
they are listed. The end column shows the proportion of bird species listed from each Red List category that
occur in the top 500 list.
Overall list Top 500
Threat Status No. spp. % spp. No. spp. % spp.
% of all spp. in threat category on red list in top 500
CR 197 1.95 54 10.8 27.41 DD 60 0.59 4 0.8 6.67 EN 389 3.84 59 11.8 15.17 EW 4 0.04 1 0.2 25.00 EX 130 1.28 1 0.2 0.77 LC 7677 75.8 183 36.6 2.38 NT 880 8.69 96 19.2 10.91 VU 727 7.18 101 20.2 13.89 not classified 64 0.63 1 0.2 1.56
Table 16. Red List threat classifications of the species in the overall and top 500 lists.
Overall list Top 500
Threats No. spp. % spp. No. spp. % spp.
Biological Resource use 1847 22.33 158 23.90
Transportation and service corridors 296 3.58 30 4.54
Pollution 294 3.55 26 3.93
Other threats 0 0 0 0
Natural system modifications 1181 14.28 80 12.10
Invasive species 782 9.45 63 9.53
Human intrusions and disturbance 281 3.40 21 3.177
Residential and commercial
development 396 4.79 33 4.99
Geological events 35 0.42 3 0.45
Energy production and mining 349 4.22 37 5.59
Agriculture and aquaculture 1783 21.56 149 22.54
Climate Change 1027 12.42 61 9.23
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Figure 4. The global distribution of Galliformes species in the top 500 birds list. The categories relate to the
number of Galliformes species (in the top 500) that are found in each country. Countries coloured white
have no Galliformes listed in the top 500. (The map was produced using the package rworldmap South, A.
(2011) rworldmap: A New R package for Mapping Global Data. The R Journal Vol. 3/1: 35-43.)
category
1
2
3
4
5
6
7
8
Galliformes
MAPISCo Final report: 4. Results - Example priority lists
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4.3. Example 3: Geographic case study – SE Asia 4.3.1. General findings
The final output list for SE Asia contains 12496 species, which is 17.7% of the overall database.
The score resolution for the full list is 23.66% (2956 unique ranks for 12496 species) and for the
top 500 is 49.20% (246 unique ranks). The five highest priority species are the Togian Islands
Babirus Babyrousa togeanensis (0.782), Anoa Bubalus depressicornis (0.782), Mountain Anoa B.
quarlesi (0.782), Javan pig Sus verrucosus (0.782) and Aceh pheasant Lophura hoogerwerfi
(0.503) (see Table 17 for the top 20 species).
4.3.2. Taxonomic composition
The overall list consists of 20.42% “other” species (2552 spp.), 20.27% plants (2533 spp.), 20.09%
birds (2510 spp.), 18.17% fish (2271 spp.), 8.86% mammals (1107 spp.), 6.24% reptiles (780 spp.)
and 5.95% amphibians (743 spp.) (Table 18). The top 500 list has a rather different composition,
with birds and mammals contributing the largest proportions (36.8 %, 184 spp. and 35.0%, 175 spp.
respectively), followed by plants (10.4%, 52 spp.), “other” species (6.8%, 34 spp.), amphibians
(4.8%, 24 spp.), fish (4.2%, 21 spp.) and reptiles (2.0%, 10 spp.). The highest scoring species in
each taxon are shown in Table 18. The highest scoring mammals, birds and fish are all in the top
20, while the highest scoring amphibian Duttaphrynus sumatranus is ranked at 86, “other” species
Protosticta gracilis at 107, plant Taxus wallichiana at 44 and reptile Emoia ruficauda 193.
4.3.3. Geographic composition
The species in the overall and top 500 lists have been recorded in 11 countries - Brunei, Cambodia,
Indonesia, Lao, Malaysia, Myanmar, Philippines, Singapore, Thailand, East Timor and Viet Nam.
In the overall list, the greatest proportion of species have been recorded in Indonesia (18.28%)
followed by Malaysia (14.05%), Thailand (13.13%), Viet Nam (11.42%), Myanmar (10.15%),
Philippines (9.72%), Cambodia (6.40%), PDR Lao (6.39%), Singapore (5.64%), Brunei (3.55%)
and East Timor (1.27%) (Table 19). In the top 500 list the country in which the largest proportion of
species have been recorded is Indonesia (32.41%), followed by Malaysia (11.18%), Myanmar
(10.15%), Thailand and Viet Nam (both 9.44%), Philippines (7.28%), Cambodia (6.46%),
Myanmar (6.83%), Lao PDR (6.36%), Brunei (2.77%), Singapore (2.67%) and East Timor (1.85%)
(Table 19). The top ranked species in each country are listed in Table 20.
4.3.4. IUCN Red List threat categories and classifications
The threat status of the overall and top 500 lists is shown in Table 21. The majority of species in
the overall list are classed as Least Concern (47.7%), followed by Data Deficient (18.55%),
Vulnerable (13.43%), Near Threatened (6.55%), Endangered (4.64%) and Critically Endangered
(4.03%). The remaining categories make up less than 6%. In the top 500 list, species classed as
Vulnerable made up the largest proportion (29%), followed by Critically Endangered (27%),
Endangered (22%), Least Concern (14.2%), Near Threatened (6.6%), and Data Deficient (1.2%).
No species in the top 500 were classed in the any of the remaining categories. In the overall list,
the threat classification against which the majority of species are listed is Biological resource use
(29.90%) followed by Agriculture and aquaculture (13.25%), Pollution (11.95%),
Residential/commercial development (11.82%), Natural system modifications (8.46%), Climate
change (7.34%), Human intrusions/disturbance (6.55%), Invasive species (6.18%), Energy
MAPISCo Final report: 4. Results - Example priority lists
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production and mining (2.74%). The remaining four categories make up less than 4% (see Table
22 for full list). For the top 500 list, the threat classification against which the majority of species
were listed is also Biological resource use (25.6%), followed by Agriculture and aquaculture
(20.79%), Natural system modifications (11.60%), Residential and commercial development and
invasive species (both 8.35%), Climate change (7.64%), Pollution (5.20%), Energy production and
mining (4.95%), Human intrusions and disturbance (3.68%), Transportation and service corridors
(3.11%) and Geological events (0.71%) (Table 22).
4.3.5. Co-benefits
In the overall list, 100% are scored for Threat Status, 66.12% for ES Provisioning, 7.18% for
Habitat and Area Conservation, 5.27% for Sustainable Harvesting and 1.58% for Genetic Diversity.
In the top 500 list, 100% of species are scored for Conservation Status, 99.8% for ES Provisioning,
28.6% for Habitat and Area Conservation, 8.8% for Sustainable Harvesting and 13.6% for Genetic
Diversity.
4.3.6. Key findings and how they relate to policy
In the top 500 list, the country in which the largest proportions of species have been recorded is
Indonesia (32.41%). These 316 species (138 mammals, 112 birds, 23 plants, 20 amphibians, 11
fish, 9 “other” species and 3 reptiles) share similar threats (37.34% of these species are threatened
by Biological resource use and 29.43% by Agriculture and aquaculture). For the highest ranked 10
species in the top 500 list (all from Indonesia), hunting and/or habitat destruction are the major
threats listed by the IUCN Red List. Conservation actions that reduce habitat destruction and target
unsustainable hunting of species in Indonesia should therefore benefit these priority species.
Several species on the list, e.g. Javan warty pig Sus verrucosus and Sulawesi babirusa Babyrousa
celebensis are illegally hunted in protected areas and therefore better community engagement and
conservation law enforcement would benefit these species.
MAPISCo Final report: 4. Results - Example priority lists
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Table 17. The top 20 highest scoring species, the order to which they belong, the country(ies) in which they
have been recorded, scores each of the five co-benefits as well as resultant priority score and rank. Note
that all values have been rounded to three decimal points. All co-benefit weighting factors set to 1 (default).
Rank Species English name Taxon Country
Thre
at s
tatu
s
Hab
itat
Har
vest
ing
Gen
. div
ersi
ty
ES
pro
visi
on
ing
Sco
re
1 Babyrousa
togeanensis
Togian Islands
Babirusa mammals Indonesia 0.778 0.333 0.747 0.782
1 Bubalus
depressicornis Anoa mammals Indonesia 0.778 0.333 0.747 0.782
1 Bubalus quarlesi Mountain Anoa mammals Indonesia 0.778 0.333 0.747 0.782
1 Sus verrucosus Javan Pig mammals Indonesia 0.778 0.333 0.747 0.782
5 Lophura
hoogerwerfi Aceh Pheasant Birds Indonesia 0.667 0.016 0.333 0.747 0.503
6 Lophura inornata Salvadori's Pheasant Birds Indonesia 0.667 0.012 0.333 0.747 0.441
7 Arborophila
orientalis
Grey-breasted
Partridge Birds Indonesia 0.667 0.007 0.333 0.747 0.374
8 Babyrousa
babyrussa Babiroussa mammals Indonesia 0.667 0.333 0.747 0.275
8 Babyrousa
celebensis Sulawesi Babirusa mammals Indonesia 0.667 0.333 0.747 0.275
8 Callosciurus
melanogaster Mentawai Squirrel mammals Indonesia 0.667 0.333 0.747 0.275
11 Polyplectron
schleiermacheri
Bornean Peacock-
pheasant Birds Indonesia, Malaysia 0.778 0.013 0.333 0.684 0.251
12 Bubalus
mindorensis
Mindoro Dwarf
Buffalo mammals Philippines 1.000 0.333 0.608 0.208
12 Sus cebifrons Visayan Warty Pig mammals Philippines 1.000 0.333 0.608 0.208
14 Lophura edwardsi Edwards's Pheasant Birds Viet Nam 1.000 0.007 0.333 0.595 0.172
15 Bos sauveli Grey Ox mammals Cambodia, Lao PDR,
Thailand, Viet Nam 1.000 0.333 0.597 0.082
16 Epinephelus
coioides Estuary Cod Fish
Brunei Darussalam,
Cambodia, Indonesia,
Malaysia, Myanmar,
Philippines, Singapore,
Thailand, Viet Nam
0.556 0.611 0.614 -0.119
17 Sus celebensis Celebes Pig mammals Indonesia 0.556 0.333 0.747 -0.233
18 Cairina scutulata White-winged Duck Birds
Cambodia, Indonesia,
Lao PDR, Malaysia,
Myanmar, Thailand,
Viet Nam
0.778 0.017 0.333 0.622 -0.407
19 Melanoperdix niger Black Partridge Birds
Brunei Darussalam,
Indonesia, Malaysia,
Singapore
0.667 0.020 0.333 0.660 -0.441
20 Lophura
erythrophthalma Crestless Fireback Birds
Brunei Darussalam,
Indonesia, Malaysia,
Singapore
0.667 0.019 0.333 0.660 -0.448
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Table 18. Proportion of species in the overall and top 500 species lists by taxonomic group, along with the highest rank and score per group, highest scoring
species and the country in which it is found.
Taxonomic group
Overall list Top 500
Highest rank Highest score Highest scoring species Country (ies)
No. spp. % spp.
No.
spp. % spp.
Amphibians 743 5.95 24 4.8 86 -2.056 Duttaphrynus sumatranus
Sumartrian toad Indonesia
Birds 2510 20.09 184 36.8 5 0.503 Lophura hoogerwerfi Aceh
pheasant Indonesia
Fish 2271 18.17 21 4.2 16 -0.119 Epinephelus coioides
Estuary Cod
Brunei Darussalam; Cambodia;
Indonesia; Malaysia; Myanmar;
Philippines; Singapore; Thailand;
Viet Nam
Mammals 1107 8.86 175 35 1 0.782 Babyrousa togeanensis
Togian Islands Babirusa Indonesia
Other 2552 20.42 34 6.8 107 -2.375 Protosticta gracilis
Arthropod Indonesia
Plants 2533 20.27 52 10.4 44 -0.842 Taxus wallichiana
Himalayan Yew
Indonesia; Myanmar;
Philippines; Viet Nam
Reptiles 780 6.24 10 2 193 -3.334 Emoia ruficauda Red-tailed
Swamp Skink Philippines
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Table 19. The 11 countries which feature in the overall and top 500 lists and the number and percentage of species which have been recorded as occurring within them.
Overall list Top 500 list
Country
rank Country No. spp. % spp. Country No. spp. % spp.
1 Indonesia 6298 18.28 Indonesia 316 32.41
2 Malaysia 4842 14.05 Malaysia 109 11.18
3 Thailand 4522 13.13 Myanmar 99 10.15
4 Viet Nam 3934 11.42 Thailand 92 9.44
5 Myanmar 3498 10.15 Viet Nam 92 9.44
6 Philippines 3347 9.72 Philippines 71 7.28
7 Cambodia 2205 6.40 Cambodia 63 6.46
8 Lao PDR 2202 6.39 Lao PDR 62 6.36
9 Singapore 1944 5.64 Brunei 27 2.77
10 Brunei 1223 3.55 Singapore 26 2.67
11 East Timor 436 1.27 East Timor 18 1.85
Table 20. The top ranking species in each country.
Country
Highest Priority
score Rank Species
Brunei -0.119 16 Epinephelus coioides, Estuary Cod
Cambodia 0.081 15 Bos sauveli, Grey Ox
Indonesia 0.782 1 Babyrousa togeanensis, Togian
Islands Babirusa
People's Democratic Republic of Lao 0.081 15 Bos sauveli, Grey Ox
Malaysia 0.251 11 Polyplectron schleiermacheri,
Bornean Peacock-pheasant
Myanmar 0.356 28 Epinephelus coioides, Estuary Cod
Philippines 0.207 12 Bubalus mindorensis, Mindoro
Dwarf Buffalo
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Table 21. The proportion of species in the overall and top 500 lists and the IUCN threat category in which
they are listed.
Table 22. Red List threat classifications of the species in the overall and top 500 lists.
Overall list Top 500
Threat Category No. spp. % spp. No. spp. % spp.
CR 503 4.03 135 27.00
DD 2318 18.55 6 1.20
EN 580 4.64 110 22.00
EW 2 0.02 0 0.00
EX 7 0.06 0 0.00
LC 5960 47.70 71 14.20
NT 819 6.55 33 6.60
VU 1678 13.43 145 29.00
not evaluated 122 0.98 0 0.00
LR/cd 122 0.98 0 0.00
LR/lc 378 3.02 0 0.00
LR/nt 129 1.03 0 0.00
Overall list Top 500
Threats No. spp. % spp. No. spp. % spp.
Biological Resource use 4371 29.90 181 25.60
Transportation and service corridors 224 1.53 22 3.11
Pollution 1747 11.95 37 5.23
Other threats 6 0.04 0 0.00
Natural system modifications 1236 8.46 82 11.60
Invasive species 903 6.18 59 8.35
Human intrusions and disturbance 957 6.55 26 3.68
Residential/commercial development 1728 11.82 59 8.35
Geological events 34 0.23 5 0.71
Energy production and mining 401 2.74 35 4.95
Agriculture and aquaculture 1937 13.25 147 20.79
Climate Change 1073 7.34 54 7.64
MAPISCo Final report: 5. Using the method
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5. Using the method
5.1. Expandable –How does the does the priority list respond to the inclusion of additional co-benefit data? A plant example.
The method developed in this project has the capacity for additional datasets (either taxonomic or
co-benefit) to be included within it should they become available. We see this expansion as a
critical element of the method because at present, the number of datasets that contribute to the
overall priority scores is relatively small (12 data sources). As discussed in section 4 (page 27),
this has resulted in taxonomic and geographic constraints on the lists, which must be considered
before this method can become fully integrated in conservation policy. One way to address these
constraints is to add new co-benefit data to the database. At present, we believe we have included
all currently available, verified data, by focussing on either the transcribing of existing datasets into
a format usable by the method, or on the collection of new data, additional data would make a
huge improvement to the database. As described in section 4 (page 27) this should focus on the
taxa that are underrepresented in the current database - plants for example, are vastly
underrepresented in the database in its current form – just 6.3% of all plants species currently
described worldwide are on the priority list. This is not the case for other taxa, birds, the coverage
for mammals and amphibians all approaching 100% (see Box 3 Table B3-1; page 28).
For this reason, we have chosen to investigate the effect additional plant data will have on the
composition of priority lists
Method: To assess the effect of inclusion of further co-benefit data, we asked the IUCN SSC Palm
Specialist Group (Bill Baker, Kew Gardens & IUCN-SSC Palm Specialist Group, pers. comm.) to
use its specialist knowledge to score a selection of palm species based on their contribution to one
of the five co-benefits – Harvesting. The group selected 64 species for inclusion in this assessment,
based on the flagship species for palm conservation. However, only 52 of these species were
already on the overall list. As this exercise was to address the addition of data to the list, we
concentrated on these 52 species. The group were asked to score species on a scale of 0 to 2
where 0 is a species of zero value to harvesting and 2 is a species of the maximum value to
harvesting. These values were then rescaled to fit with the original harvesting data. New co-benefit
scores for harvesting were then calculated using these rescaled data set scores, following the
procedure outlined in section 3 (page 12). Final priority scores for the full list were then calculated.
Capsule.
Expandable. We demonstrate how additional species or co-benefit data can be added to the database,
and outline how such changes impact on the ranking of priority lists.
Adaptable. We examine the effect changing individual co-benefit weightings (i.e. making certain co-
benefits “more important” in the calculation of priority lists than others) has on priority list ranking.
Usable. Here we outline the development of a web-based interface, which, using a variety of tabs and
graphics, allows users to fully explore the priority lists created by the methodology under a number of
different scenarios. We view this as a critical feature of the GUI, as it makes it adaptable to policy
aspirations.
MAPISCo Final report: 5. Using the method
51
Results: For the 52 palm species previously included on the priority list, inclusion of the new
harvesting co-benefit scores results in harvesting co-benefit scores being increased (mean value
from 0.111 to 0.414). The mean final priority score for these species in the overall list also
increased, from -7.043 to -1.224. This had a significant change in the overall species ranking within
the list. Firstly, two palm species now ranked equal first at the top of the overall list - Carpoxylon
macrospermum and Ceroxylon sasaimae (both previously ranked 37816th). Secondly, inclusion of
this new data set increased the representation of plants in the top 500 by 4% (from 10 to 30
species), largely at the expense of fish (-2.4%) and amphibians (-1.8%).
Discussion: These results show clearly that the expansion of the database through the addition of
new information will have considerable effects on species priority lists.
5.2. Adaptable - How does the priority list respond to changes in co-benefit weightings? The facility to change the relative weight given to of each co-benefit in is built into the database.
This allows the priority lists to be adapted to explore policy scenarios. If, for example, a
policy wished to prioritise species based on their contribution to ecosystem services, the weighting
this co-benefit was given in the methodology could be increased in relation to the other co-benefits.
This would then give an ecosystem service-centric list. We illustrate this adaptability using a
sensitivity analysis and by carrying out a worked example.
5.2.1. Sensitivity analysis
The species represented in the top 500 list changes as the co-benefit weightings are varied. We
can illustrate the strength of this effect across the five co-benefits by decreasing the weight of one
co-benefit by 0.1 intervals from 1 to 0 whilst keeping all others constant (at 1). This gives a total of
51 combinations (10 decreases in weighting by 0.1, for each of the 5 co-benefits, in addition to all
co-benefit weights set to 1).
Decreasing the weighting of each co-benefit (relative to all others held at a constant weight of 1)
from 1 to 0.1 results in a similar pattern of absolute change in rank for each co-benefit (see Figure
5 below). The greatest absolute change in species in the top 500 list occurs when Ecosystem
services is reduced to a weight of 0.1 (the mean change in rank is 1588.01). In the 51
combinations of weightings tested, a total of 1064 different species occur in the top 500, with some
species occurring in many or all iterations (up to 51 times). The distribution of species occurrence
in the top 500 is distinctly bimodal: 504 (41.6%) species occurred in the top 500 in more than 30
iterations and 602 (49.4%) occurred fewer than 10 times. This suggests a surprising degree of
stability of species representation in the top of the list irrespective of modest levels of variations in
weighting. This most likely reflects the non-independence of the co-benefits outlined above.
Appendix 9, Table A9-4 shows the list of species occurring more than 30 times in the top 500.
5.2.2. Worked examples – threat status and ecosystem services
If policy-makers wish to prioritise threat status above all other co-benefits then they could adjust
the weight applied to it allowing it to have a greater influence on the final priority score. By setting
the weight of Threat Status to 1.0 and all other co-benefits to 0.5, this changes the priority list in
the following ways;
MAPISCo Final report: 5. Using the method
52
1. The percentage of Critically Endangered species in the top 500 increases by 24.2%
compared to when all co-benefits are equally weighted (1.0). The percentage of Vulnerable,
Near Threatened or Least Concern species decreases, but the number of Endangered
species remains constant (see Table 23).
2. The taxonomic focus of the top 500 does not change extensively, with birds continuing to
contribute the largest proportion of species, followed by amphibians, mammals and fish
(Table 23)
If policy-makers wish to prioritise Ecosystem service provision and adjust the weight applied to
it as described above, this changes the priority list in the following ways;
1. The percentage of Critically Endangered species in the top 500 decreases by 14.2%,
compared to when all co-benefits are equally weighted (1.0). The percentage of
Endangered and Vulnerable species also decreases, while the number of Near Threatened
and Data Deficient species increase. The number of Least Concern species remains
relatively constant (see Table 24).
2. The taxonomic focus of the top 500 list shifts from birds (48.4 % of species when all co-
benefits are weighted equally) to amphibians (42.4 %) (see Table 24).
MAPISCo Final report: 5. Using the method
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Figure 5. Mean absolute change in the rank of species in the top 500, following decreases in co-benefit weightings.
Table 23. The proportion of species in the top 500 lists and the IUCN threat category in which they are
listed when Threat Status or Ecosystem Services are given priority over other co-benefits (CBs).
IUCN Red list Threat
Status categories
Threat status weighted 1 (all other
CBs weighted 0.5)
Ecosystem Services weighted 1 (all
other CBs weighted 0.5)
All CBs
weighted 1
CR 54.2 % 15.8% 30%
EN 26.6% 14.2% 26.2%
VU 12.6% 20.6% 21.2%
NT 5.6% 14.6% 10.4%
EW 0.4% 0.2% 0.4%
LC 0.4% 9% 10.4%
DD 0.2% 25.6% 1.2%
0
500
1000
1500
-0.1 -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 -0.9
Change in weight (from 1)
Me
an
ab
so
lute
ch
an
ge
in
ra
nk
Status
Hab
Har
GenD
ES
MAPISCo Final report: 5. Using the method
54
Table 24. The proportion of species in the top 500 lists in each taxonomic group when Threat status or
Ecosystem services are given priority over other co-benefits (CBs).
Taxonomic
group
Threat status weighted 1 (all
other CBs weighted 0.5)
Ecosystem Services weighted 1 (all
other CBs weighted 0.5)
All CBs
weighted 1
amphibians 29.8 42.4 23.2
birds 40.6 35.2 48.4
fish 6.4 0.6 7.2
mammals 17.2 14 16
other 1.6 1.6 0.8
plants 2.2 4.6 1.8
reptiles 2.2 1.6 2.6
5.3. Usable - Development of Graphic User Interface (GUI)
A GUI is defined as “a type of user interface that allows users to interact with electronic devices
using images rather than text commands”. In the context of the MAPISCo database, we see a
GUI functioning as a way of enabling non-technical users to explore the data within it without
having to individually alter each component manually. We envisaged the GUI using a
combination of lists, graphs and maps to display the data in an easily interpreted form, allowing
users to investigate questions such as: What are the highest priority species? Where in the world
does these species occur? What effect does altering particular co-benefit scores have on the
overall ranking of the lists? Where does a particular species fall in the ranking?
5.3.1. GUI Development
The GUI was developed using the same open-source statistical environment as the original
database - R. This allowed user interface and analysis routines to be integrated easily. R also has
rich graphical routines, a rapid development time, good transparency of method, and the potential
for modification by other team members. R is freely available for all common operating systems,
relatively easy to install, used in universities worldwide and increasingly by major commercial
organisations such as Google and the New York Times. Using the new R Package ‘Shiny’,
released in November 2012 for user interface development allows user interfaces to be run locally
as well as on a web server. In the latter case users do not need to have R installed.
An initial prototype user interface was presented by the developer Andy South at the MAPISCo
steering group meeting in November 2012.The initial prototype allowed users to select a species
from the list and then choose one of the following display options (tabs):
Graphic: the position of this species in the MAPISCo priority ranking (with all weighting factors set to 1), as a bar chart with each individual species represented by one bar. Bars are coloured according to taxonomic group.
Map: a world map showing which countries this species occurs in and an option to label the countries with their names.
MAPISCo Final report: 5. Using the method
55
Score co-benefits: shows how the priority score for this species is calculated from the five co-benefits. This allows the user to see, for example, whether a particular species receives a high priority score because of its values for Threat Status, Habitat or Harvesting.
A subsequent version was made available in time for the CITES COP at the start of March 2013.
The new version added the following functionality requested at the steering group meeting.
Two stage selection process: first allowing users to select a taxonomic group, continent and country. A species list is displayed based on these selections allowing the user to then select an individual species and view the outputs outlined above.
Weightings sliders for each of the five co-benefits. The sliders can be moved between zero and one (with one being the default starting value). Changing the weightings leads to re-calculation of the priority scores and all other UI components update with the re-calculated rankings.
Rank Table tab: displays a table of the selected species list ranked by priority scores. The table contains the scientific name, taxonomic group, English name and scores for each of the five co-benefits as well as the overall priority score.
About tab: gives a brief outline of the project, contact details for project participants and acknowledgements.
The current, development version, of the GUI can be viewed and tried out at: www.mapisco.org.uk.
5.3.2. Constraints and legacy
The resources available for this part of the project have been limited relative to the usual resources
required to develop a fully featured, robust, useable software product. 14 days of developer time
for GUI development were available from project funds. Therefore, the GUI that has been
produced should be seen as a prototype to be developed further. We are keen to develop the user
interface when funds can be sourced.
5.3.3. Future development options
With relatively few extra days development time the following options can be added to the GUI in
the short term. Costed proposals for these options were provided to the project steering committee
in February 2013 (options that were taken up at that stage and are included in the implementation
described above are not included here).
Outputting priority lists as a PDF or CSV file including metadata detailing the user, time of
creation and weighting options selected.
Produce a version of the user interface targeted specifically at Overseas Territories (OTs),
only including species occurring in OTs and allowing specific OTs to be selected.
Allow output of a single reference page for a species chosen, giving text, map and
graphics, including specifying the co-benefit values and which weightings options chosen.
MAPISCo Final report: 5. Using the method
56
Creation of a reference document/atlas containing a page for all species subject to a
maximum PDF size of ~2GB. The page would have text, map, and a scorings graph.
Provide a button that will link to other databases (WCMC, IUCN Red List) and/or image
search for the selected species.
Creation of an R package containing the MAPISCo database and analysis routines,
documentation and helpfiles. Submission to international repository. This will make the
database and methods easily accessible to researchers worldwide and will help to ensure
project legacy.
These short term development options could provide a bridge to longer term developments for
which there is considerable potential.
5.3.4. Future hosting options
The beta-test version of the GUI is currently hosted on a test server and redirected from the
www.mapisco.org.uk domain name. There is no guarantee how long this test server will remain
available. To make the user interface freely available online in the long term there are two options.
The first option is to make it available on a project specific server running R and shiny thus
incurring no extra costs beyond hosting. The second option is to use the Shiny hosting service,
which would incur an as yet unknown monthly fee. The related issue of where the database should
be hosted is considered in section 6.52, page 61.
MAPISCo Final Report: 6. Discussion
57
6. Discussion
Capsule
The method developed allows the prioritisation of species based on their contribution to five co-benefits - conservation effort directed at high ranking species is expected, therefore, to contribute most to biodiversity, via the selected co-benefits.
The database that we have compiled contains information on 70000 species that has been consolidated from a suite of databases held by other organisations. Creating, curating and updating primary databases is time-consuming and expensive and so the coverage of species and co-benefit scores is variable across major species-groups. For example, birds are well-covered, plants much less so.
There is clear scope for Defra to build on the progress made to date so that scientific knowledge and practice can better support UK government objectives. In order to do this, the database requires modest technical development and a permanent home, the scientific rationale linking species and co-benefits should be strengthened, and the policy arenas where it can be used should be defined more closely.
6.1. Fit to original project brief
The original contract specification for this project required the creation of a methodology to
prioritise species conservation effort for the greatest contribution to “consequential benefits for
other species (or taxa), habitats, wider ecosystems, and ecosystem services”. The methodology
was to be: expandable allowing the incorporation of future data, adaptable to changing policy
aims and usable by non-technical practitioners.
The methodology presented in this report meets the specification outlined above by focusing on a
selection of five priority co-benefits (habitat conservation, genetic diversity, harvesting, species
extinction risk and ecosystem services). The steps involved in developing a priority list of species
for conservation investment included: (i) identifying 2-3 data sources which could be used to
quantify the value of a given species to each co-benefit, (ii) computing standardised scores for
each species on each co-benefit (across data sources), (iii) summing these scores to create a final
ranked priority score, weighted as required. In theory, conservation effort directed at species
ranked highly on the priority list could be expected to contribute most to the selected co-
benefits. This would permit greater contributions to:
1) the prevention of species extinctions by on average focusing effort on more highly
threatened species (Aichi Target 12),
2) the conservation of habitats by focusing effort on those species used to identify a
selection of Key Biodiversity Areas in which larger number of species co-occur
(Aichi Targets 5 and 7),
3) the promotion of sustainable harvesting by focusing on harvested species of the
greatest economic value (Aichi Target 6),
MAPISCo Final Report: 6. Discussion
58
4) the conservation of genetic diversity of species of economic or social value, by
focusing on wild relatives of crops and domesticated animals, and medicinal plant
species (Aichi Target 13) and
5) the protection of ecosystem service provisioning by focusing on species occurring in
forest- and wetland habitats in countries with higher estimated rates of carbon loss
through deforestation or lower freshwater availability (Aichi Target 14).
Therefore, conservation of the species highlighted by our approach presents the greatest
potential to contribute to Aichi 5-7 and 12-14, as well as being an effective way to help
direct conservation policy to contribute to international conservation agreements.
6.2. How does the method compare with ‘business as usual’?
One of the original drivers for this project was the perceived view that resources were often
directed towards a few charismatic species. In the current methodology “politically interesting”
or flagship species often championed by interest groups do not generally rank highly (e.g. Asian
Elephant Elephas maximus is ranked 397th, African Elephant 553rd, Tiger 1759th, Giant Panda
Ailuropoda melanoleuca 9473rd, African Lion Panthera leo 6724th, Eastern Gorilla G. beringei
3819th, Lowland Gorilla Gorilla gorilla 2741st, Black Rhinoceros Diceros bicornis 37816th, Polar
Bear Ursus maritimus 45625th and White Rhinoceros Ceratotherium simum 51510th). This is
because they are associated with only a small number, if any, of the co-benefits considered here.
The method we have devised is based on objective criteria which can be transparently
adapted as policy aspirations change. This can be done by putting more or less value on each
co-benefit by varying the associated co-benefit weighting. For example, clearly using objective
criteria based on a range of co-benefits places a few of the charismatic species illustrated here in
context: they are less likely to fulfil a range of goals as set out in the Aichi Targets which we focus
on than a large number of other species.
6.3 How do co-benefits relate to IUCN threat status?
An aim of the MAPISCo approach was to prioritise species based on the consequential benefits
associated with their survival in addition to their IUCN Red List threat status. We would therefore
expect that resulting priority lists are not simply a reflection of threat status.
To test this expectation we subdivided the overall database into four sub-databases – 1) one
containing all species which score on Red List Threat Status AND Habitat Conservation, 2) one
containing all species which score on Red List Threat Status AND Harvesting, 3) one containing all
species which score on Red List Threat Status AND Genetic Diversity, and 4) one containing all
species which score on Red List Threat Status AND Ecosystem Services. This meant that each
sub database contained data only for species that score on Red List Threat Status and each of the
other four co-benefits in turn. For each of these four databases we created three new priority lists
based on -. 1) Red List threat category scores alone (for which the scores will be 1 for Critically
Endangered, 0.88 for Endangered etc). This represents how prioritisation decisions could be made
if the Red List alone was used to rank species. 2) Scores from the co-benefit in that database
MAPISCo Final Report: 6. Discussion
59
alone (e.g. scores for Genetic Diversity). 3) Scores from the threat category and co-benefit
combined (the average of the two). The results of this are shown in Table 25 below.
Both the Habitat and Ecosystem Services co-benefits are significantly negatively related to Threat
Status, meaning that more traditional approaches to conservation (based on extinction risk- the
IUCN Red List) do not capture more recent concerns about protecting a range of co-benefits from
each species.
When threat status and each co-benefit are combined, three of them (Habitat, Harvesting and
Ecosystem Services) are positively correlated to threat status suggesting that the MAPISCo
database does encompass extinction risk and these co-benefits as well. The exception is Genetic
Diversity, which is negatively correlated to both IUCN status (although not significantly) and to
IUCN status and Genetic Diversity suggesting that the relationship with this score and others is not
straightforward. This is probably due to the scoring for this co-benefit which tends to be binary
(either not related at all or quite highly related – section 3.2.3., page 17).
Table 25. Spearman’s rank correlation coefficient between IUCN status and each co-benefit (* = p < 0.05).
Priority score IUCN status
Habitat (n = 5553)
Harvesting (n = 1504)
Genetic diversity (n = 811)
Ecosystem services (n = 38946)
Habitat -0.154*
Harvesting 0.013
Genetic diversity -0.066
Ecosystem services -0.60*
IUCN status + Habitat 0.890* 0.246*
IUCN status + Harvesting 0.862* 0.450*
IUCN status + Genetic Diversity 0.902* -0.328*
IUCN status + Ecosystem services 0.863* 0.389*
6.4. Operating constraints It is important to bear in mind that, in its current format, the method developed by this project is
biased towards those taxa that have the greatest representation in the databases used to calculate
priority scores. This is because some species and some co-benefits have been subject to more
study and data collation than others. This issue is particularly acute for plants, which have a very
low proportional representation in the current version of the database (this is discussed in full in
section 4, page 27). This will inevitably result in the relative (overall mean) downgrading of plants
in any species prioritisation process until more plants have been assessed on the Red List and in
other databases. Therefore, we urge caution when using the method with all taxa: it is better to
currently use it to ask specific questions such as prioritisation within well studied groups, such as
birds.
MAPISCo Final Report: 6. Discussion
60
6.5. Integration of MAPISCo into decision-making – next steps
As biodiversity issues become mainstream in political processes there is a recognition that the
interface between science and policy must be strengthened (e.g. Koetz et al. 2008). Drawing on
experience with the Intergovernmental Panel on Climate Change (IPCC), the necessary elements
include research produced by bodies external to the policy body; a structure for collecting new data
and observations; and an assessment body to make information and knowledge accessible for
policy makers (Lariguaderie & Mooney 2010a;b). Perhaps most importantly, it is vital to recognise
that developing science-based policy is an iterative and adaptive process that relies on improving
knowledge so as to reduce uncertainty coupled with dialogue between the research and policy
community (Koetz, Farrell & Bridgewater (2011).
Thus, use of the MAPISCo methodology should be seen as an iterative process (Figure 6). As
discussed in sections 4 and 5, the use of data sources to inform the co-benefits should be
continually assessed, improved and expanded, in response to (changing) expert opinion. Because
the lists generated by the current method are based on a relatively small number of data sources
(12), this expansion is crucial to give greater robustness to decision-making. It would be sensible,
therefore, for Defra to give priority to supporting the efficient collation and curation (and even
collection) of such data. It may also be possible to develop formal means to incorporate expert
opinion in the way in which data sets are used and scored (e.g. Howes, Maron, & Mcalpine 2010;
Aguilera et al. 2011) (as with the Palms example, section 5.1, page 50).
Figure 6. Non-linear science-policy interface showing how MAPISCo may benefit from stronger dialogue
between these two fields. Black arrows form part of the traditional “linear” interface, red arrows are the
feedback required.
MAPISCo Final Report: 6. Discussion
61
To allow MAPISCo to be used successfully in the longer term, three areas require further attention:
1) Science, 2) Practical and 3) Policy. This will put MAPISCo on a sustainable footing and
enable it to contribute to policy development.
6.5.1. SCIENCE. Ensuring that the methodology fully accounts for scientific advances
Linking species data to co-benefits
Information on the links between conservation of individual species and co-benefits is varied. Thus
the ability to prioritise conservation on the basis of some co-benefits will be more limited in some
species groups than others (see section 4, page 27). However, the knowledge base relating
species conservation to co-benefits may improve in the future as interest in work on ecosystem
services gains ground (including a thorough review commissioned by this project, see Appendix 3).
If Defra is to progress with the MAPISCo method, further research may be appropriate refine the
concepts and framework.
Conceptual advances
The analytical field of prioritisation in biodiversity conservation is moving rapidly and there are
frequent advances in standardising variables and dealing with unknowns and uncertainty. It is
important that relevant advances are tracked so that any necessary adjustments to the MAPISCo
methodology can be made. Defra could achieve this by establishing a MAPSICo Secretariat, that
undertakes the necessary surveillance or by commissioning regular updates.
Links to other Aichi Targets
The project has taken place within the context of Defra’s desire to maximise overall conservation
benefit from its spend on species. Currently five co-benefits, linked to four Aichi Targets are
included in the methodology so the priority lists are only relevant within the context of these
particular targets. Different species would be ranked more highly if contributions to Target 9
(control of invasive species) or others were to be included.
Non-independence of data sets
There is a degree of overlap in type of data used for the calculation of co-benefit scores (e.g.
extinction risk as based on the IUCN Red List categories is used for Threat Status calculations, but
is also used in the identification of e.g. IBA and AZE species). In statistical analyses, such
interdependence would be considered a problem. However, in this case, this interdependence
results from the co-benefits themselves (and the Aichi Targets from which they are derived) being
non-independent. For example, by addressing Aichi Target 12 (preventing species extinctions),
many species relevant to Target 13 (Genetic Diversity) would also be covered (as many of these
are listed on the IUCN Red List).
6.5.2. PRACTICAL - Maintenance of database and incorporation of additional data. Where will the database be housed?
There is an immediate need to determine where the database will be housed and what form
technical support will be required. There are several options for hosting the database either within
MAPISCo Final Report: 6. Discussion
62
Defra, or with external hosts, such as the IUCN Red List Office (Cambridge), Newcastle University
or UNEP-WCMC. The suitability of these options will depend on an assessment of the following
factors:
Cost;
Capability;
Ability to provide scientific support (see below);
Understanding of Defra’s policy needs and way of working; and
Duration of hosting contract that Defra proposes.
Scientific support needs
Some level of ongoing scientific support to Defra may be required to allow the MAPISCO
methodology to be fully operational. It may be that the standards and other documentation (see
below) together with training in the application and use of the methodology to provide prioritisation
lists would be sufficient, rather than an ongoing ‘help desk’ approach. Defra will need to consider
what support it is likely to require, for how long and in what form.
Define standards and documentation
To ensure that the database and methodology are used appropriately and to best effect it is
desirable to develop technical documents that define the standards to be used and specify in what
format any outputs should appear. This is also important to show that all queries run in MAPISCo
are transparent and that the decisions on weightings are documented fully. Good examples of how
this can be done and how useful it may be can be drawn from the IUCN Red List which has
standard definitions and classifications (see http://www.iucnredlist.org/technical-
documents/classification-schemes and links therein) and also produces outputs of searches in a
standardised way with a recommended citation. With some consideration, it would be possible to
produce a similarly standardised output from the graphical user interface that gives all decisions
made on weighting and identifies the person running the query as the author.
Incorporating new data and updating existing data
Data availability and accessibility is a concern. A very limited number of datasets exist which
contain the information required for the MAPSICo methodology. Fewer still have been brought
together, been adequately assembled and documented, and then made accessible. This is
important because the availability and choice of data sources included in the database has
consequences for the ranking of species. Thus, the ability to prioritise conservation on the basis of
some co-benefits will be more limited in some species groups than others.
The paucity of data, for some taxa such as plants, means that small improvements in the
availability of data can have a large impact on the resulting species priority list. The inclusion of
specialist data on palm tree species into the database resulted in a considerable change in their
place in the species ranking from no species in the top 2000; to two in the top five (see section 5.2,
page 51). By exploring other scenarios where small improvements in the availability and/or quality
of data linking species to co-benefits, it will be possible to focus resource investment in the
MAPISCo Final Report: 6. Discussion
63
gathering and/or collation of data that can maximise impact on prioritisation ability. In addition, the
method derived here for ‘within-group’ prioritisation could be a useful way forward.
All suitable datasets have been incorporated in the current database. Addressing remaining gaps
may require various approaches. For example, taxonomic coverage is not uniform, the specific
content of the database (the data fields) vary and there is substantial need to improve the curation
and accessibility of many datasets before they could be considered for inclusion in the MAPSICo
database. Finally, there will be varying motivations of data holders to share their data with Defra on
a gratis basis. A practical first step would be to look at institutions close to Defra (and current/future
partners) and produce a detailed analysis of what their data holdings are and how they can be
made accessible to MAPSICo. A strong candidate here would be Kew Gardens and the data
currently being assessed by the IUCN Red List in Cambridge. Access to these sources may add
significantly to the MAPSICo coverage. Filling other gaps would require a more strategic approach
and this would depend on immediate Defra priorities.
6.5.3. POLICY - Integrating MAPISCo into policy and resource allocation decisions Demonstrating the potential of MAPISCo
MAPISCo would benefit from external review by scientists and policy-makers. Therefore, it is now
very important to demonstrate the method to policy makers and senior officials in Defra and other
potential users. This could involve a demonstration of how lists can be generated, the sorts of
decisions that can be taken on co-benefit weightings and the impact varying these may have, and
how the outputs can be used. One or more workshops with potential end users would likely be the
best way to promote the method. If this could be combined with working through one or more
current Defra species issues, it would be a very strong demonstration of the method.
Strengthening the ability of MAPISCo to underpin policy
The strengthening of links between both policy requirements and the framing of the scientific inputs,
and between MAPSICo’s outputs and the impact they have on policy decision-making will be key
integrating MAPISCo into working policy. Providing a broader array of policy situations in which to
demonstrate how the method may be used would be helpful, as this would allow the range of
assumptions and decisions to be assessed through the range of weightings applied by policy
makers to MAPISCo at the input stage. At the other, output, end of the process, it would allow
much greater understanding of the range of uses to which the priority lists (and associated data)
generated would then inform the decisions that have to be taken. Exploring this with a range of
users in a variety of contexts would allow the potential and the limits of the method to be defined
more clearly.
A further exploration and demonstration of the value of MAPISCo would be a clearly defined
project in which priority lists would be generated with various weightings (reflecting different policy
demands) and comparing these against existing Defra priorities. This would permit assessment of
both how well aligned they are and, perhaps more informatively, reasons for variation between
current priorities and various MAPISCo outputs. Such an assessment should help bring into sharp
focus unstated assumptions or other factors that may need to be incorporated into the
methodology to account for the full range of contexts in which it may be used.
MAPISCo Final Report: 6. Discussion
64
Ease of use
The methodology is far more likely to be used if it is intuitive and clear. Therefore the interface
(graphical user interface: GUI see section 5.3, page 54 for further discussion of GUI legacy) and
the explanatory documentation needs to be easy to understand. The need here is for a short
period of testing documentation and the GUI are a good fit to the users. There may be a need for
refinement based on this testing.
6.6. Concluding remarks
We have delivered a first version of a methodology that can identify priorities for species
conservation efforts based on expected contributions to a selection of five co-benefits. Our finding
that around 1064 species are commonly ranked highly irrespective of variations in how the co-
benefits are weighted, suggests that the proposed methodology does provide a blunt tool for
identifying species where conservation effort could be expected to make significant contributions to
the Aichi Targets.
This project is at the cutting edge of the science-policy interface. Although species prioritisation
efforts are common, the vast majority of previous efforts are either geographically or taxonomically
limited (e.g. Dunn, Hussell, & Welsh 1999; Knapp, Russell, & Swihart 2003; Rodríguez, Rojas-
Suárez, & Sharpe 2004; Jimenez-Alfaro, Colubi, & Gonzalez-Rodriguez 2010), and are often
limited to biological considerations only (Mace & Collar 2002; Mace, Possingham, & Leader-
Williams 2006).
Although this project set out to establish clear objective criteria to determine how to prioritise
species conservation investments, we have also shown that the choice of both co-benefit
weighting and the data sources has a strong effect on which species are identified as higher
priorities. As a result, the development of this methodology has brought the mismatch between the
data requirements and data availability/accessibility for ambitious species prioritisation exercises
into sharp focus.
Implied in the original project brief is an assumption of a relatively straightforward and linear
science-policy interface, where science can directly meet policy needs and inform policy changes.
Both the mismatch between the data required to fully service the original project brief and our
results presented here highlight the need for a re-evaluation of this interface. As we have outlined
above, at this stage of the methodology, further guidance and refinement of policy aims are
required for science to make progress. In other words, a number of feedbacks from science into
policy and back again need to be incorporated into a non-linear interface. As a result, the
methodology described here becomes part of an iterative process where conservation science and
policy meet and continuously refine each other’s needs, rather than a final answer to global
species prioritisation problems.
MAPISCo Final Report: 7. References
65
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