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Spatial Criteria Used in IUCN Assessment Overestimate Area of Occupancy for Freshwater Taxa
By
Jun Cheng
A thesis submitted in conformity with the requirements for the degree of Masters of Science
Ecology and Evolutionary Biology University of Toronto
© Copyright Jun Cheng 2013
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Spatial Criteria Used in IUCN Assessment Overestimate Area of
Occupancy for Freshwater Taxa
Jun Cheng
Masters of Science
Ecology and Evolutionary Biology
University of Toronto
2013
Abstract
Area of Occupancy (AO) is a frequently used indicator to assess and inform designation of
conservation status to wildlife species by the International Union for Conservation of Nature
(IUCN). The applicability of the current grid-based AO measurement on freshwater organisms
has been questioned due to the restricted dimensionality of freshwater habitats. I investigated the
extent to which AO influenced conservation status for freshwater taxa at a national level in
Canada. I then used distribution data of 20 imperiled freshwater fish species of southwestern
Ontario to (1) demonstrate biases produced by grid-based AO and (2) develop a biologically
relevant AO index. My results showed grid-based AOs were sensitive to spatial scale, grid cell
positioning, and number of records, and were subject to inconsistent decision making. Use of
the biologically relevant AO changed conservation status for four freshwater fish species and
may have important implications on the subsequent conservation practices.
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Acknowledgments
I would like to thank many people who have supported and helped me with the production
of this Master’s thesis. First is to my supervisor, Dr. Donald Jackson, who was the person that
inspired me to study aquatic ecology and conservation biology in the first place, despite my
background in environmental toxicology. Don, your mentorship has always been thoughtful,
motivating and well-placed, leading me through the transition and through the further research
process. To my co-supervisor, Dr. Nicholas Mandrak, your depth of knowledge and enthusiasms
towards fishes have constantly excited me and shaped my vision. I cannot express how grateful I
am for your generous and inspiring inputs, as well as your encouraging and patient guidance. I
am honored to be a graduate student of both of you. To my advisory committee members, Dr.
Ken Minns and Dr. Keith Somers, I am blessed to have your insightful comments and questions
that contributed tremendously to making this thesis complete.
To the Jackson lab (Karen, Lifei, Brad, Jaewoo, Brie, Cindy, Sarah, and Georgina) and the
extended members (Liset, Sarah, Danielle, Caren, Jonathon, Caroline, Henrique, Jordan and
many more): thank you all for being great colleagues and friends that made grad school an
enjoyable journey. I will always remember and deeply appreciate what I have learnt from you
both in academically and personally.
To my family: Mom, Dad, Grandpa, Juan and Rey. My most sincere appreciation goes to
Mom and Dad. Thank you for the years of supporting my education and believing in me while I
was away from home. You have given me the best experience growing up independently yet
feeling supported. Dad and Grandpa, you are the initial inspirations for me to be interested in
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science and research. Juan and Rey, you are the most awesome aunt and uncle, and the most
awesome friends. Thank you for giving me a feeling of home ever since I came to Canada.
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Table of Contents
Acknowledgments....................................................................................................................iii
List of Tables…………………………………………………………………………..…..…vi
List of Figures………………………………………………………………………...…......viii
List of Appendices……………………………………………………………………..…..….x
Introduction................................................................................................................................1
Methods......................................................................................................................................8
Results......................................................................................................................................19
Discussion................................................................................................................................43
References................................................................................................................................62
Appendices...............................................................................................................................70
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List of Tables
Table 1. Summary of the five quantitative criteria used by the Committee on the Status of
Endangered Wildlife in Canada (COSEWIC) for assessing conservation status of wildlife
species, which is adapted from the IUCN Red List Categories and Criteria………….........3
Table 2. Total number of occurrence record, number of geographically distinct localities, 2km-
IAO, 1km-IAO, and 0.5km-IAO calculated using grid cells of the same position and
orientation, ratio of 2km-IAO to 1km-IAO, and 2km-IAO to 0.5km-IAO in percentage for
20 at-risk freshwater fish species in Ontario……………………………………..….…....25
Table 3. Mean, standard deviation, maximum, minimum values of 2km-IAO calculated using
13 layers of grid cells at varying positions for 20 at-risk freshwater fish species in Ontario.
Also shown are COSEWIC reported 2km-IAO, and ratio of COSEWIC-reported values to
the calculated mean IAO values in percentage…..………………………………....……..28
Table 4. Habitat classification for the 20 at-risk freshwater fish species included in this
study…………………………………………………………………………………….....33
Table 5. Home range estimates based on average body length reported in Ontario (Holm et al.
2010) and the equation of Woolnough et al. (2009), and the adjusted buffer scales used for
BioAO calculation for the 20 at-risk freshwater fish species included in this study……...34
Table 6. Stream width (m) predicted by relationship with Strahler stream order. Values are
anti-loge transformed and corrected with bias estimator (Sprugel 1983)…. ………..……36
Table 7. Proposed BioAO as sum of stream occupancy calculated in terms of occupied stream
length x stream width, and lake/wetland occupancy calculated in terms of suitable habitat
area within circular buffer, in comparison to COSEWIC reported biological AO for 20 at-
risk freshwater fish species in Ontario. …………………………………………..….........37
Table 8. Summary of species status designated under COSEWIC and after application of
BioAO, and the corresponding reasons for designation for 20 at-risk freshwater fish
species in Ontario………………………………………………………..……………..….40
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Table 9. Summary of advantages and drawbacks of each AO measurement approach for
freshwater taxa………………………………………………………………………….....50
viii
List of Figures
Figure 1. Study area……………………………………………………………………..……...10
Figure 2. Example of Index of Area of Occupancy (IAO) measurements for Black Redhorse in
the Grand River, Ontario, at three spatial scales: 0.5km, 1km and 2km……………...……11
Figure 3. Example of BioAO measurement for stream occupancy of Black Redhorse in the
Grand River, Ontario. The highlighted stream stretch indicates the segments considered
occupied habitat. Localities distant from others were considered single locations.……….15
Figure 4. Example of BioAO calculation for lakeshore occupancy for Grass Pickerel in Long
Point Bay, Lake Erie……………………………………………………………………….17
Figure 5. Number of freshwater fish (a) and mollusc (b) species listed under COSEWIC in each
threat category and the number of Endangered (EN) and Threatened (TH) freshwater fish
(c) and mollusc (d) species designated by criterion………………………………………..21
Figure 6. Primary and secondary threats identified responsible for species decline in freshwater
fish (a) and mollusc (b) species of Canada that are classified as extinct, extirpated,
endangered, threatened and special concerned…………..…………………………………22
Figure 7. Grid-based IAO calculations for 20 freshwater fish species measured at three spatial
scales: 2km X 2km (circles); 1km X 1km (triangles); 0.5km X 0.5km (squares), with
species arranged on the x-axis in the order of decreasing 2km-IAO size. Dashed lines
indicate threshold values for IUCN criteria……………...……………………………..…..24
Figure 8. Mean values of calculated 2km-IAO (triangles) based on 13 placements of grids,
COSEWIC-reported 2km-IAO (squares), proposed BioAO (circles), COSEWIC-reported
biological AO (diamonds), and HR-BioAO (crosses) for 20 at-risk freshwater fish species
in Ontario, with species arranged on the x-axis in the order of decreasing mean 2km-IAO
size. Dashed lines indicate threshold values for IUCN criteria….…………………………31
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Figure 9. Linear regression (solid line) between Strahler stream order and loge-transformed
stream width (m) based on 2431 DFO survey records in Ontario (open circles), P <
0.0001.Predicted stream width (see Table 4) at each Strahler order is estimated by the
regression………….……………………………………………………………………..…35
Figure 10. Frequency distribution of total BioAO using adjusted buffer
size…………………………………………………………………….……………………38
Figure 11. Linear regression (solid lines) between number of geographically unique occurrence
sites and the resultant (a) mean 2km-IAO calculated using 13 gird layers at varying
positions (all 20 at-risk freshwater fish species in Ontario, P < 0.0001); (b) Stream BioAO
in stream length multiplied by stream width (17 of the 20 species, P =0.097); (c) suitable
habitat area within circular buffer (17 of the 20 species, P < 0.001)…..…………………..42
Figure 12. Point distribution records of a freshwater mussel species the Wavy-rayed
Lampmussel, Lampsilis fasciola, in the Thames River basin (black circle), and the grid-
based IAO of 1km2(shaded grids) and 2km2 (open grids) scales considered by
COSEWIC………………………………………………………………………………….48
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List of Appendices
Appendix 1. COSEWIC designated conservation status and threat factors identified for
freshwater fish species in Canada…...……………………………………………………..70
Appendix 2. COSEWIC designated conservation status and reported threat factors for
freshwater mollusc species in Canada……………………………………………………...81
Appendix 3. Type of habitats used by freshwater species at risk in Canada. a) fishes; b)
molluscs………………………………………………………………………..…………...83
Appendix 4. Proportion of the two components of BioAO: stream occupancy and lake/wetland
occupancy for 20 fish species at risk in Ontario.……………...……………………………84
Appendix 5. Breakdown of stream BioAO for 17 species inhabited stream environment: area for
raw length of occupied stream segment, buffer segment area and area for single
locations.…………….……………………………………………………………………...85
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1. Introduction
Anthropogenic activities have accelerated species extinction rates to an historically high
level, leading to an increased demand for conservation actions (Butchart et al. 2010).
Prioritizing conservation efforts has become one of the most crucial steps in preserving
biodiversity (Hoffmann et al. 2010; Keith et al. 2004; Mace et al. 2008; Miller et al. 2007). One
way of achieving such a goal is through assessing and ranking species by the likelihood of
extinction (Keith et al. 2004; Mace and Lande 1991; Mace 1994). Over the past four decades, a
number of risk-ranking protocols and subsequent lists have been developed across various
geographical scales, ranging from global to regional and local scales (Gärdenfors et al. 2001;
Mace 1994; Miller et al. 2007; Millsap et al. 1990; Milner-Gulland et al. 2006). These lists
provide guidance for developing recovery strategies, designating protected habitat area, and
informing policy makers (Lamoureux et al. 2003).
The International Union for Conservation of Nature (IUCN) Red List Categories and
Criteria is the most recognized conservation status assessment framework (Martín-López 2011).
The IUCN Red List status assignment incorporates a set of quantitative criteria to predict
extinction probabilities (IUCN Standards and Petitions Working Group 2010). Predictors, such
as population size, fragmentation, geographic distribution, and rate of decline, are determined
and compared against a series of numerical thresholds to differentiate species into various risk
levels. Such systematic procedures have been developed to facilitate objective evaluations and
to standardize assessments across taxonomic groups (Mace 1994).
IUCN Criteria consist of five major rules, two of which involves spatial analyses of the
species’ geographic distribution (Table 1; IUCN Standards and Petitions Working Group 2010).
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Two spatial indices are used: extent of occurrence (EO); and, area of occupancy (AO). EO
measures the total range of a species’ geographic distribution that encompasses all occurrence
records, whereas AO is defined as the actual habitat area occupied by individuals of the species.
The two parameters differ in that AO recognizes that not all areas within the geographic range
are suitable habitat. These spatial indices are incorporated into conservation assessments to
provide insight on population size and its trends, particularly when adequate abundance data are
lacking (Gaston and Fuller 2009, Mace et al. 2008; Pritt and Frimpong 2009). Geographic
distribution is one of the fundamental ecological and evolutionary characteristics of a species
that is both directly and indirectly linked to population health (Hengeveld and Haeck 1982;
Gaston and Lawton 1990b; Gaston et al. 2000). It is often regarded as a surrogate for population
abundance in conservation risk assessments, based on the notion that a population with a greater
number of individuals tends to be more widespread (Cardoso et al. 2011). Numerous studies
have demonstrated this positive correlation between range size and population abundance (Bock
and Ricklefs 1983; Gaston 1994; Gaston et al. 2000; Lacy and Bock 1986; Schoener 1987).
Both spatial parameters are assessed under IUCN Criterion B, which classifies a species into at-
risk categories if the geographic range distribution is very limited (IUCN Standards and
Petitions Working Group 2010). AO is also considered under subcriterion D2, which qualifies
species with extremely restricted population distribution as threatened to reflect the extinction
risk associated with demographic stochasticity and the elevated susceptibility to single threat
event (Lande 1993; IUCN Standards and Petitions Working Group 2010). These two spatial
criteria are the most frequently used reasons for qualifying a species into threatened categories
under IUCN Red List (Abeli et al. 2009; Gaston and Fuller 2009). Criterion B alone was
identified as the measure influencing the listing of more than 40% of all at-risk species (Gaston
and Fuller 2009).
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Table 1. Summary of the five quantitative criteria used by the Committee on the Status of
Endangered Wildlife in Canada (COSEWIC) for assessing conservation status of wildlife
species, which is adapted from the IUCN Red List Categories and Criteria.
Criterion Reasons for designation Indices analyzed
A Decline in total number of mature
individuals
Number of mature individuals
B Small distribution range and decline
or fluctuation
EO, AO, and number of locations
C Small and declining number of
mature individuals
Number of mature individuals
D Very small or restricted total
population
Number of mature individuals, or, AO
and number of locations
E Quantitative analysis The probability of extinction
The current approaches used for calculating the spatial criteria have received numerous
criticisms on their effectiveness and applicability (Abeli et al. 2009; Cardoso et al. 2011; Hartley
and Kunin 2003; Keith et al. 2000; Mace et al. 2008). EO, measuring the overall range of the
species, is usually estimated by a minimal convex polygon (MCP) that encloses all distributional
records (IUCN Standards and Petitions Working Group 2010). Inclusion of inappropriate
habitats due to discontinuities and disjunctions in the range distribution leads to biases in EO
calculation (Burgman and Fox 2003). Although IUCN advices area of unsuitable environments
to be excluded from EO estimates, explicit instruction on how to do so is not provided (Willis et
al. 2003).
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In the case of AO, a grid-based method is recommended. It is performed by overlaying
uniform-sized grids on the range of a species and summing the area of the cells in which the
species occurred (IUCN Standards and Petitions Working Group 2010). This approach is
frequently opposed because it is extremely sensitive to the resolution of grid cells (Hengeveld
and Heack 1982; Joseph and Possingham 2008; Keith et al. 2000; Whittaker et al. 2005). The
scale at which AO is measured varies greatly across taxonomic groups, where cell sizes ranged
from 0.1 to 1000 km2 in a review of 47 species (Gaston and Fuller 2009). Consequently,
standardization to a reference scale (currently 2km by 2km grid size as recommended by IUCN)
based on scale-area relationship is required (Mace et al. 2008). The ability to detect population
decline through grid-AO is also questionable. Joseph and Possingham (2008) showed the AO
measured at the traditionally recommended scale was insufficient to accurately detect declines
in population abundance.
Freshwater ecosystems support diverse groups of species and, yet, are under the most
substantial extinction crisis (Bruton 1995; Duncan and Lockwood 2001). Great proportions of
freshwater fauna are imperiled, including freshwater fishes, molluscs, crayfishes, and
amphibians (IUCN 2012; Jelks et al. 2008; Régnier et al. 2009; Ricciardi and Rasmussen 1999).
The projected global extinction rate is strikingly higher in these taxa compared to their terrestrial
counterparts (Ricciardi and Rasmussen 1999). In North America, 61 species and subspecies of
freshwater fishes and 21 species of freshwater molluscs have become extinct (Jelks et al. 2008).
The leading factors contributing to the endangerment of aquatic organisms have been identified
as habitat alteration and degradation, followed by overexploitation, pollution, and introduction
of alien species (Dextrase and Mandrak 2006; Jelks et al. 2008). However, freshwater fauna
have received a disproportionately small amount of conservation attention (Duncan and
Lockwood 2001; Maitland 1995). Only 33% of all described freshwater fish species and 3% of
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molluscs have been assessed under the IUCN classification regime, in comparison to 100%
coverage in mammals and avian species, and 94% in amphibians (IUCN 2012).
The suitability of current spatial criteria for these freshwater species has been debated. In
addition to the shortcomings described above, the measurement for AO is thought to be
especially problematic for freshwater organisms. During the early development of IUCN Red
List Criteria, grid-measured AO was derived from an array of studies on abundance –
distribution relationship measured by number of occupied grids (Mace 1994). However, most of
these studies were conducted for land birds over broad geographic ranges (Bock 1984; Brown
1984; Ford 1990; Schoener 1987) and this relationship was rarely shown in aquatic species
(Gaston and Lawton 1990a). The fundamental problem is that the grid-adjacency measure of
AO does not account for the confined geographic characteristics of aquatic habitats (Gaston and
Lawton 1990a). Unlike those in terrestrial and marine environments, where movements are
relatively free over large areas, freshwater organisms are restricted to limited space within clear
boundaries and dispersal across space is directional and hierarchical within watercourse
networks (Hitt and Angermeier 2008). Freshwater organisms are also often associated with
linear habitat ranges, such as streams and lakeshore areas (Burgman and Fox 2003; Joseph and
Possingham 2003; Mace et al. 2008) or confined to small waterbodies. As a result, grid-based
occupancy measurement may fail to capture the dimensionality of such habitats and to reflect
the underlying geographic patterns in species distribution and abundance, leading to subsequent
misinterpretation in extinction risk.
The Committee on the Status of Endangered Wildlife in Canada (COSEWIC) is
responsible for identification of threatened wildlife species at the national level in Canada, and
adopts the IUCN Red List protocol (COSEWIC 2010). The risk categories assigned by
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COSEWIC follows that of IUCN, including Extinct (EX), Extirpated (ET), Endangered (EN),
Threatened (TH), Special Concern (SC), Not at Risk (NR), and Data Deficient (DD). EN and
TH are considered the at-risk groups, where critical thresholds in conservation risk criteria are
evaluated. SC also represents an at-risk category, which is composed of species with known
declines and/or existing threats, but do not meet thresholds for designation in higher risk
categories. COSEWIC conservation assessments provide the basis for listing under the federal
Species at Risk Act (SARA), and for development and implementation of species recovery
strategies and recovery action plans. COSEWIC separates species into designatable units (DUs),
where appropriate, and assesses each DU separately. For simplicity, heretofore all species,
subspecies, designatable units, and populations are referred to as species.
Like the IUCN framework, COSEWIC incorporates an occupancy parameter, the Index
of Area of Occupancy (IAO), measured following the grid concept. IAO is required to be
calculated at a scale of a 2km-grid (i.e. 2km by 2km). An alternative scale of 1km-grid size is
recommended in cases where biological relevance can be argued (COSEWIC 2010). However,
given that most streams in Canada average less than 100m in width, IAO values measured at
these resolutions are likely to include more terrestrial habitat than actual occupied aquatic
habitats, grossly overestimating AO for most freshwater taxa. In this study, we used COSEWIC
assessments for freshwater organisms in Canada as a case study to: 1) understand the relative
importance of spatial criteria for status designation; 2) further explore the potential biases and
shortfalls associated with the grid IAO approach; and, 3) develop a biologically relevant
measure of AO that reflects the risk of extinction and can be universally applied with relative
ease. The first objective was achieved by compiling all existing status assessments available for
freshwater species, including fishes and molluscs. The latter two objectives involved measuring
7
IAO and a proposed biological AO for 20 imperiled freshwater fish species native to
southwestern Ontario.
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2. Methods
COSEWIC Assessments for Freshwater Species
I compiled all available COSEWIC status assessment reports on Canadian freshwater
species, totaling 131 fishes and 22 molluscs (Appendix 1, Appendix 2). Assessment reports
were obtained from the SARA Registry (www.sararegistry.gc.ca), as of January 2012. For each
species evaluated, the following data were gathered: 1) current conservation status; 2) criteria
for designation; 3) reported measures and trends (decline and/or fluctuation) in spatial indices,
including EO, AO, number of locations and habitat quality, and method used to calculate indices
when applicable; 4) threat factors suspected to have led to observed or projected population
decline; and, 5) preferred habitat. Species from all risk categories were included in this
compilation. A species could be classified into an at-risk category by meeting the threshold for
one or more criteria. The reason(s) for designation was derived from the technical summary
section of the assessment report or inferred from the report when not explicitly stated. Threat
factors were classified into eight categories, adapted from IUCN (2012): habitat loss and
degradation; alien species invasion; over-harvesting; pollution; natural disaster; change in native
species dynamic; persecution; and, other human disturbances. Threats were further identified as
either primary or secondary causes of endangerment for each given species, similar to the
approach taken by Dextrase and Mandrak (2006). A primary threat was defined as a major
factor known to cause risk of extinction, while secondary threat referred to factors having a
minor role or projected effect, or of unknown significance.
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Evaluating IAO Calculations
I used distribution data for 20 imperiled freshwater fish species in Ontario to reproduce
IAO calculations, with the goal of investigating issues surrounding the grid adjacency approach.
Occurrence data of these species were obtained from the Department of Fisheries and Oceans
(DFO) (Mandrak, unpublished data). The distribution data were originally compiled for
conducting COSEWIC assessments, which included both historical and recent records collected
from all available sources, ranging from community surveys and targeted sampling conducted
by various organizations including DFO and Ontario Ministry of Natural Resources (OMNR),
independent surveys conducted for research purposes, and museum records from Canadian
Museum of Nature and Royal Ontario Museum (Doolittle et al. 2007). The area covered by the
distribution localities was restricted to southern Ontario (Figure 1). This area is known to have
undergone urbanization, dam construction, water extraction, pollution, and invasion by several
alien species (Dextrase and Mandrak 2006) that have decreased the ecosystem health and
imperiled the local freshwater fauna.
IAO measurements were performed using a Geographic Information System (GIS),
following the IUCN, and hence COSEWIC, guidelines. The distribution data were
georeferenced latitude-longitude point records and were projected in ArcGIS v10. Vector layers
of uniform grid cells were created using E.T. GeoWizard and overlaid onto species distribution
maps. Using the Spatial Analyst extension, cells were then spatially joined to species occurrence
records, which permitted objective tallying of occupied cells. The E.T. GeoWizard tool pack
was advantageous in this calculation because it created grid cells as vector features, with the
ability to customize grid sizes and location of cell boundaries.
10
Figure 1. Study area.
Three major issues associated with the grid-adjacency method were examined: 1) scale-
dependence; 2) influence of position and orientation; and, 3) reproducibility. To address the
scale-dependency issue, I calculated IAO at three spatial scales: 2km, 1km, and 0.5km. These
grid sizes reflected the currently recommended scale, the frequently utilized alternative scale for
freshwater organisms, and a conservative scale matching the average width of streams,
respectively (see Figure 2 for an example). For each species, 2km-IAO was compared to IAO
values resulted from the finer resolutions. IAO values at all three resolutions were compared
against the critical thresholds. The second issue, grid location and orientation, was approached
by producing grid layers of the same resolution with shifted border positions. This was achieved
by specifying coordinates of the grid layers’ range in the E.T. GeoWizard. Thirteen 2km-grid
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layers were generated in this fashion, eleven of which were north-south oriented, whereas, the
remaining two were tilted at an angle, which was sometimes used in COSEWIC assessments
(Mandrak, unpublished data). For each species, mean value, maximum, minimum, and standard
deviation of the resultant IAO values were recorded for comparison against the critical
thresholds. Lastly, I evaluated the reproducibility of the grid-based approach by identifying
discrepancies between the calculated IAOs and the COSEWIC-reported values. The average
values of all 2km-IAO calculated for each species were compared to that reported by
COSEWIC.
Figure 2. Example of Index of Area of Occupancy (IAO) measurements for Black Redhorse in
the Grand River, Ontario, at three spatial scales: 0.5km, 1km and 2km.
12
Biological AO
As an alternative to IAO, I propose a biologically relevant measure of Area of
Occupancy for aquatic species living in confined habitats, heretofore referred to as the BioAO. I
categorized species occurrences into four major groups of freshwater habitats: stream,
lakeshore, wetland, and open lake. Separate approaches were taken for occurrences in streams,
and in lakes (lakeshore and open lake) and wetlands. Such separation of habitat type recognized
the fundamental differences in geographical shapes of the available habitat and the differences
in constraints on fish movements. Total BioAO for a specific species was the sum of the two
components.
Biological Area of Occupancy in Streams
BioAO for stream occupancy was calculated as the product of occupied stream segment
length (m) and estimated stream width (m). Occupied stream segment length was measured as
distances between species distribution points through a stream network following a least-cost
routing application (ArcGIS, Network Analyst). Distribution records of the 20 fish species were
projected onto a stream network layer of the Great Lakes drainage basin. This stream network
was created by converting line features of stream segments developed by Aquatic Landscape
Inventory Software (ALIS) into an interconnecting network. ALIS is a stream segmentation
application that classifies stream segments based on a set of variables including hydrography,
surficial geology, connectivity, flow barriers, and thermal regime (Valley Segment Committee
2001). The stream network covered in our study area was developed with hydrographic maps at
a fine scale (1:10000), which was crucial for recognizing small headwater and accurate stream
13
order classifications (Hughs et al. 2011). Each stream segment in ALIS was described by
watershed code, segment length, and Strahler and Shreve stream orders.
As point distribution data typically underestimate actual distribution, a buffered
approach was used to determine occupied stream segment length. I introduced a term, buffer
distance, to quantify the length of the stream segment I assumed an individual could travel
through. This method utilized species-specific spatial scales by considering variations in
mobility among species. Assuming each occurrence location represented at least one individual,
the capacity by which the individual might move between these interconnected locations
through the streams was considered to determine the size of its occupancy. If two occurrence
sites of a species were separated by a distance greater than twice that of the buffer length, the
stream segment in between was considered unoccupied and excluded from being part of BioAO.
The buffer distance was also introduced to each end of an occupied segment to account for
potential habitat usage. Localities with single occurrence record were considered as ‘single
locations’, for which the size of occupied stream reach was assigned as one buffer (see Figure 3
for an example).
Buffer distance used in BioAO analysis was determined based on the predicted home
ranges. This approach is consistent with the notion that area of occupancy is essentially the sum
of home range areas of all individuals within a population (Gaston 1991). Home range is defined
as the area over which an individual travels or lives in and has been found to correlate positively
with body size in freshwater fish species (Minns 1995, Woolnough et al. 2009). I used the
following allometric relationship constructed for lotic species (Woolnough et al. 2009) to
predict home range as a function of body size:
log home range (m) = -0.678 + 0.73 x log body size (cm3).
14
Body size was calculated following method described by Woolnough et al. (2009) using average
fish body length (mm) reported in Ontario by Holm et al. (2010). Back-transformation of log-
transformed home range sizes were corrected for bias (Sprugel 1983). A set of biological AO
was calculated using the predicted home range as buffer length, heretofore, referred as HR-
BioAO.
The final proposed BioAO was an adjusted buffer length based on home range sizes in
the following manner: the buffer range for a given species was arbitrarily set to 1km or 2km if
home range was less than 60m, or between 60m and 2000m, respectively. For species with
home range estimated greater than 2000m, home range size was used directly as the buffer
distance. The 1- and 2-km river lengths corresponded to those scales currently being applied
under COSEWIC and IUCN assessments for grid measurements, which provide rationale for
comparing against the current thresholds. These scales are also similar to those referred to as
local segment scales used in other studies, such as linear stream buffer of 5km used by Fagan et
al. (2005) and 3.25km used by Groce et al. (2012).
I included a layer of hydrological structures (OMNR 2012) to reflect barriers to fish
movements and dispersal in stream habitats. Point locations of dams were used as barriers
within the stream network when conducting the least-cost calculation to indicate terminals of
occupied stream segments.
15
Figure 3. Example of BioAO measurement for stream occupancy of Black Redhorse in the
Grand River, Ontario. The highlighted stream stretch indicates the segments considered
occupied habitat. Localities distant from others were considered single locations.
Estimates of stream width for the occupied segments were the other component of
BioAO measure. A dataset of stream width information for over 100 Ontario streams was
attained (N.E. Mandrak, DFO, unpublished data). It contained information collected from a total
of 2431 sampling events, with records of waterbody names, date of the sampling, and latitude-
longitude coordinates. Two approaches were taken to estimate occupied stream segment width
under different scenarios. If an occupied stream was sampled in the width dataset, then the
average width for that particular stream was used. If the information was not immediately
available, I predicted stream width as a function of Strahler stream order.
16
A linear regression analysis between Strahler order and the stream width was constructed
(ln-transformed; Hugh et al. 2011) for streams in Ontario. I projected the sampling records as
point vectors onto ALIS stream network and performed spatial join to associate width data with
the Strahler order of the stream reach in which it was measured. I manually relocated points that
were, based on the waterbody name, snapped onto incorrect waterbodies during the spatial join.
Stream widths estimated through this relationship were corrected using the anti-logarithmic bias
corrector (Sprugel 1983).
Lake and Wetland Occupancy
I combined all lakeshore, wetland, and open-lake occurrence sites and measured
occupancy using a circular buffer approach (see Figure 4 for an example). For each occurrence
locality, a circular buffer was created with a species-specific range. I determined the radius of
the buffer size based on the species movement size calculated the same way for the stream
occupancy. HR-BioAO was calculated based on a radius of the predicted home range size of the
species, and BioAO was calculated based on a radius size of the adjusted buffer length. To
eliminate terrestrial area from the AO measure, the buffered zone was overlaid onto the map of
major and minor waterbodies from Canada Water Maps (2012). Only areas of the waterbodies
intersected within circular buffer were considered as occupied habitat.
17
Figure 4. Example of BioAO calculation for lakeshore occupancy for Grass Pickerel in Long
Point Bay, Lake Erie.
Applicability of BioAO
I compared the risk of extinction and conservation status according to the COSEWIC
guidelines based on IAO and my proposed BioAO. Note that COSEWIC analyses for six of the
20 species (Bridle Shiner, Channel Darter, Grass Pickerel, Northern Brook Lamprey, River
Redhorse, Silver Lamprey) included DUs covering areas for which I did not have point
distribution data. For this reason, for the other fourteen species with complete distribution data,
the calculated IAO values, BioAO values were compared to the COSEWIC-reported AOs and
their conservation status were reassessed.
18
In addition, I investigated the extent to which the different methods were influenced by
the number of distribution localities available. Linear regression of the 2km-IAO, stream
BioAO, and lake BioAO against the number of geographically distinct occurrence localities
were performed.
19
3. Results
Summary of COSEWIC Assessments for Freshwater Species
The review of COSEWIC reports for freshwater fishes and molluscs revealed the high
degree of endangerment of these aquatic species (Figure 5a, b). Among 131 freshwater fish
species assessed under COSEWIC, 12 species were extinct or extirpated, and 79 existing species
were classified in the imperiled categories (EN, TH, or SC). Out of the 22 freshwater mollusc
species assessed under COSEWIC, one species was considered extirpated, and 20 species were
classified in the imperiled categories (EN, TH, or SC), among which 16 species were
Endangered.
Compilation of these assessments confirmed that the two spatial criteria (Criterion B and
D2) were used most frequently as the factor for designation of at-risk aquatic organisms (Figure
5c, d). Both Criterion B and D2 were used alone, or in conjunction with other criteria, to
determine the imperilment of species; 54% of the endangered and 48% of the threatened fish
species were assessed using Criterion B, and 57% of the threatened fish species were assessed
using Criterion D2. Criterion B was also used for assessing 88% of the endangered, and the only
threatened, freshwater molluscs.
AO measures reported in the COSEWIC assessments for the freshwater taxa varied in
the methods used. Ninety of the 131 freshwater fishes and 20 of the 22 freshwater molluscs
were reported with at least one measure of AO. The most common AO measure reported in
COSEWIC assessments was IAO measured at 2km-scale (38 fish species; 10 mollusc species),
whereas 1km-IAO (25 fish species; 5 mollusc species), biological AO in the form of estimated
20
stream area (21 fish species; 13 mollusc species), and preferred habitat area (25 fish species; 1
molluscs species) were also presented. Ten freshwater fish species had IAO reported to be
greater than their EO.
A number of threat factors were identified to have contributed to the imperilment of
Canadian freshwater fauna (Figure 6). This review only considered species in categories at
Special Concern or higher because information was not always available for species that were
Not at Risk or Data Deficient. Multiple threats were listed for a majority of the species (71 of
the 88 freshwater fishes, 20 of the 21 freshwater molluscs). Habitat loss and degradation were
the most significant threat to imperilment (45 fish species; 17 mollusc species; Figure 6).
Introduced species was cited as the primary threat for 19 freshwater fish and 13 mollusc species;
Pollution was identified as the primary threat for 15 freshwater fish and 9 mollusc species; and
freshwater fishes also suffered from over-exploitation.
21
(a) (c)
(b) (d)
Figure 5. Number of freshwater fish (a) and mollusc (b) species listed under COSEWIC in each threat category, and the number of
Endangered (EN) and Threatened (TH) freshwater fish (c) and mollusc (d) species designated by criterion.
Data Defficient
Not at Risk
Special Concern
Threatened
Endangered
Extirpated
Extinct
Number of Species
0 5 10 15 20
E
D
C
B
A
Number of Species
Des
ign
atin
g C
rite
ria
EN
TH
Data Defficient
Not at Risk
Special Concern
Threatened
Endangered
Extirpated
Extinct
Number of Species
0 5 10 15 20
E
D
C
B
A
Number of Species D
esig
nat
ing
Cri
teri
a
EN
TH
22
(a)
(b)
Figure 6. Primary and secondary threats identified responsible for species decline in freshwater fish (a) and mollusc (b) species of
Canada that are classified as Extinct, Extirpated, Endangered, Threatened and Special Concern.
0 10 20 30 40 50
Human disturbances
Persecution
Change in native species
Natural disaster
Pollution
Harvest
Alien invasive species
Habitat loss/degradation
Number of Species
Primary
Secondary
0 5 10 15 20
Human disturbances
Persecution
Change in native species
Natural disaster
Pollution
Harvest
Alien invasive species
Habitat loss/degradation
Number of Species
Primary
Secondary
23
IAO calculations
The IAO measures increased with increasing grid scale (Figure 7, Table 2). All 2km-
IAO values were below the TH threshold under subcriterion B2 (2000km2) and above that for
D2 (20km2), and ranged from 64km
2 (Warmouth) to 1196km
2 (Redside Dace). Based on the
2km-IAO, 16 species met the criteria for Endangered under B2 (500km2). Based on the 1km-
IAO, all species met the threshold for being Endangered (B2), ranging from 25km2 to 409km
2.
Based on the 0.5km-IAO, almost half of the species (8 out of 20) qualified for being
geographically restricted (8.5km2 to 124.75km
2). On average, 2km-IAO values were 325% of
the corresponding 1km-IAO values, and 1120% of the corresponding 0.5km-IAO values.
Variation in grid placement yielded insignificant amounts of variation in 2km-IAO
measures for majority of the species (Table 3; Figure 8). Maximum difference among measures
was greatest for Redside Dace (80km2) and Grass Pickerel (60km
2). Blackstripe Topminnow,
Eastern Sand Darter, and Silver Chub also showed variation in IAO results to a certain degree
(greater than 40 km2 maximum differences). For Silver Shiner, maximum and minimum values
were 476km2 and 508km
2, respectively, which were below or above the critical threshold for
EN.
Relatively poor reproducibility of the grid-approach was demonstrated by the
discrepancies between the mean calculated 2km-IAO measures and COSEWIC-reported values
(Table 3). The mean 2km-IAO values that I calculated strongly deviated from the IAOs
reported by COSEWIC for five species: Blackstripe Topminnow, Bridle Shiner, Lake
Chubsucker, Pugnose Minnow, and Silver Shiner, where the COSEWIC reported IAO values
exceeded my calculations by more than 50%.
24
Figure 7. Grid-based IAO calculations for 20 freshwater fish species measured at three spatial scales: 2km X 2km (circles); 1km X
1km (triangles); 0.5km X 0.5km (squares), with species arranged on the x-axis in the order of decreasing 2km-IAO size. Dashed lines
indicate threshold values for IUCN criteria.
1
10
100
1000
10000
IAO
(km
2)
2X2 IAO (km2)
1X1 IAO (km2)
0.5X0.5 IAO (km2)
TH: B2
EN: B2
TH: D2
25
Table 2. Total number of occurrence record, number of geographically distinct localities, 2km-IAO, 1km-IAO, and 0.5km-IAO
calculated using grid cells of the same position and orientation, ratio of 2km-IAO to 1km-IAO, and 2km-IAO to 0.5km-IAO in
percentage for 20 at-risk freshwater fish species in Ontario.
Common name Scientific name COSEWIC
status
Total
number of
occurrences
Number of
geographically
unique
localities
2km-IAO
(km2)
1km-IAO
(km2)
0.5km-
IAO
(km2)
2km-IAO to
1km-IAO
(%)
2km-IAO
to 0.5km-
IAO
(%)
Black Redhorse Moxostoma
duquesnei
TH 338 208 464 133 36.25 348.87% 1280.00%
Blackstripe
Topminnow
Fundulus
notatus
SC 287 166 328 101 30.5 324.75% 1075.41%
Bridle Shiner1 Notropis
bifrenatus
SC 132 111 292 83 22.5 351.81% 1297.78%
Channel
Darter1
Percina
copelandi
TH 156 105 228 62 17.25 367.74% 1321.74%
Eastern Sand
Darter
Ammocrypta
pellucida
TH 945 576 544 172 53.5 316.28% 1016.82%
Grass Pickerel1 Esox
americanus
vermiculatus
SC 533 460 768 245 71.5 313.47% 1074.13%
Lake
Chubsucker
Erimyzon
sucetta
EN 247 200 240 80 25.25 300.00% 950.50%
26
Lake Sturgeon
(Great Lakes -
Upper St.
Lawrence
populations)
Acipenser
fulvescens
TH 71 60 180 51 13.25 352.94% 1358.49%
Northern Brook
Lamprey (Great
Lakes - Upper
St. Lawrence
populations)1
Ichthyomyzon
fossor
SC 54 42 112 32 8.5 350.00% 1317.65%
Northern
Madtom
Noturus
stigmosus
EN 127 103 124 43 14.25 288.37% 870.18%
Pugnose
Minnow
Opsopoeodus
emiliae
TH 72 53 132 37 9.5 356.76% 1389.47%
Pugnose Shiner Notropis
anogenus
EN 302 261 352 118 37 298.31% 951.35%
Redside Dace Clinostomus
elongatus
EN 1314 972 1196 409 124.75 292.42% 958.72%
River
Redhorse1
Moxostoma
carinatum
SC 66 46 124 32 8.75 387.50% 1417.14%
Silver Chub
Macrhybopsis
storeriana
EN 759 359 816 259 72.75 315.06% 1121.65%
27
Silver Lamprey
(Great Lakes -
Upper St.
Lawrence
populations)1
Ichthyomyzon
unicuspis
SC 122 62 192 51 13.25 376.47% 1449.06%
Silver Shiner Notropis
photogenis
TH 534 271 484 150 42.5 322.67% 1138.82%
Spotted Gar Lepisosteus
oculatus
TH 525 362 120 53 23.5 226.42% 510.64%
Spotted Sucker Minytrema
melanops
SC 173 116 272 79 21 344.30% 1295.24%
Warmouth Lepomis
gulosus
SC 796 413 64 25 10.5 256.00% 609.52%
1 COSEWIC species assessments included distribution records outside of Ontario which were not available to be incorporated in this analysis. 2 COSEWIC reported 2km-IAO included historical data. 3 IAO in 1km-scale, calculated with single grid layer or reported by COSEWIC.
* 2km-IAO value for Ontario occurrence only (Mandrak, unpublished data).
28
Table 3. Mean, standard deviation, maximum, minimum values of 2km-IAO calculated using 13 layers of grid cells at varying
positions for 20 at-risk freshwater fish species in Ontario. Also shown are COSEWIC reported 2km-IAO, and ratio of COSEWIC-
reported values to the calculated mean IAO values in percentage.
Common name COSEWIC
status
Mean 2km-
IAO
(km2)
Standard
deviation in
2km-IAO
(km2)
Max 2km-AO
(km2)
Min 2km-IAO
(km2)
COSEWIC
2km-IAO
(km2)
COSEWIC-
reported 2km-
IAO to mean
2km-IAO
(%)
Black Redhorse TH 456 10.07 468 440
Blackstripe Topminnow SC 306 12.92 328 288 516 169%
Bridle Shiner1 SC 279 9.54 300 264 6242* 224%
Channel Darter1 TH 230 7.41 244 220
Eastern Sand Darter TH 540
(1723)
11.66 560 520 556
(3043)
103%
(177%3)
Grass Pickerel1 SC 750 18.38 780 720
Lake Chubsucker EN 243
(803)
5.26 252 236 400
(2433)
165%
(304%3)
29
Lake Sturgeon
(Great Lakes - Upper St.
Lawrence populations)
TH 186 4.77 192 180
Northern Brook
Lamprey (Great Lakes -
Upper St. Lawrence
populations)1
SC 121 6.41 132 112
Northern Madtom EN 127 7 136 116 180 141%
Pugnose Minnow TH 126 6.84 136 112 2752 219%
Pugnose Shiner EN 348 11.78 368 332 308 89%
Redside Dace EN 1207
(4093)
24.39 1256 1176 (4413) (108%3)
River Redhorse1 SC 122 4.18 128 116
Silver Chub EN 824 12.33 848 804 8362
Silver Lamprey
(Great Lakes - Upper St.
Lawrence populations)1
SC 193 3.79 200 188
Silver Shiner TH 490 8.57 508 476 896 183%
30
Spotted Gar TH 122 6.03 136 112
Spotted Sucker SC 278 3.87 284 272
Warmouth SC 62 5.55 76 56
1 COSEWIC species assessments included distribution records outside of Ontario which were not available to be incorporated in this analysis. 2 COSEWIC reported 2km-IAO included historical data. 3 IAO in 1km-scale calculated with single grid layer or reported by COSEWIC.
* 2km-IAO value for Ontario occurrence only (Mandrak, unpublished data).
31
Figure 8. Mean values of calculated 2km-IAO (triangles) based on 13 placements of grids, COSEWIC-reported 2km-IAO (squares),
proposed BioAO (circles), COSEWIC-reported biological AO (diamonds), and HR-BioAO (crosses) for 20 at-risk freshwater fish
species in Ontario, with species arranged on the x-axis in the order of decreasing mean 2km-IAO size. Dashed lines indicate threshold
values for IUCN criteria.
0
1
10
100
1000A
rea
of
Occ
up
ancy
(km
2)
Mean 2km-IAO
COSEWIC reported 2km-IAO
BioAO
COSEWIC reported biologicalAO
HR-BioAO
TH: B2
EN: B2
TH: D2
32
Proposed Biological AO calculations
Seventeen of the 20 species had stream occupancies, of which Black Redhorse and
Blackstripe Topminnow were strictly stream species (Table 4), and 18 species occurred in lakes
and wetland habitats, of which Spotted Gar and Warmouth were restricted to wetlands. Linear
individual home range calculations based on body length ranged from 29.3m to 36798.4m
(Table 5). Four cyprinid species and two percid species had linear home range estimates of less
than 60m and, therefore, were assigned with a minimum buffer distance of 1km. Four large-
bodied species had home ranges predicted to be greater than 2km, and the remaining species had
adjusted buffer distance set to 2km.
The total biological AO calculated as the sum of occupancies in two habitat types
yielded AO measures of small values (Table 8 and Appendix 4). All BioAO calculated with the
adjusted buffer length were below the TH threshold under subcriterion B2 (2000km2). All
species except for Silver Chub (1643km2), had BioAO below the EN threshold under
subcriterion B2 (500km2). Three species that met the TH threshold under subcriterion D2
(20km2) were Blackstripe Topminnow (3.28km
2), Northern Brook Lamprey (18.01km
2), and
Pugnose Minnow (12.31km2). Another three species also demonstrated relatively limited
occupancy: Black Redhorse (25.68km2), Redside Dace (30.06km
2), and Silver Shiner
(27.92km2). Frequency distribution showed the majority of the assessed species (13 out of 20)
had BioAO sizes between 20km2 and 200km
2 (Figure 10).When home range predicted from the
allometric relationship was used directly as buffer distance, all HR-BioAO were smaller than the
EN threshold under subcriterion B2, eleven of which fell below the TH threshold of subcriterion
D2.
33
Table 4. Habitat classification for the 20 at-risk freshwater fish species included in this study.
Common Name Habitat Type
Stream Nearshore lake Wetland/marsh Open lake
Black Redhorse X
Blackstripe Topminnow X
Bridle Shiner X X
Channel Darter X X
Eastern Sand Darter X X X
Grass Pickerel X X X
Lake Chubsucker X X X
Lake Sturgeon X X
X
Northern Brook Lamprey X X X
Northern Madtom X X
Pugnose Minnow X X
Pugnose Shiner X X X
Redside Dace X
River Redhorse X X
Silver Chub
X
Silver Lamprey X X
X
Silver Shiner X
Spotted Gar
X
Spotted Sucker X X
X
Warmouth
X
34
Table 5. Home range estimates based on average body length reported in Ontario (Holm et al.
2010) and the equation of Woolnough et al. (2009), and the adjusted buffer scales used for
BioAO calculation for the 20 at-risk freshwater fish species included in this study.
Common name Average body
length in Ontario (mm)
Raw home range predicted
(m)
Home range after bias
correction (m)
Adjusted buffer distance (m)
Black Redhorse 400 679.99 3507.63 3507.63
Blackstripe Topminnow 50 7.16 36.92 1000
Bridle Shiner 50 7.16 36.92 1000
Channel Darter 45 5.68 29.31 1000
Eastern Sand Darter 60 10.67 55.04 1000
Grass Pickerel 175 111.24 573.79 2000
Lake Chubsucker 200 149.02 768.70 2000
Lake Sturgeon 1170 7133.81 36798.43 36798.43
Northern Brook Lamprey
150 79.37 409.39 2000
Northern Madtom 80 20.03 103.34 2000
Pugnose Minnow 50 7.16 36.92 1000
Pugnose Shiner 50 7.16 36.92 1000
Redside Dace 75 17.39 89.72 2000
River Redhorse 450 880.10 4539.81 4539.81
Silver Chub 120 48.69 251.14 2000
Silver Lamprey 255 253.70 1308.66 2000
Silver Shiner 100 32.66 168.46 2000
Spotted Gar 510 1157.64 5971.47 5971.47
Spotted Sucker 255 253.70 1308.66 2000
Warmouth 155 85.27 439.87 2000
35
Stream-width records were associated with streams, with Strahler stream order ranging
from 1 to 9 on the 1:10 000 scale ALIS map. Regression analysis showed that Strahler order was
a significant predictor for stream width (r2 = 0.62, P < 0.0001) (Figure 9). The predicted width
estimates ranged from 3.66m for 1st order streams to 340.36m for 9
th order streams (Table 6).
Figure 9. Linear regression (solid line) between Strahler stream order and loge-transformed
stream width (m) based on 2431 DFO survey records in Ontario (open circles), P < 0.0001.
Predicted stream width (see Table 6) at each Strahler order is estimated by the regression.
y = 0.5667x + 0.2897 r² = 0.6227
-1
0
1
2
3
4
5
6
7
8
9
0 1 2 3 4 5 6 7 8 9
loge
-tra
nsf
orm
ed W
idth
(m
)
Strahler Order
36
Table 6. Stream width (m) predicted by relationship with Strahler stream order. Values are anti-
loge transformed and corrected with bias estimator (Sprugel 1983).
Strahler order Predicted Width
(m)
1 3.66
2 6.44
3 11.36
4 20.02
5 35.28
6 62.17
7 109.58
8 193.12
9 340.37
37
Table 7. Proposed BioAO as sum of stream occupancy calculated in terms of occupied stream
length x stream width, and lake/wetland occupancy calculated in terms of suitable habitat area
within circular buffer, in comparison to COSEWIC reported biological AO for 20 at-risk
freshwater fish species in Ontario.
Common Name COSEWIC
Status Stream BioAO
Lake/ Wetland
BioAO
Total BioAO
COSEWIC reported biological
AO
Black Redhorse TH 25.68 0.00 25.68 18.5a
Blackstripe Topminnow SC 3.28 0.00 3.28
Bridle Shiner1 SC 22.52 46.33 68.85
Channel Darter1 TH 16.89 45.70 62.59
Eastern Sand Darter TH 9.87 59.23 69.11 21a
Grass Pickerel1 SC 37.88 232.79 270.67
Lake Chubsucker EN 1.74 159.21 160.95 200b
Lake Sturgeon TH 208.81 130.63 339.44
Northern Brook Lamprey1
SC 5.92 12.10 18.02 26a
Northern Madtom EN 44.97 33.20 78.17
Pugnose Minnow TH 6.14 6.17 12.31
Pugnose Shiner EN 18.57 98.37 116.94
Redside Dace EN 30.36 0.00 30.36 4a
River Redhorse1 SC 25.54 29.50 55.03 178.5a
Silver Chub EN 0.00 1643.20 1643.20
Silver Lamprey1 SC 32.12 168.39 200.51 36962b
Silver Shiner TH 23.60 4.32 27.92 19.3a
Spotted Gar TH 0.00 117.51 117.51 51.57b
Spotted Sucker SC 44.62 92.39 137.01 1090a
Warmouth SC 0.00 55.08 55.08 52.99b
1 COSEWIC species assessments included distribution records outside of Ontario and were not available to be incorporated in this analysis.
aCOSEWIC-reported biological AO calculated as length of stream between uppermost and lowermost sites X average width of stream.
bCOSEWIC-reported biological AO based on available habitat area.
38
Figure 10.Frequency distribution of total BioAO using adjusted buffer size.
Application of the BioAO measures to the COSEWIC criteria generated new
conservation assessments for four species (Table 8). Eastern Sand Darter and Silver Shiner,
which were classified by COSEWIC as TH using Criterion B, now, qualify for EN. The species
classified as SC, Blackstripe Topminnow, qualify for TH under subcriterion D2. Black
Redhorse assessed as TH, now, only qualify for SC because the BioAO exceeding TH threshold
of subcriterion D2. In addition, the EN status of Northern Madtom due to its small EO was
further supported by the BioAO. On the other hand, Silver Chub, which was assessed as EN by
both criteria A and B, remained EN status due to criterion A, whereas its BioAO exceeded the
EN threshold under subcriterion B2.
3
13
3
1
0 0
2
4
6
8
10
12
14
20 200 500 2000 More
Freq
uen
cy
BioAO (km2)
D2: TH B2: EN B2: TH
39
Linear regression analyses showed the lack of independence between AO measures and
the number of occurrence sites (Figure 11). The mean 2km-IAO measures increased with
increasing number of occurrence sites with r2 of 0.64 (P < 0.0001). The wetland/lake BioAO
calculated using the circular buffer also increased proportionally with number of occurrence
sites in lakes and wetlands for each species (r2
= 0.46, P < 0.001). Linear BioAO for stream
occupancies, however, did not correlate with number of stream localities (r2 = 0.00054, P =
0.922).
40
Table 8. Summary of species status designated under COSEWIC and after application of BioAO, and the corresponding reasons for
designation for 20 at-risk freshwater fish species in Ontario.
Common Name COSEWIC Status
Reason(s) for COSEWIC Designation BioAO-based Status
Reason(s) for New Designation
Black Redhorse TH TH: D2 (small AO and highly fragmented habitat)
SC* AO meet B2: EN, b; but not a or c; AO does not meet D2
Blackstripe Topminnow SC N/A TH* D2
Bridle Shiner1 SC N/A N/A BioAO calculation did not cover entire DU to generate new status
Channel Darter1 TH Original COSEWIC data met EN: B2abc, reason for down-listing not stated
N/A BioAO calculation did not cover entire DU to generate new status
Eastern Sand Darter TH TH: B2ab(i, iii, iv, v) EN* B2ab (I, iii, iv, v); same if COSEWIC used 1km-IAO or reported biological AO
Grass Pickerel1 SC TH: B2ab(ii - v) but rescue effect N/A BioAO calculation did not cover entire DU to generate new status
Lake Chubsucker EN EN: B2ab(ii-iv) EN EN: B2ab(ii-iv)
Lake Sturgeon TH TH: A2abcd TH AO meet B2: EN, b; but no a, c
Northern Brook Lamprey1 SC N/A N/A BioAO calculation did not cover entire DU to generate new status
Northern Madtom EN EN: B1ab (iii) EN B1ab iii+2ab iii
Pugnose Minnow TH TH: B1ab(i, ii, iii) + 2ab (I, ii, iii) TH D2
Pugnose Shiner EN EN: B2ab(iii-v) EN EN: B2ab(iii-v)
Redside Dace EN EN: B2ab(i-v) EN EN: B2ab(i-v)
41
River Redhorse1 SC N/A N/A BioAO calculation did not cover entire DU to generate new status
Silver Chub EN EN: A2bce + B2ab (v) EN EN: A2ace - AO no longer meets EN: B2
Silver Lamprey1 SC N/A SC BioAO calculation did not cover entire DU to generate new status
Silver Shiner TH TH: B1+2ab(iii) EN* B2ab iii - Same if COSEWIC used reported biological AO
Spotted Gar TH TH: D2 (# locations and threats to habitat)
TH AO meet B2: EN, b, but not a or c; AO does not meet D2
Spotted Sucker SC N/A SC Does not meet any spatial criteria. May meet other criteria
Warmouth SC AO meets TH: D2, but rescue effect is likely
SC AO meets TH: D2, but rescue effect is likely
1 COSEWIC species assessments included distribution records outside of Ontario and were not available to be incorporated in this analysis. *Species status changed after applying BioAO
42
a) b) c)
Figure 11. Linear regression (solid lines) between number of geographically unique occurrence sites and the resultant (a) mean 2km-
IAO calculated using 13 gird layers at varying positions (all 20 at-risk freshwater fish species in Ontario, P < 0.0001); (b) Stream
BioAO in stream length multiplied by stream width (17 of the 20 species, P = 0.922); (c) suitable habitat area within circular buffer
(17 of the 20 species, P < 0.001).
y = 1.0149x + 97.96 r² = 0.64
0
200
400
600
800
1000
1200
1400
0 500 1000
Mea
n 2
km-I
AO
(km
2)
Number of Occurrence Site
y = -0.0045x + 28.65 r² = 0.00054
0
50
100
150
200
250
0 500 1000
STre
am B
ioA
O (
km2
)
Number of Occurrence Site
y = 2.1985x - 0.7047 r² = 0.46
0
200
400
600
800
1000
1200
1400
1600
1800
0 200 400
Lake
/Wet
lan
d B
ioA
O (
km2
)
Number of Sites in Lakes and Wetlands
43
4. Discussion
Area of Occupancy was the most frequently utilized criterion for listing freshwater fishes
and molluscs under imperiled categories; however, the measurement of AO was not consistent
across assessment reports. By reproducing AO measures with actual distribution records for 20
imperiled freshwater fish species, my results showed that the currently recommended grid-based
AO approach was dependent on the spatial scale, relative position of grids to the distribution
records, and number of occurrence localities. I also found inconsistencies between estimated
IAO values and those reported by COSEWIC. Finally, I propose a more biologically relevant
approach to measure AO. The BioAO were smaller than IAO values, all below the threatened
threshold under B2. Incorporation of BioAO changed the conservation status for four species,
suggesting up-listing of three species and down-listing of one species.
The review of COSEWIC assessment reports on the freshwater taxa showed that AO
was the most used criterion for designation of conservation status. The frequent use of this
spatial criterion for species conservation status ranking was also seen in previous studies. In a
conservation risk assessment for freshwater fishes in France, size and fluctuations of AO were
revealed to be important deciding factors of final status designation (Keith and Marion 2002).
Giam et al. (2011) reported extinction risk of freshwater fish fauna at the national level in
Singapore to be correlated strongly with local geographic range. AO also served as an important
indicator in other taxonomic groups exhibiting small ranges, such as butterflies (Lewis and
Senior 2010), arthropods and spiders (Cardoso et al. 2011), peripheral isolated plants (Abeli et
al. 2009), and herbarium species (Willis et al. 2003). In my study, IAO measures for the 20
COSEWIC designated at-risk species increased with decreasing grid resolution as expected. I
44
did not include more variants in cell sizes because the relationship between grain resolution and
resultant AO values has been illustrated in previous studies (Callaghan 2008; Hartley and Kunin
2003; Kunin 1998; Willis et al. 2003). Rather, I aimed to understand how the different spatial
scales affected species ranking. Even at the coarser scales, (2km- and 1km-grids), all species
occupancy metrics met the TH threshold. If the grid method and the corresponding threshold
values were biologically appropriate, I would anticipate that some species (especially those
assessed as Special Concern) to have IAO greater than the minimum thresholds of the B2
criterion. The use of IAO is not informative given the current set of thresholds, leaving listing
by criterion B2 entirely based on other signs of population imperilment, such as severe
fragmentation, continuous decline and/or fluctuations in population abundance, range size
indices, and habitat quality.
Choosing an adequate spatial resolution for grid-based AO measurements suitable for
freshwater organisms is challenging. The narrow dimensionality of freshwater habitats justifies
the use of finer scales for aquatic species compared to their terrestrial counterparts. Other
studies have discussed the inclusion of large proportion of terrestrial area as inappropriate
habitat for aquatic species when coarse grid scales were used (Mace et al. 2008; Zaragozi et al.
2012), leading to over-estimation of AO. This phenomenon was confirmed by our initial
compilation of COSEWIC reports, which revealed 10 species with IAO values exceeding their
total range as measured by EO.
Alternatively, COSEWIC (2010) allows IAO measured at 1km2
or a biological AO for
freshwater organisms. However, utilization of this finer scale is not universal, nor do explicit
guidelines exist for when it is appropriate. I found a number of COSEWIC assessments for
aquatic species included AO measured with alternative methods, but were not used for final
45
designation of species status (Table 8). For example, the 1km-IAO reported for Eastern Sand
Darter was 304km2, below the minimum threshold for EN under criterion B2 (500km
2) and met
the indication of severely fragmentation and declining population, which would qualify the
species as Endangered. Yet, the designation by COSEWIC was based on the reported 2km-IAO
of 556km2 (COSEWIC 2009). Similar mismatches in status ranking were also found for Silver
Shiner, where the reported biological AO would have warranted a higher threat category.
IAO calculated with high grid resolution was also found to be misleading. For freshwater
species occupying stream habitats, I observed that as grid size decreases, the grid cells
incorporated less stream structures between distribution points, and subsequently the occupied
sites became disconnected through the stream system (Figure 1). Finer spatial scales are also
less robust to incomplete sampling (Kunin 1998; Kunin et al 2000). The IUCN states the scale
at which AO is measured needs to be coarser than the census data (IUCN Standards and
Petitions Working Group 2010) to prevent underestimation of occupied area and subsequent
over-listing of threatened species (Mace et al. 2008, Willis et al. 2003). As a result, extensive
distribution data are, therefore, required; however, accurate and comprehensive distribution data
are rarely available for freshwater taxa.
The grid-IAO approach was shown to be dependent on the number of distribution points
used in the calculation. Species with higher numbers of distribution points available tended to
result in larger IAO measures, which might lead to incorrect designation of the subsequent
conservation status based on distribution data alone. Species of particular conservation concerns
typically received greater amount of conservation attention, which resulted in greater sampling
efforts and hence documented occurrence. An example from my case study is the species
Redside Dace, an endangered cyprinid species found only in small tributaries in Ontario, where
46
heavy urban development has led to loss of several populations (COSEWIC 2007). Its habitat
area has undergone extensive surveys in effort of protection and restoration of the species,
resulting in an exceptionally high number of distribution points available for this species (972
localities). This resulted in a 2km-IAO of 1207 km2, in contrast to COSEWIC-reported
biological AO of 4km2 and BioAO of 30.36. This 2km-IAO measure exceeded the EN threshold
under criterion B, contradicting the evident imperilment of this species.
The IAO calculation was also found to be sensitive to grid placement, which might have
influenced the assessment of conservation status. For example, differential grid positioning for
Silver Shiner, whose IAO was at the margin of EN threshold, could lead to a different
conclusion in its status ranking. Currently, there is no explicit guideline under COSEWIC or
IUCN for how to position grid cells. Without proper spatial reference, such variation can also
result in irreproducibility and misinterpretation of changes in occupied area and pose difficulties
when comparing across species or to future assessments (Rivers et al. 2011; Willis et al. 2003).
Therefore, I recommend the implementation of explicit approaches for placing the grid cells
used in IAO calculations in order to facilitate comparisons across species and time, or
alternatively, the estimation based on the use of several (e.g. 10) random starting points in order
to assess the sensitivity of the results and, hence the assessment, to the starting points for grid
placement.
Significant discrepancies between the COSEWIC-reported IAO and those calculated
using my procedure were observed. For the four species (Blackstripe Topminnow, Bridle
Shiner, Lake Chubsucker and Silver Shiner) with the greatest disagreement, COSEWIC-IAO
values were twice as high on average and resulted in lower status ranking. Such inconsistency
might have arisen, in part, due to involvement of subjective expert opinion during COSEWIC
47
IAO analyses. When performing the IAO calculation for riverine species, COSEWIC assessors
sometimes count the unoccupied grid cells between distribution points to include habitat thought
to be suitable (Figure 12; T. Morris, Department of Fisheries and Oceans, Burlington, Ontario;
personal communication 2010). Although this approach attempts to account for the more
ecologically important habitat, it brings in subjectivity into the standard calculation and
potentially resulted in overestimation of occupancy.
he main rationale for using grid-based AO approach is to ensure easy, quantitative
measurement of occupancy in order to avoid involvement of subjective opinion (IUCN
Standards and Petitions Working Group 2010). However, the inherent shortfalls of the grid-
based AO approach have led to the opposite. Lukey and Crawford (2009) revealed mismatches
between conservation status predicted based on objective application of the criteria and the
actual COSEWIC designated status, suggesting influence by expert opinion. Lukey et al. (2011)
showed that discrepancies in status designation were more likely to occur when there was high
degree of uncertainty in the risk-indicating variables. Regan et al. (2005) reported similar
inconsistency in status designation among assessors that adopted the IUCN Red List protocol,
and suggested that subjective opinion was more likely to be involved when parameter value
hovered around the critical thresholds.
48
Figure 12. Point distribution records of a freshwater mussel species the Wavy-rayed
Lampmussel, Lampsilis fasciola, in the Thames River basin (black circle), and the grid-based
IAO of 1km2(shaded grids) and 2km
2 (open grids) scales considered by COSEWIC (from
COSEWIC).
Towards a Biological Approach
One of my main objectives was to develop a quantitative occupancy index that is
suitable for aquatic environments and can be used consistently with explicit guidelines for
designating freshwater species status. The proposed approach is the first one to incorporate life
history-informed spatial scale and habitat connectivity into simple GIS-based calculations for
49
area of occupancy. I developed separate methods for stream occupancy and lake/wetland
occupancy procedures to account for differences in fish movements and limitations posed by the
dimensionality of these habitats, for which I argue provide better approximations to the
biologically defensible areas of occupied habitat for freshwater species. In general, my BioAO
measurements generated smaller values than IAO, but were in better agreement with biological
AO when reported in a COSEWIC report. The BioAO approaches are discussed in comparison
to other alternative occupancy indices from previous studies, including the grid-IAO approach,
biological AO based on occupied stream length between outermost distribution points,
biological AO based on available area of preferred habitat, and AO resulting from distribution
modeling (Table 9). Here, I argue that my proposed BioAO measure presented advantages over
the other approaches in calculating AO for the purpose of conservation status ranking, because it
accounts for species-specific scale, uses distribution of all localities in the calculation, is robust
against number of available records, grid placement, and can be performed with relative ease in
respect to both sampling and computation.
50
Table 9. Summary of advantages and drawbacks of each AO measurement approach for
freshwater taxa.
Attribute Method
Buffered occupied
segment X width of habitat
Circular buffer
intersected with aquatic
habitat
2km X 2km Grid-based
IAO
Total occupied length X width of habitat
Available preferred
habitat area
Habitat modeling
Habitat type stream,
nearshore lake
wetland, nearshore and open
lake
all stream,
nearshore lake
wetland, nearshor
e and open lake
all
Species-specific Yes Yes No No Yes Yes
Depend on number of
records No Yes Yes No - -
Account for all localities
Yes Yes Yes No - -
Sensitive to grid placement
No No Yes No - -
Demand extensive sampling
No No No No Yes Yes
Ease to conduct
Yes Yes Yes Yes Yes No
Habitat-Specific Occupancy Methods
Occupancy for stream species measured as the product of linear distance through the
ecosystem and the width of the habitat more accurately captured actual area of occupancy as
defined by COSEWIC compared to the two-dimensional grid method. The resultant BioAO
accounted strictly for the area within the aquatic environment. This approach was recommended
by the freshwater division of IUCN (Fagan et al. 2005) and applied in a number of COSEWIC
assessments. A similar approach was taken by Fagan et al. (2005) to evaluate population
51
persistence of native fish species in the lower Colorado River. This procedure is also more
suitable for species found in other types of linear habitats such as those living in intertidal,
riparian, and coastal areas. For example, Callaghan (2008) studied the scale-area relationship
using linear scales measured as river length for a moss species that inhabits river banks.
Distributions in the littoral zones of lake habitats can also be considered linear habitat.
Specifically, nearshore occupancy can be determined by breadth of the littoral zone inferred
from bathymetry layers and the distance along the shoreline where the species occurred. In this
study, I did not separate littoral distribution from the rest of the lake occupancy measures
because preliminary attempts revealed that identification of these distributions was complicated
by record accuracy and involved considerable numbers of subjective judgments.
My proposed buffered approach to linear occupancy accounted for the habitat
heterogeneity in streams by excluding unoccupied stream reaches between buffered distribution
points. The resultant AO metric was not as dependent on sample number and accounted for all
distribution records (Table 9). In contrast, when reporting biological AO for riverine organism,
COSEWIC defines the occupied stream length as the entire reach between uppermost and
lowermost distribution sites along a stream. Although this approach may account for imperfect
distribution data, it is sensitive to the locations of the outermost occurrence sites and is
insensitive at recognizing isolated subpopulations within one waterbody. For instance, the
distribution of Black Redhorse includes a 39km unoccupied stretch between the river mouth and
the nearest upstream distribution site. I suggest that considering this segment as part of AO is
inappropriate because it not only exceeds the longest migration distance reported for the species
(15km; COSEWIC 2005a) and home range (3508m), but is fragmented by the presence of
Dunnville Dam.
52
Human barriers are important threats that alter distributional patterns of freshwater
fishes, leading to elevated risk of extinction (Clavero et al. 2004). Fragmentation restricts access
to upstream spawning areas and overwintering grounds in many migratory species that have
large home ranges, such as members of Acipenseridae, Catostomidae, and Petromyzontidae
(Jager et al. 2008; Reid et al. 2008a; Woolnough et al. 2009). Absence or decreased abundance
of fish species in upstream habitats of fragmented streams has been documented in these species
(Gido et al. 2010; Quinn and Kwak 2003; Reid et al. 2006). Dams also increase the rate of local
extinction by disrupting metapopulation structure (Angermeier 1995) as reported for Cyprinidae
(Frassen 2012) and Percidae (Haponski et al. 2007). Many of the major streams in our study
area were heavily fragmented by dams and impoundments, contributing to population decline.
Of the 17 species associated with streams in our analysis, dams were identified as the major
threat factor for five of them and secondary threat for two species (Appendix 1). Although
permeability of the dams may be improved with the construction of fish passways (Reid et al.
2008a; Reid et al. 2008b), the degree to which it maintains in-stream connectivity is debatable
(Bunt et al. 2000). Therefore, positions of dams were incorporated in our BioAO calculation
where fragmented stream segments were excluded as part of AO. Consideration of barriers
should also be made when dealing with other stream taxa. Barriers have been shown to pose
significant threats to unionid mussels because these species depend upon fish hosts for dispersal
during larval stage (Schwalb et al. 2011; Vaughn 2012).
Differential stream width estimations could explain differences observed between my
proposed BioAO and those used by COSEWIC. Stream width used in COSEWIC assessments
were typically rough approximations, whereas, my method was based on direct observations or
predicted by Strahler order. The allometric relationship between stream width and Strahler order
was fairly robust because it covered a continuous gradient of waterbody sizes and Strahler
53
orders (Hughes et al. 2011). The regression was not perfect, however, and tended to
underestimate sizes in large rivers, such as the St Lawrence River, Detroit River, and St Clair
River, which all average about 1000m in width. In these cases, average stream width was used
based on field survey data.
I adopted a circular buffer method for occupancies in lakes and wetlands to replace the
use of grid cells. Unlike stream species, fish movements in lakes and wetlands are generally
non-directional (Moilanen et al. 2008). Circular buffers better estimated the potential area in
which an individual may occur, compared to imaginary grid boundaries (Gaston 1991) and this
approach avoided the issue of sensitivity to grid placement (Rivers et al. 2010). The additional
step of removing terrestrial area from the circular buffer accounted for these species being
confined by clear boundaries of the aquatic habitat, allowing more biologically accurate
occupancy measure. This approach required maps of the aquatic habitats, such as lakeshore
contours and flooded areas in wetlands, which are often available for major Canadian
waterbodies (Canada Water Maps 2012). It was performed with relative ease using GIS-based
functions and can be applied to other species when suitable habitat boundaries are well defined.
COSEWIC sometimes reports AO in terms of available area of preferred habitat for
species occurring in wetlands and lakes. An endangered species, Spotted Gar, was reported to
occupy 51.57km2 in Ontario, measured as the vegetated area of the only three wetlands where it
occurred. The AO of species endemic to a single lake, such as the Misty Lake Threespine
Stickleback species pair, was measured as the area of the lake. For species living in large lakes,
estimation of suitable habitat is often based on total area at preferred depth, which provides a
useful estimation of potential occupancy if depth preference is well defined. However, a species
may be absent at sites of suitable habitats because distribution can be affected by factors other
54
than habitat availability, such as dispersal limitation (Joy and Death 2004), biological
interactions with prey, predator, and competitor species (Guisan and Zimmerman 2000; Jackson
et al. 2001), and local extirpation caused by extreme events (Joy and Death 2004).
Consequently, AO reported in this manner may not have the resolution to mirror changes in
population sizes, and subsequently provides limited insights to the species conservation
conditions. An example is the Upper Great Lakes Kiyi, a subspecies of declining population
size that lives in the deep, cold waters of the Great Lakes. Biological AO reported by
COSEWIC was determined by the area of lake of greater than 100m depth, generating an AO of
67, 755km2 (COSEWIC 2005b). This measure of AO was entirely based on bathymetric maps
of the lakes, and therefore lacked the ability to reflect the decreasing abundance observed in the
species.
Another commonly adopted practice for determining size of AO is through distribution
and occupancy modeling. Species distribution models (SDM) link environmental variables with
species occurrence data statistically to characterize suitable aquatic habitat. The modeling
approach generates highly species-specific analyses and aids in mechanistic understanding for
population decline (Lecis and Norris 2004). Many studies predicted local species occupancies
for freshwater taxa based on SDM, including stream fishes (Anderson et al. 2012; Hopkins and
Burr 2009), freshwater molluscs (Vaughn 2012; Wilson et al. 2011), and aquatic amphibians
(Lecis and Norris 2004). However, conducting SDM analysis for all species to be assessed for
conservation status is unrealistic because building SDMs require comprehensive surveys of the
species and habitat to understand species-habitat association (Anderson et al. 2012). Further,
similar to the area of suitable habitat approach, SDM-based AO is likely to be less sensitive at
reflecting population decline because it represents preferred habitat area instead of actual
occupancy. For these reasons, most existing SDM research is focused on species that were
55
reported imperiled or known to be rare, in attempt to designate protected areas (Carvalho et al.
2010; Moilanen et al. 2008), rather than of being used for determination of conservation status
designation.
Gaston (1991) described the area of occupancy as “… the sum of the areas of the home
ranges (or equivalent) of all individuals, reduced to account for zones where these overlap one
another”. Based on this notion, I used home range size to determine the biologically appropriate
scale for calculating occupancy measures. Assuming each distribution record represented an
individual, buffered area covered within the home range distance was regarded as the occupied
area for that individual. This method provided rationale for species-specific consideration of
scales. Previous studies showed it was at the spatial scale similar to the home range size of a
species where AO is proportionate to abundance (Hartley and Kunin 2003; Joseph and
Possingham 2008). Meyer and Thuiller (2006) concluded the home range scale to be the most
appropriate in predicting suitable habitat through a meta-analysis of distribution modeling
studies. Using home range to determine stream and lake/wetland occupancies can also be useful
for explicitly identifying number of locations, as seen in studies on freshwater fishes using
buffer stream distance (Fagan et al. 2005) and on herbaria using circular buffers (Rivers et al.
2011). Using estimates of home range in AO calculation is already utilized in status
designations for bird species, in terms of number of breeding pairs multiplied by their average
home range (COSEWIC 2010).
Body size and home range have been shown to be strongly related in freshwater fish
species (Minns 1995; Woolnough et al. 2009). The allometric relationship used in my analysis
was particularly applicable because it was composed exclusively on home range reports for
stream fish species (Woolnough et al. 2009). It also predicted home range in linear units, which
56
simplified the procedure in GIS-based calculations. However, drawbacks are associated with the
use of allometric relationship from literature data compilations. First, the large variations in
methods of data collection and methods of home range measure among the original studies
compiled for constructing the allometric relationship might prevent accurate estimation of home
range. Also, extrapolation was also required because some species sizes exceeded those used in
the data compilations. Further, the reported allometric relationship did not account for different
body shape in fish species, although correction for body shape did not seem to make significant
differences in the original analysis (D.A. Woolnough, Central Michigan University, Mount
Pleasant, Michigan; personal communication 2012).
Arbitrary rules to increase buffer size instead of using home range directly in BioAO
calculation were necessary to prevent underestimation of AO due to uncertainties in the
distribution data, which are inherently inevitable for imperiled species as a result of typically
low detectability. Currently, COSEWIC suggests use of ‘the best available’ data, which includes
all presence-only records from various sources, in order to overcome such detection difficulties
(COSEWIC 2010). These data are often the result of multiple surveys undertaken for different
purposes and using a variety of sampling techniques. As a result, the point records can never
represent the distribution of all individuals in the population, which is the assumption of the HR-
BioAO calculation. Further, some home ranges predicted from the allometric relationship were
extremely small, in the range of 29m to 60m, especially in small-bodied cyprinid and percid
species. It is unlikely that these distribution data are based on surveys conducted at such a fine
scale. Since AO should always be measured at a spatial scale equivalent or greater than the scale
of sampling (IUCN Standards and Petition Working Group 2010), allometric home range was
inappropriate for these species.
57
However, it is challenging to confirm the suitability of these arbitrary rules as to how
well the resulting segments represented the actual AO, without absence data or information on
species detectability for these at-risk species. In the future, I suggest conducting targeted field
surveys for species under assessment designed to sample repeatedly at each site to allow the
computation of probability of detection. It will also be useful to include absence sites on species
distribution maps when information is available. Detectability and presence-absence data
together can be utilized to determine buffer length that predicts occupied stream segments by
using absent records with sufficient sampling effort to further refine the extents of the buffers.
Nonetheless, body size-home range relationships provided explicit basis for modeling species
distributions at spatial scales that are biologically meaningful. Such rationale for choosing
biologically relevant spatial scales may extend to conservation risk assessments of various taxa,
given the large amount of body size-home range relationship data available (Hendriks et al.
2009).
Other estimators could be considered to buffer distribution points, such as area per
individual (API) and minimum area for population viability (MAPV). Unlike home range which
considers movement capability, API is determined by habitat resource availability (Minns et al.
1996) and tends to be smaller than home range in freshwater fish species, because of high
degree of habitat overlap (Hendriks et al. 2009; Velez-Espino et al. 2008). Using API as
indicator of occupancy scale is potentially more precise based on the COSEWIC definition of
AO. However, utilization of API as buffer for each distribution point requires occurrence
records that more closely reflect abundance, which are typically not available for at-risk species.
MAPV can be calculated as the product of API and minimal viable population (MVP) or the
product of inverse of density and MVP (Velez-Espino et al. 2008). Using this metric as buffer
length would tend to result in overestimation of AO because it assumes each distribution point
58
to represent a viable population, which is erroneous especially in imperiled species. This method
would generate very large buffer distance because the aim of this measure is to determine
critical habitat area necessary for species recovery (Velez-Espino et al. 2008).
Re-evaluation of Losses – Application of BioAO
Using the proposed biological AO measurements, conservation status was reassessed
under the COSEWIC framework, using the current set of thresholds. Only Eastern Sand Darter
and Silver Shiner were reassessed as endangered under subcriterion B2, meeting the AO
threshold as well as satisfying the additional conditions a-c (Table 8). Although BioAO for
another three species (Black Redhorse, Lake Sturgeon, and Spotted Gar) also fell below the
critical threshold for being endangered, there was not enough evidence for population
fragmentation, decline or fluctuation in population size, range or habitat quality to support up-
listing based on subcriterion B2 (Table 8). Consideration of these indicators of population
decline complementary to the spatial indices is required under criterion B because distribution
ranges do not correlate to risk of extinction as tightly as population abundance measures (Mace
et al. 2008). Up-listing of status ranking into the threatened category occurred when the BioAO
met the threshold of being extremely restricted (subcriterion D2), as in the case of Blackstripe
Topminnow, which had limited distributions in the Lake Erie drainage, yet were classified as
Special Concern by COSEWIC based on the IAO measures. On the other hand, BioAO for the
threatened species, Black Redhorse, exceeded the threshold for D2, suggesting downgrading in
status rank to Special Concern. The increase in BioAO compared to the COSEWIC assessment
could be the result of a number of reasons. First, the COSEWIC biological AO did not account
for locations with single records, which were included in our calculations. Second, the
59
COSEWIC report estimated 50m width for the major streams where Black Redhorse occurred,
different from what we used (77m for Grand River and 40m for Thames River). Third, the
buffer zone added to the ends of occupied segments could have raised the length of streams
deemed occupied (Appendix 5).
The challenge for adopting the BioAO measure for freshwater taxa lies in the set of
standardized threshold values that defines risk categories. Freshwater species with smaller body
sizes often have lower spatial requirements due to higher density of individuals in preferred
habitat (Hendriks et al. 2009) and most freshwater fishes, by nature, are more geographically
restricted than their terrestrial counterparts (Olden et al. 2010), resulting in generally smaller
BioAO. Consequently, although standard thresholds provide the opportunity to compare across
taxa, they may become insufficient to differentiate the freshwater fish species into different
conservation categories (Keith et al. 2000) and may lead to over- or under-listing depending on
taxon (Mace et al. 2008). As observed in my study, the calculated HR-BioAO all fell below the
EN threshold under criterion B, among which 11 species would qualify as geographically
restricted under criterion D. Although this measure of AO represented the most conservative
case where the underlying assumption that the distribution data represent all individuals in a
single population is unlikely to be realistic, the order of magnitude of the difference between
HR-BioAO and the threshold values was intriguing. When applying the proposed BioAO
calculated with the adjusted buffer size, current thresholds again failed to discriminate species
into different risk categories. As stated earlier, since species included in this study covered risk
categories from SC to EN, I expected BioAO size of these species to cross the threshold levels.
Both cases suggest that the current standard thresholds may be inappropriate for assessing
extinction risk in freshwater fish species. In addition, no rationale for the current thresholds
values was found during the literature review of this study. These threshold sizes were
60
determined presumably based on the early distribution - abundance studies, which were mainly
conducted for land birds and large terrestrial mammals with grid sizes of 10s of kilometers
(Gaston and Lawton 1990a, 1990b). Similar threshold issue was also noted in status assessments
for other taxa with small ranges. Reduction in thresholds for criterion B was suggested for Red
List evaluation in small-bodied arthropods and spiders (Cardoso et al. 2011) and sessile vascular
plants in Britain (Keith et al. 2000).
To differentiate species into appropriate conservation categories, new sets of critical
thresholds should be adopted to reflect biologically meaningful scales, and should match the
spatial scales at which AO is measured. One way of achieving this goal may be to use home
range –body size allometric relationships specific to different taxonomic groups (Hendriks et al.
2009) to correct the threshold levels to appropriate scales. A simpler approach to this problem is
currently used by NatureServe, which provides another frequently used conservation status
assessment procedure using a set of indices rated individually and then weighted to produce the
final status rank for a species (Faber-Langendoen et al. 2012). In their assessments, differential
sets of threshold scales for AO were adopted, based on the types of ecosystems: matrix, large
patch, small patch, and linear ecosystem (Master et al. 2012). The recommended threshold scale
for species found in linear habitats (2km2 and smaller is considered imperiled) is 100 times finer
than that used for matrix ecosystems (Master et al. 2012).
Conclusion
This study comprehensively reviewed the measurement of area of occupancy used in the
assessment of conservation status for freshwater organisms. Characterizing local conservation
biogeographic patterns demands careful considerations of where species actually occur.
61
Inappropriate measures of area of occupancy compromises its usefulness in conservation
ranking practices that precede conservation actions. For the freshwater taxa that are inherently
restricted by natural habitat boundaries, current methodological approaches are likely to produce
incorrect estimations on the relative likelihood of extinction, leaving imperiled species
misclassified and resulting in misallocation of limited conservation resources. I recommend
replacement of the traditional grid-based approach with an ecologically meaningful
measurement that accounts for habitat dimensionality and mobility on a species basis. The
proposed method would also apply to other species that occur in linear and small patchy
habitats. Subsequent revision of critical thresholds will be a crucial next step for adapting to
scales that are biologically meaningful for these species.
62
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6. Appendices
Appendix 1. COSEWIC designated conservation status and threat factors identified for freshwater fish species in Canada.
Common Name
Scientific Name COSEWIC
status
Threats
Habitat Loss/
Degradation
Alien Invasive Species
Harvest Pollution Natural Disaster
Change in Native Species
Dynamics
Persecution Human
Disturbance
Banff Longnose Dace
Rhinichthys cataractae smithi
EX P P S
Blue Walleye Sander vitreus glaucus
EX S P S
Deepwater Cisco
Coregonus johannae
EX P P
Hadley Lake Benthic Threespine Stickleback
Gasterosteus aculeatus
EX P
Hadley Lake Limnetic Threespine Stickleback
Gasterosteus aculeatus
EX P
Lake Ontario Kiyi
Coregonus kiyi orientalis
EX S P
71
Gravel Chub Erimystax x-punctatus
ET P S
Paddlefish Polyodon spathula
ET S S
Striped Bass (St. Lawrence Estruary population)
Morone saxatilis ET S P
Atlantic Whitefish
Coregonus huntsmani
EN P S S
Aurora Trout Salvelinus fontinalis timagamiensis
EN S S P
Copper Redhorse
Moxostoma hubbsi
EN P S S P
Enos Lake Benthic Threespine Stickleback
Gasterosteus aculeatus
EN P P P
Enos Lake Limnetic Threespine Stickleback
Gasterosteus aculeatus
EN P P P
Lake Chubsucker
Erimyzon sucetta EN P S S
72
Lake Sturgeon (Nelson River populations)
Acipenser fulvescens
EN S P
Lake Sturgeon (Saskatchewan River populations)
Acipenser fulvescens
EN S P
Lake Sturgeon (Red-Assiniboine Rivers - Lake Winnipeg populations)
Acipenser fulvescens
EN S P S S
Lake Sturgeon (Winnipeg River - English River populations)
Acipenser fulvescens
EN S P S S
Lake Sturgeon (Western Hudson Bay populations)
Acipenser fulvescens
EN S P
Misty Lake Lentic Threespine Stickleback
Gasterosteus aculeatus
EN S P S
Misty Lake Lotic Threespine Stickleback
Gasterosteus aculeatus
EN S P S
73
Nooksack Dace
Rhinichthys cataractae ssp
EN P S S S
Northern Madtom
Noturus stigmosus
EN S P
Paxton Lake Benthic Threespine Stickleback
Gasterosteus aculeatus
EN P P S
Paxton Lake Limnetic Threespine Stickleback
Gasterosteus aculeatus
EN P P S
Pugnose Shiner
Notropis anogenus
EN P S S
Redside Dace Clinostomus elongatus
EN P S P S S
Salish Sucker Catostomus catostomus
EN P S P
Shortnose Cisco
Coregonus reighardi
EN S P S
Speckled Dace Rhinichthys osculus
EN P S P
Spring Cisco Coregonus sp. EN S P P
74
Vananda Creek Benthic Threespine Stickleback
Gasterosteus aculeatus
EN P P S
Vananda Creek Limnetic Threespine Stickleback
Gasterosteus aculeatus
EN P P S
Western Brook Lamprey (Morrison Creek population)
Lampetra richardsoni
EN P S
Western Silvery Minnow
Hybognathus argyritis
EN P P
White Sturgeon
Acipenser transmontanus
EN P P
Atlantic Sturgeon (St Lawrence population)
Acipenser oxyrinchus
TH S P S S
Atlantic Sturgeon (Maritime population)
Acipenser oxyrinchus
TH S P S S
Black Redhorse
Moxostoma duquesnei
TH P S P
75
Carmine Shiner
Notropis percobromus
TH P S S S S
Channel Darter
Percina copelandi
TH P S
Coastrange Sculpin (Cultus population)
Cottus aleuticus TH S S S
Eastern Sand Darter (Ontario population)
Ammocrypta pellucida
TH P S P
Eastern Sand Darter (Quebec population)
Ammocrypta pellucida
TH P S P
Lake Sturgeon (Great Lakes - Upper St. Lawrence populations)
Acipenser fulvescens
TH S P S
Mountain Sucker (Milk River populations)
Catostomus platyrhynchus
TH P S S
Rainbow Smelt (Lake Utopia Small-bodied population)
Osmerus mordax TH P P P
76
Rainbow Smelt (Lake Utopia Large-bodied population)
Osmerus mordax TH P P P
Mountain Sculpin (Eastslope populations)
Cuttus sp TH P P
Shortjaw Cisco Coregonus zenithicus
TH S P P S
Silver Shiner Notropis photogenis
TH P S P
Spotted Gar Lepisosteus oculatus
TH P
Striped Bass (Southern Gulf of St. Lawrence population)
Morone saxatilis TH P
Stiped Bass (Bay of Fundy population)
Morone saxatilis TH P S S
Umatilla Dace Rhinichthys umatilla
TH P P
Vancouver Lamprey
Lampetra macrostoma
TH P S
77
Westslope Cutthroat Trout (Alberta population)
Oncorhynchus clarkii lewisi
TH P P P S
American Eel Anguilla rostrata SC P P S
Banded Killifish (Newfoundland population)
Fundulus diaphanus
SC P
Bering Cisco Coregonus laurettae
SC S S
Bigmouth Buffalo (Saskatchewan - Nelson River populations)
Ictiobus cyprinellus
SC P S S S S S
Blackstripe Topminnow
Fundulus notatus SC S
Bridle Shiner Notropis bifrenatus
SC P S S P
Charlotte Unarmoured Threespine Stickleback
Gasterosteus aculeatus
SC S S S
78
Columbia Sculpin
Cottus hubbsi SC P S
Deepwater Sculpin (Great Lakes - Western St. Lawrence populations)
Myoxocephalus thompsonii
SC P S S
Dolly Varden (Western Arctic population)
Salvelinus malmamalma
SC P P S
Giant Threespine Stickleback
Gasterosteus aculeatus
SC S
Grass Pickerel Esox americanus vermiculatus
SC P
Green Sturgeon
Acipenser medirostris
SC S S S
Upper Great Lakes Kiyi
Coregonus kiyi kiyi
SC S P
Lake Sturgeon (Rainy River-Lake of the Woods populations)
Acipenser fulvescens
SC P P S
79
Lake Sturgeon (Southern Hudson Bay and James Bay populations)
Acipenser fulvescens
SC S P
Mountain Sucker (Pacific populations)
Catostomus platyrhynchus
SC P S S S
Northern Brook Lamprey (Great Lakes - Upper St. Lawrence populations)
Ichthyomyzon fossor
SC S S S
Orangespotted Sunfish
Lepomis humilis SC S S S
Pugnose Minnow
Opsopoeodus emiliae
TH S
River Redhorse
Moxostoma carinatum
SC P S P S
Rocky Mountain Sculpin (Westslope populations)
Cottus sp SC P
Shorthead Sculpin
Cottus confusus SC S S
80
Shortnose Sturgeon
Acipenser brevirostrum
SC S S S
Silver Chub Macrhybopsis storeriana
EN P P
Silver Lamprey (Great Lakes - Upper St. Lawrence populations)
Ichthyomyzon unicuspis
SC S S P
Spotted Sucker
Minytrema melanops
SC P
Squanga Whitefish
Coregonus sp. SC S S
Warmouth Lepomis gulosus SC S
Westslope Cutthroat Trout (British Columbia population)
Oncorhynchus clarkii lewisi
SC P P P S
P - primary threat factor; S - secondary threat factor
81
Appendix 2. COSEWIC designated conservation status and reported threat factors for freshwater mollusc species in Canada.
Common Name Scientific Name COSEWIC Status
Threats
Habitat Loss/
Degradation
Alien Invasive Species
Harvest Pollution Natural Disaster
Change In Native Species
Dynamics
Human Disturbance
Intrinsic Factors
Dwarf Wedgemussel
Alasmidonta heterodon
EX P
Banff Springs Snail
Physella johnsoni EN P S S
Eastern Pondmussel
Ligumia nasuta EN S S
Fawnsfoot Truncilla donaciformis
EN P P S
Hickorynut Obovaria olivaria EN P P P P
Hotwater Physa Physella wrighti EN S S S S
Kidneyshell Ptychobranchus fasciolaris
EN P P P S
Lake Winnipeg Physa Snail
Physa sp. EN P P
Mapleleaf Mussel
Quadrula quadrula
EN P P
Norhtern Riffleshell
Epioblasma torulosa rangiana
EN P P P S
82
Rainbow Mussel Villosa iris EN P P
Rayed Bean Villosa fabalis EN P P
Rocky Mountain Ridged mussel
Gonidea angulata
EN P P S
Round Hickorynut
Obovaria subrotunda
EN P P P S
Round Pigtoe Pleurobema sintoxia
EN P P P
Salamander Mussel
Simpsonaias ambigua
EN P P P S
Snuffbox Epioblasma triquetra
EN P P P S
Mapleleaf Mussel
Quadrula quadrula
TH P P
Brook Floater Alasmidonta varicosa
SC P S S S
Wavy-rayed Lampmussel
Lampsilis fasciola
SC P S P
Yellow Lampmussel
Lampsilis cariosa SC S S
P - primary threat factor; S - secondary threat factor
83
Appendix 3. Type of habitats used by freshwater species at risk in Canada. a) fishes; b) molluscs.
(a)
(b)
0
5
10
15
20
25
Stream Lake wetland Estuary Marinewater
Nu
mb
er o
f Ta
xa
EN
TH
SC
0
2
4
6
8
10
12
14
Stream Lake Hot spring
Nu
mb
er o
f Sp
ecie
s
EN
TH
SC
84
Appendix 4. Proportion of the two components of BioAO: stream occupancy and lake/wetland occupancy for 20 fish species at risk in
Ontario.
0% 20% 40% 60% 80% 100%
Black Redhorse
Blackstripe Topminnow
Bridle Shiner
Channel Darter
Eastern Sand Darter
Grass Pickerel
Lake Chubsucker
Lake Sturgeon
North Brook Lamprey
Northern Madtom
Pugnose Minnow
Pugnose Shiner
Redside Dace
River Redhorse
Silver Chub
Silver Lamprey
Silver Shiner
Spotted Gar
Spotted Sucker
Warmouth
Lake/Wetland
Stream
85
Appendix 5. Breakdown of stream BioAO for 17 species inhabited stream environment: area for raw length of occupied stream
segment, buffer segment area and area for single locations.
0% 20% 40% 60% 80% 100%
Black Redhorse
Blackstripe Topminnow
Bridle Shiner
Channel Darter
Eastern Sand Darter
Grass Pickerel
Lake Chubsucker
Lake Sturgeon
North Brook Lamprey
Northern Madtom
Pugnose Minnow
Pugnose Shiner
Redside Dace
River Redhorse
Silver Lamprey
Silver Shiner
Spotted Sucker
Raw Stream Segment
Single Location
Buffer Stream Segment