Conservation Assessment and Planning

• Selection of sites to meet conservation goals

• Design of a network of sites that has a high probability of maintaining biodiversity and natural processes over time

In the past, the selection of sites for protection as reserves has been ad hoc, opportunistic, politically-biased, and/or not based on biological criteria

So, scientists started getting more involved in reserve selection.

Early approaches involved the use of

maps, mylar overlays, magic markers, and expert opinion.

When sites were compared with one

another or prioritized, multi-criteria scoring procedures were used.

From Noss (1993)

However, scoring procedures are inefficient and do not assure that all species and features are represented in the collection of sites – the highest-scoring sites may have all the same features!

Key Features of Systematic Conservation Planning (from Margules, Pressey, and others)

Explicit, quantitative goals

Assessment of how well goals are met in existing reserves

Efficiency and cost-effectiveness – “most bang for the buck”

Complementarity – sites are chosen to complement existing protected areas and other selected sites

Key Features of Systematic Conservation Planning (cont.)

Flexibility – various ways to achieve goals

Irreplaceability – extent to which a site is needed to achieve goals (or contributes to goals)

Persistence – viability over the long term

Scheduling – minimizing losses while the reserve network takes shape

Conservation Planning to Protect Species and Ecosystems

Protection of special elements—identifying, mapping, and protecting populations of imperiled species , imperiled natural communities, and other sites of high biodiversity value (usually fine-scale)

Representation of a full spectrum of ecosystem types (e.g., vegetation, abiotic habitats, aquatic habitats) at multiple spatial scales

Conservation of focal species—meeting conservation needs of species with high ecological importance or sensitivity to disturbance by humans

Site-Selection Algorithms: Integrate Multiple Datasets to Meet Conservation Planning Goals

Computerized mathematical algorithms linked to GIS (spatially explicit)

Highly efficient in achieving stated goals for each conservation target (feature)

Transparent; can be applied interactively in a workshop format

Address 2 kinds of problems: - Meet a variety of biodiversity goals while

minimizing net expected costs – the minimum set problem

- Maximize biodiversity benefit (e.g., representation of features) within a fixed budget – the maximal coverage problem

Prominent Site-Selection Algorithms

Marxan (Ball and Possingham, U. of Queensland): http://www.uq.edu.au/marxan/

Zonation (Moilanen, U. of Helsinki): http://www.helsinki.fi/bioscience/consplan/

Zonation

Zonation produces a hierarchical prioritization of the landscape based on the conservation value of sites (cells), iteratively removing the least valuable cell (accounting for complementary) from the landscape until no cells remain. In this way, landscapes can be zoned according to their value for conservation.

The program produces, among other things, basic raster files

from each run, which can be imported to GIS software to create maps or to conduct further analyses. The data requirements for the program are realistic and it can be run with large datasets containing up to 2 000 species or 16 mil. element landscapes on an ordinary desktop PC.

Simulated Annealing Agorithms

(e.g., Marxan)

i j

b lengthboundarywjelementfortPenaltyisiteCostCostTotal cos

Or in plain language, Total Portfolio Cost = (cost of selected sites) + (penalty cost for not meeting the stated conservation goals for each element) + (cost of spatial dispersion of the selected sites as measured by the total boundary length of the portfolio).

Algorithm attempts to minimize the total “cost” of a reserve “portfolio”

Planning Units

Divide region into planning units (i.e., watersheds or other natural units or grid of squares or hexagons).

These are the basic building blocks for assembling a “portfolio” of sites or a reserve system.

Displaying a Solution

After the algorithm has selected an alternative portfolio, you can display the selected planning units.

50%, blm .0003

Effect of Boundary Modifier

Because it is desirable for nature reserves to be both compact and contiguous, one objective of the annealing algorithm is to minimize the total length of the boundary of the portfolio. This slide shows how increasing the boundary modifier (BM) affects the clustering in the solution.

BM = 0.0 BM = 0.2 BM = 1.0

Displaying a “Summed” Solution

Planners may not want just a good solution but also to see how robust the solution is. The 'Sum Runs' option shows the number of times each planning unit was selected out of the total number of runs. The results are displayed as a color gradient from white (never selected) to dark red (most frequently selected). This adds flexibility to reserve design and also provides a measure of irreplaceability

50%, blm .0003

Yellow: Places almost always

chosen.

Pink: Areas chosen about ½ the

time.

Blue: Areas can be considered

useful in only some reserve

networks.

93 data layers

Conservation Hotspots

6 different size targets x 4 levels clumping x 100 runs each = 2,400 solutions

Summed Solutions from 93 data layers: A

Measure of Irreplaceability

CIT Marine Analysis

Coastal Information Team

Ecosystem Spatial Analyses

Special Element Analyses • Plants: 29

• Vascular: 24

• Non-vascular: 5

• Animals: 53 • Amphibians: 2

• Mammals: 8

• Fish: 10

• Focal species: 7

• Birds: 6

• Invertebrates: 20

• Rare plant communities: 29

Bird colonies

Seabird Colonies

Population Density and Marine Usage

CIT Marine Analysis

D. Gunn

Other classes of special elements? Spatial surrogates of ecological and

evolutionary processes (Pressey et al. 2003)

Wind Corridor to Coachella Valley

Freshwater Ecosystem Types

•Drainage Area •Biogeoclimatic Zone •Geology •Gradient •Glacial Connectivity •Presence of Dominant Lake / Wetland Features

Categories of Focal Species (Lambeck 1997)

• Area-limited species • Dispersal-limited species • Resource-limited species • Process-limited species Also: • Highly interactive species (foundation species

and keystone species) • Endemic species (if not adequately addressed

as special elements)

Focal Species

• Grizzly Bear • Black Bear • Mountain Goat • Black-Tailed Deer • Northern Goshawk • Tailed Frog (coastal population) • Salmon

Why carnivores as focal species?

• Large carnivores: area-limited, often resource-limited, sometimes keystone species, charismatic, sensitive to human persecution, often imperiled

• Forest mesocarnivores: often dispersal-limited, often resource-limited, bioindicators of forest condition, often imperiled

Modeling Methods

• Habitat (niche) models: static but spatially explicit – e.g., resource selection functions, maximum entropy models (Maxent), Mahalanobis distance measures – developed from species distribution and environmental data

• Spatially Explicit Population Models (SEPMs): dynamic demographic simulation models and viability analyses

Spatially Explicit Population Models

Variability in Costs vs. Benefits of Site Selection (Naidoo et al. 2006, Perhans et al. 2008)

Sites can vary tremendously in acquisition or opportunity costs

Sites may vary even more in the benefits (i.e., conservation values) they provide.

When the cost of obtaining more information, e.g., through biological surveys, is low compared to the cost of protecting sites, obtaining more data is cost-efficient; but there will be a threshold beyond which more data-collection is too costly

In general, when costs of sites are more variable than site quality, then costs should drive the site selection process

Conversely, then site quality is more variable than costs, then site quality is more important in prioritization

How much time and money do we spend collecting additional biodiversity data? More data allow for more informed and cost-

effective decisions

However, the cost of collecting more data is that sites are lost to habitat destruction in the meantime – hence, data collection has diminishing returns

In a simulation of a 81,000 km2 landscape with ongoing habitat destruction, 1-2 years of data collection at a cost of ca. $100K USD was optimal; there was little increase in effectiveness of conservation prioritizations with increasing investment, and the full data set was 25 times more costly (Grantham et al. 2008)

How Much is Enough?

Or, how much protected area is needed to meet our conservation goals?

In the past, we had to rely on:

• What we thought was politically feasible, e.g., the IUCN estimate of 10% of the earth’s land surface, or the Brundtland Commission’s estimate of 12%

• Best guesses of biologists, which varied depending on what they studied

• Analyses of single species or community types (not biodiversity generally)

Determinants of How Much is Enough

• The inherent abiotic and biotic heterogeneity (e.g., richness and endemism) of the region

Determinants of How Much is Enough

• The fineness of classification of habitat types used in the representation analysis

Determinants of How Much is Enough

• The reservation threshold, or proportion of each habitat type that must be reserved in order to be considered represented

Determinants of How Much is Enough

• The replication threshold, or number of sites in which a habitat type, species, or other feature must be reserved in order to be considered represented

Determinants of How Much is Enough

• Area requirements and population viability criteria for individual species

Determinants of How Much is Enough

• The natural disturbance regime

Determinants of How Much is Enough

• Previous conservation decisions, i.e., what kinds of ecosystems and species are present (and potentially “over-represented”) in existing protected areas.

Current Amount of Protected Area Globally

ca. 5% of land surface, with

conservative criteria (ca. 12% with less conservative criteria

of what counts as a protected area; i.e., IUCN changed its criteria!)

Estimates of how much is enough Source Region Goal Recommended

Area Odum (1970) Georgia Optimize

ecosystem services and quality of life

40%

Odum and Odum (1972)

South Florida Optimize ecosystem services, etc.

50%

Margules et al. (1988)

Australian river valleys

Represent all plant species and wetland types at least once

44.9% (plants) 75.3% (both)

Ryti (1992) San Diego Canyons

Represent all bird, mammal, and plant species at least once

62.5%

Source Region Goal Recommended Area

Ryti (1992) Islands in Gulf of California

Represent all bird, mammal, reptile, and plant species at least once

99.7%

Metzgar and Bader (1992)

Northern Rocky Mountains of U.S.

Maintain an effective population of 500 grizzly bears (total pop. = 2000)

ca. 60% of region (32 million acres)

Cox et al. (1994)

Florida Protect rare species and natural communities

33.3%

Hoctor et al. (2000)

Florida Capture biological priority areas and provide connectivity

57.5%

Source Region Goal Recommended Area

Noss et al. (1999)

Klamath-Siskiyou Ecoregion (CA-OR)

Protect roadless areas that meet all special elements, representation, and focal species goals

60-65% in 2 classes of reserves plus linkages

Rodrigues & Gaston (2001)

Review of 21 studies worldwide

Represent each species at least once

Mean = 13.6%, Range = 0.3% - 66%

Rodrigues & Gaston (2001)

Tropical rainforests

Represent each plant (and vertebrate) species

at least once

92.7%

(17.8%)

Noss et al. (2002)

Greater Yellowstone Ecosystem

Meet all special elements, representation, and focal species

goals

70%

Source Region Goal Recommended Area

Carroll et al. (2003)

U.S.-Canada Rocky Mountains

Protect highest-quality habitat and source areas to maintain viable populations of

carnivores

36.4%

The Nature Conservancy (2003, unpublished)

> 50 ecoregional assessments in 11 countries

Meet multiple conservation goals, especially representation and protection of

special elements

Mean = 30% -40% (up to ca. 70%)

To Conserve Biodiversity, We Need:

Much more conserved land (generally 25-75% of a region – average about 50%) Lands around conservation areas kept at low-density development or other low-intensity land use Limit high-density development to lands within strictly defined urban growth boundaries – and with well-designed “open space” in reserves and corridors

Land that is “Protected” Must Also be Managed (and often Restored)