Navigation Bar (see also links at bottom of page) Home Page Contacts News SiteIndex Training Support Prices Research Software

 

GIS Enhances Endangered Species Conservation Efforts

  Originally published in GIS World in November 1994 (vol. 7, no. 11, pages 36-40). Reproduced with permission of GIS World, Inc.

By H. Resit Akçakaya, an ecologist with Applied Biomathematics, 100 North Country Road, Setauket, NY 11733, USA.








Helmeted honeyeater (Photo courtesy of Ian Smales.)
 

Current GIS applications in conservation biology and wildlife management include various aspects of habitat description, delineation and monitoring. For example, the U.S. Fish and Wildlife Service's GAP analysis program (Davis et al. 1990) identifies areas used by various species. Overlaying these maps with land ownership maps allows users to determine areas and species that are protected poorly.

Such efforts, though extremely valuable, use only part of GIS's potential, as they concentrate on describing of existing conditions. Conservation biologists and wildlife managers are concerned with questions that involve predicting the future of endangered and threatened species (Akçakaya 1992). Such questions include:

  • What is the spotted owl's chance of recovery from its current threatened status?
  • What is the Florida panther's risk of extinction in the next 50 years?
  • Is it better to prohibit hunting or to provide more habitat for African elephants?
  • Is captive breeding and reintroduction to natural habitat patches a viable strategy for conserving black-footed ferrets? If so, is it better to reintroduce 100 black-footed ferrets to one habitat patch or 50 each to two habitat patches?
  • Is it worthwhile to relocate endangered helmeted honeyeaters from their current populations to empty habitat patches to spread the risk of local extinctions?
  • Is it better to preserve one large fragment of old-growth forest, or several smaller fragments of the same total area?
  • Is it better to add another habitat patch to the nature reserve system, or enhance habitat corridors to increase dispersal among existing patches?

Spotted owl

Population Viability Analysis


Florida panther
Such questions can be addressed by population viability analysis (PVA), a systematic examination of interacting factors that place a population or species at risk of extinction (Soulé 1986; Shaffer 1990). These factors may be natural and anthropogenic in origin, and their examination often involves mathematical models that predict the future changes in the abundance and distribution of the species in question, given information about its ecology and demography (Burgman et al. 1993).

The mathematical models are complicated by the complexity of ecological systems the models attempt to describe. As a result, for most real-world applications, the models need to be implemented as computer programs. Computer implementation is especially necessary when the models include several populations of the species. A collection of populations of the same species occupying different habitat patches is called a metapopulation (see Figure 1). In recent years, the metapopulation concept has become one of the most important paradigms in conservation biology (Hanksi 1989), perhaps as a result of the increased fragmentation of the habitats used by threatened and endangered species. For example, the spotted owl lives in an increasingly fragmented landscape, and models developed to estimate its risk of extinction or chance of recovery must account for multiple patches (Lamberson et al. 1992; LaHaye et al. 1994).


Figure 1. Abstract representation of a metapopulation. The circles represent different populations, and the lines represent migratory connections among populations.

Adding spatial structure to models is where the potential of GIS for conservation-related issues is the greatest. The spatial structure of the metapopulation has important effects on extinction risks, and recovery chances, as well as the impact of human disturbances on endangered species and the efficiency of conservation measures to prevent extinctions. GIS can be a valuable tool in helping ecologists determine the spatial structure of the landscape an endangered species lives in.

Spatial Structure and Metapopulation Dynamics

Spatial structure is simply the number, location, size and shape of habitat patches. These geographic attributes determine a number of biological characteristics, such as abundance (the number of individuals that live in a patch), or the maximum number of individuals that can be supported by the resources of a patch (called "carrying capacity" by biologists). Another important spatial factor is the distance among patches. Often, isolated patches have fewer individuals, or they go extinct more frequently, because they do not receive immigrants from other patches. Migration or dispersal is an important factor that may lead to recolonization of empty patches. Some ecologists advocate "habitat corridors", elongated areas of suitable habitat that connect two otherwise isolated habitat patches. For example, isolated woodlots in an agricultural landscape may be connected by hedgerows around fields, enabling species that can only breed in the woods to move from one wood to another, in search of vacant territories, mates and food.

For empty patches to be recolonized, there must be some occupied patches around; in other words, patches must not go extinct at the same time. If all populations of a metapopulation are affected by the same environmental factors, and have the same sequence of good and bad years, there is a high risk that several populations will go extinct around the same time, limiting the opportunities for recolonization (Gilpin 1988; Akçakaya and Ginzburg 1991). Often what determines whether environmental factors affecting two patches are correlated or not is the distance between the two patches, because nearby patches are more likely to be affected by the same factors. For example, the sequence of wet and dry years may be similar in two nearby mountain ranges. This presents a problem. On the one hand, nearby patches can exchange migrants, which increases their chance of survival (because migrants can recolonize empty patches). On the other hand, nearby patches often have correlated environments, which decreases their survival chances (because they can all go extinct at the same time). This is only one of the many complexities of metapopulation dynamics that necessitates the use of models, because common-sense and intuition, although valuable, are not enough to make decisions about the future of species living in fragmented habitats.

Linking GIS to Population Viability


Figure 2. Helmeted honeyeater (Photo courtesy of Ian Smales.)
Most models for population viability analysis do not make explicit use of habitat data organized in geographic information systems, and most GIS-based data on the habitat of endangered species are not explicitly used in predicting species viability and persistence. How can GIS and population viability analyses be linked? One starting point is to use spatial data organized by a GIS to identify the structure of a species' habitat; in other words, to calculate the number, location, size and shape of habitat patches from the point of view of the species. This approach was used recently to assess the effectiveness of translocating individuals as a management option for the endangered helmeted honeyeater (Lichenostomus melanops cassidix) in Australia (Akçakaya et al. 1995; see Figures 2 and 3).

Figure 3. The helmeted honeyeater is restricted to the Yellinbo nature reserve (yellow dot), where there were 60 individuals in 1991. (Courtesy of Michael McCarthy/Prism Desktop Publishing)
To use GIS to determine the spatial structure with this approach, first it is necessary to distinguish the habitat characteristics important for the species. That can be done by collecting habitat and species occurrence data at a large number of locations in the landscape. The data then may be analyzed with multiple regression, which gives a function (called the habitat suitability function) that links the habitat characteristics to the suitability of the habitat. In the case of the helmeted honeyeater, the variables of this function were the presence of ground water, density of Eucalyptus trees, and the amount of decortication of their bark (see Figure 4).
Figure 4. The helmeted honeyeater's habitat consists of riparian Eucalptus swamplands. (Photo courtesy of Peter Menkhorst)

The habitat suitability (HS) function in effect converts the maps of various habitat characteristics into a single map of habitat suitability (see Figure 5). The next step is to analyze the patchiness or spatial structure in this habitat suitability map. The determination of patch structure in this map involves finding clusters or groups of nearby cells (e.g., within foraging distance of each other) that have high enough HS values for the species to breed (Akçakaya 1994). These cell clusters can be labeled as patches (see Figure 5).


Figure 5. A map of habitat suitability (composed from habitat maps of a hypothetical species), superimposed with the spatial structure identified by a patch-recognition algorithm. This patch structure forms the basis of the metapopulation model represented in Figure 1.

The next step is to relate the habitat suitability to demographic characteristics, e.g., by defining the carrying capacity of each patch as a function of the total habitat suitability (summed over all cells) in that patch. Another aspect of the spatial structure that can be determined based on the patch identification is the minimum (edge-to-edge) distances between patches, based on the location and shape of their edges.

These calculations may form the basis of a metapopulation model that can address questions such as those listed at the beginning of this article. Of course, more species-specific data must be added to the model before it can used to estimate extinction risks or recovery chances. These data include birth and death rates (which may also be based on the habitat suitability in each patch), migration or dispersal rates (which may be based on distances calculated from the patch structure), as well as year-to-year fluctuations that have been observed in these demographic rates.

The details of the type of data needed for assessments of extinction risk and population viability depend on the species and landscape in question. What is important in all cases is to combine all the available information into a model, instead of using intuition or simple rules of thumb. Such models, however simple, hopefully will advise conservation biologists about future data needs by helping them identify parameters to which the risk results seem to be most sensitive. Although no program or model is a substitute for empirical work, a methodological approach that uses the full potential of GIS may help biologists use empirical information more efficiently.

References

Akçakaya, H.R. 1992. Population viability analysis and risk assessment. Wildlife 2001: Populations, D.R. McCullough and R.H. Barrett (eds), Elsevier Publishers, London.

Akçakaya, H.R. 1994. RAMAS/GIS: Linking Landscape Data With Population Viability Analysis. Applied Biomathematics, New York.

  Top of Page
Software · Prices · Training · What's New · Forum
  Research · Support · Index · Contact Us · Home
   
©1999 by Applied Biomathematics

webmaster@ramas.com
Date modified: 3-24-00