GIS‐Generated, Expert‐Based Models for Identifying Wildlife Habitat Linkages and Planning Mitigation Passages

Abstract

We developed three black bear ( Ursus americanus) habitat models in the context of a geographic information system to identify linkage areas across a major transportation corridor. One model was based on empirical habitat data, and the other two (opinion‐ and literature‐based) were based on expert information developed in a multicriteria decision‐making process. We validated the performance of the models with an independent data set. Four classes of highway linkage zones were generated. Class 3 linkages were the most accurate for mapping cross‐highway movement. Our tests showed that the model based on expert literature most closely approximated the empirical model, both in the results of statistical tests and the description of the class 3 linkages. In addition, the expert literature–based model was consistently more similar to the empirical model than either of two seasonal, expert opinion–based models. Among the expert models, the literature‐based model had the strongest correlation with the empirical model. Expert‐opinion models were less in agreement with the empirical model. The poor performance of the expert‐opinion model may be explained by an overestimation of the importance of riparian habitat by experts compared with the literature. A small portion of the empirical data to test the models was from the pre‐berry season and may have affected how well the model predicted linkage areas. Our empirical and expert models represent useful tools for resource and transportation planners charged with determining the location of mitigation passages for wildlife when baseline information is lacking and when time constraints do not allow for data collection before construction.

Isolation by Resistance

“…The model's most promising contribution, however, is its ability to generate indices of gene flow and genetic differentiation using large spatial datasets that provide only relative values of local demographic parameters. Rarely are actual deme sizes, population densities, or migration rates known for natural populations, but relative suitabilities of different habitat types and their permeabilities to movement can be readily generated using ecological data, published literature, or expert opinion (e.g., Boone and Hunter 1996;Vos et al 2001;Clevenger et al 2002;Arnaud 2003;Coulon et al 2004).…”

Fine-scale landscape genetics of the American badger (Taxidea taxus): disentangling landscape effects and sampling artifacts in a poorly understood species

“…For many species, however, we lack information about processes that might affect gene flow such as habitat preferences, distribution and population structure within a specific study area. Most commonly, expert opinion has been used as a proxy for relevant field data (Zeller et al, 2012) despite criticism over decreased accuracy (Pearce et al, 2001;Clevenger et al, 2002), inherent biases and inability to assess accuracy in expert opinion (Spear et al, 2010;Zeller et al, 2012). One potential solution to relying on expert opinion in poorly understood species is to employ alternative statistical methods that do not require extensive parameterization of landscape resistance models.…”

Use of Occupancy Models to Evaluate Expert Knowledge-based Species-Habitat Relationships

“…Experts draw from their observations, readings, discussions, and other means, and offer opinions or specific knowledge about ecological patterns and processes (Perera et al 2012). The purported species-habitat relationships from these experts are viewed as working hypotheses that serve as priors describing the initial state of knowledge, but are rarely tested empirically (Clevenger et al 2002, Johnson and Gillingham 2004, Low-Choy et al 2009, Schlossberg and King 2009. Tests are of interest because planners and decision makers draw from this initial state of knowledge to draft conservation plans and predict a species' response to conservation actions (e.g., Clevenger et al 2002, Zabel et al 2003, Low-Choy et al 2009, Allen et al 2011.…”