Transferability of Species Distribution Models: a Functional Habitat Approach for Two Regionally Threatened Butterflies

Abstract

Numerous models for predicting species distribution have been developed for conservation purposes. Most of them make use of environmental data (e.g., climate, topography, land use) at a coarse grid resolution (often kilometres). Such approaches are useful for conservation policy issues including reserve-network selection. The efficiency of predictive models for species distribution is usually tested on the area for which they were developed. Although highly interesting from the point of view of conservation efficiency, transferability of such models to independent areas is still under debate. We tested the transferability of habitat-based predictive distribution models for two regionally threatened butterflies, the green hairstreak (Callophrys rubi) and the grayling (Hipparchia semele), within and among three nature reserves in northeastern Belgium. We built predictive models based on spatially detailed maps of area-wide distribution and density of ecological resources. We used resources directly related to ecological functions (host plants, nectar sources, shelter, microclimate) rather than environmental surrogate variables. We obtained models that performed well with few resource variables. All models were transferable--although to different degrees--among the independent areas within the same broad geographical region. We argue that habitat models based on essential functional resources could transfer better in space than models that use indirect environmental variables. Because functional variables can easily be interpreted and even be directly affected by terrain managers, these models can be useful tools to guide species-adapted reserve management.

Outstanding Challenges in the Transferability of Ecological Models

“…The process of transferring models typically stems from the need to support resource management in the face of pervasive data deficiencies, limited research funding, and accelerating global change [5]. Spatial transfers have been used to guide the design of protected areas, search for species on the brink of extinction, inform species relocations or reintroductions, outline hotspots of invasive pests, design field sampling campaigns, and assist the regulation of human activities (e.g., [78,79]). For instance, cetacean density models developed off the east coast of the United States were recently extrapolated throughout the western North Atlantic high seas to assist the management of potentially harmful sonar exercises performed by the military [80].…”

Environmental Correlation Structure and Ecological Niche Model Projections

“…Local adaptations, biotic interactions, sink populations, and historical constraints all can reduce transferability of models (Randin et al 2006;Staruss and Biedermann 2007;Vanreusel et al 2007). However, besides these factors, when working with correlative models and indirect variables, a more basic consideration is needed: the maintenance of correlation structure of the set of factors.…”

Assessing the potential of translocating vulnerable forest birds by searching for novel and enduring climatic ranges

“…Lastly, we ensured the models behaved in expected ways by projecting distributions on a set of diverse climate scenarios including a “cooling” scenario which showed an expected increase in the range of species at lower elevations, demonstrating our models are not inherently pessimistic or with low transferability. For presence‐only SDM techniques, model transferability improves based on the suitability and relevance of selected predictors (Randin et al., 2006; Vanreusel, Maes, & Van Dyck, 2007). The well‐documented link of temperature and precipitation to avian malaria and consequently native bird distribution (Ahumada et al., 2004; LaPointe, Atkinson, & Samuel, 2012; LaPointe, Goff, & Atkinson, 2010) further ensures high model transferability of our SDMs.…”