Generality of Models that Predict the Distribution of Species: Conservation Activity and Reduction of Model Transferability for a Threatened Bustard

Abstract

Predictive models can help clarify the distribution of poorly known species but should display strong transferability when applied to independent data. Nevertheless, model transferability for threatened tropical species is poorly studied. We built models predicting the incidence of the critically endangered Bengal Florican (Houbaropsis bengalensis) within the Tonle Sap (TLS) floodplain, Cambodia. Separate models were constructed with soil, land-use, and landscape data and species incidence sampled over the entire floodplain (12,000 km(2)) and from the Kompong Thom (KT) province (4000 km(2)). In each case, the probability of Bengal Florican presence within randomly selected 1 x 1 km squares was modeled by binary logistic regression with multimodel inference. We assessed the transferability of the KT model by comparing predictions with observed incidence elsewhere in the floodplain. In terms of standard model-validation statistics, the KT model showed good spatial transferability. Nevertheless, it overpredicted florican presence outside the KT calibration region, classifying 491 km(2) as suitable habitat compared with 237 km(2) predicted as suitable by the TLS model. This resulted from higher species incidence within the calibration region, probably owing to a program of conservation education and enforcement that has reduced persecution there. Because both research and conservation activity frequently focus on areas with higher density, such effects could be widespread, reducing transferability of predictive distribution models.

Where will conflicts between alien and rare species occur after climate and land-use change? A test with a novel combined modelling approach

“…Implications of the novel combined approach for nature conservation Predictive models have been used in a wide array of applications directed at evaluating and planning the conservation of valuable elements of biodiversity and ecosystems (Pearce and Ferrier 2001;Muñoz et al 2005;Gray et al 2008), as well as for forecasting their responses to environmental changes (Guisan and Theurillat 2000;Araújo et al 2005;Broennimann et al 2006). The more informative character of the combined models compared to the non-combined, traditional models, can make them particularly valuable for multiple applications in fields related to conservation, management and monitoring.…”

Modelling future changes of habitat and fauna in the Tonle Sap wetland of the Mekong

“…Published studies with the required information for our analysis were only available for selected animal species, including a limited number of fishes (Lamberts 2001;Lim et al 1999), water snakes (Brooks et al 2009), Bengal florican (Gray et al 2009), grey-headed fisheagle (Tingay et al 2010), aquatic invertebrates (Tanaka & Ohtaka 2009), and zooplankton/zoobenthos (Ohtaka et al 2010). Data for other species were inferred by synthesizing Tonle Sap biodiversity data with generally available lifehistory characteristics (Bonheur & Lane 2002;Campbell et al 2006;Davidson 2006).…”

“…Some fish species of the Tonle Sap are reliant on the nutrient-rich habitats made available during the flood pulse aquatic phase to complete their life-cycles; others require the refugial ponds created by the inundation of the habitat to survive the dry season. Various endangered bird species inhabit the Tonle Sap, most of which have particular habitat preferences; for example, the greyheaded fish-eagle (Ichthyophaga ichthyaetus) is typically found in forested areas (Tingay et al 2010), whereas the Bengal florican (Houbaropsis bengalensis) is predominantly found in grasslands (Gray et al 2009). The close linkage of these species to their habitats and the restrictions that this imposes suggests that flooding and annual cyclical changes of habitats in the Tonle Sap are crucial for maintaining biological productivity and biodiversity.…”

Combining citizen science, bioclimatic envelope models and observed habitat preferences to determine the distribution of an inconspicuous, recently detected introduced bee (Halictus smaragdulus Vachal Hymenoptera: Halictidae) in Australia

“…Peterson 2003Peterson , 2006Sutherst 2003), others have suggested that models will not be able to predict the full extent of invasions when species adapt or evolve to cope with new conditions or face a new suite of interacting species (Broennimann et al 2007;Broennimann and Guisan 2008). Indeed, there is increasing evidence that models cannot always be transferred to new geographic locations (Gray et al 2009;Randin et al 2006;Zanini et al 2009). …”