Adding small species to the big picture: Species distribution modelling in an age of landscape scale conservation. (January 2018)

• Record Type: Journal Article
• Authors: Eaton, Sally; Ellis, Christopher; Genney, David; Thompson, Richard; Yahr, Rebecca; Haydon, Daniel T.
• Is Part Of: Biological conservation. Volume 217(2018)
• Page Start: 251
• Page End: 258
• Publication Date: 2018-01
• Subjects: AUC area under the receiver operating curve -- CT Classification Tree -- GAM Generalised Additive Model -- NPMR nonparametric multiplicative regression -- NWSS Native Woodland Survey Scotland -- SLSC's small/less charismatic species -- SDM species distribution model -- TSS true skill statistic; Compound variables -- Generalised Additive Model (GAM) -- Lichen epiphytes -- Native Woodland Survey Scotland (NWSS) -- Temperate rainforests; Conservation of natural resources -- Periodicals; Nature conservation -- Periodicals; Ecology -- Periodicals; Environment -- Periodicals; Environmental Pollution -- Periodicals; Electronic journals; 333.9516
• DOI: 10.1016/j.biocon.2017.11.012
• Languages: English
• ISSNs: 0006-3207

Abstract

Abstract: A recent shift in conservation policy from the site scale to the ecosystem or landscape scale requires underpinning by large-scale species distribution data. This poses a significant challenge in conserving small/less charismatic species (SLCS's) whose often cryptic nature can result in spatially restricted sampling, thus preventing landscape scale conservation projects from being realised for these ecologically important groups. Species distribution models (SDMs) can provide a powerful tool to bridge this gap. However, in the case of SLCS's (here lichen epiphytes in temperate rainforests of western Scotland are used as a model system), direct predictor variables exist at micro-scales (millimetres to centimetres), which are not extensively available in landscape-scale datasets. Here we identify a group of well-mapped larger-scale 'compound variables' which capture the effect of multiple direct predictors (such as bark pH and topography), and test whether they can be successfully used to predict species distributions at the landscape scale, circumventing the need for direct (micro-scale) predictor data. By testing the SDMs more widely within western Scotland, accurate predictions of species presence/absence could be made throughout the region for 5 of the 9 lichen epiphytes, making these SDMs extremely valuable as a conservation planning tool. Species distribution models utilising compound variables as predictors offer a solution to the paucity of species distributional data for SLCS's, and present a valuable resource in conservation planning for such species. The importance of testing the SDMs outside of a training region to prevent prediction error is highlighted however.