The effects of climate change on Australia’s only endemic Pokémon: Measuring bias in species distribution models

Abstract

1. Species distribution models (SDMs) are frequently used to predict the effects of climate change on species of conservation concern. Biases inherent in the process of constructing SDMs and transferring them to new climate scenarios may result in undesirable conservation outcomes. We explore these issues and demonstrate new methods to estimate biases induced by the design of SDM studies. We present these methods in the context of estimating the effects of climate change on Australia's only endemic Pokémon.2. Using a citizen science dataset, we build species distribution models for Garura kangaskhani to predict the effects of climate change on the suitability of habitat for the species. We demonstrate a novel Monte Carlo procedure for estimating the biases implicit in a given study design, and compare the results seen for Pokémon to those seen from our Monte Carlo tests as well as previous studies in the same region using both simulated and real data.3. Our models suggest that climate change will impact the suitability of habitat for G. kangaskhani, which may compound the effects of threats such as habitat loss and their use in blood sport. However, we also find that using SDMs to estimate the effects of climate change can be accompanied by biases so strong that the data themselves have minimal impact on modelling outcomes. 4. We show that the direction and magnitude of bias in estimates of climate change impacts are affected by every aspect of the modelling process, and suggest that bias estimates should be included in future studies of this type. Given the widespread use of SDMs, systemic biases could have substantial financial and opportunity costs. By demonstrating these biases and presenting a novel statistical tool to estimate them, we hope to provide a more secure future for G. kangaskhani and the rest of the world's biodiversity.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Predicting the Potential Distribution of Endangered and Rare Satyrium Species in China under Climate Change

“…The plant distribution patterns can be detected by the environmental factors like temperature and precipitation (26,27). Geographical distribution of species can be determined by the climate change and geographical distribution of species also response to change in climate (28). The structures of terrestrial ecosystems have been changed by global warming, and the habitat and geographical distribution functions of species have been changed by the structure of terrestrial ecosystems in turn (29).…”

Incorporating sampling bias into permutation tests for niche and distribution models

“…10 km at the equator) (Fick and Hijmans 2017). For simulations testing the Warren et al (2021b) method of measuring methodological bias in transferred models, we projected each model to the same set of variables for the year 2070, using the CMIP5 climate model and representative concentration pathway (RCP) 8.5.…”

“…Failing to account for these biases can result in models that parameterize the environmental correlates of sampling bias in addition to, or even at the expense of, estimating species’ environmental tolerances. While methods have been developed that attempt to either reduce the bias in these data sets (e.g., Aiello-Lammens et al 2015) or correct for bias in the modeling process (e.g., Phillips et al 2009; Ranc et al 2017; Vollering et al 2019; Zurell et al 2010; Pagel and Schurr 2012; Warren et al 2021b; Osborne et al 2022), dealing with spatial sampling bias remains one of the most difficult and confusing aspects of ENM studies.…”

“…By ignoring the pattern of spatial sampling bias, these null distributions of points therefore most likely underestimate the level of spatial autocorrelation due to spatial sampling bias that would be seen in a typical uninformative data set, which may be problematic for permutation tests. This could result in tests that overestimate model quality (Raes and ter Steege 2007; Bohl et al 2019; Warren et al 2021a), underestimate bias in transferring models (Warren et al 2021b), and mistake shared sampling bias for ecological similarity (Warren et al 2008).…”

“…We focus on scenarios in which the occurrence of the simulated species is unrelated to the environmental predictors, but where there are varying levels and spatial scales of geographic sampling bias. We hypothesize that in this case failing to account for spatial sampling bias will lead to increased type I error for the hypothesis tests regarding niche evolution and model fit (Warren et al 2008; Warren et al 2021a; Raes and ter Steege 2007; Bohl et al 2019), and will lead to poor estimates of methodological bias using the method from bias estimation method Warren et al (2021b). We then present a new method that includes spatial sampling bias in the generation of random points, rather than overall levels of autocorrelation in the empirical occurrence data as in Osborne et al (2022), and evaluate its effects on type 1 error and estimates of methodological bias.…”