Validation of species–climate impact models under climate change

Abstract

Increasing concern over the implications of climate change for biodiversity has led to the use of species-climate envelope models to project species extinction risk under climatechange scenarios. However, recent studies have demonstrated significant variability in model predictions and there remains a pressing need to validate models and to reduce uncertainties. Model validation is problematic as predictions are made for events that have not yet occurred. Resubstituition and data partitioning of present-day data sets are, therefore, commonly used to test the predictive performance of models. However, these approaches suffer from the problems of spatial and temporal autocorrelation in the calibration and validation sets. Using observed distribution shifts among 116 British breeding-bird species over the past $ 20 years, we are able to provide a first independent validation of four envelope modelling techniques under climate change. Results showed good to fair predictive performance on independent validation, although rules used to assess model performance are difficult to interpret in a decision-planning context. We also showed that measures of performance on nonindependent data provided optimistic estimates of models' predictive ability on independent data. Artificial neural networks and generalized additive models provided generally more accurate predictions of species range shifts than generalized linear models or classification tree analysis. Data for independent model validation and replication of this study are rare and we argue that perfect validation may not in fact be conceptually possible. We also note that usefulness of models is contingent on both the questions being asked and the techniques used. Implementations of species-climate envelope models for testing hypotheses and predicting future events may prove wrong, while being potentially useful if put into appropriate context.

Macroclimate Determines the Global Range Limit of Aedes aegypti

“…In order to avoid pervasive effects of spatial autocorrelation, species distribution models should be evaluated with data spatially independent from the calibration dataset (Araú jo et al 2005). Accordingly, we evaluated how well our model predicted the distribution of A. aegypti in the Americas based on the macroclimates the species occupied on the rest of the world.…”

Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods

“…These or similar variables are typically used in distribution modelling exercises (e.g. Thomas et al 2004;Thuiller et al 2005;Araujo et al 2005;Beale et al 2008b;Huntley et al 2008) not because they are always expected to directly impact bird distributions, but they are perceived to have strong indirect effects on birds and other taxa through effects on food availability or habitat type (Araujo et al, 2005;Beale et al, 2008b;Huntley et al, 2008). Other biologically relevant climate variables could also be used but are usually strongly correlated with one 7 or other of these and have little affect on the strength of associations realised (Beale et al, 2008b).…”

Habitat changes and changing predatory habits in North American fossil canids

“…However, although predictive models can give important clues to short-term biotic reactions 4,5 , only the fossil record can provide empirical data on evolutionary responses during long periods of profound climatic and environmental change [6][7][8][9] . Here we demonstrate that the evolution of predatory behaviour in North American canids (for example, foxes and wolves; family Canidae) has been influenced by climatic and environmental transformation over the later Cenozoic (the past B37 million years (Myr ago)).…”