Predicting the future of species diversity: macroecological theory, climate change, and direct tests of alternative forecasting methods

Abstract

Accurate predictions of future shifts in species diversity in response to global change are critical if useful conservation strategies are to be developed. The most widely used prediction method is to model individual species niches from point observations and project these models forward using future climate scenarios. The resulting changes in individual ranges are then summed to predict diversity changes; multiple models can be combined to produce ensemble forecasts. Predictions based on environment-richness regressions are rarer. However, richness regression models, based on macroecological diversity theory, have a long track record of making reliable spatial predictions of diversity patterns. If these empirical theories capture true functional relationships between environment and diversity, then they should make consistent predictions through time as well as space and could complement individual species-based predictions. Here, we use climate change throughout the 20th century to directly test the ability of these different approaches to predict shifts of Canadian butterfly diversity. We found that all approaches performed reasonably well, but the most accurate predictions were made using the single best richness-environment regression model, after accounting for the effects of spatial autocorrelation. Spatially trained regression models based on macroecological theory accurately predict diversity shifts for large species assemblages. Global changes provide pseudo-experimental tests of those macroecological theories that can then generate robust predictions of future conditions.

Space can substitute for time in predicting climate-change effects on biodiversity

“…There are other good reasons to exercise caution when applying empirically calibrated models to project biodiversity responses to 21st century climate change, including the prospect of shifting realized niches and emergent species interactions, the challenge of extrapolating models to no-analog climates, and the overarching question of whether ecological systems are computationally irreducible (15,24,26,40). Nevertheless, this study complements studies that have focused on modeling changes in species richness (16,18) and adds support to the prospect of projecting impacts to biodiversity as a result of climate change.…”

“…Increasingly, space-for-time substitution is being applied in biodiversity modeling to project climate-driven changes in species distributions, species richness, and compositional turnover (7)(8)(9)(10)(11). Examination of transferability of models for individual species has exposed concerns regarding the projection of these spatial models across time (12)(13)(14)(15), and it has been suggested that models based on collective biodiversity properties might be more robust (9,16,17). However, the fundamental assumption that spatial relationships between climate and biodiversity can be used to project temporal trajectories of biodiversity under changing climates remains largely untested (but see refs.…”

Can Species Richness Patterns Be Interpolated From a Limited Number of Well-Known Areas? Mapping Diversity Using GLM and Kriging

“…Hortal et al 2011) or geostatistical methods (e.g. Fuentes 2001Hernández-Stefanoni et al 2011)-or both environmental responses and spatial structure altogether (Algar et al 2009) provide better predictions of species richness patterns; and (ii) investigate the processes causing such non-stationarity in detail. It could be argued that the effects of ecological assembly rules may, at least in part, account for the geographical non-stationarity in species richness gradients (Guisan & Rahbek 2011), so an investigation on the relationship between the errors in the extrapolation of species richness and differences in the-phylogenetic, functional or ecological-structure of the assemblages may provide insights on why it is so difficult to predict the geographical patterns of diversity.…”

Future of biodiversity in the Barents Region

“…When contacted, the developers of these scenarios lacked confidence in the projections themselves. We therefore had to resort to including these data as static variables, which is not uncommonly done to represent anthropogenic impacts in predictive studies (Algar et al 2009;Morueta-Holme et al 2010). Furthermore, a number of species, especially birds, were associated with coastal environments.…”