Modeling plant ranges over 75 years of climate change in California, USA: temporal transferability and species traits

Abstract

Species distribution model (SDM) projections under future climate scenarios are increasingly being used to inform resource management and conservation strategies. A critical assumption for projecting climate change responses is that SDMs are transferable through time, an assumption that is largely untested because investigators often lack temporally independent data for assessing transferability. Further, understanding how the ecology of species influences temporal transferability is critical yet almost wholly lacking. This raises two questions. (1) Are SDM projections transferable in time? (2) Does temporal transferability relate to species ecological traits? To address these questions we developed SDMs for 133 vascular plant species using data from the mountain ranges of California (USA) from two time periods: the 1930s and the present day. We forecast historical models over 75 years of measured climate change and assessed their projections against current distributions. Similarly, we hindcast contemporary models and compared their projections to historical data. We quantified transferability and related it to species ecological traits including physiognomy, endemism, dispersal capacity, fire adaptation, and commonness. We found that non‐endemic species with greater dispersal capacity, intermediate levels of prevalence, and little fire adaptation had higher transferability than endemic species with limited dispersal capacity that rely on fire for reproduction. We demonstrate that variability in model performance was driven principally by differences among species as compared to model algorithms or time period of model calibration. Further, our results suggest that the traits correlated with prediction accuracy in a single time period may not be related to transferability between time periods. Our findings provide a priori guidance for the suitability of SDM as an approach for forecasting climate change responses for certain taxa.

Alternative biological assumptions strongly influence models of climate change effects on mountain gorillas

“…The predictions for future conditions by the 3-variable standard SDMs were radically different from the 18-variable models. Methods to decide which and how many variables to use for predicting climate change effects are not well established, as such methods tend to rely on cross-validation of known observations for current conditions, which does not establish a model's ability to predict across time (Dobrowski et al 2011), but methods that seek to reduce the number of variables used (e.g., Warren and Seifert 2011) seem promising.…”

Climatic Risk and Distribution Atlas of European Bumblebees

“…At best, the model predictions should be evaluated against an independent data set. However, assessing predictive ability of a model for future conditions is not possible, but transferring models from historical conditions to current conditions and vice versa could be a solution (Dobrowski et al, 2011). However, in many cases, which apply also to the bumblebee data, historical data often suffer from lower sampling intensities, at least in some regions.…”

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

“…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.…”