Patterns of crop cover under future climates.

We study changes in crop cover under future climate and socio-economic projections. This study is not only organised around the global and regional adaptation or vulnerability to climate change but also includes the influence of projected changes in socio-economic, technological and biophysical drivers, especially regional gross domestic product. The climatic data are obtained from simulations of RCP4.5 and 8.5 by four global circulation models/earth system models from 2000 to 2100. We use Random Forest, an empirical statistical model, to project the future crop cover. Our results show that, at the global scale, increases and decreases in crop cover cancel each other out. Crop cover in the Northern Hemisphere is projected to be impacted more by future climate than the in Southern Hemisphere because of the disparity in the warming rate and precipitation patterns between the two Hemispheres. We found that crop cover in temperate regions is projected to decrease more than in tropical regions. We identified regions of concern and opportunities for climate change adaptation and investment.

Improving the use of species distribution models in conservation planning and management under climate change.

Choice of variables, climate models and emissions scenarios all influence the results of species distribution models under future climatic conditions. However, an overview of applied studies suggests that the uncertainty associated with these factors is not always appropriately incorporated or even considered. We examine the effects of choice of variables, climate models and emissions scenarios can have on future species distribution models using two endangered species: one a short-lived invertebrate species (Ptunarra Brown Butterfly), and the other a long-lived paleo-endemic tree species (King Billy Pine). We show the range in projected distributions that result from different variable selection, climate models and emissions scenarios. The extent to which results are affected by these choices depends on the characteristics of the species modelled, but they all have the potential to substantially alter conclusions about the impacts of climate change. We discuss implications for conservation planning and management, and provide recommendations to conservation practitioners on variable selection and accommodating uncertainty when using future climate projections in species distribution models.

A tool for simulating and communicating uncertainty when modelling species distributions under future climates.

Tools for exploring and communicating the impact of uncertainty on spatial prediction are urgently needed, particularly when projecting species distributions to future conditions.We provide a tool for simulating uncertainty, focusing on uncertainty due to data quality. We illustrate the use of the tool using a Tasmanian endemic species as a case study. Our simulations provide probabilistic, spatially explicit illustrations of the impact of uncertainty on model projections. We also illustrate differences in model projections using six different global climate models and two contrasting emissions scenarios.Our case study results illustrate how different sources of uncertainty have different impacts on model output and how the geographic distribution of uncertainty can vary.Synthesis and applications: We provide a conceptual framework for understanding sources of uncertainty based on a review of potential sources of uncertainty in species distribution modelling; a tool for simulating uncertainty in species distribution models; and protocols for dealing with uncertainty due to climate models and emissions scenarios. Our tool provides a step forward in understanding and communicating the impacts of uncertainty on species distribution models under future climates which will be particularly helpful for informing discussions between researchers, policy makers, and conservation practitioners.