Linking patterns to process: incorporating physiological mechanism into climate-based distribution models

Species distribution models (SDMs) have played a pivotal role in predicting how species might respond to climate change. These models most often rely on correlative methods, whereby a statistical relationship between species’ known occurrences and the climate of those locations is determined and then extrapolated to predict future distributions under climate change scenarios. Such modelling approaches, which seek to find and recreate patterns in species distributions, are not directly associated with the physiological processes that ultimately underlie species’ responses to climate. By incorporating these processes more explicitly into SDMs, the proximal limitations on species distributions can be better understood and subsequent predictions will be more robust over space and time. This thesis presents research to promote and support the integration of physiological processes into SDMs. It explores major themes in model construction and use and demonstrates how process-based (mechanistic) models might be applied to predict future crop suitability. In Chapter 2, I review the plant SDM literature and find that physiologically important variables are frequently neglected in models. Ten physiologically relevant variables for plants are identified and in Chapter 3, I present a new global climate classification (CCS) that accounts for variation in these aspects of climate. I show how the popular Köppen CCSs, for which boundaries of zones were chosen to reflect major vegetation patterns, do not entirely reflect the physiological processes that determine plant distributions. I discuss how predictions of climate-driven changes in plant distributions may be unreliable in areas where zone assignment using physiologically relevant variables is different to that of the Köppen systems. In Chapter 4, I demonstrate the use of microclimate modelling techniques to generate 100m spatial resolution climate data for present and future time periods. I use these data to run the mechanistic crop model WOrld FOod STudies (WOFOST) and show how, by capturing spatial variation in climate suitability, microclimate data could provide better approximations of predicted yields and inform agricultural decision-making. Then, in Chapter 5, I show that incorporating interannual variability into climate suitability assessments or understanding the extent to which average climate data might obscure this variance is also important to consider, even when using mechanistic models. Finally, I consider how mechanistic crop models might be applied to inform agricultural decisions. In Chapter 6, I demonstrate how the results from a mechanistic crop model may be combined with an expert-informed qualitative assessment of crop suitability to give a holistic understanding of the best crops to grow based on climatic and non-climatic factors. In Chapter 7, I examine how climate-driven changes to crop suitability may lead to conflict between agricultural land use and conservation. I model global crop suitability for current and future time periods and show that agricultural expansion is a major threat to remaining wilderness. I conclude that to protect wilderness and its many values, agricultural systems will need to be transformed. Overall, this thesis shows why physiological process should become central in endeavours to understand the effects of climate change on species distributions and presents methods to achieve this in ecological research. It shows how reliable predictions of the impacts of climate change on crop suitability could help reconcile food security and conservation goals.Cornwall Counci