A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models

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Predicting biological invasions remains a challenge to applied ecologists and limits pre‐emptive management of biosecurity threats. In the last decade, spotted‐wing drosophila Drosophila suzukii has emerged as an internationally significant agricultural pest as it rapidly spread across Europe and the Americas. However, the underlying drivers of its global invasion remain unstudied, while countries like Australia, presently free from D. suzukii, require robust estimates of spread and establishment potential to aid development of effective preparedness strategies. Here, we analysed the ecoclimatic and human‐mediated drivers of the global invasion of D. suzukii to understand historical spread patterns and improve forecasts of future spread potential. Using a modular approach, climate‐driven population dynamics were linked in space via dispersal processes to simulate spread at continental scales. Combined with biological parameters measured in laboratory studies, the spread model was parameterized and validated on international spread data. Model accuracy was high and was able to predict 83% of regional presence–absence through time in the United States and, without further model fitting, 78% of the variation in the Europe incursion. Omitting human‐assisted spread from the model reduced predictability by over 20%, highlighting the large anthropogenic influence in this modern biological invasion. Economic activity (GDP) rather than human population density was more strongly associated with human‐mediated spread. Simulations predicted that eastern Australian coastal regions, particularly those near major cities with high economic activity, will result in the fastest spread of D. suzukii. Synthesis and applications. Incursions of Drosophila suzukii into Australia will have significant consequences for horticultural industries with the predicted speed of spread making eradication programs extremely difficult. However, the identified areas of significant fruit production, and high environmental suitability and economic activity will form a logical means for prioritizing industry preparedness. In light of our findings, a key component of preparedness strategies will be the ability of fruit producers to rapidly transition to effective management of D. suzukii.

Climate is a major factor determining the distribution of plant species. Correlative models are frequently used to model the relationships between species distributions and climatic drivers but, increasingly, their use for prediction in novel scenarios such as climate change is being questioned. Mechanistic models, where processes limiting plant distribution are explicitly included, are regarded as preferable but more challenging. The availability of tools for simulating microclimates with high spatial and temporal definition has also opened new possibilities for simulating the limiting environmental stresses experienced by plant over their ontogeny. However, the field of mechanistic species distribution modelling is relatively new and the tools and theory for constructing these models are underdeveloped. In this paper we explore the potential for using a Dynamic Energy Budget model of organism growth integrated with microclimate and photosynthesis models. We model the interactions of plant growth and microclimatic stressors over the life stages of plant growth, and scale them up to demonstrate predictions of distribution at the continental scale. We develop the model using Julia, a new language for scientific computing, as a set of generic modelling packages. These have a modular, toolkit structure that has the potential to increase the efficiency and transparency of developing mechanistic SDMs.

Climate is a major factor determining the distribution of plant species. Correlative models are frequently used to model the relationships between species distributions and climatic drivers but, increasingly, their use for prediction in novel scenarios such as climate change is being questioned. Mechanistic models, where processes limiting plant distribution are explicitly included, are regarded as preferable but more challenging.The availability of tools for simulating microclimates with high spatial and temporal definition has also opened new possibilities for simulating the limiting environmental stresses experienced by plant over their ontogeny. However, the field of mechanistic species distribution modelling is relatively new and the tools and theory for constructing these models are underdeveloped.In this paper we explore the potential for using a Dynamic Energy Budget model of organism growth integrated with microclimate and photosynthesis models. We model the interactions of plant growth and microclimatic stressors over the life stages of plant growth, and scale them up to demonstrate predictions of distribution at the continental scale. We develop the model using Julia, a new language for scientific computing, as a set of generic modelling packages. These have a modular, toolkit structure that has the potential to increase the efficiency and transparency of developing mechanistic SDMs.