Mechanistic forecasts of species responses to climate change: The promise of biophysical ecology.

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 based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shifting constraints, and novel abiotic or biotic environments.

Regional and seasonal activity predictions for fall armyworm in Australia.

Since 2016, the fall armyworm (FAW), Spodoptera frugiperda, has undergone a significant range expansion from its native range in the Americas, to continental Africa, Asia, and in February 2020, mainland Australia. The large dispersal potential of FAW adults, wide host range of immature feeding stages, and unique environmental conditions in its invasive range creates large uncertainties in the expected impact on Australian plant production industries. Here, using a spatial model of population growth and spread potential informed by existing biological and climatic data, we simulate seasonal population activity potential of FAW, with a focus on Australia's grain production regions. Our results show that, in Australia, the large spread potential of FAW will allow it to exploit temporarily favourable conditions for population growth across highly variable climatic conditions. It is estimated that FAW populations would be present in a wide range of grain growing regions at certain times of year, but importantly, the expected seasonal activity will vary markedly between regions and years depending on climatic conditions. The window of activity for FAW will be longer for growing regions further north, with some regions possessing conditions conducive to year-round population survival. Seasonal migrations from this permanent range into southern regions, where large areas of annual grain crops are grown annually, are predicted to commence from October, i.e. spring, with populations subsequently building up into summer. The early stage of the FAW incursion into Australia means our predictions of seasonal activity potential will need to be refined as more Australian-specific information is accumulated. This study has contributed to our early understanding of FAW movement and population dynamics in Australia. Importantly, the models established here provide a useful framework that will be available to other countries should FAW invade in the future. To increase the robustness of our model, field sampling to identify conditions under which population growth occurs, and the location of source populations for migration events is required. This will enable accurate forecasting and early warning to farmers, which should improve pest monitoring and control programs of FAW.