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To understand how ectotherms will respond to warming temperatures, we require information on thermal habitat quality at spatial resolutions and extents relevant to the organism. Measuring thermal habitat quality is either limited to small spatial extents, such as with ground‐based 3D operative temperature (Te) replicas, representing the temperature of the animal at equilibrium with its environment, or is based on microclimate derived from physical models that use land cover variables and downscale coarse climate data. We draw on aspects of both these approaches and test the ability of unoccupied aerial vehicle (UAV) data (optical RGB) to predict fine‐scale heterogeneity in sub‐canopy lizard (Anolis bicaorum) Te in tropical forest using random forest models. Anolis bicaorum is an endemic, critically endangered, species, facing significant threats of habitat loss and degradation, and work was conducted as part of a larger project. Our findings indicate that a model incorporating solely air temperature, measured at the centre of the 20 × 20 m plot, and ground‐based leaf area index (LAI) measurements, measured at directly above the 3D replica, predicted Te well. However, a model with air temperature and UAV‐derived canopy metrics performed slightly better with the added advantage of enabling the mapping of Te with continuous spatial extent at high spatial resolutions, across the whole of the UAV orthomosaic, allowing us to capture and map Te across the whole of the survey plot, rather than purely at 3D replica locations. Our work provides a feasible workflow to map sub‐canopy lizard Te in tropical environments at spatial scales relevant to the organism, and across continuous areas. This can be applied to other species and can represent species within the same community that have evolved a similar thermal niche. Such methods will be imperative in risk modelling of such species to anthropogenic land cover and climate change. [ABSTRACT FROM AUTHOR]

Abstract To understand how ectotherms will respond to warming temperatures, we require information on thermal habitat quality at spatial resolutions and extents relevant to the organism. Measuring thermal habitat quality is either limited to small spatial extents, such as with ground‐based 3D operative temperature (Te) replicas, representing the temperature of the animal at equilibrium with its environment, or is based on microclimate derived from physical models that use land cover variables and downscale coarse climate data. We draw on aspects of both these approaches and test the ability of unoccupied aerial vehicle (UAV) data (optical RGB) to predict fine‐scale heterogeneity in sub‐canopy lizard (Anolis bicaorum) Te in tropical forest using random forest models. Anolis bicaorum is an endemic, critically endangered, species, facing significant threats of habitat loss and degradation, and work was conducted as part of a larger project. Our findings indicate that a model incorporating solely air temperature, measured at the centre of the 20 × 20 m plot, and ground‐based leaf area index (LAI) measurements, measured at directly above the 3D replica, predicted Te well. However, a model with air temperature and UAV‐derived canopy metrics performed slightly better with the added advantage of enabling the mapping of Te with continuous spatial extent at high spatial resolutions, across the whole of the UAV orthomosaic, allowing us to capture and map Te across the whole of the survey plot, rather than purely at 3D replica locations. Our work provides a feasible workflow to map sub‐canopy lizard Te in tropical environments at spatial scales relevant to the organism, and across continuous areas. This can be applied to other species and can represent species within the same community that have evolved a similar thermal niche. Such methods will be imperative in risk modelling of such species to anthropogenic land cover and climate change.

Abstract The emergence of Russia as a major grain exporter is not only crucial for the world commercial agriculture and food security, but also for the country's economy. Here we examine the past-to-future thermal suitability for winter wheat (Triticum aestivum , L. 1753) cultivation over Russia and compare it with the recent trends of wheat yields and harvested area. The analyses use a multi-model ensemble median of the most updated bias-corrected outputs from five CMIP5 Earth System Models (1950–2099) under two representative concentration pathways (RCP 4.5 and RCP 8.5) and the Era-Interim dataset (1979–2016). Our results show that the thermal suitability has increased by ∼10 Mha per decade since 1980. Consistently, winter wheat yields and harvested area have also increased over the last decade by ∼0.5 t/ha and ∼4 Mha, respectively. Moreover, a potential for the Russian wheat sector may still be exploited if we consider the abandoned land (∼27 Mha) after the collapse of the Soviet Union. Our results also show that the increase in heat availability and the reduction of the frost constraint will likely move the thermal suitability toward the north-western and the Far East regions. Conversely, increases of extreme heat events are projected in the southern regions of Russia, which currently represent the most productive and intensively managed wheat cultivation area. Our findings imply both opportunities and risks for the Russian wheat sector that calls for sustainable and farsighted land management strategies to comprehensively face the consequences of global warming. [ABSTRACT FROM AUTHOR]