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Climate change disproportionately affects northern and alpine environments, with faster rates of warming than the global average. Because alpine and northern species are particularly well adapted to cool temperatures, most species must modify their behavior when temperatures exceed a critical threshold. Evaluating how temperature increases affect species inhabiting northern and alpine environments is therefore essential to understand the effects of projected climate change on these ecosystems. We analyzed the influence of temperature on the activity patterns and habitat selection of four populations of a cold-adapted, mountain specialist, the mountain goat (Oreamnos americanus). We collected GPS location and activity sensor data during 2010–2019 from 223 mountain goats from two distinct ecotypes: coastal and continental. Using a resource selection modeling approach, we determined that mountain goats of both ecotypes decreased selection for alpine meadows when temperatures increased. Reduced selection for open, forage rich habitat was associated with increased selection for habitat dominated by snow/ice patches in coastal areas, and by forests in continental sites. Mountain goats in continental environments selected higher elevation habitats only when temperature increased, whereas goats in coastal environments selected higher elevation habitat at all temperatures. Mountain goats of both ecotypes reduced the proportion of time spent active when temperatures increased during the middle of the day. Our study reveals that mountain goats use diverse tactics to mitigate thermal stress, and that these tactics vary between ecotypes, highlighting the need for considering adaptation to specific environments within a species when assessing climate change impacts on populations.

The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both image acquisition and analysis phases. Development of artificial intelligence-assisted super-resolution microscopy is often limited by access to large biological datasets, as well as by difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic stimulated emission depletion microscopy simulation platform, pySTED, for the development and deployment of artificial intelligence strategies for super-resolution microscopy. pySTED integrates theoretically and empirically validated models for photobleaching and point spread function generation in stimulated emission depletion microscopy, as well as simulating realistic point-scanning dynamics and using a deep learning model to replicate the underlying structures of real images. This simulation environment can be used for data augmentation to train deep neural networks, for the development of online optimization strategies and to train reinforcement learning models. Using pySTED as a training environment allows the reinforcement learning models to bridge the gap between simulation and reality, as showcased by its successful deployment on a real microscope system without fine tuning. Stimulated emission depletion microscopy is a super-resolution imaging technique that utilizes point scanning in fluorescence microscopy. pySTED is developed to aid in the development and benchmarking of optical microscopy experiments, testing it in both synthetic and real settings.

The integration of artificial intelligence (AI) into microscopy systems significantly enhances performance, optimizing both the image acquisition and analysis phases. Development of AI-assisted super-resolution microscopy is often limited by the access to large biological datasets, as well as by the difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic STED simulation platform, pySTED, for the development and deployment of AI-strategies for super-resolution microscopy. The simulation environment provided by pySTED allows the augmentation of data for the training of deep neural networks, the development of online optimization strategies, and the training of reinforcement learning models, that can be deployed successfully on a real microscope.

Simultaneous super-resolution imaging of multiple fluorophores remains a major challenge in STimulated Emission Depletion (STED) microscopy due to spectral overlap of STED-compatible fluorophores. The combination of STED microscopy and Fluorescence Lifetime Imaging Microscopy (FLIM) offers a powerful alternative for super-resolved, multiplexed imaging of biological samples but is hindered by lifetime convergence at high depletion powers. Here, we present an analysis method, two-species Separation of Photons by LIfetime Tuning (SPLIT)-STED, that uses a linear system of equations in phasor-based STED-FLIM to enhance both fluorophore unmixing and spatial resolution. It defines the fluorescence signal as a mixture of three lifetime components: the two target fluorophores and a short-lifetime contribution from undepleted peripheral fluorescence photons. Two-species SPLIT-STED disentangles overlapping lifetimes and selectively filters low-resolution signal. The method enables accurate unmixing of spectrally overlapping fluorophores and, by enhancing resolution through lifetime-based filtering, allows the use of lower depletion powers, thereby improving fluorescence lifetime separation.