Climate change is negatively affecting many species. The increase in mean air temperature is often associated with shifts in distribution, changes in phenology, and local extinctions. Other factors that only partially correlate with air temperature, like water shortage, may also contribute to the negative consequences of climate change. Although the effect of temperature on lizards’ ecophysiology is highly studied, many lizards are also at risks of increased water loss and dehydration, which are predicted to increase under climate change. Here we aimed for the first time to explore if lacertid lizards exposed to dehydration thermoregulate less precisely than hydrated lizards and if dehydrated lizards are less active, change the daily pattern of thermoregulation and balance water balance against thermoregulation. We exposed four lizard species with differences in the thermal preference to thermal gradients with or without a source of water. We measured preferred body temperatures, daily pattern of thermoregulation, and the use of space. Dehydration negatively affected thermoregulation in all investigated species. Dehydrated lizards reduced their preferred body temperature and showed a species-specific pattern of hourly change in thermal preference. Furthermore, they more frequently used the colder parts of the gradients and spent more time hidden. Lizards experiencing dehydration may suffer a reduction in survival and fitness because of poor thermoregulation. Similarly, they may spend more time hidden, waiting for more favourable weather conditions. Such inactivity may carry ecological costs especially in those regions that undergo either short or prolonged periods of droughts.

Species distribution models have grown in complexity by incorporating fine‐scale variables, including data on microclimate, physiology and species interactions. Recent studies have acknowledged the importance of the spatial scale by including higher resolution maps and more complex climatic variables. However, models rarely consider the consequences of including data related to time. Indeed, species phenology – and potential shifts in phenology due, for example, to climate change – is potentially one of the most neglected aspects of ecological modelling. We present a literature review of relevant phenological aspects at different temporal scales and across several taxa. Such elements should be considered to define better the environmental niche and project present, future and past distribution models. We considered the available studies on plants, insects, reptiles, birds and mammals to evaluate how they dealt with the phenology of the investigated species, as well as the phenology of other resources and interacting species, to infer present, past and future projections. Here we focus on four main phenological aspects that, if not considered, may easily bias any projection, namely: 1) phenology can be accompanied by a shift in distribution within the year (e.g. migratory species); 2) activity may be restricted to a portion of the year (e.g. most ectotherms from temperate climates); 3) survival and reproduction success may depend on the synchrony with other species phenology (e.g. plants–pollinators interactions); 4) changes in climatic conditions can lead to shifts in phenology (e.g. anticipated or delayed blooms or changes in migration timing). In this review, we show how neglecting such factors may quickly lead to project a biased distribution. Finally, we provide a guide on evaluating whether the case study may be affected by such factors and what actions may improve the models.

Photographic capture–recapture is a valuable tool for obtaining demographic information on wildlife populations due to its noninvasive nature and cost‐effectiveness. Recently, several computer‐aided photo‐matching algorithms have been developed to more efficiently match images of unique individuals in databases with thousands of images. However, the identification accuracy of these algorithms can severely bias estimates of vital rates and population size. Therefore, it is important to understand the performance and limitations of state‐of‐the‐art photo‐matching algorithms prior to implementation in capture–recapture studies involving possibly thousands of images. Here, we compared the performance of four photo‐matching algorithms; Wild‐ID, I3S Pattern+, APHIS, and AmphIdent using multiple amphibian databases of varying image quality. We measured the performance of each algorithm and evaluated the performance in relation to database size and the number of matching images in the database. We found that algorithm performance differed greatly by algorithm and image database, with recognition rates ranging from 100% to 22.6% when limiting the review to the 10 highest ranking images. We found that recognition rate degraded marginally with increased database size and could be improved considerably with a higher number of matching images in the database. In our study, the pixel‐based algorithm of AmphIdent exhibited superior recognition rates compared to the other approaches. We recommend carefully evaluating algorithm performance prior to using it to match a complete database. By choosing a suitable matching algorithm, databases of sizes that are unfeasible to match “by eye” can be easily translated to accurate individual capture histories necessary for robust demographic estimates.