Predicting daily activity time through ecological niche modelling and microclimatic data

Abstract

Abstract Climate temporality is a phenomenon that affects species activity and distribution patterns across spatial and temporal scales. Despite the global availability of microclimatic data, their use to predict activity patterns and distributions remains scarce, particularly at fine temporal scales (e.g. < month). Predicting activity patterns based on climatic data may allow us to foresee some of the consequences of climate change, particularly for ectothermic vertebrates. The Gila monster exhibits marked daily and seasonal activity patterns linked to physiology and reproduction. Here we evaluate whether ecological niche models fitted using microclimate data can predict temporal activity patterns using the Gila monster Heloderma suspectum as a study system. Furthermore, we identified whether the activity patterns are related to physiological constraints. We used dated occurrences from museum specimens and human observations to generate and test ecological niche models using minimum volume ellipsoids. We generated hourly microclimatic data for each occurrence site for 10 years using the NicheMapR package. For ecological niche modelling, we compared the traditional seasonal approach versus a daily activity pattern strategy for model construction. We tested both using the omission rate of independent observations (citizen science data). Finally, we tested whether unimodal and bimodal activity patterns for each season could be recreated through ecological niche modelling and whether these patterns followed known physiological constraints. The unimodal and bimodal activity patterns previously reported directly from tracking individuals across the year were recovered using niche modelling and microclimate across the species' geographical range. We found that upper thermal tolerances can explain the daily activity patterns of this species. We conclude that ecological niche models trained with microclimatic data can be used to predict activity patterns at high temporal resolutions, particularly on ectotherm species of arid zones coping with rapid climate modifications. Furthermore, the use of high temporal resolution variables can lead to a better niche delimitation, enhancing the results of any research objective that uses correlative models.