Predicting animal movements and spatial distributions is crucial for our comprehension of ecological processes and provides key evidence for conserving and managing populations, species and ecosystems. Notwithstanding considerable progress in movement ecology in recent decades, developing robust predictions for rapidly changing environments remains challenging. To accurately predict the effects of anthropogenic change, it is important to first identify the defining features of human‐modified environments and their consequences on the drivers of animal movement. We review and discuss these features within the movement ecology framework, describing relationships between external environment, internal state, navigation and motion capacity. Developing robust predictions under novel situations requires models moving beyond purely correlative approaches to a dynamical systems perspective. This requires increased mechanistic modelling, using functional parameters derived from first principles of animal movement and decision‐making. Theory and empirical observations should be better integrated by using experimental approaches. Models should be fitted to new and historic data gathered across a wide range of contrasting environmental conditions. We need therefore a targeted and supervised approach to data collection, increasing the range of studied taxa and carefully considering issues of scale and bias, and mechanistic modelling. Thus, we caution against the indiscriminate non‐supervised use of citizen science data, AI and machine learning models. We highlight the challenges and opportunities of incorporating movement predictions into management actions and policy. Rewilding and translocation schemes offer exciting opportunities to collect data from novel environments, enabling tests of model predictions across varied contexts and scales. Adaptive management frameworks in particular, based on a stepwise iterative process, including predictions and refinements, provide exciting opportunities of mutual benefit to movement ecology and conservation. In conclusion, movement ecology is on the verge of transforming from a descriptive to a predictive science. This is a timely progression, given that robust predictions under rapidly changing environmental conditions are now more urgently needed than ever for evidence‐based management and policy decisions. Our key aim now is not to describe the existing data as well as possible, but rather to understand the underlying mechanisms and develop models with reliable predictive ability in novel situations.

Current plant – herbivore interaction models and experiments with mammalian herbivores grazing plant monocultures show the superiority of a maximizing forage quality strategy (MFQ) over a maximizing intake strategy (MI). However, there is a lack of evidence whether grazers comply with the model predictions under field conditions. We assessed diet selection of sheep (Ovis aries) using plant functional traits in productive mesic vs. low-productivity dry species-rich grasslands dominated by resource-exploitative vs. resource-conservative species respectively. Each grassland type was studied in two replicates for two years. We investigated the first grazing cycle in a set of 288 plots with a diameter of 30 cm, i.e. the size of sheep feeding station. In mesic grasslands, high plot defoliation was associated with community weighted means of leaf traits referring to high forage quality, i.e. low leaf dry matter content (LDMC) and high specific leaf area (SLA), with a high proportion of legumes and the most with high community weighted mean of forage indicator value. In contrast in dry grasslands, high community weighted mean of canopy height, an estimate of forage quantity, was the best predictor of plot defoliation. Similar differences in selection on forage quality vs. quantity were detected within plots. Sheep selected plants with higher forage indicator values than the plot specific community weighted mean of forage indicator value in mesic grasslands whereas taller plants were selected in dry grasslands. However, at this scale sheep avoided legumes and plants with higher SLA, preferred plants with higher LDMC while grazing plants with higher forage indicator values in mesic grasslands. Our findings indicate that MFQ appears superior over MI only in habitats with a predominance of resource-exploitative species. Furthermore, plant functional traits (LDMC, SLA, nitrogen fixer) seem to be helpful correlates of forage quality only at the community level.