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.

Biological and Environmental Sciences Faculty of Natural Sciences University of Stirling Stirling UK

Biomathematics and Statistics Scotland Edinburgh UK

British Trust for Ornithology UK

CEFE Univ Montpellier CNRS EPHE IRD Montpellier France

Centre for Biological Diversity School of Biology University of St Andrews St Andrews UK

Department of Biodiversity and Environmental Management University of León León Spain

Department of Biology Ecology and Evolution University of Liege Liege Belgium

Department of Biosciences Swansea University Swansea UK

Department of Geography Durham University Durham UK

Department of Wildlife Fish and Environmental Studies Swedish University of Agricultural Sciences Umeå Sweden

Faculty of Tropical AgriSciences Czech University of Life Sciences Prague Prague Czechia

Heron Ecological Kingston Idaho USA

Norwegian Institute for Nature Research Oslo Norway

School of Biodiversity One Health and Veterinary Medicine University of Glasgow Glasgow UK

School of Biological and Marine Sciences University of Plymouth Plymouth UK

School of Biological Sciences University of Reading Reading UK

School of Biology and Environmental Science University College Dublin Dublin Ireland

School of Environmental Sciences University of East Anglia Norwich UK

School of Mathematical and Physical Sciences University of Sheffield Sheffield UK

School of Natural and Environmental Sciences Newcastle University Newcastle upon Tyne UK

Spatial Ecology and Conservation Lab Department of Biodiversity Institute of Biosciences São Paulo State University UNESP Rio Claro São Paulo Brazil

UK Centre for Ecology and Hydrology Penicuik UK

University of the Philippines Quezon City Philippines

Zoological Society of London London UK

Zoology Department Nelson Mandela University Port Elizabeth South Africa

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