biologging; conservation; human‐modified landscapes; modelling; movement ecology; Ecology, Evolution, Behavior and Systematics; Animal Science and Zoology
Abstract :
[en] 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.
Disciplines :
Environmental sciences & ecology
Author, co-author :
Gomez, Sara ; CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
English, Holly M ; School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
Bejarano Alegre, Vanesa ; Spatial Ecology and Conservation Lab (LEEC), Department of Biodiversity, Institute of Biosciences, São Paulo State University-UNESP, Rio Claro, São Paulo, Brazil
Blackwell, Paul G ; School of Mathematical and Physical Sciences, University of Sheffield, Sheffield, UK
Bracken, Anna M ; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK
Bray, Eloise; School of Mathematical and Physical Sciences, University of Sheffield, Sheffield, UK
Evans, Luke C ; School of Biological Sciences, University of Reading, Reading, UK
Gan, Jelaine L; School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK ; University of the Philippines, Quezon City, Philippines
Grecian, W James ; Department of Geography, Durham University, Durham, UK
Gutmann Roberts, Catherine ; School of Biological and Marine Sciences, University of Plymouth, Plymouth, UK
Harju, Seth M ; Heron Ecological, Kingston, Idaho, USA
Hejcmanová, Pavla ; Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Prague, Czechia
Fundação de Amparo à Pesquisa do Estado de São Paulo Natural Environment Research Council Gordon and Betty Moore Foundation National Geographic Society Rural and Environment Science and Analytical Services Division
Funding text :
V.B.A. received support from the S\u00E3o Paulo Research Foundation (processes number: 2020/07586\u20104). L.C.E. was supported by the Natural Environment Research Council Grant (award number: NE/V006916/1). Z.O. was funded by the Regional Government of Andalusia and NextGenerationEU. P.H. received support from the Faculty of Tropical AgriSciences\u2014Czech University of Life Sciences Prague (award number: IGA20243107). C.J.G.R. was supported by the Natural Environment Research Council and the ARIES Doctoral Training Partnership (award number: NE/S007334/1). C.R. acknowledges funding from the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society (NGS\u201082515R\u201020). K.F.W. was supported by the Scottish Government's Rural and Environment Science and Analytical Services Division (RESAS).
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