A call for increased integration of experimental approaches in movement ecology

Biological reviews/Biological reviews of the Cambridge Philosophical Society, Год журнала: 2025, Номер unknown

Опубликована: Апрель 29, 2025

ABSTRACT Rapid developments in animal‐tracking technology have enabled major advances the field of movement ecology, which seeks to understand drivers and consequences across scales, taxa, ecosystems. The has made ground‐breaking discoveries, yet majority studies ecology remain reliant on observational approaches. While important, are limited compared experimental methods that can reveal causal relationships underlying mechanisms. As such, we advocate for a renewed focus approaches animal ecology. We illustrate way forward two fundamental levels biological organisation: individuals social groups. then explore application experiments study anthropogenic influences wildlife movement, enhance our mechanistic understanding conservation interventions. In each these examples, draw upon previous research effectively employed approaches, while highlighting outstanding questions could be answered by further experimentation. conclude ways manipulations both laboratory natural settings provide promising generate understandings drivers, consequences, movement.

Язык: Английский

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Practical guidelines for validation of supervised machine learning models in accelerometer‐based animal behaviour classification

Journal of Animal Ecology, Год журнала: 2025, Номер unknown

Опубликована: Май 19, 2025

Abstract Supervised machine learning has been used to detect fine‐scale animal behaviour from accelerometer data, but a standardised protocol for implementing this workflow is currently lacking. As the application of ecological problems expands, it essential establish technical protocols and validation standards that align with those in other ‘big data’ fields. Overfitting prevalent often misunderstood challenge learning. Overfit models overly adapt training data memorise specific instances rather than discern underlying signal. Associated results can indicate high performance on set, yet these are unlikely generalise new data. be detected through rigorous using independent test sets. Our systematic review 119 studies accelerometer‐based supervised classify reveals 79% (94 papers) did not validate their sufficiently well robustly identify potential overfitting. Although does inherently imply overfit, absence sets limits interpretability results. To address challenges, we provide theoretical overview overfitting context accelerometry propose guidelines optimal techniques. aim equip ecologists tools necessary general theory requirements biologging, facilitating reliable detection advancing field.

Язык: Английский

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Detection and classification of captive coppery titi monkey calls

Bioacoustics, Год журнала: 2025, Номер unknown, С. 1 - 19

Опубликована: Май 22, 2025

Язык: Английский

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Former les "conservateurs informatiques" d'Afrique

Nature Africa, Год журнала: 2024, Номер unknown

Опубликована: Дек. 3, 2024

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Training Africa’s ‘computer conservationists’

Nature Africa, Год журнала: 2024, Номер unknown

Опубликована: Дек. 3, 2024

Язык: Английский

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