Integrating animal tracking and trait data to facilitate global ecological discoveries

Journal of Experimental Biology, Journal Year: 2025, Volume and Issue: 228(Suppl_1)

Published: Feb. 15, 2025

ABSTRACT Understanding animal movement is at the core of ecology, evolution and conservation science. Big data approaches for tracking have facilitated impactful synthesis research on spatial biology behavior in ecologically important human-impacted regions. Similarly, databases traits (e.g. body size, limb length, locomotion method, lifespan) been used a wide range comparative questions, with emerging being shared level individuals populations. Here, we argue that proliferation both types publicly available creates exciting opportunities to unlock new avenues research, such as planning ecological forecasting. We assessed feasibility combining trait develop test hypotheses across geographic, temporal biological allometric scales. identified multiple questions addressing performance distribution constraints could be answered by integrating data. For example, how do physiological metabolic rates) biomechanical form) influence migration distances? illustrate potential our framework three case studies effectively integrate research. An challenge ahead lack taxonomic overlap databases. identify critical next steps future integration databases, most open interlinked individual-level Coordinated efforts combine will accelerate global evolutionary insights inform management decisions changing world.

Language: Английский

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A new data-driven paradigm for the study of avian migratory navigation

Movement Ecology, Journal Year: 2025, Volume and Issue: 13(1)

Published: March 11, 2025

Abstract Avian navigation has fascinated researchers for many years. Yet, despite a vast amount of literature on the topic it remains mystery how birds are able to find their way across long distances while relying only cues available locally and reacting those fly. Navigation is multi-modal, in that may use different at times as response environmental conditions they themselves in. It also operates spatial temporal scales, where strategies be used parts journey. This multi-modal multi-scale nature however been challenging study, since would require long-term tracking data along with contemporaneous co-located information cues. In this paper we propose new alternative data-driven paradigm study avian navigation. That is, instead taking traditional theory-based approach based posing research question then collecting navigation, approach, large amounts data, not purposedly collected specific question, analysed identify as-yet-unknown patterns behaviour. Current technological developments have led collections both animal which openly scientists. These open combined exploratory using mining, machine learning artificial intelligence methods, can support identification unexpected during migration, lead better understanding navigational decision-making scales.

Language: Английский

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Understanding and predicting animal movements and distributions in the Anthropocene

Journal of Animal Ecology, Journal Year: 2025, Volume and Issue: unknown

Published: April 4, 2025

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

Language: Английский

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How can we make GPS tracking studies more open, reproducible, and collaborative? A vision for the OpenGPS platform

Data in Brief, Journal Year: 2025, Volume and Issue: unknown, P. 111603 - 111603

Published: May 1, 2025

Language: Английский

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Maintaining robust terrestrial ecological monitoring amid technological advancements

Trends in Ecology & Evolution, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

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