About
From our oceans to savannas, animals must cope with dynamic environments that are undergoing unprecedented rates of change. An understanding of how these environmental processes shape animal behavior, ecological interactions, and population persistence is urgently needed to support
Research topics
- Environmental science
- Geography
- Computer Science
- Sociology
- Biology
- Ecology
- Environmental resource management
- Environmental planning
- Mathematics
- Statistics
Selected publications
PLOS Climate · 2026-01-21
articleOpen accessMarine cold-spells are an understudied phenomena which can both negatively impact marine wildlife and provide thermal refugia for species displaced by climate change. To develop forward-looking and climate-ready management schemes, it is critical to examine how marine species respond to cold-spells, how long-term warming will affect marine cold-spells over the next century, and how these future cold-spells will in turn affect species of conservation concern, particularly in marine protected areas. To this end, we detect marine cold-spells across the California Current System, a productive Eastern Boundary Upwelling System, relative to a fixed baseline (1980–2009) and to a detrended time series that isolates cold-spells from long-term climate change. We then project the impact of future marine cold-spells on habitat suitability for two endangered top predators: leatherback turtles ( Dermochelys coriacea ) and blue whales ( Balaenoptera musculus ). Models project that 68–99% of the California Current System will no longer experience fixed baseline marine cold-spells by 2099 under a high emissions scenario, though marine cold-spells will still occur relative to a detrended time series. Blue whales lost 5% of their core habitat in National Marine Sanctuaries during historical marine cold-spells and are projected to gain 1–2% more core habitat during future, fixed baseline marine cold-spells. Leatherback sea turtles had little core habitat change during historical marine cold-spells but are projected to gain 4–5% more core habitat during future marine cold-spells. It is plausible that both species gain habitat during future marine cold-spells because these events provide thermal refugia to their prey. We urge conservationists and ecologists to increase their attention to marine cold-spells as potential thermal refugia and prioritize collecting data on endangered species’ prey in order to understand more deeply how species will respond to extreme temperature events.
Zenodo (CERN European Organization for Nuclear Research) · 2026-03-20
datasetOpen accessSenior authorData for the paper Warming temperatures increase close encounters between two top predator species via changes in spatial behaviour. The associated R code is available at: https://github.com/KasimResearch/TemperatureSpace
Resource variability shapes the ecology of social information and collective sensing
Trends in Ecology & Evolution · 2026-03-12
articleOpen accessSocial information expands individual sensing of resources in dynamic ecosystems, yet why social strategies evolve in resource pursuit remains unsettled. We posit that resource variability along three axes mediates the emergence of collective sensing by altering the value of social information for maximizing individual resource gain and minimizing its variance. Drawing from empirical examples across taxa and scales, we offer testable predictions under the hypothesis that resource variability shapes this dual value of social information. Variance-induced risks to survival represent an underappreciated factor amplifying the value of social signals and cues, especially when resources are patchy, ephemeral, and abundant. This perspective bridges classical ecological models and burgeoning interest in collective behavior, providing the 'why' underlying the 'how' of sensory collectives.
Zenodo (CERN European Organization for Nuclear Research) · 2026-03-20
datasetOpen accessSenior authorData for the paper Warming temperatures increase close encounters between two top predator species via changes in spatial behaviour. The associated R code is available at: https://github.com/KasimResearch/TemperatureSpace
Movement Ecology · 2026-04-11
articleOpen accessSenior authorLeveraging machine learning and accelerometry to classify animal behaviours with uncertainty
Methods in Ecology and Evolution · 2025-12-08 · 2 citations
articleOpen accessAbstract Animal‐worn sensors have revolutionised the study of animal behaviour and ecology. Accelerometers, which measure changes in acceleration across planes of movement, are increasingly being used in conjunction with machine learning models to classify animal behaviours across taxa and research questions. However, the widespread adoption of these methods faces challenges from imbalanced training data, unquantified uncertainties in model outputs, shifts in model performance across contexts and noisy classifications in continuous data streams, where predicted behaviours change abruptly within a sequence. To address these challenges, we introduce an open‐source approach for classifying animal behaviour from raw acceleration data. Our approach integrates machine learning and statistical inference techniques to evaluate and mitigate class imbalances, changes in model performance across ecological settings and noisy classifications. Importantly, we extend predictions from single behaviour classifications to prediction sets: sets of behaviour labels guaranteed to contain the true behaviour with a pre‐specified probability, in a framework analogous to the use of prediction intervals in statistical analyses. We evaluate our approach via simulation and highlight its utility using data collected from a free‐ranging large carnivore, African wild dogs ( Lycaon pictus ), in the Okavango Delta, Botswana. We demonstrate significantly improved predictions along with associated uncertainty metrics in African wild dog behaviour classification, particularly for rare and ecologically important behaviours such as feeding, where correct classifications more than doubled following quality checks and data rebalancing introduced in our pipeline. Our approach is applicable across taxa and represents a key step towards advancing the burgeoning use of machine learning to remotely observe around‐the‐clock behaviours of free‐ranging animals. Future work could include the integration of multiple data streams, such as accelerometer, audio and GPS data, for model training and could be incorporated directly into our pipeline.
2025-04-30
peer-review1st authorCorrespondingNature Ecology & Evolution · 2025-02-19 · 5 citations
article2025-06-09
peer-review2025-07-03
peer-review
Recent grants
Frequent coauthors
- 112 shared
Elliott L. Hazen
NOAA National Marine Fisheries Service Southwest Fisheries Science Center
- 56 shared
Kasim Rafiq
Botswana Predator Conservation Trust
- 49 shared
Jameal F. Samhouri
NOAA National Marine Fisheries Service Northwest Fisheries Science Center
- 45 shared
Neil R. Jordan
Botswana Predator Conservation Trust
- 36 shared
Karin A. Forney
NOAA National Marine Fisheries Service Southwest Fisheries Science Center
- 34 shared
Blake E. Feist
- 34 shared
Steven J. Bograd
NOAA National Marine Fisheries Service Southwest Fisheries Science Center
- 30 shared
Matthew S. Savoca
Pacific University
Labs
Awards & honors
- Humboldt Fellowship for Experienced Researchers (2025)
- Packard Fellowship in Science and Engineering (2023)
- Alfred P. Sloan Research Fellowship (2022)
- International Bio-Logging Society Early Career Award (2021)
- 2023 Packard Fellow
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