Challenges in Modelling Spatio‐Temporal Climatic Correlates of Local Losses of Wild Bees Using Dynamic Occupancy Models Article Swipe
Gary D. Powney
,
James M. Bullock
,
Robin J. Boyd
,
Claire Carvell
,
Mike Edwards
,
Rowan Edwards
,
Bill Kunin
,
Nick J. B. Isaac
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/ddi.70055
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/ddi.70055
Aim Impacts of climate change on biodiversity are well documented. Much of the evidence linking climate change to species distribution changes derives from studies using long‐term climate averages in a correlative spatial framework. While useful, these static species distribution models (SDMs) give little insight into the process behind the correlations. Here, we model changes in wild bee occupancy dynamics as a function of temperature covariates that vary in space and time. We aim to detect fine‐scale, climate‐associated distribution changes in wild bees beyond those captured by traditional SDMs and aim to assess the challenges of applying dynamic occupancy models to large‐scale opportunistic datasets. Location Great Britain. Methods We use dynamic occupancy models to examine the relationship between temperature and local losses for 106 wild bee species. We focus on one spatial metric (mean long‐term temperature average, akin to SDM approaches) and one spatio‐temporal metric (the annual temperature anomaly). We use a risk‐of‐bias assessment to evaluate how data limitations may affect inference in our dynamic occupancy models. Results Mean long‐term temperature for a site was associated with the probability of local loss for > 60% of species. In general, mean temperature was negatively associated with the probability of local loss for southerly distributed species (meaning a lower probability of loss at warmer sites), while the relationship was reversed for northerly species. The annual temperature anomaly was only influential for one species. Main Conclusions Our results mirror the large‐scale spatial pattern of SDMs, and although ecologically plausible, we find little signal of fine‐scale, climate‐associated wild bee distribution changes. We attribute this lack of signal to data‐model mismatches as revealed by the detailed risk‐of‐bias assessment. We conclude that while dynamic occupancy models offer promise for integrating spatio‐temporal covariates, their use may be limited when applied to large‐scale opportunistic datasets that lack systematic sampling effort.
Related Topics
Concepts
Occupancy
Climate change
Ecology
Anomaly (physics)
Species distribution
Biodiversity
Metric (unit)
Spatial ecology
Environmental science
Habitat
Inference
Environmental niche modelling
Biology
Computer science
Ecological niche
Physics
Economics
Operations management
Artificial intelligence
Condensed matter physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/ddi.70055
- OA Status
- gold
- References
- 82
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413272331
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413272331Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/ddi.70055Digital Object Identifier
- Title
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Challenges in Modelling Spatio‐Temporal Climatic Correlates of Local Losses of Wild Bees Using Dynamic Occupancy ModelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-01Full publication date if available
- Authors
-
Gary D. Powney, James M. Bullock, Robin J. Boyd, Claire Carvell, Mike Edwards, Rowan Edwards, Bill Kunin, Nick J. B. IsaacList of authors in order
- Landing page
-
https://doi.org/10.1111/ddi.70055Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1111/ddi.70055Direct OA link when available
- Concepts
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Occupancy, Climate change, Ecology, Anomaly (physics), Species distribution, Biodiversity, Metric (unit), Spatial ecology, Environmental science, Habitat, Inference, Environmental niche modelling, Biology, Computer science, Ecological niche, Physics, Economics, Operations management, Artificial intelligence, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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