ArticleEcology and evolution2025
Spatially Varying Drivers of Temporal β Diversity in Forest Avian Communities.
Article in Ecology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Spatially Varying Drivers of Temporal β Diversity in Forest Avian Communities.Ecology and evolution · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preserving biodiversity is crucial for ecosystem function, necessitating its measurement and monitoring to comprehend human impacts on the environment. β-diversity, a key metric, can be used to measure changes in community diversity and assemblages over time. Multiple lines of evidence show that climate, land use land cover (LULC), and topography independently influence diversity; more recent work also shows that LULC can decouple climate effects, creating refugia. Spatial structures also affect the relationships between ecological processes and environmental factors. This study uses two discrete generations of breeding bird atlas data in Ohio, 24 years apart, to investigate the effects of environmental changes and spatial heterogeneity on statewide forest bird community diversity. Utilizing machine learning, we identified elevation variability, mean forest patch size, and total forest area as the most significant variables affecting temporal β diversity dissimilarity (referred to as temporal dissimilarity). The smallest changes in temporal dissimilarity occurred in landscapes with increased mean forest patch size, forest class area, and varied elevation. Furthermore, the results revealed a weak interaction between total forest area and elevation variability, with the largest temporal dissimilarity occurring in sites with the highest loss of total forest area and invariable elevation. We pinpointed regions in Ohio experiencing the greatest amount of composition changes in intensified agricultural and developed lands. A geographically weighted regression model indicated that the relationships between temporal dissimilarity and elevation variability, mean forest patch size, total forest area, and annual maximum temperature varied spatially within the state. Our study integrates ecological patterns with spatial patterns, showing that landscapes can interact with climate to mitigate regional climatic impacts and suggest effective conservation areas. Our findings also indicate that the relationships between ecological processes and the environment are not always spatially consistent.
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Registered trials
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