Spatio‐Temporal Comparisons Between Microclimate Species Distribution Models and Mechanistic Models of Potential Surface Activity
ABSTRACT With projected decreases in biodiversity looming due to changing environmental conditions, it is important for conservation managers to have accurate predictions of species' distributions. Species distribution models (SDM) and mechanistic models that account for biophysical factors are...
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Wiley
2025-07-01
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| Series: | Ecology and Evolution |
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| Online Access: | https://doi.org/10.1002/ece3.71813 |
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| author | Samuel FitzSimons Stickley John A. Crawford William E. Peterman Jennifer M. Fraterrigo |
| author_facet | Samuel FitzSimons Stickley John A. Crawford William E. Peterman Jennifer M. Fraterrigo |
| author_sort | Samuel FitzSimons Stickley |
| collection | DOAJ |
| description | ABSTRACT With projected decreases in biodiversity looming due to changing environmental conditions, it is important for conservation managers to have accurate predictions of species' distributions. Species distribution models (SDM) and mechanistic models that account for biophysical factors are important tools for predicting potential distributions for many species. Incorporating microclimate data into SDMs and mechanistic models has become an important step for developing biologically relevant models for organisms reliant on microclimatic regimes. However, there remains a need to compare and integrate the predictions from microclimate‐derived SDMs and mechanistic models at fine spatio‐temporal scales to improve predictive accuracy and quantify model uncertainty. We developed correlative SDMs and mechanistic models of potential resistance to surface activity for two salamanders using fine‐resolution (3 m) microclimate data. Models were produced for the Great Smoky Mountains National Park, USA during the 2010, 2030, and 2050 time periods. We determined the spatio‐temporal agreement between SDMs and mechanistic models to assess model uncertainty at varying spatial resolutions. We also modeled and assessed spatio‐temporal variability within potential activity corridors. We found that agreement between fine‐resolution microclimate SDMs and mechanistic models was generally poor and varied temporally, but model agreement increased and converged at varying coarser spatial resolutions. Furthermore, potential activity corridors spatio‐temporally varied and demonstrated increased habitat fragmentation under future projections. The findings from this study highlight a contradiction in which we may need to model species distributions with microclimate data at finer, more biologically meaningful resolutions, but model agreement between correlative and mechanistic approaches may be weakened at these fine scales. Researchers may therefore need to strike a balance between increasing spatial resolution and study extent when integrating model approaches. Further quantifying model uncertainty and identifying alternative methods for integrating SDMs and mechanistic models will be an important step towards accurately predicting species distributions under changing environmental conditions. |
| format | Article |
| id | doaj-art-d657ec15988e489fa45fcbc46d45444c |
| institution | DOAJ |
| issn | 2045-7758 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj-art-d657ec15988e489fa45fcbc46d45444c2025-08-20T02:46:14ZengWileyEcology and Evolution2045-77582025-07-01157n/an/a10.1002/ece3.71813Spatio‐Temporal Comparisons Between Microclimate Species Distribution Models and Mechanistic Models of Potential Surface ActivitySamuel FitzSimons Stickley0John A. Crawford1William E. Peterman2Jennifer M. Fraterrigo3Department of Natural Resources and Environmental Sciences University of Illinois, Urbana‐Champaign Urbana Illinois USANational Great Rivers Research and Education Center East Alton Illinois USASchool of Environment and Natural Resources The Ohio State University Columbus Ohio USADepartment of Natural Resources and Environmental Sciences, Program in Ecology, Evolution, and Conservation Biology University of Illinois, Urbana‐Champaign Urbana Illinois USAABSTRACT With projected decreases in biodiversity looming due to changing environmental conditions, it is important for conservation managers to have accurate predictions of species' distributions. Species distribution models (SDM) and mechanistic models that account for biophysical factors are important tools for predicting potential distributions for many species. Incorporating microclimate data into SDMs and mechanistic models has become an important step for developing biologically relevant models for organisms reliant on microclimatic regimes. However, there remains a need to compare and integrate the predictions from microclimate‐derived SDMs and mechanistic models at fine spatio‐temporal scales to improve predictive accuracy and quantify model uncertainty. We developed correlative SDMs and mechanistic models of potential resistance to surface activity for two salamanders using fine‐resolution (3 m) microclimate data. Models were produced for the Great Smoky Mountains National Park, USA during the 2010, 2030, and 2050 time periods. We determined the spatio‐temporal agreement between SDMs and mechanistic models to assess model uncertainty at varying spatial resolutions. We also modeled and assessed spatio‐temporal variability within potential activity corridors. We found that agreement between fine‐resolution microclimate SDMs and mechanistic models was generally poor and varied temporally, but model agreement increased and converged at varying coarser spatial resolutions. Furthermore, potential activity corridors spatio‐temporally varied and demonstrated increased habitat fragmentation under future projections. The findings from this study highlight a contradiction in which we may need to model species distributions with microclimate data at finer, more biologically meaningful resolutions, but model agreement between correlative and mechanistic approaches may be weakened at these fine scales. Researchers may therefore need to strike a balance between increasing spatial resolution and study extent when integrating model approaches. Further quantifying model uncertainty and identifying alternative methods for integrating SDMs and mechanistic models will be an important step towards accurately predicting species distributions under changing environmental conditions.https://doi.org/10.1002/ece3.71813climate changeecological niche modelsGreat Smoky Mountains National Parkmicrohabitatphysiologyplethodontid salamanders |
| spellingShingle | Samuel FitzSimons Stickley John A. Crawford William E. Peterman Jennifer M. Fraterrigo Spatio‐Temporal Comparisons Between Microclimate Species Distribution Models and Mechanistic Models of Potential Surface Activity Ecology and Evolution climate change ecological niche models Great Smoky Mountains National Park microhabitat physiology plethodontid salamanders |
| title | Spatio‐Temporal Comparisons Between Microclimate Species Distribution Models and Mechanistic Models of Potential Surface Activity |
| title_full | Spatio‐Temporal Comparisons Between Microclimate Species Distribution Models and Mechanistic Models of Potential Surface Activity |
| title_fullStr | Spatio‐Temporal Comparisons Between Microclimate Species Distribution Models and Mechanistic Models of Potential Surface Activity |
| title_full_unstemmed | Spatio‐Temporal Comparisons Between Microclimate Species Distribution Models and Mechanistic Models of Potential Surface Activity |
| title_short | Spatio‐Temporal Comparisons Between Microclimate Species Distribution Models and Mechanistic Models of Potential Surface Activity |
| title_sort | spatio temporal comparisons between microclimate species distribution models and mechanistic models of potential surface activity |
| topic | climate change ecological niche models Great Smoky Mountains National Park microhabitat physiology plethodontid salamanders |
| url | https://doi.org/10.1002/ece3.71813 |
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