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Updated: Sep 15, 2025

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Spatio-Temporal Comparisons Between Microclimate Species Distribution Models and Mechanistic Models of Potential

Samuel FitzSimons Stickley1, John A Crawford2, William E Peterman3

  • 1Department of Natural Resources and Environmental Sciences University of Illinois, Urbana-Champaign Urbana Illinois USA.

Ecology and Evolution
|July 17, 2025
PubMed
Summary

Predicting species distributions requires integrating microclimate data into species distribution models (SDM) and mechanistic models. Model agreement was poor at fine scales but improved at coarser resolutions, highlighting a need for balanced approaches.

Keywords:
Great Smoky Mountains National Parkclimate changeecological niche modelsmicrohabitatphysiologyplethodontid salamanders

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Area of Science:

  • Ecology
  • Conservation Biology
  • Environmental Modeling

Background:

  • Projected biodiversity loss necessitates accurate species distribution predictions.
  • Microclimate data integration into species distribution models (SDM) and mechanistic models is crucial for predicting distributions of microclimate-reliant organisms.
  • Comparing microclimate-derived SDM and mechanistic model predictions at fine scales is needed to improve accuracy and quantify uncertainty.

Purpose of the Study:

  • To develop and compare correlative SDMs and mechanistic models of salamander surface activity resistance using fine-resolution microclimate data.
  • To assess spatio-temporal agreement between these models and quantify uncertainty at various spatial resolutions.
  • To model and assess spatio-temporal variability and habitat fragmentation in potential activity corridors under future projections.

Main Methods:

  • Developed correlative species distribution models (SDMs) and mechanistic models for two salamander species.
  • Utilized fine-resolution (3m) microclimate data for the Great Smoky Mountains National Park across 2010, 2030, and 2050.
  • Analyzed spatio-temporal agreement between models and assessed variability in potential activity corridors.

Main Results:

  • Agreement between fine-resolution microclimate SDMs and mechanistic models was generally poor and varied temporally.
  • Model agreement increased and converged at coarser spatial resolutions.
  • Potential activity corridors showed spatio-temporal variability and increased fragmentation under future climate projections.

Conclusions:

  • Integrating microclimate data into SDMs and mechanistic models presents a challenge due to decreased agreement at finer resolutions.
  • A balance between spatial resolution and study extent is necessary when integrating correlative and mechanistic modeling approaches.
  • Further research on quantifying model uncertainty and developing integrated methods is vital for accurate species distribution predictions under environmental change.