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Predicting daily activity time through ecological niche modelling and microclimatic data.

Felipe A Toro-Cardona1,2, Juan L Parra2, Octavio R Rojas-Soto1

  • 1Laboratorio de Bioclimatología, Red de Biología Evolutiva, Instituto de Ecología, A. C. Xalapa, Veracruz, Mexico.

The Journal of Animal Ecology
|February 6, 2023
PubMed
Summary
This summary is machine-generated.

Ecological niche models using microclimate data accurately predict Gila monster activity patterns. This approach, using fine-grained climate data, helps understand climate change impacts on arid-zone ectotherms.

Keywords:
Heloderma suspectumdaily activity patternsectothermellipsoidsmicroclimateseasonality

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Area of Science:

  • Ecology
  • Climate Change Biology
  • Herpetology

Background:

  • Climate temporality influences species activity and distribution, yet microclimate data's predictive power for fine-scale activity patterns remains underutilized.
  • Ectothermic vertebrates, like the Gila monster, show distinct activity patterns tied to climate, making them crucial for studying climate change impacts.

Purpose of the Study:

  • To assess if ecological niche models (ENMs) using microclimate data can predict temporal activity patterns of the Gila monster (Heloderma suspectum).
  • To determine if predicted activity patterns align with known physiological constraints.
  • To evaluate the efficacy of daily versus seasonal approaches in ENM construction for predicting activity.

Main Methods:

  • Generated hourly microclimatic data for 10 years using NicheMapR for Gila monster occurrence sites.
  • Constructed ENMs using minimum volume ellipsoids, comparing seasonal and daily activity pattern strategies.
  • Validated models using citizen science data and assessed if unimodal/bimodal activity patterns align with physiological constraints.

Main Results:

  • ENMs incorporating microclimate data successfully recovered known unimodal and bimodal seasonal activity patterns for the Gila monster.
  • Upper thermal tolerances were identified as key drivers explaining the species' daily activity patterns.
  • The daily activity pattern strategy in ENM construction outperformed the seasonal approach in validation.

Conclusions:

  • ENMs trained with high-resolution microclimate data can effectively predict temporal activity patterns, especially for arid-zone ectotherms facing climate change.
  • Utilizing fine-grained temporal climate variables enhances niche delimitation and improves correlative model outcomes.
  • This methodology offers a robust framework for anticipating climate change consequences on species' behavior and distribution.