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使用分布滞后非线性建模方法确定热适应的关键时期。

Determining critical periods for thermal acclimatisation using a distributed lag non-linear modelling approach.

作者信息

Redana Matteo, Gibbins Chris, Lancaster Lesley T

机构信息

Department of Zoology University of Cambridge Cambridge UK.

School of Environmental and Geographical Sciences University of Nottingham Malaysia Semenyih Malaysia.

出版信息

Ecol Evol. 2024 May 31;14(6):e11451. doi: 10.1002/ece3.11451. eCollection 2024 Jun.

DOI:10.1002/ece3.11451
PMID:38826161
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11140238/
Abstract

Rapid changes in thermal environments are threatening many species worldwide. Thermal acclimatisation may partially buffer species from the impacts of these changes, but currently, the knowledge about the temporal dynamics of acclimatisation remains limited. Moreover, acclimatisation phenotypes are typically determined in laboratory conditions that lack the variability and stochasticity that characterise the natural environment. Through a distributed lag non-linear model (DLNM), we use field data to assess how the timing and magnitude of past thermal exposures influence thermal tolerance. We apply the model to two Scottish freshwater Ephemeroptera species living in natural thermal conditions. Model results provide evidence that rapid heat hardening effects are dramatic and reflect high rates of change in temperatures experienced over recent hours to days. In contrast, temperature change magnitude impacted acclimatisation over the course of weeks but had no impact on short-term responses. Our results also indicate that individuals may de-acclimatise their heat tolerance in response to cooler environments. Based on the novel insights provided by this powerful modelling approach, we recommend its wider uptake among thermal physiologists to facilitate more nuanced insights in natural contexts, with the additional benefit of providing evidence needed to improve the design of laboratory experiments.

摘要

全球范围内,热环境的快速变化正威胁着许多物种。热适应可能会部分缓冲物种免受这些变化的影响,但目前,关于适应的时间动态的知识仍然有限。此外,适应表型通常是在缺乏自然环境所特有的变异性和随机性的实验室条件下确定的。通过分布式滞后非线性模型(DLNM),我们利用实地数据来评估过去热暴露的时间和幅度如何影响热耐受性。我们将该模型应用于生活在自然热条件下的两种苏格兰淡水蜉蝣物种。模型结果表明,快速的热硬化效应显著,反映了最近几小时到几天内经历的温度变化率很高。相比之下,温度变化幅度在数周的过程中影响适应,但对短期反应没有影响。我们的结果还表明,个体可能会因环境变冷而使其耐热性去适应。基于这种强大的建模方法所提供的新见解,我们建议热生理学家更广泛地采用该方法,以便在自然环境中获得更细致入微的见解,此外还能为改进实验室实验设计提供所需的证据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33c9/11140238/5a3841b410cf/ECE3-14-e11451-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33c9/11140238/7dd79204483d/ECE3-14-e11451-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33c9/11140238/b1d7832a7e9f/ECE3-14-e11451-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33c9/11140238/11672c09d505/ECE3-14-e11451-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33c9/11140238/d84ed4a0b8ae/ECE3-14-e11451-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33c9/11140238/5a3841b410cf/ECE3-14-e11451-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33c9/11140238/7dd79204483d/ECE3-14-e11451-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33c9/11140238/b1d7832a7e9f/ECE3-14-e11451-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33c9/11140238/11672c09d505/ECE3-14-e11451-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33c9/11140238/d84ed4a0b8ae/ECE3-14-e11451-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33c9/11140238/5a3841b410cf/ECE3-14-e11451-g002.jpg

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