Effects of thermal fluctuations on biological processes: A meta-analysis of experiments manipulating thermal variability

Thermal variability is a key driver of ecological processes, affecting organisms and populations across multiple temporal scales. Despite the ubiquity of variation, biologists lack a quantitative synthesis of the observed ecological consequences of thermal variability across a wide range of taxa, phenotypic traits, and experimental designs. Here, we conduct a meta-analysis to investigate how properties of organisms, their experienced thermal regime, and whether thermal variability is experienced in either the past (prior to an assay) or present (during the assay) affect performance, relative to the performance of organisms experiencing constant thermal environments. Our results – which draw upon 1,712 effect sizes from 75 studies – indicate that the effects of thermal variability are not unidirectional and become more negative as mean temperature and fluctuation range increase. Exposure to variation in the past decreases performance to a greater extent than variation experienced in the present and increases the costs to performance more than diminishing benefits across a broad set of empirical studies. Further, we identify life history attributes that predictably modify the ecological response to variation. Our findings demonstrate that effects of thermal variability on performance are context-dependent, yet negative outcomes may be heightened in warmer, more variable climates.

Methods
Systematic literature review

To understand how thermal variability affects performance, defined as physiological or demographic rates or states, we conducted two systematic literature searches of the effects of thermal variation during acclimation and acute conditions. Our first search, conducted on 14 November 2020 using the ISI Web of Science (WOS) database with the search terms: AK=((temperature OR thermal) NEAR (vari* OR fluc*)) AND SU=(Life Sciences & Biomedicine) yielded 176 results. To increase sample size and decrease publication bias, we conducted a second systematic literature search on 3 June 2021 using the SCOPUS database with the search terms: KEY ("thermal performance curve" OR "thermal fluct*" OR "thermal vari*" OR "temperature vari*" OR "fluctuating temperatures" OR "thermal regime" AND ("ecology" OR "physiology")), which yielded 405 results. There were 43 papers returned in both WOS and SCOPUS searches.

Inclusion criteria

We screened abstracts and titles from both searches for inclusion using the 0.4.1 version of the revtools R package (Westgate, MJ, 2019) and excluded 189 studies (Figure 2). We then assessed eligible studies (n=306) and excluded studies that lacked a constant and fluctuating treatment (n=115), did not feature a consistent, controlled fluctuation pattern (e.g. pulse press, multiple stochastic cold exposures, etc.) (n=64), were reviews, commentaries, or perspectives (n=33), were theoretical or modelling studies (n=24), were not biologically relevant (e.g. engineering, chemical studies, etc.) (n=19), lacked reported error measurements (n=4), lacked extractable or comparable data (n=4), and featured more than 1°C difference between the mean temperatures in constant and fluctuating treatments (n=13). For studies to meet these inclusion criteria, the experimental design had to be explicitly focused on thermal variability. Subsequently, we conducted a cited reference search from the remaining eligible studies and included an additional 49 studies. In total, we included 75 studies with 1,712 effect sizes (Figure 2) (see Table S2 for a list and description of studies included). All studies included involved ectothermic organisms. We excluded any population or community-level responses and species with unresolved phylogenies or that were not identified to the species level in the Open Tree of Life database.

Data extraction

From the studies that met our inclusion criteria, we extracted mean response values, any measure of variance (SD or SEM), and sample size from tables and figures using Webplotdigitizer (Webplotdigitizer, v4.5, 2021). Any studies that reported error as SEM were converted to SD by multiplying SEM by the square root of the sample size. Further, if studies reported findings using medians and the IQR, and we could confirm the data to be approximately normally distributed, we estimated the mean based on the reported median, and the SD to be the IQR divided by 1.5 (Higgins & Green, 2011). If any extracted values were missing sample sizes or variances, the points were automatically excluded via the meta-analysis software metafor (v3.0.2, Viechtbauer, 2010).  Additionally, we collected aspects of experimental design (experiment type, duration, etc.), thermal regime (mean temperature, fluctuation range, etc.) as well as life history traits (age, size) and response metrics (trait directionality, see Analysis and Hypothesis Testing for definition) to investigate potential mechanisms mediating responses to thermal variability.

热变异性（Thermal variability）是生态过程的关键驱动因子，可在多个时间尺度上影响生物个体与种群。尽管变异普遍存在，但生物学家尚未对涵盖广泛类群、表型性状与实验设计的热变异性生态后果开展定量综合分析。本研究开展一项元分析（meta-analysis），旨在探究相较于恒定热环境下的生物表现，生物体自身属性、其所经历的热制度以及热变异性是在实验前（先前）还是实验期间（当下）被暴露，这些因素如何影响生物表现。本研究整合了75项研究的1712个效应量，结果表明热变异性的效应并非单向的，且随着平均温度与波动幅度的升高，其负面效应愈发显著。相较于当下暴露于热变异的情况，先前暴露于热变异会更大程度地降低生物表现，且在大量实证研究中，其对生物表现所带来的代价增幅超过了收益的缩减幅度。此外，本研究识别出可预见性地调控生物对热变异生态响应的生活史属性。本研究结果显示，热变异性对生物表现的效应具有情境依赖性，但在更温暖、热变异更强的气候中，负面结果可能会更为突出。

方法

系统文献综述
为阐明热变异性对生物表现（定义为生理或种群统计速率与状态）的影响，我们针对驯化阶段与急性条件下的热变异效应开展了两次系统文献检索。第一次检索于2020年11月14日在ISI Web of Science（WOS）数据库中进行，检索式为：AK=((temperature OR thermal) NEAR (vari* OR fluc*)) AND SU=(Life Sciences & Biomedicine)，共获得176条结果。为扩大样本量并降低发表偏倚，我们于2021年6月3日使用SCOPUS数据库开展第二次系统文献检索，检索式为：KEY ("thermal performance curve" OR "thermal fluct*" OR "thermal vari*" OR "temperature vari*" OR "fluctuating temperatures" OR "thermal regime") AND ("ecology" OR "physiology")，共获得405条结果。WOS与SCOPUS检索的重复结果共43篇。

纳入标准
我们使用revtools R包（0.4.1版本，Westgate, MJ, 2019）筛选两次检索的摘要与标题，排除了189项研究（见图2）。随后对符合条件的306项研究进行评估，并排除以下研究：缺乏恒定与波动处理的研究（n=115）、未采用一致可控的波动模式（如脉冲式胁迫、多次随机低温暴露等）的研究（n=64）、综述、评论或观点类文章（n=33）、理论或模型研究（n=24）、无生物学相关性的研究（如工程、化学类研究等，n=19）、未报告误差度量的研究（n=4）、缺乏可提取或可比数据的研究（n=4），以及恒定与波动处理间平均温差超过1℃的研究（n=13）。唯有实验设计明确聚焦热变异性的研究方能满足上述纳入标准。随后，我们对剩余符合条件的研究开展参考文献追溯检索，额外纳入49项研究。最终本研究共纳入75项研究，包含1712个效应量（见图2；纳入研究的列表与描述详见表S2）。所有纳入研究均针对外温动物。我们排除了任何种群或群落水平的响应数据，以及生命之树（Open Tree of Life）数据库中系统发育未明确或未鉴定到物种水平的物种。

数据提取
从符合纳入标准的研究中，我们使用Webplotdigitizer（v4.5, 2021）从表格与图表中提取平均响应值、任意形式的方差度量（标准差SD或标准误SEM）以及样本量。所有以标准误报告误差的研究均通过将SEM乘以样本量的平方根转换为标准差。此外，若研究以中位数与四分位距（IQR）报告结果，且我们可确认数据近似服从正态分布，则我们将基于报告的中位数估算均值，将四分位距除以1.5作为标准差（Higgins & Green, 2011）。若提取的数值缺失样本量或方差，则元分析软件metafor（v3.0.2, Viechtbauer, 2010）会自动排除对应数据点。此外，我们还收集了实验设计相关信息（实验类型、持续时间等）、热制度参数（平均温度、波动幅度等），以及生活史性状（年龄、体型）与响应指标（性状方向性，详见分析与假设检验部分的定义），以探究介导热变异性响应的潜在机制。