DOI: 10.5061/dryad.m63xsj46d
title: Effects of thermal fluctuations on biological processes: A meta-analysis of experiments manipulating thermal variability

description: 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.
description: <strong>Systematic literature
 review</strong> 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.
 <strong>Inclusion criteria</strong>
 We screened abstracts and titles from both searches for inclusion
 using the 0.4.1 version of the <em>revtools</em> 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. <strong>Data
 extraction</strong> 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 (<em>Webplotdigitizer</em>, 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 <em>metafor</em> (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.
description: R