DOI: 10.5061/dryad.xksn02vq3
title: Data from: Oxidative status: A general but overlooked indicator of welfare across animal species?

description: Datasets resulting from the review process aiming to quantify the use of
 markers of oxidative status in animal welfare studies and in adjacent
 fields focused on wild animals (ecophysiology, conservation
 physiology). Datasets used for the metanalyses aiming to examine
 variation in markers of oxidative status across three conditions
 associated with a negative valence (social isolation, noise exposure,
 predation exposure).
description: <strong>Review:</strong> The
 reviewing process was conducted in journals assumed to be representative
 of their respective research fields (animal welfare, ecophysiology,
 conservation physiology) and actively publishing research in the last
 decade (2013-2022) to reflect current trends. Only research articles or
 short communications were considered in the reviewing process to reflect
 active research in each field (as opposed to reviews, commentaries, or
 perspective articles). Plant articles were also excluded. Because of the
 overrepresentation of ecophysiological studies relative to other fields
 and because such studies can often be found in journals also publishing
 exclusively physiological studies, the reviewing process of
 ecophysiological studies was limited to journals with a clear
 ecophysiological section. <em>Biology Letters</em> does not
 have an ecophysiological section but, because of the strong evolutionary
 and ecological focus of this journal, its <em>Physiology</em>
 section was considered as an ecophysiological section. The reviewing
 process was also limited to the <em>Animal Welfare</em>
 section of the journal <em>Animals</em> publishing articles in
 a variety of other disciplines. For general conservation and welfare
 journals (<em>i.e</em>., not primarily focusing on
 physiology), only articles including physiological parameters were
 considered to reflect the fields of conservation physiology and animal
 welfare physiology. The advanced search tool on the
 website of each journal was used to search for the keywords: “oxidative
 stress”, “oxidative status”, and “antioxidant”. For
 each article, the conditions considered to be susceptible to affecting
 markers of oxidative status were assigned to the following 27 categories:
 condition (physical/clinical condition), infection (infection,
 parasitism), activity (physical activity), restraint, density,
 predictability, predation, noise, temperature, radioactive radiation,
 hypoxia/reoxygenation, UVB exposure, salinity, pH, chemicals, environment
 (environment physical properties other than those detailed in the list),
 time (year, season), location, urbanity/tourism, water deprivation, food
 availability/supplementation, antioxidant supplementation, reproduction,
 migration, social aspects, isolation, growth/hatching
 asynchrony. For each article, the considered animal
 species were classified in the following taxa: mammals, birds, reptiles,
 amphibians, fish, insects, crustaceans, mollusks, helminths and
 cnidaria. The number of biomarkers of oxidative status
 was also quantified in each article and arranged in the following
 categories: -        Reactive Oxygen Species
 (ROS)/pro-oxidants: superoxide anion, hydrogen peroxide
 (H<sub>2</sub>O<sub>2</sub>), xanthine oxidase,
 labile plasma anion, -        Antioxidants: antioxidant
 capacity, superoxide dismutase (SOD), catalase (CAT), glutathione
 peroxidase (GPX), glutathione S-transferase (GST), glutathione reductase
 (GR), glucose-6-phosphate dehydrogenase (G6PDH), glutathione (GSH)/thiols,
 uric acid (UA), aryl hydrocarbon receptor (AHR), vitamin E (Vit. E),
 carotenoids (Car.), omics, -        Oxidative damage:
 MDA/lipid peroxidation, hydroperoxides (HP), protein carbonyls (PC), DNA
 damage. Some of these markers were not necessarily
 measured using the same techniques (<em>e.g</em>., antioxidant
 capacity: FRAP, TEAC, ORAC, BAP, OXY; hydroperoxides: HYDROP, ROM; DNA
 damage: 8-OHdG, comet assay).
 <strong>Meta-analysis:</strong>
 The reviewing process was conducted by searching in Google
 Scholar for articles on oxidative markers measured in response to three
 conditions (social isolation, noise exposure, predation) by entering the
 following search terms: -        (1) “oxidative stress”
 “social isolation” -        (2) “oxidative stress”
 “noise” -        (3) “oxidative stress”
 “predation” Despite its practicability, Google Scholar
 may not necessarily be recommended as a sole literature source to conduct
 meta-analyses. <sup>[1]</sup> However, it provides more
 outputs than other academic search systems, which reflects the overall
 existing literature when checking at least the first 200 results provided
 and checking the reference list of the selected papers.
 <sup>[2]</sup> This is the strategy that was used here, by
 checking at least the first 880 Google Scholars outputs for each search,
 thereby ensuring maximal coverage of the existing literature. To minimize
 the effects of potential confounding factors resulting in spurious
 relationships, only experimental studies were included while correlative
 studies were excluded. Moreover, only oxidative markers measured in given
 biological material of given animal species and with sufficient sample
 sizes (≥ 5) <sup>[3]</sup> were retained. Finally, only
 studies measuring oxidative markers in nervous tissues (thereby
 potentially reflecting affective states) or in peripheral tissues
 accessible without sacrificing animals (<em>e.g</em>., plasma)
 were retained (as these tissues may, in most cases, be the only tissues
 available to assess animal welfare in the wild). An exception to this rule
 was made regarding oxidative markers measured in body homogenates in small
 animals (<em>e.g</em>., tadpoles, insects) exposed to
 predators (as body homogenates are typically used to measure markers of
 oxidative status in these taxa). Because of all these limitations,
 analyses had to be restricted to superoxide dismutase (SOD) activity,
 catalase (CAT) activity, glutathione (GSH) concentration and
 malondialdehyde (MDA) concentration, measured -       
 (1) in the hippocampus (HP) and prefrontal cortex (PFC) in laboratory
 rodents (rats and mice) exposed to social isolation,
 -        (2) in the hippocampus (HP), brain and blood
 plasma/serum of laboratory rodents exposed to noise,
 -        (3) in body homogenates of tadpoles, crustaceans and
 insects exposed to predators. The mean values and
 standard deviations of the control group and the treatment group(s) were
 extracted from each study (from the content of the article, from the
 related supplementary material, from the figures using Graphreader V2
 (version 0.9, Jan 2023, Kristian P. Larsen www.graphreader.com) or by contacting the authors). Standardized Mean Differences (SMDs, Hedges’ g) were then calculated for the difference between experimental and control groups included in each study, using the <em>escalc</em> function in the <em>metafor</em> package <sup>[4]</sup> in R version 4.3.1 <sup>[5]</sup> using R Studio (version 2023.06.0). Results were not sensitive to the use of SMD or SMDH (correcting SMD for heteroskedacity). SMD effect sizes were then used to conduct meta-analyses with the <em>rma.mv</em> function of <em>metafor</em>. Because in most analyses, some studies provided more than one data point, study ID was added as a random factor in those models. In order to be able to more easily compare with selection models (described below), all other models were fitted with effect size ID (one unique ID per effect size) as a random effect. For models where study ID was also used as a random effect, effect size ID captures the additional variation between effect sizes within studies, while the use of effect size ID in models with only one effect size per study is identical to study ID, so only effect size ID was kept in those models. All models were fitted with restricted maximum likelihood (REML) estimation. The t-value was used as a test statistic to construct confidence intervals and p-values. <sup>[6]</sup> This first meta-analytical approach provided a general effect size for each of the four oxidative markers (SOD, CAT, GSH, MDA) in response to the three considered conditions (social isolation, noise exposure, predation exposure) (when the number of data points ≥ 5), resulting in a total of 11 models. For the instances where sample size &lt; 5 (<em>e.g</em>., GSH ~ predation model), models were not run. In a second step, meta-regressions were conducted by adding moderators in the model to assess differences in effect size between tissues (hippocampus and prefrontal cortex in the social isolation models; plasma, whole brain and hippocampus for noise exposure models) or taxa (crustaceans, amphibians and insects for predation exposure models), resulting in a total of nine models. Finally, in a third step, further meta-analyses were conducted for each oxidative marker within each moderator to calculate effect sizes within each biological tissue or taxon (when the number of data points ≥ 5). Phylogenetic information was not included in any of the models, as they included only laboratory rodents (mostly rats and some mice; social isolation, noise exposure) or because “taxon” was a variable of interest in the model (predation exposure). Because the visual examination of funnel plots (and related methods, such as the Egger’s test), does not accurately capture publication bias in meta-analyses, publication bias was assessed here using selection models (using the <em>selmodel</em> function from the package <em>metafor</em> after using the <em>rma</em> function in R) to adjust for studies with given P-values being more likely to be published. <sup>[7]</sup> Because most studies included in the meta-analyses reported results for more than one marker of oxidative status regardless of the significance of the outcomes and of the variation direction (increase or decrease), two-tailed selection models were preferred over one-tailed selection models (note, however, that both model types provided qualitatively similar estimates). These models were run for each oxidative marker on the whole dataset of each of the three considered conditions (total of 11 models with a number of data points between 12 and 58) as described in <sup>[8]</sup>. The cutoff P-value, initially set at 0.05, was in some instances adjusted to lower values to ensure a sufficient sample size (N ≥ 5) within each P-value interval (see Table S2 for the chosen cut-off points). In five cases, evidence of publication bias was detected. In those cases, effect sizes estimated by the function <em>selmodel</em> are provided instead of effect sizes estimated by the <em>rma.mv</em> function. Because it was not possible to adjust for publication bias in the corresponding metaregression analyses (impossible to use the <em>selmodel</em> function after using the <em>rma.mv</em> function necessary for metaregressions), the coefficients and confidence intervals provided should be interpreted with caution. <strong>References</strong> 1. Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. <em>Research Synthesis Methods</em>, <em>11</em>(2), 181–217. https://doi.org/10.1002/jrsm.1378 2. Ungar, O. J., Muhanna, N., & Chaushu, H. (2023). Google Scholar as a sole literature source for meta-analyses in otolaryngology head and neck surgery. <em>Indian Journal of Otolaryngology and Head & Neck Surgery</em>, <em>75</em>(2), 864–870. https://doi.org/10.1007/s12070-023-03532-8 3. Curtis, M. J., Bond, R. A., Spina, D., Ahluwalia, A., Alexander, S. P. A., Giembycz, M. A., Gilchrist, A., Hoyer, D., Insel, P. A., Izzo, A. A., Lawrence, A. J., MacEwan, D. J., Moon, L. D. F., Wonnacott, S., Weston, A. H., & McGrath, J. C. (2015). Experimental design and analysis and their reporting: New guidance for publication in BJP: Editorial. <em>British Journal of Pharmacology</em>, <em>172</em>(14), 3461–3471. https://doi.org/10.1111/bph.12856 4. Viechtbauer, W. (2010). Conducting meta-analyses in <em>R</em> with the <strong>metafor</strong> package. <em>Journal of Statistical Software</em>, <em>36</em>(3). https://doi.org/10.18637/jss.v036.i03 5. R Core Team. (2023). <em>R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria</em> [Computer software]. https://www.R-project.org/ 6. Assink, M., & Wibbelink, C. J. M. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. <em>The Quantitative Methods for Psychology</em>, <em>12</em>(3), 154–174. https://doi.org/10.20982/tqmp.12.3.p154 7. Maier, M., VanderWeele, T. J., & Mathur, M. B. (2022). Using selection models to assess sensitivity to publication bias: A tutorial and call for more routine use. <em>Campbell Systematic Reviews</em>, <em>18</em>(3). https://doi.org/10.1002/cl2.1256 8. Harrer, M., Cuijpers, P., Furukawa, T. A., & Ebert, D. D. (2021). Publication Bias. In <em>Doing Meta-Analysis with R: A Hands-On Guide</em> (Chapman&Hall). CRC Press. https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/
description: # Data from: Oxidative status: A general but overlooked indicator of
 welfare across animal species?
 [https://doi.org/10.5061/dryad.xksn02vq3](https://doi.org/10.5061/dryad.xksn02vq3) ## Description of the data and file structure Review Data: data are presented by research fields, journal and year of publication. For each publication, the following information is provided: title, animal taxon being considered, markers of oxidative status being measured, the biological matrix were markers of oxidative status were measured, and the conditions in response to which they were measured Meta-analysis data: data are presented for each of the three negatively-valenced conditions under consideration (social isolation, noise exposure, predation exposure). The title of each study is provided, as well as the tissue in which markers of oxidative status were measured. For each of the four markers of oxidative status that were considered here (MDA, GSH, SOD, CAT) , the sample size, the mean effect and the standard deviation are provided for the control and the treatment groups. ## Sharing/Access information Data was derived from the following sources: * Peer-reviewed articles ## Code/Software R codes used for the meta-analyses and meta-regressions are provided for each marker of oxidative status (MDA, GSH, SOD, CAT) and for each negatively-valenced condition (social isolation, noise exposure, predation exposure) considered here.