DOI: 10.5061/dryad.51c59zwh2
title: The relationship between neutral genetic diversity and performance in wild arthropod populations

description: Larger effective populations (Ne) are characterised by higher genetic
 diversity, which is expected to predict population performance (average
 individual performance that influences fitness). Empirical studies of the
 relationship between neutral diversity and performance mostly represent
 species with small Ne, while there is limited data from the species-rich
 and ecologically important arthropods that are assumed to have large Ne
 but are threatened by massive declines. We performed a systematic
 literature search and used meta-analytical models to test the prediction
 of a positive association between neutral genetic diversity and
 performance in wild arthropods. From 14 relevant studies of 286
 populations, we detected a weak (r = 0.15) but non-significant positive
 association both in the full data set (121 effect sizes) and a reduced
 data set accounting for dependency (14 effect sizes). Theory predicts that
 traits closely associated with fitness show relatively stronger
 correlation with neutral diversity, this relationship was upheld for
 longevity and marginally for reproduction. Our analyses point to major
 knowledge gaps in our understanding of relationships between neutral
 diversity and performance. Future studies using genome-wide data sets
 across populations could guide more powerful designs to evaluate
 relationships between adaptive, deleterious and neutral diversity and
 performance.
description: <strong>Study eligibility criteria</strong>
 To review studies on the association between neutral genetic
 diversity and performance in natural arthropod populations, we strictly
 included studies that presented data on both population-level neutral
 genetic diversity and measure(s) of performance in at least two
 populations of an arthropod species. Performance traits were selected as
 proxies for fitness and included reproductive, life history, immunity,
 morphology, and behavioural traits, listed in Table 1 (adapted from
 Mousseau & Roff, 1987). The suitability of these traits as proxies
 for fitness in natural populations is supported by associations between
 offspring production and longevity, morphology (body size) or behaviour
 (number of mates) in wild crickets (Rodríguez-Muñoz et al., 2010) but
 otherwise rely on extrapolations from experimental or comparative studies
 (e.g., Fabian et al., 2018; Pekkala et al., 2011). Performance traits were
 measured either in the natural setting of the population or under
 controlled conditions in the lab or common gardens. We adopted very broad
 inclusion criteria for estimates of neutral genetic diversity by accepting
 low-coverage markers that are traditionally used for this purpose (e.g.,
 microsatellites and allozymes) as well as estimates based on high genome
 coverage (e.g., ddRAD sequencing). Some of these markers may not always be
 strictly neutral just as e.g. ddRAD sequences may include a smaller subset
 of loci under selection. However, when averaged over several or many loci
 these estimates are here considered to reflect neutral genetic diversity.
 We rejected studies that sampled non-representatively and non-randomly
 (e.g., using isofemale lines or supersisters to generate populations, or
 using juvenile clutches or colonies without specifying how many
 populations they were sampled from). We removed studies that artificially
 manipulated genetic diversity of populations (‘experimental’ in Figure 1),
 studies that used too few markers to infer genetic diversity i.e., less
 than 3 loci except when used to determine parentage (‘insufficient genetic
 data’ in Figure 1) and studies based on exactly the same data (‘repeated
 data’ in Figure 1). <strong>Selection of search
 terms</strong> To find eligible studies, we
 developed four parallel search strings: a string for relevant Web of
 Science categories, a genetic diversity string, a performance trait
 string, and a taxa group string (see Appendix 1). The genetic diversity
 string keywords were adapted from Hughes et al. (2008). Search terms for
 performance traits were designed to capture traits within the nine
 categories described in Table 1. Behavioural traits were included when a
 direct effect on survival or reproduction could be assumed. Morphological
 traits were assumed to mostly reflect body size and hence be proxies for
 fecundity or offspring production (Marshall & Gittleman, 1994;
 Rodríguez-Muñoz et al., 2010; Walker et al., 2003). Traits that do not
 have an impact on fitness that is straightforward to interpret were
 excluded from the meta-analysis. These include learning, memory retention,
 nest mate recognition, sex ratio or colony aggression.
 Given the large number of taxonomic groups among arthropods, we
 optimized the taxa group search string to remove redundant taxa keywords
 so that the search string did not surpass the keyword limit of Web of
 Science. We selected scientific names for arthropod subphyla, classes and
 commonly used sub-classes and orders using BIOSIS Previews (2020),
 Marshall (2006) and Wheeler et al. (2001) (Appendix 2 Table 1). Frequently
 used common names for these groups were then collated using Marshall
 (2006) and Wikipedia contributors (2023) as supporting information and
 included the genera ‘Drosophila’ and ‘Daphnia’. Each common name was
 individually searched with the genetic diversity string and the Web of
 Science categories string, against an exclusionary string with the
 respective scientific names for class (excluding ‘Insecta’), sub-class and
 order (e.g., Beetle NOT Coleoptera). The same process was repeated vice
 versa for the scientific names against their respective common names. The
 keyword(s) that yielded no relevant unique hits were dropped. As a last
 step, the selected keywords were searched with the genetic diversity
 string and the Web of Science categories against the sub-phyla and against
 the class ‘Insecta’. The full list of scientific and common names that
 were optimised is presented in Appendix 2 (Table 1). All optimisation
 searches were conducted in ‘All Fields’ field on Web of Science using
 abbreviations that would concisely capture relevant unique hits. For
 example, ‘Metaboli*’ was used instead of ‘Metabolism’ and ‘Metabolic rate’
 separately. <strong>Literature search and study
 selection</strong> We performed the literature
 search in Web of Science (accessed through XX Library) covering the period
 1900 to December 2022. We first performed a search with the Web of Science
 categories, genetic diversity, performance trait and taxa group strings in
 the ‘Abstract’ field. We then added results from a second search with the
 Web of Science categories, genetic diversity and performance trait strings
 still in the ‘Abstract’ field, but with the taxa group string in the
 ‘Title’ field plus an exclusionary taxa group string in the ‘Abstract’
 field. This was to capture articles that only used the taxa names in their
 titles. Lastly, we searched the reference lists of papers that met our
 study selection criteria and of relevant review papers for additional
 articles that our Web of Science searches failed to capture (Charlesworth
 & Charlesworth, 1987; Crnokrak & Roff, 1999; DeWoody et
 al., 2021; Gibson & Nguyen, 2021; Hughes et al., 2008; Keller
 & Waller, 2002; King & Lively, 2012; Reed &
 Frankham, 2003; Soper et al., 2021). Following the
 PRISMA 2020 guidelines for systematic reviews (Page et al., 2021), we
 obtained a total of 6,698 studies published in English from Web of Science
 and reference list searches (Figure 1). Based on the search criteria
 explained above, more than 6400 studies were deemed ‘irrelevant’ and of
 235 records retained, 219 were experimental and therefore also excluded
 (Figure 1). In total, we acquired 14 unique studies with both neutral
 genetic diversity and performance data in natural arthropod
 populations. <strong>Data extraction and effect
 size calculation</strong> We extracted relevant
 information from each study in order to calculate the correlation
 (Pearson's <em>r</em>) between genetic diversity and
 performance, which we normalised using Fisher’s<em> Z
 </em>transform: <em>Zr</em> = (1/2) x
 log((1+<em>r</em>) / (1-<em>r</em>)). The sampling
 variance for this effect size is 1 / (<em>N</em>-3) and is
 used to weight our statistical models. <em>N</em> is the
 number of populations from which each effect size is calculated. The
 correlation coefficient was calculated from: data provided in the study,
 data in the supplementary information, from reported test statistics and
 figures (e.g., Spearman’s rank correlations, <em>t</em>
 values, <em>F</em>-ratio from one-way ANOVA) or from our own
 calculations based on genetic data kindly provided by the authors (Pearson
 correlations presented in Appendix 3, Arteaga et al., 2019; Dobelmann et
 al., 2019; Freilij et al., 2022; Koch et al., 2020). Full details of the
 calculations and conversions underlying each effect size are provided in
 the supplementary data extraction file (supplementary data file A). To
 standardize representation of data, we reversed the direction of the
 statistical association for traits that have a negative expected impact on
 fitness (e.g., parasite prevalence, extinction probability), making it
 comparable to traits with positive expected impact on fitness (e.g.,
 number of eggs produced). In total we calculated 121 effect sizes from the
 14 studies involving 14 arthropod species (Table S1).
 We extracted the following information from each study: i) how
 performance and genetic diversity were measured, ii) the observed range of
 genetic diversity, and iii) taxonomic information. In our sample of
 studies, performance traits were in the reproduction, longevity, immunity
 or morphology categories (see Table 1). We divided genetic diversity
 measures into four categories: heterozygosity (<em>Ho</em>,
 <em>He</em>), inbreeding coefficients (GIS, FIS,
 homozygosity), lineage-level diversity (polyandry), and molecular-level
 diversity (% polymorphic loci).
 <strong>Non-independence</strong>
 There are four levels of non-independence in our dataset: i)
 phylogenetic non-independence due to shared evolutionary history, ii)
 species-level non-independence due to multiple effect sizes per species
 (12 of the 14 species), iii) study-level non-independence due to multiple
 effect sizes per study (12 of the 14 studies), and iv) within-study
 non-independence due to multiple effect sizes being calculated from the
 same individuals. To address phylogenetic non-independence we downloaded a
 phylogenetic tree of the 14 species in our dataset using the
 <em>rotl</em> R package (Michonneau et al., 2016) from which
 we derived a phylogenetic correlation matrix, using functions in the
 <em>APE</em> R package (Paradis & Schliep, 2019) to
 use as a random effect in our statistical models. The study and species
 levels of non-independence are confounded with each other because most
 studies are on unique species (10 of the 14 studies) with the exceptions
 being two studies on two species each and two studies on the same species
 (Table S1). To account for study and species levels of non-independence we
 included species ID as a random effect in our statistical models. Finally,
 to explore the impact of within-study non-independence on our results, we
 constructed a reduced dataset with one observation per species. This
 dataset avoids non-independence due to multiple measurements made on the
 same individuals and multiple measurements per study / species since
 <em>N<sub>study</sub></em> =
 <em>N<sub>species</sub></em> =
 <em>N<sub>effect sizes</sub> = 14</em> and only
 phylogeny needs to be accounted for. To do this, we averaged or removed as
 many effect sizes as possible from each study while keeping unique levels
 of how performance and genetic diversity were measured (supplementary data
 file B). For example, if a study measured longevity and reproduction on
 the same individuals, both effect sizes were kept. From this, we sampled
 one effect size at random from each species. We report the results of the
 analyses described below on the full- and species-level datasets side by
 side. <strong>Publication bias and the
 measurement of genetic diversity</strong> The
 tendency not to publish non-significant findings (publication bias) can
 distort the distribution of effect sizes in meta-analyses (Koricheva et
 al., 2013). We detected limited evidence of publication bias using three
 different methods: the funnel plot (standard error vs effect size) was
 symmetrical, zero studies were estimated to be missing from our sample of
 studies based on a trim and fill analysis, and the intercept from Egger’s
 regression was non-significant (estimate = 0.34, se = 0.54, p = 0.54).
 These tests were conducted on the species-level dataset to avoid issues of
 non-independence. The association between performance
 and genetic diversity was not strongly affected by how genetic diversity
 was measured. We examined this by modelling <em>Zr</em> as a
 function of genetic diversity measure category (four levels, see above)
 with the global intercept suppressed to estimate the mean effect size for
 each level. The only statistically significant association between
 performance and genetic diversity was when diversity was measured at the
 molecular level (% polymorphic loci) and only in the full data set (full
 data molecular level diversity <em>Zr</em> = 0.68, 95% CI =
 0.16 to 1.06; species data molecular level diversity<em>
 Zr</em> = 0.36, 95% CI = -0.53 to 1.05, Table S2).
 We calculated a scaled genetic diversity range that is comparable
 across species to investigate if strength of the correlation between
 genetic diversity and performance depends on range of genetic diversity
 sampled. We did this by taking the difference between the maximum and
 minimum values of genetic diversity sampled for each effect size and
 dividing this difference by the maximum possible difference for a given
 measure of genetic diversity:
 (<em>max<sub>observed</sub></em> -
 <em>min<sub>observed</sub></em>) /
 (<em>max<sub>possible</sub></em> -
 <em>min<sub>possible</sub></em>). This expresses
 the observed difference as a fraction of the maximum possible difference,
 and ranges between 0 and 1, with 1 indicating that the sampled range is
 equal to the maximum possible range of genetic diversity. For example, for
 <em>Bombus jonellus</em>, <em>He</em> was measured
 which ranges between 0 and 1 and the minimum and maximum observed values
 in the study population were 0.696 and 0.766 respectively. This gives a
 scaled genetic diversity range of 0.070 = (0.766-0.696) / (1-0). It was
 not possible to calculate a theoretically possible range of genetic values
 for effect sizes based on polyandry and allelic richness
 (<em>Ar</em>). This reduced our sample size for this analysis
 to 85 effect sizes from 12 studies on 12 species.
 <strong>Data analysis</strong> We
 constructed three statistical models in the R environment (R Core Team,
 2023) to answer our questions (see the supplementary R code). To estimate
 the mean effect size on the correlation between genetic diversity and
 performance, we constructed an intercept only model with
 <em>Zr</em> as the response variable. To determine if traits
 closely associated with fitness show a stronger positive correlation with
 genetic diversity, we modelled <em>Zr</em> as a function of
 performance category (four levels: immunity, reproduction, longevity,
 morphology). We suppressed the global intercept in this model to estimate
 the mean effect size for each level. Finally, to determine if we can
 explain variation in the association between genetic diversity and
 performance based on the genetic diversity range sampled, we modelled
 <em>Zr</em> as a function of the scaled genetic diversity
 range (continuous). Note that it was not possible to fit interactions
 between how performance was measured and the observed range of genetic
 diversity because of limited replication in the different performance
 categories. In all models, <em>Zr</em> was
 weighted by its inverse sampling variance and the phylogenetic correlation
 matrix was included as a random effect. When analysing the full dataset,
 but not the species-level data, species ID is included as an additional
 random effect. We report <em>I<sup>2</sup></em>
 estimates (Nakagawa & Santos, 2012) for these variance components
 in Tables S3-S5. Models were run in the <em>MCMCglmm</em>
 (Hadfield, 2010) and <em>metafor</em> (Viechtbauer, 2010) R
 packages to compare Bayesian vs. restricted maximum likelihood approaches
 to parameter estimation. We report the parameter estimates (posterior mode
 ± 95% credible intervals) from <em>MCMCglmm</em> in the main
 text and both are reported in the Tables S3-S5. In all models, the
 parameter estimates from <em>MCMCglmm</em> and
 <em>metafor</em> were consistent.
description: # The relationship between neutral genetic diversity and performance in
 wild arthropod populations File name: supp_R_code.R This R script contains
 all the code needed to replicate the analyses (including packages and
 functions). Models described in the methods section of the manuscript are
 located in this code. The code is organised into five main parts: * Data
 manipulation (lines 50 to 65) * Publication bias tests (lines 70 to 89) *
 Differences between genetic diversity measures (lines 95 to 210) *
 Analysis of all effect sizes (lines 216 to 390) * Analysis of reduced
 dataset (lines 395 to 548)
 \#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# File name: supp_tables.xlsx This Excel document contains the following four sheets: * 'Table_S1' * the data are organised in long format (134 rows) * column descriptions: A. study = study reference from which relevant estimates were extracted B. category = performance trait category C. performance = fitness trait D. genetic diversity = the metric used to estimate genetic diversity in the study E. genetic diversity minimum = lowest level of genetic diversity respectively F. genetic diversity maximum = highest level of genetic diversity respectively G. theoretical minimum = lowest theoretical level of genetic diversity for respective metric. NAs are used when there is no theoretical minimum H. theoretical maximum = highest theoretical level of genetic diversity for respective metric. NAs are used when there is no theoretical maximum I. Fisher's z-transformation of r = effect size for study. NAs refer to undefined numbers that result from calculations where one or more input values were 0 J. expected impact on fitness = expected direction of correlation between respective genetic diversity and fitness trait measured. + refers to positive expected impact, - refers to negative expected impact K. adjusted Fisher's z-transformation of r = z-statistic corrected according to the expected impact on fitness L. sampling variance = sampling variance of Fisher's z-transformation of r M. latin binomial = scientific name of each species N. family = taxonomic family of each species O. order = taxonomic order of each species P. data type = data analysis method Q. source = where the relevant estimates were extracted from in the study R. genetic type = genetic marker type used to estimate genetic diversity in study S. marker number = number of genetic markers used to estimate genetic diversity in study * 'all_data for R' * subset of Table S1 * the data are organised in long format (122 rows) * export as .csv files and read into R (some of the column headings will need changing to match with the R code) * column descriptions: A. study = study reference from which relevant estimates were extracted B. category = performance trait group C. genetic diversity category = the type of metric used to estimate genetic diversity in the study D. genetic diversity minimum = lowest level of genetic diversity respectively E. genetic diversity maximum = highest level of genetic diversity respectively F. theoretical minimum = lowest theoretical level of genetic diversity for respective metric. NAs are used when there is no theoretical minimum G. theoretical maximum = highest theoretical level of genetic diversity for respective metric. NAs are used when there is no theoretical maximum H. Fisher's z-transformation of r = effect size for study I. expected impact on fitness = expected direction of correlation between respective genetic diversity fitness trait measured J. adjusted Fisher's z-transformation of r = z-statistic corrected according the expected impact on fitness K. sampling variance = sampling variance of Fisher's z-transformation of r L. latin binomial = scientific name of each species * 'reduced_data for R' * the reduced data to account for within-study dependencies * see 'supp_data_B.txt' for a description of how the data were reduced * the data are organised in long format (15 rows) * export as .csv files and read into R (some of the column headings will need changing to match with the R code) * column descriptions: A. study = study reference from which relevant estimates were extracted B. category = performance trait group C. genetic diversity = the metric used to estimate genetic diversity in the study D. genetic diversity category = the type of metric used to estimate genetic diversity in the study E. genetic diversity minimum = lowest level of genetic diversity respectively F. genetic diversity maximum = highest level of genetic diversity respectively G. theoretical minimum = lowest theoretical level of genetic diversity for respective metric. NAs are used when there is no theoretical minimum H. theoretical maximum = highest theoretical level of genetic diversity for respective metric. NAs are used when there is no theoretical maximum I. Fisher's z-transformation of r = effect size for study J. expected impact on fitness = expected direction of correlation between respective genetic diversity fitness trait measured K. adjusted Fisher's z-transformation of r = z-statistic corrected according the expected impact on fitness L. sampling variance = sampling variance of Fisher's z-transformation of r M. latin binomial = scientific name of each species \#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# File name: supp_data_A.txt This plain text document contains details of the figures, tables, and text fragments from which data were obtained, and the calculations used to pool data, organised alphabetically by latin name. \#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# File name: supp_data_B.txt This plain text document contains details of how the data in supp_data_A.txt were collapsed to have one effect size per species \#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-# File name: tree.nex phylogenetic tree to read into R.