DOI: 10.5061/dryad.gtht76hvb
title: Predicting the effects of climate change on the fertility of aquatic animals using a meta-analytic approach

description: Given that reproductive physiology is highly sensitive to thermal stress,
 there is increasing concern about the effects of climate change on animal
 fertility. Even a slight reduction in fertility can have consequences for
 population growth and survival, so it is critical to better understand and
 predict the potential effects of climate change on reproductive traits. We
 synthesised 1894 effect sizes across 276 studies on 241 species to examine
 thermal effects on fertility in aquatic animals. Our meta-analysis
 revealed that external fertilisers tend to be more vulnerable to warming
 than internal fertilisers, especially in freshwater species. We also found
 that increased temperature is particularly detrimental for gametes, and
 that under certain conditions, female fertility is more sensitive to
 warming than male fertility, challenging the prevailing view that males
 are more vulnerable. This work provides valuable new insights into the
 effects of temperature on fertility, with potential consequences for
 population viability. 
description: <em>Literature Search and Data
 Collection </em> This
 study was not pre-registered. To identify relevant studies for our
 meta-analysis, we used a large systematic map (Dougherty et al. 2024),
 which was created by searching published literature using the ISI Web of
 Science Core and following the ROSES Reporting Standards for Systematic
 Evidence Syntheses guidelines and the PRISMA guidelines (Haddaway et al.
 2018, O’Dea et al. 2021,Supplementary Table 8). Studies included in this
 systematic map were peer-reviewed journal articles or book chapters that
 measured one or more reproductive traits at two or more temperatures in
 non-human animals. The systematic map includes 427 papers that contain
 data on the effects of temperature on fertility in aquatic animals. We
 excluded 151 of these from our meta-analysis for a variety of reasons
 (Supplementary Figure 1, Supplementary Table 9). For example, we excluded
 studies with data on gonad developmental stages and gonad gene expression,
 which have no clear link to fertility. We included gonad traits such as
 gonadosomatic index and gonad size, which have a clearer relationship with
 reproductive success. We also excluded studies examining seasonal
 temperature variations outside the breeding season, which was not relevant
 to our question. For studies with insufficient information for calculating
 effect sizes, we contacted the authors to request the missing data. Out of
 37 authors, four responded, with one providing the necessary data. We thus
 had to exclude the remaining 36 studies (Supplementary Figure 1).
   Our final analysis included
 276 papers, and each paper was assigned to one of the authors (AC, IG, EM,
 or CH). The data were then compiled, cross-checked, and validated by the
 first (AC) and last author (NP). A total of 1894 effect sizes were
 extracted and used in the final analysis. We extracted data on the effects
 of temperature on gamete traits (e.g., gamete size, quantity, or quality),
 gonad traits (e.g., gonad size, gonadosomatic index), and reproductive
 output (Supplementary Table 2). For each effect size, we recorded
 information on taxonomic family, class and phylum, fertilisation mode,
 which sex was exposed to the temperature change, whether temperature
 variation was natural or experimentally manipulated, the duration of
 exposure to the temperature change, and the developmental stage during
 which individuals were exposed to the temperature change. 
 <em>Effect size
 calculation </em> Data
 were extracted from tables, text, or figures using WebPlotDigitizer
 (Rohatgi 2022). For studies using two or three discrete temperature
 treatments (e.g., low versus high temperatures), we extracted the mean and
 standard deviation and used these to calculate a biserial correlation
 coefficient and its sampling variance using the ‘escalc’ function in the
 ‘metafor’ package vers.4.6-0 (Viechtbauer 2010). The ‘escalc’ function
 uses equation 12 from Jacobs and Viechtbauer (2017) to calculate the
 sampling variance. To account for shared-control non-independence, we
 first identified all cases where a control group was compared to several
 treatment groups. We then reduced the weight of the control group in the
 analyses by dividing the sample size of such a group by as many times that
 group has been compared to a treatment group before calculating effect
 sizes.   For studies where
 fertility was measured across a range of temperatures (i.e. where it was
 appropriate to treat temperature as a continuous variable), we calculated
 a Pearson’s correlation coefficient. For any studies that only provided
 test statistics, such as a chi-square test, we converted these to
 correlation coefficients, using the formulas provided in Borenstein et al.
 (2011). For both biserial correlation coefficients and Pearson’s
 correlation coefficients, a positive value indicated higher fertility at
 higher temperatures, whereas a negative value indicated a reduction in
 fertility at higher temperatures.   
 In total, we obtained 1326 effect sizes for marine species, 473
 effect sizes for freshwater species, and 95 effect sizes for species that
 were neither freshwater nor marine (e.g., hypersaline or estuarine). This
 included 241 species from nine phyla with Chordata (38.2%) having the
 largest representation, followed by Arthropoda (20.7%) and Mollusca
 (17.2%) (Supplementary Figure 2). All species in our dataset were
 ectotherms.  <em>Data
 analysis </em> All
 analyses were completed in R version 4.3.1 (R Core Team 2023), and figures
 were generated using the ‘orchaRd’ package vers. 2 (Nakagawa et al. 2023)
 and the ‘ggplot2’ package vers. 3.5.1 (Wickham 2016). These analyses and
 all figures use a combination of biserial correlation coefficients (where
 means and standard deviations were available, k=1583) and Pearson’s
 correlation coefficients (k=311). To construct a phylogenetic tree for the
 species included in our dataset, we used the tree for the 1,191 species in
 the full systematic map (Dougherty et al. 2024) and then used the
 ‘set.diff’ and ‘drop.tip’ functions in the ‘ape’ package vers. 5.7-1
 (Paradis et al. 2004) to remove non-aquatic species (Figure 1). The
 variance-covariance matrix representing the phylogenetic relatedness
 between all species in our dataset was used as a random effect in all of
 our meta-analytic models described below (Paradis et al. 2004). We also
 included the following random effects in all models: ‘paper code’ to
 account for multiple effect sizes from the same study, ‘animal group ID’
 to control for effect sizes from the same paper that used different groups
 of animals, ‘species ID’ to account for the same species being used across
 different papers, ‘shared control ID’ to account for effect sizes from the
 same paper which shared a control treatment, and ‘effect size code’ as a
 unit level effect to measure residual heterogeneity (Viechtbauer
 2010).    We first assessed
 the overall effect of temperature on fertility with a random effects model
 using the ‘rma.mv’ function in ‘metafor’. We estimated heterogeneity in
 our main effects model using I2 as an estimate of the proportion of
 variance explained due to differences between the levels of a random
 effect (Nakagawa and Santos 2012). We also display prediction intervals on
 figures as a measure of heterogeneity (Cooper et al. 2019). We then tested
 a range of moderators that may influence the effect of temperature on
 fertility, using multi-level meta-regression models with the same random
 effects described above. For each moderator, we calculated a marginal R2
 to describe the percentage of heterogeneity explained by the moderator
 model compared to the main effects model. Statistical significance of
 moderators was determined using omnibus tests of meta-regressions with
 intercepts. We obtained mean effect size estimates (β) and 95% confidence
 intervals for each level of the moderator by removing the intercept from
 the meta-regression model. For statistically significant moderators with
 more than two levels, we then examined which factor levels differed from
 one another. We did this using the L argument in the ‘anova’ command to
 perform linear contrasts in meta-regressions without an
 intercept.   We tested ten
 categorical moderators and four continuous moderators (Supplementary Table
 1). We first looked at three categorical moderators to examine whether
 there were any differences in the effects of temperature on fertility that
 could be explained by the type of fertility trait being examined: (i) “sex
 of the trait” (male, female, both) and (ii) “type of fertility trait”
 (gamete, gonad or reproductive output). The category “both” for “sex of
 trait” refers to combined fertility traits, influenced by both male and
 female fertility changes, such as fertilisation success. Sex of trait
 differs from the sex exposed to temperature as some papers only exposed
 one sex but measured traits that could be affected by both sexes. For
 fertility traits that could not be assigned to a specific sex, such as
 fertilisation success or offspring number (k=479), we ran a model testing
 whether the effect on these fertility traits varied depending on whether
 only males, only females, or both sexes had been exposed to increased
 temperature (iii) “Sex exposed (for combined traits)”. This allowed us to
 test for additive or multiplicative effects of exposing both sexes to a
 higher temperature compared to just one sex.   
 Five categorical moderators and one continuous
 variable were used to test whether variation in the effect of temperature
 on fertility could be explained by the organism’s physiological or life
 history traits: (i) “developmental stage of animal exposed” to compare
 effects due to the exposure of gametes, embryos and juveniles, or adults,
 (ii) “habitat type” to test for differences between freshwater and marine
 species, (iii) “fertilisation mode” to test for differences between
 internal and external fertilisers, (iv) “phylum” to test for different
 effects across taxonomic groups (v) “climate zone” to test for differences
 among species from tropical, subtropical, temperature, or polar regions,
 and (vi) each species’ thermal tolerance (CTmax). To examine the relevance
 of thermal tolerance to effects on fertility, we subtracted the elevated
 temperature organisms were exposed to from that species’ critical thermal
 limit (CTmax), using data for 13 species from four phyla in our dataset
 from the GlobTherm database (Bennett et al. 2018, Supplementary Figure
 3).  As with the main dataset, the majority of the effect sizes (k=184 out
 of 250) came from Chordata species (n=6), and four Mollusc species
 contributed 47 effect sizes. The other species were Echinoderms (n=2,
 k=18) and Arthropods (n=1, k=1). In addition to examining the effects of
 each of these moderators individually, we looked at the interaction
 between fertilisation mode and habitat type to test whether the effects of
 temperature on internal versus external fertilisers differed between
 freshwater and marine environments.  
 We then examined the effects of a study’s experimental design,
 and the type of temperature exposure experienced by the organism, focusing
 on the duration of the temperature change, the magnitude of the
 temperature change, whether organisms experienced constant or fluctuating
 temperatures, and whether the temperature variation was experimentally
 manipulated or experienced under natural conditions. We also looked at
 whether the interaction between the magnitude of temperature change and
 the duration of the temperature change influenced the effect of
 temperature on fertility.  
 Lastly, we examined sex-specific effects using a series of
 multi-level meta-analytic models with interaction effects. More
 specifically, we tested the interaction between focal sex (male or female)
 and (i) “type of fertility trait” (gamete, gonad or reproductive success),
 (ii) “fertilisation mode” (internal or external fertilisation), and (iii)
 “developmental stage of animal exposed” (gamete, early development, or
 adulthood). Alluvial plots show the distribution of effect sizes across
 the moderator levels for these interactions (Supplementary Figures 4-6).
 For statistically significant interactions, we conducted a leave-one-out
 analysis to assess the stability of the interactions and determine if the
 results changed significantly when excluding one group. This was done
 using the ‘metafor’ and ‘dplyr’ packages vers. 1.1.4 for data manipulation
 (Wickham et al. 2023).  
 <em>Publication bias tests </em>
 Given the well-documented pattern that
 studies with statistically significant results or larger effects tend to
 be published earlier (Jennions and Møller 2002), we used a meta-regression
 with mean-centred publication year as a continuous moderator to test for
 time-lag bias using the same random effects described above. We also used
 the square root of the inverse of the sample size for each effect size as
 a moderator to test for small-study effects (Nakagawa et al.
 2022). <em>Sensitivity
 analyses </em> We chose
 to use effect sizes based on correlation coefficients rather than
 standardised mean differences for our analyses, because we were interested
 in examining the strength and direction of the relationship between
 temperature and fertility, rather than testing for differences between two
 groups. However, given that most of our effect sizes were based on means
 and standard deviations, we performed a sensitivity analysis to test
 whether our results are robust regardless of the type of effect size
 chosen. To this end, we re-ran all of our models using standardised mean
 differences (Hedges’ g effect sizes). Hedges’ g is more robust to unequal
 sampling and small sample sizes than Cohen’s d (Rosenberg et al. 2013).
 Correlation coefficients or test statistics were converted to Hedges’ g
 effect sizes using the equations provided by Borenstein et al.
 (2011).   To ensure our
 results were robust given non-independence of effect sizes extracted from
 the same studies, we conducted cluster robust estimate of the variance
 models (RVE) for our moderator models using the ‘robust’ function in
 ‘metafor’ and compared them to the standard random effects models (Hedges
 et al. 2010).The RVE models provide a more conservative estimate of
 significance with larger standard errors and wider confidence intervals
 and can prevent Type 1 errors (Tanner-Smith and Tipton 2014). Given that
 tolerance of exposure to the thermal stressor (Bigelow 1921, Cerdá et al.
 1998, Tang et al. 2000, 2000, Cerdá and Retana 2000, Armstrong et al.
 2009, Rezende et al. 2014), we also conducted a sensitivity analysis using
 the duration of the temperature change as an additional fixed effect in
 our moderator models. These models only included 1,362 out of the 1,894
 effect sizes in our full dataset, because we lacked information on the
 duration of temperature exposure for the remaining effect sizes – 28% of
 papers in our dataset did not report this information. 
 Lastly, we ran two additional
 multi-level meta-regression models to examine whether there were any
 systematic differences depending on how our effect sizes were calculated.
 The moderator for the first model was the final effect size type (biserial
 correlation coefficient or Pearson’s correlation coefficient), and the
 moderator for the second one was the type of statistical test that the
 effect sizes were originally converted from (e.g. chi-square test,
 Spearman’s correlation). Both of these models included the same random
 effects as described above. 
description: # Predicting the effects of climate change on the fertility of aquatic
 animals using a meta-analytic approach Welcome to the project repository
 for "Predicting the Effects of Climate Change on the Fertility of
 Marine and Freshwater Animals Using a Meta-Analytic Approach." This
 README provides an overview of the project, explains how to run the code,
 and outlines the data used in the analysis. ## Table of Contents *
 [Project Overview](#project-overview) * [Files in the
 Repository](#files-in-the-repository) * [Prerequisites](#prerequisites) *
 [Installation Instructions](#installation-instructions) * [Running the
 Code](#running-the-code) * [Understanding the
 Outputs](#understanding-the-outputs) * [Troubleshooting](#troubleshooting)
 * [References](#references) ## Project Overview This project explores the
 impact of climate change on the fertility of marine and freshwater animals
 using a meta-analytic approach. The analysis considers data from multiple
 studies, conducting meta-regressions with various moderators to identify
 factors influencing fertility changes. ## Files in the Repository **`Code
 for Predicting the effects of climate change on fertility.R`**: This R
 script contains code to: * Load the data. * Calculate biserial
 correlations. * Create a phylogenetic tree. * Perform meta-analysis and
 meta-regressions and interaction models. * Perform sensitivity analysis *
 Generate orchard plots for each meta-regression. * Generate summary graphs
 and plots **`Functions for Predicting the effects of climate change on
 fertility.R`** : **This R script contains code to: * create R2 and I2
 functions * functions to edit the appearance of the meta-regression
 orchard plots and the interaction orchard plots **`TEMP.csv`**: This CSV
 file contains the data extracted from the papers including information
 from various studies such as species, temperature, location, and fertility
 metrics. without the biserial correlations used to calculate the biserial
 correlations and variance in R. Cells with "NA" mean the data
 was not available and empty cells mean that column is not applicable to
 that effect size. **`TEMP_with_EffectSizes.csv`***: *This CSV file
 contains the data used for the meta-analysis, including the biserial
 correlations and variance, along with a column of combined effect sizes
 and combined variances (ES and ES_VAR), which contains a combination of
 biserial correlation coefficients (where means and standard deviations
 were available, *k*=1583) and Pearson’s correlation coefficients
 (*k*=311). Cells with "NA" mean the data was not available and
 empty cells mean that column is not applicable to that effect size.
 **`Final_tree`**: This is the full phylogenetic tree for the large
 systematic map (Dougherty et al. 2024), which we trimmed to create Figure
 1. **`Phylo Data.csv`**: This CSV file contains the retained phylogenetic
 tree tips and their corresponding phylum and whether the species are
 marine, freshwater or other aquatic animals used to create the visual
 phylogenetic tree. ## TEMP and TEMP\_with\_EffectSizes CSV File Headings
 and Descriptions | Heading | Description | |
 ----------------------------------- |
 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Paper code | A unique identifier for each paper or study. | | Paper | The name of the paper or publication where the study is documented. | | Title | The title of the paper or study. | | Publication year | The year the paper or study was published. | | DOI | The Digital Object Identifier for the paper, providing a unique link to the publication. | | GENUS SPECIES1 | The genus and species of the organism studied. | | GENUS SPECIES | The genus and species of the organism studied repeated for the phylogenetic matrix. | | Tmax | Maximum temperature reached during the study for this effect size. Units: degrees Celsius. | | CTMaxMaxTemp | Critical thermal maximum, indicating the highest temperature tolerated by the organism minus the maximum temperature. Units: degrees Celsius. | | Phylum | The taxonomic phylum of the organism(s) studied. | | Sex exposed | The sex of the organism(s) exposed to the experimental conditions. | | Fertilisation mode | The mode of fertilization (e.g., internal or external). | | Stressor variation | Description of the type of stressor variation in the study (e.g., experimental, natural). | | Climate Zone | The preferred climate zone of the species exposed (e.g., tropical, temperate, polar, etc.). | | Life-stage of animal exposed | The development stage of the organism(s) exposed in the study (e.g., gamete, juvenile, adult). | | Constant or fluctuating | Indicates whether the temperature conditions were constant or fluctuating. | | Marine or Freshwater | Indicates whether the species was marine or freshwater or other (brackish, hypersaline). | | Minimum Temperature | The minimum temperature during the study for this effect size. Units: degrees Celsius. | | Maximum Temperature | The maximum temperature during the study for this effect size. Units: degrees Celsius. | | Magnitude of Change | The degree of temperature change during the study: maximum temperature - minimum temperature. Units: degrees Celsius. | | Duration of Temperature Change Days | The length of time (in days) over which the temperature change occurred. Units: Days | | Sex of Trait | The sex associated with the trait being studied (e.g., male, female, or both). | | Fertility Trait | The specific fertility trait being measured (e.g., ovary width, sperm velocity, hatchling number). | | Units | The units of the specific fertility trait being measured (e.g., ovary width, sperm velocity, hatchling number). | | Trait category | The category of the fertility trait (e.g., gonad, gamete, reproductive output). | | Effect size code | The specific code used to denote unique effect size. | | Experiment Code | A unique code for identifying the experiment and/or group of animals from which the data came. | | Source | The source of the data or experimental results within each paper. | | Mean\_lowtemp | The mean measurement of the fertility trait at low temperature conditions. Units: depend on the specific fertility trait and are listed in the units column where available.  | | Standard Error\_lowtemp | The standard error for mean measurements at low temperature. | | SD\_lowtemp | Standard deviation for mean low temperature conditions. | | ADJSDlow | Standard deviation for mean low temperature conditions adjusted using the adjusted lown to calculate where converting from standard error. | | Confidence Intervals\_lowtemp 95% | The 95% confidence intervals for measurements at low temperature. | | Proportion\_lowtemp | Proportion data for the low-temperature fertility trait where means and standard deviations are not available and only percentage/proportion data is available. | | n\_lowtemp | The number of samples at low temperature. | | adjustedlown | The number of samples at low temperature adjusted by dividing the n\_lowtemp by count\_shared\_control. | | Mean\_hightemp | The mean measurement of the fertility trait at high temperature conditions. Units: depend on the specific fertility trait and are listed in the units column where available.  | | Standard Error\_hightemp | The standard error for measurements at high temperature. | | SD\_hightemp | Standard deviation for high temperature conditions. | | ADJSDhigh | Standard deviation for low temperature conditions adjusted using the adjusted lown to calculate where converting from standard error. | | Confidence Intervals\_hightemp 95% | The 95% confidence intervals for measurements at high temperature. | | Proportion\_hightemp | Proportion data for the high-temperature fertility trait where means and standard deviations are not available and only percentage/proportion data is available. | | n\_hightemp | The number of samples at high temperature. | | adjustedhighn | The number of samples at high temperature adjusted by dividing the n\_hightemp by count\_shared\_control. | | Test | The type of statistical test used in the study. | | Final.Effect.Size | The type of statistical test used in the final analysis (Biserial or Pearsons). | | Test\_value | The value derived from the statistical test. | | Significance | Indicates whether the result is statistically significant. | | Effect size direction | Indicates whether the effect size is positive or negative. | | Number.of.Animals | Total number of animals in the study, distinct from the total of the n\_lowtemp or n\_hightemp if discrepancies arise due to group or replicate comparisons. | | Total.Number.of.Replications | Total number of replications for the effect size. | | N\_total | Total sample size at the individual level. | | N\_totaladj | Total sample size used in the effect size calculations at the individual level (adjustedhighn + adjustedlown or Number.of.Animals where necessary). | | Pooled.Standard.Deviation | The standard deviation derived from pooling across all samples, using the adjusted sample sizes. | | Bias Correction Factor | A correction factor used to account for unequal sample sizes when calculating Hedges g effect sizes. Formula: 1 - (3 / (4\*(adjustedhighn + adjustedlown - 2) - 9)). | | PreD | Hedges g effect size before negative trait correction. Formula: ((Mean\_hightemp - Mean\_lowtemp) / Pooled Standard Deviation) \* Bias Correction Factor. | | D effect size | The standardized effect size using Hedges g times the negative trait correction. If a decrease in the trait has a positive effect on fertility, the effect size direction is reversed. | | D\_VAR | The variance of Hedges g. Formula: ((adjustedhighn + adjustedlown) / (adjustedhighn \* adjustedlown)) + (D effect size^2) / (2 \* (adjustedhighn + adjustedlown - 2)). | | r | The correlation coefficient extracted from papers. | | r\_VAR | The variance of the correlation coefficient. Formula: (1 − r^2)^2 / (N\_totaladj − 1). | | Biserial\_Correlation | Biserial correlations calculated in R using escalc. | | Biserial\_Correlation\_Variance | Biserial correlation variance calculated in R using escalc. | | ESPre | Combined column of Biserial correlations calculated in R and Pearsons correlations for effect sizes without means and standard deviations, before negative trait correction. | | Negative trait Correction | Indicates whether a correction was applied based on whether an increase in the fertility trait measured would have a positive or negative effect on fertility. | | ES | Biserial correlations calculated in R without means and standard deviations, multiplied by the negative trait correction. | | ES\_VAR | The variance associated with the biserial and Pearsons correlations for the effect sizes. | | shared.control.id | A unique identifier of effect sizes that share a control value. | | count\_shared\_control | The number of times the control value in that row has been used. | ## Phylo Data CSV File Headings and Descriptions | Heading | Description | | -------------------- | -------------------------------------------------------------------------------- | | TipLabel | The unique species as they appear on the phylogenetic tree tips. | | Phylum | Phylum of the individual species. | | Marine.or.Freshwater | The habitat type of each species and whether it is marine, freshwater, or other. | ```` ## Prerequisites To run the code in this repository, you need: * R (version 4.0 or later) * RStudio (optional but recommended) * A complete set of R packages: ```r # Install required packages install.packages(c("ape" = "5.7-1", "bookdown" = "0.39", "car" = "3.1-2", "dplyr" = "1.1.4", "forcats" = "1.0.0", "ggplot2" = "3.4.0", "ggpubr" = "0.6.0", "ggtree" = "2.2.1", "metafor" = "3.0-2", "tidyverse" = "1.3.1")) # Load pacman to simplify loading other packages pacman::p_load(devtools, tidyverse, metafor, patchwork, R.rsp, emmeans) # Install orchaRd from GitHub devtools::install_github("daniel1noble/orchaRd", force = TRUE) # Load libraries library(readr) library(ape) library(orchaRd) library(metafor) library(car) library(patchwork) library(rotl) library(ggplot2) library(MCMCglmm) library(Hmisc) library(dplyr) library(tidyr) library(lme4) library(MuMIn) library(treebase) library(viridis) library(bookdown) library(metadat) library(stringr) library(Matrix) library(tidyverse) library(forcats) library(purrr) library(tibble) library(magrittr) library(knitr) library(writexl) library(RColorBrewer) ```` ## Installation Instructions Ensure R and RStudio are installed. Use the code block above to install all required packages and additional packages using `pacman`. This includes installing `orchaRd` from GitHub. ## Running the Code 1. Open the `Code for Predicting the effects of climate change on fertility.R` and for Functions for `Predicting the effects of climate change on fertility.R` script in RStudio or any other R environment. 2. Ensure the path to the `TEMP.csv` and `TEMP_with_EffectSizes.csv` file is correct. 3. Follow the instructions within the script to: * Load the data. * Calculate the effect sizes * Create the phylogenetic tree. * Perform meta-analysis and meta-regressions. * Run functions * Generate orchard plots. 4. Execute the script to obtain the results. ## Understanding the Outputs * **Phylogenetic Tree**: A visualization of relationships among species in the dataset. * **Meta-Analysis Results**: Summary statistics for the overall effect size and heterogeneity. * **Meta-Regressions**: An analysis showing the impact of different moderators on effect size. * **Orchard Plots**: Graphical representations of meta-regressions, illustrating relationships between moderators and fertility outcomes. ## Troubleshooting * If you encounter errors during package installation, ensure you have an active internet connection and appropriate permissions. * Verify the path to `Predicting the effects of climate change on the fertility data.csv` is correct. * Ensure the R script and data file are in the same directory, or update the script with the correct path. * If you experience errors related to package loading, double-check package installation and attempt to reinstall any packages causing issues. ## References For more information on methodologies and the R ecosystem, refer to: * "Meta-Analysis in Ecology and Evolution" by Julia Koricheva, Jessica Gurevitch, and Kerrie Mengersen. * R Documentation at [r-project.org](https://www.r-project.org/). For further questions, issues, or suggestions, please contact the project maintainer or submit an issue on the repository's issue tracker. Thank you for using this repository. I hope this README helps you successfully run the analysis and understand the results. Good luck with your work!