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Increasingly intense and frequent ocean heatwaves are causing widespread coral mortality. These heatwaves are just one of the many stressors — among for instance ocean acidifi cation, nutrient pollution and destructive fi shing practices — that have caused widespread decline of coral reefs over the past century. This destruction of reefs threatens the remarkable biodiversity of organisms that depend upon coral reefs. However, recent research suggests that many of the fi shes and invertebrates that inhabit coral reefs may play an underappreciated role in infl uencing the resistance and recovery of corals to stressors, especially those caused by global climate change such as ocean heatwaves. Unraveling the threads that link these coral inhabitants to the corals’ response to stressors has the potential to weave a more comprehensive model of resilience that integrates the plight of coral reefs with the breathtaking diversity of life they host. Here, we aim to elucidate the critical roles that coral-associated fishes and invertebrates play in mediating coral resilience to environmental stressors. By integrating recent research findings, we aim to showcase how these often-overlooked organisms influence coral resilience in the face of climate change. 

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Quantitatively summarizing results from a collection of primary studies with meta-analysis can help answer ecological questions and identify knowledge gaps. The accuracy of the answers depends on the quality of the meta-analysis. We reviewed the literature assessing the quality of ecological meta-analyses to evaluate current practices and highlight areas that need improvement. From each of the 18 review papers that evaluated the quality of meta-analyses, we calculated the percentage of meta-analyses that met criteria related to specific steps taken in the meta-analysis process (i.e., execution) and the clarity with which those steps were articulated (i.e., reporting). We also re-evaluated all the meta-analyses available from Pappalardo et al. [1] to extract new information on ten additional criteria and to assess how the meta-analyses recognized and addressed non-independence. In general, we observed better performance for criteria related to reporting than for criteria related to execution; however, there was a wide variation among criteria and meta-analyses. Meta-analyses had low compliance with regard to correcting for phylogenetic non-independence, exploring temporal trends in effect sizes, and conducting a multifactorial analysis of moderators (i.e., explanatory variables). In addition, although most meta-analyses included multiple effect sizes per study, only 66% acknowledged some type of non-independence. The types of non-independence reported were most often related to the design of the original experiment (e.g., the use of a shared control) than to other sources (e.g., phylogeny). We suggest that providing specific training and encouraging authors to follow the PRISMA EcoEvo checklist recently developed by O’Dea et al. [2] can improve the quality of ecological meta-analyses. 

Photogrammetry is an emerging tool that allows scientists to measure important habitat characteristics of coral reefs at multiple spatial scales. However, the ecological benefits of using photogrammetry to measure reef habitat have rarely been assessed through direct comparison to traditional methods, especially in settings where manual measurements are more feasible and affordable. Here, we applied multiple methods to measure coral colonies (Pocillopora spp.) and asked whether photogrammetric or manual observations better describe short-term colony growth and links between colony size and the biodiversity of coral-dwelling fishes and invertebrates. Using photogrammetry, we measured patterns in changes in coral volume that were otherwise obscured by high variation from manual measurements. Additionally, we found that photogrammetry-based estimates of colony skeletal volume best predicted the abundance and richness of animals living within the coral. This study highlights that photogrammetry can improve descriptions of coral colony size, growth, and associated biodiversity compared to manual measurements. 

Abstract In ecological meta‐analyses, nonindependence among observed effect sizes from the same source paper is common. If not accounted for, nonindependence can seriously undermine inferences. We compared the performance of four meta‐analysis methods that attempt to address such nonindependence and the standard random‐effect model that ignores nonindependence. We simulated data with various types of within‐paper nonindependence, and assessed the standard deviation of the estimated mean effect size and Type I error rate of each method. Although all four methods performed substantially better than the standard random‐effects model that assumes independence, there were differences in performance among the methods. A two‐step method that first summarizes the multiple observed effect sizes per paper using a weighted mean and then analyzes the reduced data in a standard random‐effects model, and a robust variance estimation method performed consistently well. A hierarchical model with both random paper and study effects gave precise estimates but had a higher Type I error rates, possibly reflecting limitations of currently available meta‐analysis software. Overall, we advocate the use of the two‐step method with a weighted paper mean and the robust variance estimation method as reliable ways to handle within‐paper nonindependence in ecological meta‐analyses. 

Abstract Despite the wide application of meta‐analysis in ecology, some of the traditional methods used for meta‐analysis may not perform well given the type of data characteristic of ecological meta‐analyses.We reviewed published meta‐analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies (n_i) and the number of studies or records (k) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta‐analysis methods for estimating a mean effect size and its uncertainty interval.Our literature review showed thatn_iandkwere highly variable, distributions were right‐skewed and were generally small (mediann_i = 5, mediank = 44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). Whenkwas low (<40), 95% coverage was achieved by a confidence interval (CI) based on thetdistribution that uses an adjusted standard error (the Hartung–Knapp–Sidik–Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap orzdistribution CIs had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta‐analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults.In general, for a simple random‐effects meta‐analysis, the performance of the best frequentist and Bayesian methods was similar for the same combinations of factors (kand mean replication), though the Bayesian approach had higher than nominal (>95%) coverage for the mean effect whenkwas very low (k < 15). Our literature review suggests that many meta‐analyses that usedzdistribution or bootstrapping CIs may have overestimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta‐analyses.