Summary1. Although secondary metabolites are recognized as fundamental to the defence of plants against insect and mammalian herbivores, their relative importance compared to other potential defensive plant traits (e.g. physical resistance, gross morphology, life-history, primary chemistry and physiology) are not well understood. 2. We conducted a meta-analysis to answer the question: What types of genetically variable plant traits most strongly predict resistance against herbivores? We performed a comprehensive literature search and obtained 499 separate measurements of the strength of covariation (measured as genetic correlations) between plant traits and herbivore susceptibility -these were extracted from 72 studies involving 19 plant families. 3. Surprisingly, we found no overall association between the concentrations of secondary metabolites and herbivore susceptibility -plant traits other than secondary metabolites most strongly predicted herbivore susceptibility. Specifically, genetic variation in life-history traits (e.g. flowering time, growth rate) consistently exhibited the strongest genetic correlations with susceptibility. Genetic variation in gross morphological traits (e.g. no. branches, plant size) and physical resistance traits (e.g. latex, trichomes) were also frequently correlated with variation in herbivore susceptibility, but these relationships depended on attributes of the herbivores (e.g. feeding guild) and plants (e.g. longevity). 4. These results call into question the conventional wisdom that secondary metabolites are the most important anti-herbivore defence of plants. We propose the hypothesis that herbivores select most strongly on genetic variation in life-history, morphological and physical resistance traits, but the greater pleiotropic effects of genes controlling these traits impose strong constraints on their evolution. Meanwhile, secondary metabolites could have evolved to be important defensive mechanisms not because they have the largest effect on herbivores, but because the constraints on their evolution are the weakest.

Recent research suggests that genetic diversity in plant populations can shape the diversity and abundance of consumer communities. We tested this hypothesis in a field experiment by manipulating patches of Evening Primrose (Oenothera biennis) to contain one, four or eight plant genotypes. We then surveyed 92 species of naturally colonizing arthropods. Genetically diverse plant patches had 18% more arthropod species, and a greater abundance of omnivorous and predacious arthropods, but not herbivores, compared with monocultures. The effects of genotypic diversity on arthropod communities were due to a combination of interactive and additive effects among genotypes within genetically diverse patches. Greater genetic diversity also led to a selective feedback, as mean genotype fitness was 27% higher in diverse patches than in monocultures. A comparison between our results and the literature reveals that genetic diversity and species diversity can have similar qualitative and quantitative effects on arthropod communities. Our findings also illustrate the benefit of preserving genetic variation to conserve species diversity and interactions within multitrophic communities.

Consistent with Darwin’s ideas, this meta-analysis reveals that males experience stronger sexual selection than females.

Phenology, or the timing of seasonal activities, is shifting with climate change, resulting in disruptions to the timing of migration and breeding and in emerging asynchronies between interacting species 1-5 . Recent syntheses have concluded that trophic level 1 , latitude 6 , and how phenological responses are measured 7 are key to determining the strength of phenological responses to climate change. However, despite these insights, researchers still lack a comprehensive framework that can predict responses to climate change globally and across diverse taxa. For example, little is known about whether phenological shifts are driven by different climatic factors across regions or which ecologically important species characteristics (e.g., body size) predict the strength of phenological responses. Here, we address these questions by synthesizing hundreds of published time series of animal phenology from across the planet. We find that temperature drives phenological responses at mid-latitudes, but precipitation is more important at lower latitudes, likely because these climate factors often drive seasonality in each of these regions. Body size is also negatively associated with the strength of phenological shift, suggesting emerging asynchronies between interacting species that differ . CC-BY-NC-ND 4.

Abstract. A common effect size metric used to quantify the outcome of experiments for ecological meta-analysis is the response ratio (RR): the log proportional change in the means of a treatment and control group. Estimates of the variance of RR are also important for meta-analysis because they serve as weights when effect sizes are averaged and compared. The variance of an effect size is typically a function of sampling error; however, it can also be influenced by study design. Here, I derive new variances and covariances for RR for several often-encountered experimental designs: when the treatment and control means are correlated; when multiple treatments have a common control; when means are based on repeated measures; and when the study has a correlated factorial design, or is multivariate. These developments are useful for improving the quality of data extracted from studies for metaanalysis and help address some of the common challenges meta-analysts face when quantifying a diversity of experimental designs with the response ratio.

Soil respiration (Rs) is the second-largest terrestrial carbon (C) flux. Although Rs has been extensively studied across a broad range of biomes, there is surprisingly little consensus on how the spatiotemporal patterns of Rs will be altered in a warming climate with changing precipitation regimes. Here, we present a global synthesis Rs data from studies that have manipulated precipitation in the field by collating studies from 113 increased precipitation treatments, 91 decreased precipitation treatments, and 14 prolonged drought treatments. Our meta-analysis indicated that when the increased precipitation treatments were normalized to 28% above the ambient level, the soil moisture, Rs, and the temperature sensitivity (Q10) values increased by an average of 17%, 16%, and 6%, respectively, and the soil temperature decreased by -1.3%. The greatest increases in Rs and Q10 were observed in arid areas, and the stimulation rates decreased with increases in climate humidity. When the decreased precipitation treatments were normalized to 28% below the ambient level, the soil moisture and Rs values decreased by an average of -14% and -17%, respectively, and the soil temperature and Q10 values were not altered. The reductions in soil moisture tended to be greater in more humid areas. Prolonged drought without alterations in the amount of precipitation reduced the soil moisture and Rs by -12% and -6%, respectively, but did not alter Q10. Overall, our synthesis suggests that soil moisture and Rs tend to be more sensitive to increased precipitation in more arid areas and more responsive to decreased precipitation in more humid areas. The responses of Rs and Q10 were predominantly driven by precipitation-induced changes in the soil moisture, whereas changes in the soil temperature had limited impacts. Finally, our synthesis of prolonged drought experiments also emphasizes the importance of the timing and frequency of precipitation events on ecosystem C cycles. Given these findings, we urge future studies to focus on manipulating the frequency, intensity, and seasonality of precipitation with an aim to improving our ability to predict and model feedback between Rs and climate change.

Meta-analysis has contributed substantially to shifting paradigms in ecology and has become the primary method for quantitatively synthesizing published research. However, an emerging challenge is the lack of a statistical protocol to synthesize studies and evaluate sources of bias while simultaneously accounting for phylogenetic nonindependence of taxa. Phylogenetic nonindependence arises from homology, the similarity of taxa due to shared ancestry, and treating related taxa as independent data violates assumptions of statistics. Given that an explicit goal of meta-analysis is to generalize research across a broad range of taxa, then phylogenetic nonindependence may threaten conclusions drawn from such reviews. Here I outline a statistical framework that integrates phylogenetic information into conventional meta-analysis when (a) taking a weighted average of effect sizes using fixed- and random-effects models and (b) testing for homogeneity of variances. I also outline how to test evolutionary hypotheses with meta-analysis by describing a method that evaluates phylogenetic conservatism and a model-selection framework that competes neutral and adaptive hypotheses to explain variation in meta-analytical data. Finally, I address several theoretical and practical issues relating to the application and availability of phylogenetic information for meta-analysis.

Ecologists widely use the log response ratio for summarizing the outcomes of studies for meta-analysis. However, little is known about the sampling distribution of this effect size estimator. Here I show with a Monte Carlo simulation that the log response ratio is biased when quantifying the outcome of studies with small sample sizes, and can yield erroneous variance estimates when the scale of study parameters are near zero. Given these challenges, I derive and compare two new estimators that help correct this small-sample bias, and update guidelines and diagnostics for assessing when the response ratio is appropriate for ecological meta-analysis. These new bias-corrected estimators retain much of the original utility of the response ratio and are aimed to improve the quality and reliability of inferences with effect sizes based on the log ratio of two means.