Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.

With open‐access publishing authors often pay an article processing charge and subsequently their article is freely available online. These charges are beyond the reach of most African academics. Thus, the trend towards open‐access publishing will shift the business model from a pay‐wall model, where access to literature is limited, to a pay‐to‐publish one, where African scholars cannot afford to publish. We explore the costs of publishing and the ability of African scholars to afford to publish via open access in top journals. Three‐quarters of the 40 top ecology journals required payment for open‐access publishing (average cost $3150). Paying such fees is a hardship for African scholars as grant funding is not available and it is not feasible to pay the fees themselves as salaries are low. We encourage funders and publishers to facilitate an equitable publishing model that allows African scholars to make their research available through open‐access publishing.

Directed dispersal is defined as enhanced dispersal of seeds into suitable microhabitats, resulting in higher recruitment than if seeds were dispersed randomly. While this constitutes one of the main explanations for the adaptive value of frugivore‐mediated seed dispersal, the generality of this advantage has received little study, particularly when multiple dispersers are involved. We used probability recruitment models of a long‐lived rainforest tree in Madagascar to compare recruitment success under dispersal by multiple frugivores, no dispersal, and random dispersal. Models were parameterized using a three‐year recruitment experiment and observational data of dispersal events by three frugivorous lemur species that commonly disperse its seeds. Frugivore‐mediated seed dispersal was nonrandom with respect to canopy cover and increased modeled per‐seed sapling recruitment fourfold compared to no dispersal. Seeds dispersed by one frugivore, Eulemur rubriventer, had higher modeled recruitment probability than seeds dispersed randomly. However, as a group, our models suggest that seeds dispersed by lemurs would have lower recruitment than if dispersal were random. Results demonstrate the importance of evaluating the contribution of multiple frugivores to plant recruitment for understanding plant population dynamics and the ecological and evolutionary significance of seed dispersal.