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 We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology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.

Roads are proliferating worldwide at an unprecedented rate, with potentially severe impacts on wildlife. We calculated the extent and potential impacts of road networks across the 1,160,000-km², 13-country range of the globally endangered tiger (Panthera tigris)—a conservation umbrella species. We found that roads were pervasive, totaling 134,000 km across tiger conservation landscapes (TCLs), even in tiger priority sites and protected areas. Approximately 43% of the area where tiger breeding occurs and 57% of the area in TCLs fell within the road-effect zone. Consequently, current road networks may be decreasing tiger and prey abundances by more than 20%. Nearly 24,000 km of new roads will be built in TCLs by 2050, stimulated through major investment projects such as China’s Belt and Road Initiative. Given that roads will be a pervasive challenge to tiger recovery in the future, we urge decision-makers to make sustainable road development a top priority.

Sustainable development goals such as global food security and biodiversity conservation can conflict because these efforts create situations where humans and wildlife share landscapes, often leading to interactions that detrimentally affect both groups. Therefore, coexistence between humans and wildlife is more likely when adaptation strategies produce and sustain cobenefits, rather than benefitting one group only. However, we lack a good understanding of how different social and ecological factors contribute to cobenefit outcomes, which limits our opportunities to address local issues and scale up successful conservation actions. Here, we performed the first global review of the human–wildlife interaction literature to assess which human adaptation strategies generated cobenefits and how stakeholder involvement and other context‐specific conditions mediated those outcomes. We found that active guarding, fencing, repellents, and socioeconomic mechanisms consistently led to cobenefits across species and contexts. Thus, these interventions might be the best candidates for scaling up coexistence from local to regional or national scales. Surprisingly, stakeholder involvement was less consequential than other variables, yet, overall, it played an important role in sustaining cobenefits regardless of adaptation strategy or social–ecological context. We highlight future research directions to help manage tradeoffs and achieve sustainable coexistence outcomes in shared landscapes.

Killion, A. K., K. Sterle, E. Bondank, J. Drabik, A. Bera, S. Alian, K. Goodrich, M. Hale, R. A. Myer, Q. Phung, A. M. Shew, and A. W. Thayer. 2018. Preparing the next generation of sustainability scientists. Ecology and Society 23(4):39. https://doi.org/10.5751/ES-10395-230439