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 We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology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

Competition arises when two co-occuring species share a limiting resource. Potential for competition is higher when species have coexisted for a short time, as it is the case for herbivores and livestock introduced in natural systems. Sheep, introduced in the late 19^th century in Patagonia, bear a great resemblance in size and diet to the guanaco, the main native herbivore in Patagonia. In such circumstances, it could be expected that the two species compete and one of them could be displaced. We investigated spatial overlap and habitat selection by coexisting sheep and guanaco in winter and in summer. Additionally, we studied habitat selection of the guanaco in a control situation free from sheep, both in summer and winter. We also determined overlap between species in areas with different intensity of use (named preferred and marginal areas) in order to further detect the potential level of competition in the case of overlapping. Guanaco and sheep showed significantly different habitat preferences through all seasons, in spite of their spatial overlap at landscape scale. Additionally, the habitat used by guanaco was similar regardless of the presence or absence of livestock, which further indicates that sheep is not displacing guanaco where they coexist. These results suggest that habitat segregation between guanaco and sheep is due to a differential habitat selection and not to a competitive displacement process. Therefore, the potential for competition is considered low, contrary to what has been previously observed, although this could be a density-dependent result.

The transition from fossil fuels to renewable energy is fundamental to the mitigation of global climate change. Renewable power capacity is increasing globally, and solar photovoltaics will be the dominant renewable energy source by 2050. Photovoltaic parks (PVPs) require great expanses of land, usually in drylands, creating impacts that can compromise the sustainability of surrounding ecosystems and PVPs. But both novel ecosystems in PVPs and the effect of PVPs on ecosystems are rarely studied. This paper reviews the current knowledge on the impact of PVPs on arid and semi-arid ecosystems and describes the structure and functioning of these novel ecosystems, including changes in microclimatic conditions, soil properties, vegetation and fauna, and shows how these factors hinder the full recovery of ecosystems in PVPs. Ensuring that we do not sacrifice biodiversity for clean energy production restoration is necessary; hence, we address the limitations and challenges of restoring ecosystems within PVPs and suggest the use of modern ecological restoration techniques and the incorporation of grazing into rational planning. More research is needed to fully understand the long-term impacts and interactions of PVPs with the environment, the evolution of novel ecosystems in PVPs and the restoration techniques needed to achieve the long-term sustainability of these infrastructures.