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 We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology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

We here develop a concept of an individualized niche in analogy to Hutchison’s population-level concept of the ecological niche. We consider the individualized (ecological) niche as the range of environmental conditions under which a particular individual has an expected lifetime reproductive success of ≥ 1. Our concept has primarily an ecological function, as it refers to the match of an individual phenotype to its contemporary environment (niche fit) while we discuss evolutionary fitness as an evaluative parameter of this fit. We address four specific challenges that occur when scaling the niche down from populations to individuals. In particular, we discuss (1) the consequences of uniqueness of individuals in a population and the corresponding lack of statistical replication, (2) the dynamic nature of individualized niches and how they can be studied either as time-slice niches, as prospective niches or as trajectory-based niches, (3) the dimensionality of the individualized niche, that is greater than the population niche due to the additional dimensions of intra-specific niche space, (4) how the boundaries of individualized niche space can be defined by expected lifetime reproductive success and how expected reproductive success can be inferred by marginalizing fitness functions across phenotypes or environments. We frame our discussion in the context of recent interest in the causes and consequences of individual differences in animal behavior.

Delving into the persistent impacts of colonialism within the sphere of modern science, we explore some of the deep-seated disparities between the Global North and South with regards to the scientific enterprise. Central to this inequality are the hurdles of language and financial support. As such, this work discusses the often-overlooked obstacles that Global South scientists face, including the additional efforts non-native English speakers must invest in reading and publishing, their higher rejection rates, and the widespread neglect of publications in languages other than English. These challenges not only hinder the advancement of science but also deepen existing divides. Furthermore, we examine the double-edged sword of and the geopolitical limits of open science. While these policies democratize access to scientific knowledge, they can inadvertently exacerbate the North-South inequalities due to, for example, the prohibitive costs associated with open-access publishing—a financial burden that is often unmanageable for researchers with limited funding. This funding gap severely restricts the Global South’s scientific capabilities and impact, affecting everything from conducting comprehensive research to attending scientific meetings. The culmination of these disparities not only diminishes the impact of Global South researchers in their fields but also traps them in a cycle of reduced funding and limited global networking opportunities. In addressing these complex issues, the contributions in this work highlight some of the most common and pronounced issues related to scientific inequalities, as well as suggesting possible ways of bridging these gaps in order to reach a more equitable distribution of resources and recognition in the global scientific community.

The outcomes of interspecific and intraspecific ecological interactions can be considered to fall along continua from cooperative (mutually beneficial) to antagonistic (detrimental to one or both parties). Furthermore, the position of an interaction outcome along the continuum, for example whether a symbiont provides net costs or benefits to its host, or whether two conspecifics cooperatively forage or compete for food, is often not fixed but can change over time or across contexts. In this systematic review, we investigate the role of intraspecific trait variation (‘ITV') in one or both interacting parties in determining the cooperative‐antagonistic outcome of inter‐ and intraspecific ecological interactions. Based on a literature collection of 96 empirical and theoretical publications meeting our inclusion criteria, we give an overview of the types of interaction continua involved; traits related to outcome variance; and mechanisms as well as constraints on shifts in interactions outcomes. We propose that ITV can lead to shifts in interaction outcomes via two interrelated mechanisms. First, trait frequency effects occur when there are changes in a population's composition of traits linked to cooperation or antagonism (e.g. aggressive personality types, cheater phenotypes etc.), leading to net shifts in interaction outcomes. Second, systemic variance effects occur where the level of ITV in a trait in a population (as opposed to the mean value) is the factor that influences the cooperative‐antagonistic outcome. Heritable trait differences and phenotypic plasticity are sources of phenotypic variation among individuals, and both the degree of heritability and plasticity of the trait involved may determine whether shifts between cooperation and antagonism are likely to be short‐term (i.e. context‐dependent) or lead to more persistent shifts (e.g. mutualism breakdown). To guide future research, we describe knowledge gaps and divergences between empirical and theoretical literature, highlighting the value of applying evidence synthesis methods in ecology and evolution.

Data and code are essential for ensuring the credibility of scientific results and facilitating reproducibility, areas in which journal sharing policies play a crucial role. However, in ecology and evolution, we still do not know how widespread data- and code-sharing policies are, how accessible they are, and whether journals support data and code peer review. Here, we first assessed the clarity, strictness and timing of data- and code-sharing policies across 275 journals in ecology and evolution. Second, we assessed initial compliance to journal policies using submissions from two journals: Proceedings of the Royal Society B (Mar 2023–Feb 2024: n = 2340) and Ecology Letters (Jun 2021–Nov 2023: n = 571). Our results indicate the need for improvement: across 275 journals, 22.5% encouraged and 38.2% mandated data-sharing, while 26.6% encouraged and 26.9% mandated code-sharing. Journals that mandated data- or code-sharing typically required it for peer review (59.0% and 77.0%, respectively), which decreased when journals only encouraged sharing (40.3% and 24.7%, respectively). Our evaluation of policy compliance confirmed the important role of journals in increasing data- and code-sharing but also indicated the need for meaningful changes to enhance reproducibility. We provide seven recommendations to help improve data- and code-sharing, and policy compliance.