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Biographical features like social and economic status, ethnicity, sexuality, care roles, and gender unfairly disadvantage individuals within academia. Authorship patterns should reflect the social dimension behind the publishing process and co-authorship dynamics. To detect potential gender biases in the authorship of papers and examine the extent of women's contribution in terms of the substantial volume of scientific production in Ecology, we surveyed papers from the top-ranked journal Ecology from 1999 to 2021. We developed a Women's Contribution Index (WCI) to measure gender-based individual contributions. Considering gender, allocation in the author list, and the total number of authors, the WCI calculates the sum of each woman's contribution per paper. We compared the WCI with women's expected contributions in a non-gender-biased scenario. Overall, women account for 30% of authors of Ecology, yet their contribution to papers is higher than expected by chance (i.e., over-contribution). Additionally, by comparing the WCI with an equivalent Men's Contribution Index, we found that women consistently have higher contributions compared to men. We also observed a temporal trend of increasing women's authorship and mixed-gender papers. This suggests some progress in addressing gender bias in the field of ecology. However, we emphasize the need for a better understanding of the pattern of over-contribution, which may partially stem from the phenomenon of over-compensation. In this context, women might need to outperform men to be perceived and evaluated as equals. The WCI provides a valuable tool for quantifying individual contributions and understanding gender biases in academic publishing. Moreover, the index could be customized to suit the specific question of interest. It serves to uncover a previously non-quantified type of bias (over-contribution) that, we argue, is the response to the inequitable structure of the scientific system, leading to differences in the roles of individuals within a scientific publishing team. [ABSTRACT FROM AUTHOR]

Previous studies have examined separately how pollinator generalization and abundance influence plant reproductive success, but none so far has evaluated simultaneously the relative importance of these pollinator attributes. Here we evaluated the extent to which pollinator generalization and abundance influence plant reproductive success per visit and at the population level on a generalist plant, Opuntia sulphurea (Cactaceae). We used field experiments and path analysis to evaluate whether the per-visit effect is determined by the pollinator’s degree of generalization, and whether the population level effect (pollinator impact) is determined by the pollinator’s degree of generalization and abundance. Based on the models we tested, we concluded that the per-visit effect of a pollinator on plant reproduction was not determined by the pollinators’ degree of generalization, while the population-level impact of a pollinator on plant reproduction was mainly determined by the pollinators’ degree of generalization. Thus, generalist pollinators have the greatest species impact on pollination and reproductive success of O. sulphurea. According to our analysis this greatest impact of generalist pollinators may be partly explained by pollinator abundance. However, as abundance does not suffice as an explanation of pollinator impact, we suggest that vagility, need for resource consumption, and energetic efficiency of generalist pollinators may also contribute to determine a pollinator’s impact on plant reproduction. [ABSTRACT FROM AUTHOR]

How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( < 10), with model selection favouring less than five. Using 18 high-quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large-scale community structure. [ABSTRACT FROM AUTHOR]