Postdoctoral scientists are mentors, and it is time to recognize their work.

Academia often fails to recognize the important work that supports its functioning, such as mentoring and teaching performed by postdoctoral researchers. This is a particular problem for early-career researchers, but opportunities exist to improve the status quo.

Mismatch between IUCN range maps and species interactions data illustrated using the Serengeti food web.

Background: Range maps are a useful tool to describe the spatial distribution of species. However, they need to be used with caution, as they essentially represent a rough approximation of a species' suitable habitats. When stacked together, the resulting communities in each grid cell may not always be realistic, especially when species interactions are taken into account. Here we show the extent of the mismatch between range maps, provided by the International Union for Conservation of Nature (IUCN), and species interactions data. More precisely, we show that local networks built from those stacked range maps often yield unrealistic communities, where species of higher trophic levels are completely disconnected from primary producers. Methodology: We used the well-described Serengeti food web of mammals and plants as our case study, and identify areas of data mismatch within predators' range maps by taking into account food web structure. We then used occurrence data from the Global Biodiversity Information Facility (GBIF) to investigate where data is most lacking. Results: We found that most predator ranges comprised large areas without any overlapping distribution of their prey. However, many of these areas contained GBIF occurrences of the predator. Conclusions: Our results suggest that the mismatch between both data sources could be due either to the lack of information about ecological interactions or the geographical occurrence of prey. We finally discuss general guidelines to help identify defective data among distributions and interactions data, and we recommend this method as a valuable way to assess whether the occurrence data that are being used, even if incomplete, are ecologically accurate.

Data rescue: saving environmental data from extinction.

Historical and long-term environmental datasets are imperative to understanding how natural systems respond to our changing world. Although immensely valuable, these data are at risk of being lost unless actively curated and archived in data repositories. The practice of data rescue, which we define as identifying, preserving, and sharing valuable data and associated metadata at risk of loss, is an important means of ensuring the long-term viability and accessibility of such datasets. Improvements in policies and best practices around data management will hopefully limit future need for data rescue; these changes, however, do not apply retroactively. While rescuing data is not new, the term lacks formal definition, is often conflated with other terms (i.e. data reuse), and lacks general recommendations. Here, we outline seven key guidelines for effective rescue of historically collected and unmanaged datasets. We discuss prioritization of datasets to rescue, forming effective data rescue teams, preparing the data and associated metadata, and archiving and sharing the rescued materials. In an era of rapid environmental change, the best policy solutions will require evidence from both contemporary and historical sources. It is, therefore, imperative that we identify and preserve valuable, at-risk environmental data before they are lost to science.

A roadmap towards predicting species interaction networks (across space and time).

Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species-and to describe the structure, variation, and change of the ecological networks they form-we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Beta and phylogenetic diversities tell complementary stories about ecological networks biogeography.

The beta-diversity of interactions between communities does not necessarily correspond to the differences related to their species composition because interactions show greater variability than species co-occurrence. Additionally, the structure of species interaction networks can itself vary over spatial gradients, thereby adding constraints on the dissimilarity of communities in space. We used published data on the parasitism interaction between fleas and small mammals in 51 regions of the Palearctic to investigate how beta-diversity of networks and phylogenetic diversity are related. The networks could be separated in groups based on the metrics that best described the differences between them, and these groups were also geographically structured. We also found that each network beta-diversity index relates in a particular way with phylogenetically community dissimilarity, reinforcing that some of these indexes have a strong phylogenetic component. Our results clarify important aspects of the biogeography of hosts and parasites communities in Eurasia, while suggesting that networks beta-diversity and phylogenetic dissimilarity interact with the environment in different ways.