Search for: All records

Following the failure to fully achieve any of the 20 Aichi biodiversity targets, the future of biodiversity rests in the balance. The Convention on Biological Diversity's Kunming–Montreal Global Biodiversity Framework (GBF) presents the opportunity to preserve nature's contributions to people (NCPs) for current and future generations by conserving biodiversity and averting extinctions. There is a need to safeguard the tree of life—the unique and shared evolutionary history of life on Earth—to maintain the benefits it bestows into the future. Two indicators have been adopted within the GBF to monitor progress toward safeguarding the tree of life: the phylogenetic diversity (PD) indicator and the evolutionarily distinct and globally endangered (EDGE) index. We applied both to the world's mammals, birds, and cycads to show their utility at the global and national scale. The PD indicator can be used to monitor the overall conservation status of large parts of the evolutionary tree of life, a measure of biodiversity's capacity to maintain NCPs for future generations. The EDGE index is used to monitor the performance of efforts to conserve the most distinctive species. The risk to PD of birds, cycads, and mammals increased, and mammals exhibited the greatest relative increase in threatened PD over time. These trends appeared robust to the choice of extinction risk weighting. EDGE species had predominantly worsening extinction risk. A greater proportion of EDGE mammals (12%) had increased extinction risk compared with threatened mammals in general (7%). By strengthening commitments to safeguarding the tree of life, biodiversity loss can be reduced and thus nature's capacity to provide benefits to humanity now and in the future can be preserved.

 

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’.