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’.

Two major goals in the current biology of aging are to identify general mechanisms underlying the aging process and to explain species differences in aging. Recent research in humans suggests that one important driver of aging is dysregulation, the progressive loss of homeostasis in complex biological networks. Yet, there is a lack of comparative data for this hypothesis, and we do not know whether dysregulation is widely associated with aging or how well signals of homeostasis are conserved. To address this knowledge gap, we use unusually detailed longitudinal biomarker data from 10 species of nonhuman primates housed in research centers and data from two human populations to test the hypotheses that (a) greater dysregulation is associated with aging across primates and (b) physiological states characterizing homeostasis are conserved across primates to degrees associated with phylogenetic proximity. To evaluate dysregulation, we employed a multivariate distance measure, calculated from sets of biomarkers, that is associated with aging and mortality in human populations. Dysregulation scores positively correlated with age and risk of mortality in most nonhuman primates studied, and signals of homeostatic state were significantly conserved across species, declining with phylogenetic distance. Our study provides the first broad demonstration of physiological dysregulation associated with aging and mortality risk in multiple nonhuman primates. Our results also imply that emergent signals of homeostasis are evolutionarily conserved, although with notable variation among species, and suggest promising directions for future comparative studies on dysregulation and the aging process.

Aim: Local contributions to beta diversity (LCBD) can be used to identify sites with high ecological uniqueness and exceptional species composition within a region of interest. Yet, these indices are typically used on local or regional scales with relatively few sites, as they require information on complete community compositions difficult to acquire on larger scales. Here, we investigated how LCBD indices can be predicted over broad spatial extents using species distribution modelling and citizen science data and examined the effect of scale changes on beta diversity quantification.Location: North America. Time period: 2000s.Major taxa studied: Parulidae. Methods: We used Bayesian additive regression trees (BARTs) to predict warbler species distributions in North America based on observations recorded in the eBird database. We then calculated LCBD indices for observed and predicted data and examined the sitewise difference using direct comparison, a spatial association test, and generalized linear regression. We also investigated the relationship between LCBD values and species richness in different regions and at various spatial extents and the effect of the proportion of rare species on the relationship.Results: Our results showed that the relationship between richness and LCBD values varies according to the region and the spatial extent at which it is applied. It is also affected by the proportion of rare species in the community. Species distribution models provided uniqueness estimates highly correlated with observed data with a statistically significant spatial association Main conclusions: Sites identified as unique over broad spatial extents may vary according to the regional richness, total extent size, and the proportion of rare species. Species distribution modelling can be used to predict ecological uniqueness over broad spatial extents, which could help identify beta diversity hotspots and important targets for conservation purposes in unsampled locations.

Predicting where species should be found in space is a common question in ecology and biogeography. Species distribution models (SDMs), for instance, aim to predict where environmental conditions are suitable for a given species, often on continuous geographic scales. Such analyses require the use of geo-referenced data on species distributions coupled with climate or land cover information, hence a tight integration between environmental data, species occurrence data, and spatial coordinates. Thus, it requires an efficient way to access these different data types within the same software, as well as a flexible framework on which to build various analysis workflows. Here we present SimpleSDMLayers.jl and GBIF.jl, two packages in the Julia language implementing a simple framework and type-system on which to build SDM analyses, as well as providing access to popular data sources for species occurrences and environmental conditions.

Networks of species interactions can capture meaningful information on the structure and functioning of ecosystems. Yet the scarcity of existing data, and the difficulty associated with comprehensively sampling interactions between species, means that to describe the structure, variation, and change of ecological networks over time and space, we need to rely on modeling tools with the capacity to make accurate predictions about how species interact. Here we provide a proof-of-concept, where we show a simple neural-network model makes accurate predictions about species interactions, and use this model to reconstruct a metaweb of host-parasite interactions across space, and assess the challenges and opportunities associated with improving interaction predictions. We then provide a primer on the relevant method and tools that will guide the development and integration of these tools, and provide a road map forward toward integration of multiple sources of data and methodlogical approaches (including statistical, dynamical, and inferential models) to sketch the path forward for this research program.

Aim: Local contributions to beta diversity (LCBD) can be used to identify sites with high ecological uniqueness and exceptional species composition within a region of interest. Yet, these indices are typically used on local or regional scales with relatively few sites, as they require information on complete community compositions difficult to acquire on larger scales. Here, we investigate how LCBD indices can be used to predict ecological uniqueness over broad spatial extents using species distribution modelling and citizen science data. Location: North America. Time period: 2000s. Major taxa studied: Parulidae. Methods: We used Bayesian additive regression trees (BARTs) to predict warbler species distributions in North America based on observations recorded in the eBird database. We then calculated LCBD indices for observed and predicted data and examined the site-wise difference using direct comparison, a spatial autocorrelation test, and generalized linear regression. We also investigated the relationship between LCBD values and species richness in different regions and at various spatial extents and the effect of the proportion of rare species on the relationship. Results: Our results showed that the relationship between richness and LCBD values varies according to the region and the spatial extent at which it is applied. It is also affected by the proportion of rare species in the community. Species distribution models provided highly correlated estimates with observed data, although spatially autocorrelated. Main conclusions: Sites identified as unique over broad spatial extents may vary according to the regional richness, total extent size, and the proportion of rare species. Species distribution modelling can be used to predict ecological uniqueness over broad spatial extents, which could help identify beta diversity hotspots and important targets for conservation purposes in unsampled locations.

Session motivation and objectivesAdvances in computing, statistics, and technology over the past few decades has resulted in the accumulation of massive amounts of biodiversity data, as well as novel methods for using and integrating them (Miller et al. 2019). Data that have been collected for decades or even centuries can now be analyzed and applied in brand new ways. These data include alternative sources of information such as citizen science (also called community science) programs (Butcher et al. 1990, Sullivan et al. 2014, Hudson et al. 2017). In the midst of a global biodiversity crisis, these databases may hold the key to detecting important obstacles and threats to conservation, as well as determining the best interventions before it is too late. The ecological processes in question frequently occur across broad spatial and temporal scales; migratory species' ranges can span thousands of kilometers, and the impacts of threats such as climate change cannot necessarily be assessed on a local scale. Conventional methods of biodiversity monitoring may therefore be inadequate in the face of broad scale change. The individual researcher can generally only collect data on a localized scale, yet with the proper data practices, this information can contribute to a better understanding of the bigger picture (Sutherland et al. 2009).

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.