Inferring biotic interactions from proxies

Network analyses show horizontal and vertical distribution of vascular epiphytes on their hosts in a fragment of cloud forest in Central Mexico

Cloud forests figure as one of the most diverse ecosystems, accounting not only for a high number of plant species but also with a great variety of interactions among them. A common interaction in these forests is the one between vascular epiphytes and their hosts. However, few studies have used the network approach to analyze them. Here, we analyze the horizontal and vertical structure of the vascular epiphyte - host network in a cloud forest in central Mexico. We quantified the number of epiphyte stands on each host both total and per-stratum. Complete network, group, and species metrics were estimated at both levels of analysis. The host - epiphyte networks had relatively low network size but were highly connected; moderately nested, with low specialization, and modularity; but higher vulnerability than generality, and high niche overlap. The community was composed by a high number of generalist species. To our knowledge this is the first study in which network analyses are conducted with standardized data and including all host and epiphyte species in the community. The analyses suggest that the networks are robust, and that functional redundancy might be probable, two advantageous characteristics in a very fragmented and threatened cloud forest.

The Micropaleoecology Framework: Evaluating Biotic Responses to Global Change Through Paleoproxy, Microfossil, and Ecological Data Integration

The microfossil record contains abundant, diverse, and well-preserved fossils spanning multiple trophic levels from primary producers to apex predators. In addition, microfossils often constitute and are preserved in high abundances alongside continuous high-resolution geochemical proxy records. These characteristics mean that microfossils can provide valuable context for understanding the modern climate and biodiversity crises by allowing for the interrogation of spatiotemporal scales well beyond what is available in neo-ecological research. Here, we formalize a research framework of "micropaleoecology," which builds on a holistic understanding of global change from the environment to ecosystem level. Location: Global. Time period: Neoproterozoic-Phanerozoic. Taxa studied: Fossilizing organisms/molecules. Our framework seeks to integrate geochemical proxy records with microfossil records and metrics, and draws on mechanistic models and systems-level statistical analyses to integrate disparate records. Using multiple proxies and mechanistic mathematical frameworks extends analysis beyond traditional correlation-based studies of paleoecological associations and builds a greater understanding of past ecosystem dynamics. The goal of micropaleoecology is to investigate how environmental changes impact the component and emergent properties of ecosystems through the integration of multi-trophic level body fossil records (primarily using microfossils, and incorporating additional macrofossil data where possible) with contemporaneous environmental (biogeochemical, geochemical, and sedimentological) records. Micropaleoecology, with its focus on integrating ecological metrics within the context of paleontological records, facilitates a deeper understanding of the response of ecosystems across time and space to better prepare for a future Earth under threat from anthropogenic climate change.

Ecological associations distribution modelling of marine plankton at a global scale

Marine plankton communities form intricate networks of interacting organisms at the base of the food chain, and play a central role in regulating ocean biogeochemical cycles and climate. However, predicting plankton community shifts in response to climate change remains challenging. While species distribution models are valuable tools for predicting changes in species biogeography under climate change scenarios, they generally overlook the key role of biotic interactions, which can significantly shape ecological processes and ecosystem responses. Here, we introduce a novel statistical framework, association distribution modelling (ADM), designed to model and predict ecological associations distribution in space and time. Applied on a Tara Oceans genome-resolved metagenomics dataset, the present-day biogeography of ADM-inferred marine plankton associations revealed four major biogeographic biomes organized along a latitudinal gradient. We predicted the evolution of these biome-specific communities in response to a climate change scenario, highlighting differential responses to environmental change. Finally, we explored the functional potential of impacted plankton communities, focusing on carbon fixation, outlining the predicted evolution of its geographical distribution and implications for ecosystem function.This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Spatially explicit predictions of food web structure from regional-level data

Knowledge about how ecological networks vary across global scales is currently limited given the complexity of acquiring repeated spatial data for species interactions. Yet, recent developments in metawebs highlight efficient ways to first document possible interactions within regional species pools. Downscaling metawebs towards local network predictions is a promising approach to using the current data to investigate the variation of networks across space. However, issues remain in how to represent the spatial variability and uncertainty of species interactions, especially for large-scale food webs. Here, we present a probabilistic framework to downscale a metaweb based on the Canadian mammal metaweb and species occurrences from global databases. We investigated how our approach can be used to represent the variability of networks and communities between ecoregions in Canada. Species richness and interactions followed a similar latitudinal gradient across ecoregions but simultaneously identified contrasting diversity hotspots. Network motifs revealed additional areas of variation in network structure compared with species richness and number of links. Our method offers the potential to bring global predictions down to a more actionable local scale, and increases the diversity of ecological networks that can be projected in space. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Asymmetric Biotic Interactions Cannot Be Inferred Without Accounting for Priority Effects

Understanding biotic interactions is a crucial goal in community ecology and species distribution modelling, and large strides have been made towards improving multivariate computational methods with the aim of quantifying biotic interactions and improving predictions of species occurrence. Yet, while considerable attention has been given to computational approaches and the interpretation of these quantitative tools, the importance of sampling design to reveal these biotic interactions has received little consideration. This study explores the influential role of priority effects, that is, the order of habitat colonisation, in shaping our ability to detect biotic interactions. Using a simple set of simulations, we demonstrate that commonly used cross-sectional co-occurrence data alone cannot be used to make reliable inferences on asymmetric biotic interactions, even if they perform well in predicting the occurrence of species. We then show how sampling designs that consider priority effects can recover the asymmetric effects that are lost when priority effects are ignored. Based on these findings, we urge for caution when drawing inferences on biotic interactions from cross-sectional binary co-occurrence data, and provide guidance on sampling designs that may provide the necessary data to tackle this longstanding challenge.

The role of species interactions in shaping the geographic pattern of ungulate abundance across African savannah

Macroecologists traditionally emphasized the role of environmental variables for predicting species distribution and abundance at large scale. While biotic factors have been increasingly recognized as important at macroecological scales, producing valuable biotic variables remains challenging and rarely tested. Capitalizing on the wealth of population density estimates available for African savannah ungulates, here we modeled species average population density at 100 × 100 km as a function of both environmental variables and proxies of biotic interactions (competition and predation) and estimated their relative contribution. We fitted a linear mixed effect model on 1043 population density estimates for 63 species of ungulates using Bayesian inference and estimated the percentage of total variance explained by environmental, anthropogenic, and biotic interactions variables. Environmental and anthropogenic variables were the main drivers of ungulate population density, with NDVI, Distance to permanent water bodies and Human population density showing the highest contribution to the variance. Nonetheless, biotic interactions altogether contributed to a quarter of the variance explained, with predation and competition having a negative effect on species density. Despite the limitations of modelling biotic interactions in macroecological studies, proxies of biotic interactions can enhance our understanding of biological patterns at broad spatial scales, uncovering novel predictors as well as enhancing the predictive power of large-scale models.

Predicting emergent animal biodiversity patterns across multiple scales

Restoring biodiversity-based resilience and ecosystem multi-functionality needs to be informed by more accurate predictions of animal biodiversity responses to environmental change. Ecological models make a substantial contribution to this understanding, especially when they encode the biological mechanisms and processes that give rise to emergent patterns (population, community, ecosystem properties and dynamics). Here, a distinction between 'mechanistic' and 'process-based' ecological models is established to review existing approaches. Mechanistic and process-based ecological models have made key advances to understanding the structure, function and dynamics of animal biodiversity, but are typically designed to account for specific levels of biological organisation and spatiotemporal scales. Cross-scale ecological models, which predict emergent co-occurring biodiversity patterns at interacting scales of space, time and biological organisation, is a critical next step in predictive ecology. A way forward is to first capitalise on existing models to systematically evaluate the ability of scale-explicit mechanisms and processes to predict emergent patterns at alternative scales. Such model intercomparisons will reveal mechanism to process transitions across fine to broad scales, overcome approach-specific barriers to model realism or tractability and identify gaps which necessitate the development of new fundamental principles. Key challenges surrounding model complexity and uncertainty would need to be addressed, and while opportunities from big data can streamline the integration of multiple scale-explicit biodiversity patterns, ambitious cross-scale field studies are also needed. Crucially, overcoming cross-scale ecological modelling challenges would unite disparate fields of ecology with the common goal of improving the evidence-base to safeguard biodiversity and ecosystems under novel environmental change.

Masting promotes transformation from predation to mutualism in an oak-weevil-rodent system

The significance of ecological non-monotonicity (a function whose first derivative changes signs) in shaping the structure and functions of the ecosystem has recently been recognized, but such studies involving high-order interactions are rare. Here, we have proposed a three-trophic conceptual diagram on interactions among trees, rodents, and insects in mast and non-mast years and tested the hypothesis that oak (Quercus wutaishanica) masting could result in increased mutualism and less predation in an oak-weevil-rodent system in a warm temperate forest of China. Our 14-year dataset revealed that mast years coincided with a relatively low rodent abundance but a high weevil abundance. Masting not only benefited seedling recruitment of oaks through increased dispersal by rodents but also a decrease in predation by rodents and weevils, as well as an increase in the overwintering survival of rodents. Masting appeared to have increased weevil survival by reducing predation of infested acorns by rodents. These results suggest that masting benefits all participants in the plant-insect-rodent system by increasing mutualism and reducing predation behavior (i.e., a non-monotonic function). Our study highlights the significance of masting in maintaining the diversity and function of the forest ecosystem by facilitating the transformation from predation to mutualism among trophic species.

Applying a method before its proof of concept: A cautionary tale using inferred food webs

There are significant pitfalls associated with developing food webs using inferred approaches, including violations of ecological assumptions, which considerably undermine their potentiality to resemble ecological communities and hence be practically useful. As data‐driven scientists, we must, at the very least, test against some empirical data to establish confidence that indeed the inferred food webs reflect their supposed in situ communities. Otherwise, using inferred networks to make bold claims—like the titular statement by Botella et al. (2024)  that food webs are influenced by land‐use intensity—is highly unlikely to be useful, especially when the aforementioned claim is unsubstantiated by the Authors' own statistical analyses.

Power laws in species' biotic interaction networks can be inferred from co-occurrence data

Inferring biotic interactions from species co-occurrence patterns has long intrigued ecologists. Yet recent research revealed that co-occurrences may not reliably represent pairwise biotic interactions. We propose that examining network-level co-occurrence patterns can provide valuable insights into community structure and assembly. Analysing ten bipartite networks of empirically sampled biotic interactions and associated species spatial distribution, we find that approximately 20% of co-occurrences correspond to actual interactions. Moreover, the degree distribution shifts from exponential in co-occurrence networks to power laws in networks of biotic interactions. This shift results from a strong interplay between species' biotic (their interacting partners) and abiotic (their environmental requirements) niches, and is accurately predicted by considering co-occurrence frequencies. Our work offers a mechanistic understanding of the assembly of ecological communities and suggests simple ways to infer fundamental biotic interaction network characteristics from co-occurrence data.

Using phylogeographic link-prediction in primates to prioritize human parasite screening

Objectives

The ongoing risk of emerging infectious disease has renewed calls for understanding the origins of zoonoses and identifying future zoonotic disease threats. Given their close phylogenetic relatedness and geographic overlap with humans, non-human primates (NHPs) have been the source of many infectious diseases throughout human evolution. NHPs harbor diverse parasites, with some infecting only a single host species while others infect species from multiple families.

Materials and Methods

We applied a novel link-prediction method to predict undocumented instances of parasite sharing between humans and NHPs. Our model makes predictions based on phylogenetic distances and geographic overlap among NHPs and humans in six countries with high NHP diversity: Columbia, Brazil, Democratic Republic of Congo, Madagascar, China and Indonesia.

Results

Of the 899 human parasites documented in the Global Infectious Diseases and Epidemiology Network (GIDEON) database for these countries, 12% were shared with at least one other NHP species. The link prediction model identified an additional 54 parasites that are likely to infect humans but were not reported in GIDEON. These parasites were mostly host generalists, yet their phylogenetic host breadth varied substantially.

Discussion

As human activities and populations encroach on NHP habitats, opportunities for parasite sharing between human and non-human primates will continue to increase. Our study identifies specific infectious organisms to monitor in countries with high NHP diversity, while the comparative analysis of host generalism, parasite taxonomy, and transmission mode provides insights to types of parasites that represent high zoonotic risk.

Estimating co-extinction threats in terrestrial ecosystems

The biosphere is changing rapidly due to human endeavour. Because ecological communities underlie networks of interacting species, changes that directly affect some species can have indirect effects on others. Accurate tools to predict these direct and indirect effects are therefore required to guide conservation strategies. However, most extinction-risk studies only consider the direct effects of global change-such as predicting which species will breach their thermal limits under different warming scenarios-with predictions of trophic cascades and co-extinction risks remaining mostly speculative. To predict the potential indirect effects of primary extinctions, data describing community interactions and network modelling can estimate how extinctions cascade through communities. While theoretical studies have demonstrated the usefulness of models in predicting how communities react to threats like climate change, few have applied such methods to real-world communities. This gap partly reflects challenges in constructing trophic network models of real-world food webs, highlighting the need to develop approaches for quantifying co-extinction risk more accurately. We propose a framework for constructing ecological network models representing real-world food webs in terrestrial ecosystems and subjecting these models to co-extinction scenarios triggered by probable future environmental perturbations. Adopting our framework will improve estimates of how environmental perturbations affect whole ecological communities. Identifying species at risk of co-extinction (or those that might trigger co-extinctions) will also guide conservation interventions aiming to reduce the probability of co-extinction cascades and additional species losses.

Exploring the Frequency and Distribution of Ecological Non-monotonicity in Associations among Ecosystem Constituents

Complex links between biotic and abiotic constituents are fundamental for the functioning of ecosystems. Although non-monotonic interactions and associations are known to increase the stability, diversity, and productivity of ecosystems, they are frequently ignored by community-level standard statistical approaches. Using the copula-based dependence measure qad, capable of quantifying the directed and asymmetric dependence between variables for all forms of (functional) relationships, we determined the proportion of non-monotonic associations between different constituents of an ecosystem (plants, bacteria, fungi, and environmental parameters). Here, we show that up to 59% of all statistically significant associations are non-monotonic. Further, we show that pairwise associations between plants, bacteria, fungi, and environmental parameters are specifically characterized by their strength and degree of monotonicity, for example, microbe-microbe associations are on average stronger than and differ in degree of non-monotonicity from plant-microbe associations. Considering directed and non-monotonic associations, we extended the concept of ecosystem coupling providing more complete insights into the internal order of ecosystems. Our results emphasize the importance of ecological non-monotonicity in characterizing and understanding ecosystem patterns and processes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10021-023-00867-9.

Non-random interactions within and across guilds shape the potential to coexist in multi-trophic ecological communities

Theory posits that the persistence of species in ecological communities is shaped by their interactions within and across trophic guilds. However, we lack empirical evaluations of how the structure, strength and sign of biotic interactions drive the potential to coexist in diverse multi-trophic communities. Here, we model community feasibility domains, a theoretically informed measure of multi-species coexistence probability, from grassland communities comprising more than 45 species on average from three trophic guilds (plants, pollinators and herbivores). Contrary to our hypothesis, increasing community complexity, measured either as the number of guilds or community richness, did not decrease community feasibility. Rather, we observed that high degrees of species self-regulation and niche partitioning allow for maintaining larger levels of community feasibility and higher species persistence in more diverse communities. Our results show that biotic interactions within and across guilds are not random in nature and both structures significantly contribute to maintaining multi-trophic diversity.

Not only climate: The importance of biotic interactions in shaping species distributions at macro scales

Abiotic factors are usually considered key drivers of species distribution at macro scales, while biotic interactions are mostly used at local scales. A few studies have explored the role of biotic interactions at macro scales, but all considered a limited number of species and obligate interactions. We examine the role of biotic interactions in large-scale SDMs by testing two main hypotheses: (1) biotic factors in SDMs can have an important role at continental scale; (2) the inclusion of biotic factors in large-scale SDMs is important also for generalist species. We used a maximum entropy algorithm to model the distribution of 177 bat species in Africa calibrating two SDMs for each species: one considering only abiotic variables (noBIO-SDMs) and the other (BIO-SDMs) including also biotic variables (trophic resource richness). We focused the interpretation of our results on variable importance and response curves. For each species, we also compared the potential distribution measuring the percentage of change between the two models in each pixel of the study area. All models gave AUC >0.7, with values on average higher in BIO-SDMs compared to noBIO-SDMs. Trophic resources showed an importance overall higher level than all abiotic predictors in most of the species (~68%), including generalist species. Response curves were highly interpretable in all models, confirming the ecological reliability of our models. Model comparison between the two models showed a change in potential distribution for more than 80% of the species, particularly in tropical forests and shrublands. Our results highlight the importance of considering biotic interactions in SDMs at macro scales. We demonstrated that a generic biotic proxy can be important for modeling species distribution when species-specific data are not available, but we envision that a multi-scale analysis combined with a better knowledge of the species might provide a better understanding of the role of biotic interactions.

Combining Geographic Distribution and Trait Information to Infer Predator–Prey Species-Level Interaction Properties

Biotic interactions are a key component of the proper functioning of ecosystems. However, information on biotic interactions is spatially and taxonomically biased and limited to several groups. The most efficient strategy to fill these gaps is to combine spatial information (species ranges) with different sources of information (functional and field data) to infer potential interactions. This approach is possible due to the fact that there is a correspondence between the traits of two trophic levels (e.g., predator and prey sizes are correlated). Therefore, our objective was to evaluate the performance of the joint use of spatial, functional and field data to infer properties of the predator–prey interaction for five neotropical cats. To do this, we used presence–absence matrices to obtain lists of potential prey species per grid-cell for each predator range. These lists were filtered according to different criteria (models), and for each model, an interaction property was estimated and compared with field observations. Our results show that the use of functional information and co-occurrence allows us to generate values similar to those observed in the field. We also observed that there were differences in model performance related to the intrinsic characteristics of the predator (body size) and the interaction property being evaluated.

Analyzing trophic ecosystem functions with the interaction functional space

Quantifying the vulnerability of ecosystems to global change requires a better understanding of how trophic ecosystem functions emerge. So far, trophic ecosystem functions have been studied from the perspective of either functional diversity or network ecology. To integrate these two perspectives, we propose the interaction functional space (IFS) a conceptual framework to simultaneously analyze the effects of traits and interactions on trophic functions. We exemplify the added value of our framework for seed dispersal and wood decomposition and show how species interactions influence the relationship between functional trait diversity and trophic functions. We propose future applications for a range of functions where the IFS can help to elucidate mechanisms underpinning trophic functions and facilitate understanding of functional changes in ecosystems amidst global change.

Biotic Interactions and the Future of Fishes on Coral Reefs: The Importance of Trait-Based Approaches

Biotic interactions govern the structure and function of coral reef ecosystems. As environmental conditions change, reef-associated fish populations can persist by tracking their preferred niche or adapting to new conditions. Biotic interactions will affect how these responses proceed and whether they are successful. Yet, our understanding of these effects is currently limited. Ecological and evolutionary theories make explicit predictions about the effects of biotic interactions, but many remain untested. Here, we argue that large-scale functional trait datasets enable us to investigate how biotic interactions have shaped the assembly of contemporary reef fish communities and the evolution of species within them, thus improving our ability to predict future changes. Importantly, the effects of biotic interactions on these processes have occurred simultaneously within dynamic environments. Functional traits provide a means to integrate the effects of both ecological and evolutionary processes, as well as a way to overcome some of the challenges of studying biotic interactions. Moreover, functional trait data can enhance predictive modeling of future reef fish distributions and evolvability. We hope that our vision for an integrative approach, focused on quantifying functionally relevant traits and how they mediate biotic interactions in different environmental contexts, will catalyze new research on the future of reef fishes in a changing environment.

Coextinctions dominate future vertebrate losses from climate and land use change

Although theory identifies coextinctions as a main driver of biodiversity loss, their role at the planetary scale has yet to be estimated. We subjected a global model of interconnected terrestrial vertebrate food webs to future (2020-2100) climate and land-use changes. We predict a 17.6% (± 0.16% SE) average reduction of local vertebrate diversity globally by 2100, with coextinctions increasing the effect of primary extinctions by 184.2% (± 10.9% SE) on average under an intermediate emissions scenario. Communities will lose up to a half of ecological interactions, thus reducing trophic complexity, network connectance, and community resilience. The model reveals that the extreme toll of global change for vertebrate diversity might be of secondary importance compared to the damages to ecological network structure.

Small predators dominate fish predation in coral reef communities

Ecosystem processes are challenging to quantify at a community level, particularly within complex ecosystems (e.g., rainforests, coral reefs). Predation is one of the most important types of species interactions, determining several ecosystem processes. However, while it is widely recognised, it is rarely quantified, especially in aquatic systems. To address these issues, we model predation on fish by fish, in a hyperdiverse coral reef community. We show that body sizes previously examined in fish-fish predation studies (based on a metanalysis), only represent about 5% of likely predation events. The average fish predator on coral reefs is just 3.65 cm; the average fish prey just 1.5 cm. These results call for a shift in the way we view fish predation and its ability to shape the species or functional composition of coral reef fish communities. Considered from a functional group approach, we found general agreement in the distribution of simulated and observed predation events, among both predator and prey functional groups. Predation on coral reefs is a process driven by small fish, most of which are neither seen nor quantified.

Human disturbances affect the topology of food webs

Networks describe nodes connected by links, with numbers of links per node, the degree, forming a range of distributions including random and scale-free. How network topologies emerge in natural systems still puzzles scientists. Based on previous theoretical simulations, we predict that scale-free food webs are favourably selected by random disturbances while random food webs are selected by targeted disturbances. We assume that lower human pressures are more likely associated with random disturbances, whereas higher pressures are associated with targeted ones. We examine these predictions using 351 empirical food webs, generally confirming our predictions. Should the topology of food webs respond to changes in the magnitude of disturbances in a predictable fashion, consistently across ecosystems and scales of organisation, it would provide a baseline expectation to understand and predict the consequences of human pressures on ecosystem dynamics.

Role of Bacterial Community Composition as a Driver of the Small-Sized Phytoplankton Community Structure in a Productive Coastal System

We present here the first detailed description of the seasonal patterns in bacterial community composition (BCC) in shelf waters off the Ría de Vigo (Spain), based on monthly samplings during 2 years. Moreover, we studied the relationship between bacterial and small-sized eukaryotic community composition to identify potential biotic interactions among components of these two communities. Bacterial operational taxonomic unit (OTU) richness and diversity systematically peaked in autumn-winter, likely related to low resource availability during this period. BCC showed seasonal and vertical patterns, with Rhodobacteraceae and Flavobacteriaceae families dominating in surface waters, and SAR11 clade dominating at the base of the photic zone (30 m depth). BCC variability was significantly explained by environmental variables (e.g., temperature of water, solar radiation, or dissolved organic matter). Interestingly, a strong and significant correlation was found between BCC and small-sized eukaryotic community composition (ECC), which suggests that biotic interactions may play a major role as structuring factors of the microbial plankton in this productive area. In addition, co-occurrence network analyses revealed strong and significant, mostly positive, associations between bacteria and small-sized phytoplankton. Positive associations likely result from mutualistic relationships (e.g., between Dinophyceae and Rhodobacteraceae), while some negative correlations suggest antagonistic interactions (e.g., between Pseudo-nitzchia sp. and SAR11). These results support the key role of biotic interactions as structuring factors of the small-sized eukaryotic community, mostly driven by positive associations between small-sized phytoplankton and bacteria.

Factors determining the dorsal coloration pattern of aposematic salamanders

Aposematic bright colors have a key role for animal defense and can be expressed through metabolic production or by acquiring pigments from diet. Aposematic coloration can be related to both local adaptations and availability of trophic resources. The European fire salamander (Salamandra salamandra) shows significant color variability and occurs across a broad range of habitats. Here we combined field observations with common rearing experiments to disentangle the role of environmental conditions and local adaptations in determining aposematic coloration of salamander populations. We assessed color variation and measured habitat features and food availability in adults from 25 populations. Furthermore, we reared newborn larvae from 10 populations under different food availability and analyzed color of metamorphs. To assess color pattern, we measured the percentage of yellow covering the body, and the Hue, Saturation and Value of yellow coloration. Adult showed strong variation of color pattern; variation was strongly related to the individual's size, to habitat productivity and to food availability. Under common garden conditions, differences between populations were not anymore evident, and coloration was only affected by resource availability during larval development. Our results suggest that environmental conditions and food availability are more important than local adaptations in determining differences in aposematic color pattern.

Beyond body size—new traits for new heights in trait-based modelling of predator-prey dynamics

Food webs map feeding interactions among species, providing a valuable tool for understanding and predicting community dynamics. Using species’ body sizes is a promising avenue for parameterizing food-web models, but such approaches have not yet been able to fully recover observed community dynamics. Such discrepancies suggest that traits other than body size also play important roles. For example, differences in species’ use of microhabitat or non-consumptive effects of intraguild predators may affect dynamics in ways not captured by body size. In Laubmeier et al. (2018), we developed a dynamic food-web model incorporating microhabitat and non-consumptive predator effects in addition to body size, and used simulations to suggest an optimal sampling design of a mesocosm experiment to test the model. Here, we perform the mesocosm experiment to generate empirical time-series of insect herbivore and predator abundance dynamics. We minimize least squares error between the model and time-series to determine parameter values of four alternative models, which differ in terms of including vs excluding microhabitat use and non-consumptive predator-predator effects. We use both statistical and expert-knowledge criteria to compare the models and find including both microhabitat use and non-consumptive predator-predator effects best explains observed aphid and predator population dynamics, followed by the model including microhabitat alone. This ranking suggests that microhabitat plays a larger role in driving population dynamics than non-consumptive predator-predator effects, although both are clearly important. Our results illustrate the importance of additional traits alongside body size in driving trophic interactions. They also point to the need to consider trophic interactions and population dynamics in a wider community context, where non-trophic impacts can dramatically modify the interplay between multiple predators and prey. Overall, we demonstrate the potential for utilizing traits beyond body size to improve trait-based models and the value of iterative cycling between theory, data and experiment to hone current insights into how traits affect food-web dynamics.

Biological Microbial Interactions from Cooccurrence Networks in a High Mountain Lacustrine District

A fundamental question in biology is why some species tend to occur together in the same locations, while others are never observed coexisting. This question becomes particularly relevant for microorganisms thriving in the highly diluted waters of high mountain lakes, where biotic interactions might be required to make the most of an extreme environment. We studied a high-throughput gene data set of alpine lakes (>220 Pyrenean lakes) with cooccurrence network analysis to infer potential biotic interactions, using the combination of a probabilistic method for determining significant cooccurrences and coexclusions between pairs of species and a conceptual framework for classifying the nature of the observed cooccurrences and coexclusions. This computational approach (i) determined and quantified the importance of environmental variables and spatial distribution and (ii) defined potential interacting microbial assemblages. We determined the properties and relationships between these assemblages by examining node properties at the taxonomic level, indicating associations with their potential habitat sources (i.e., aquatic versus terrestrial) and their functional strategies (i.e., parasitic versus mixotrophic). Environmental variables explained fewer pairs in bacteria than in microbial eukaryotes for the alpine data set, with pH alone explaining the highest proportion of bacterial pairs. Nutrient composition was also relevant for explaining association pairs, particularly in microeukaryotes. We identified a reduced subset of pairs with the highest probability of species interactions (“interacting guilds”) that significantly reached higher occupancies and lower mean relative abundances in agreement with the carrying capacity hypothesis. The interacting bacterial guilds could be more related to habitat and microdispersal processes (i.e., aquatic versus soil microbes), whereas for microeukaryotes trophic roles (osmotrophs, mixotrophs, and parasitics) could potentially play a major role. Overall, our approach may add helpful information to guide further efforts for a mechanistic understanding of microbial interactions in situ.

IMPORTANCE A fundamental question in biology is why some species tend to occur together in the same locations, while others are never observed to coexist. This question becomes particularly relevant for microorganisms thriving in the highly diluted waters of high mountain lakes, in which biotic interactions might be required to make the most of an extreme environment. Microbial metacommunities are too often only studied in terms of their environmental niches and geographic barriers since they show inherent difficulties to quantify biological interactions and their role as drivers of ecosystem functioning. Our study highlights that telling apart potential interactions from both environmental and geographic niches may help for the initial characterization of organisms with similar ecologies in a large scope of ecosystems, even when information about actual interactions is partial and limited. The multilayered statistical approach carried out here offers the possibility of going beyond taxonomy to understand microbiological behavior in situ.

A needle in the haystack? Applying species co-occurrence frameworks with fish assemblage data to identify species associations and sharpen ecological hypotheses

Different species can associate or interact in many ways, and methods exist for inferring associations and underlying mechanisms from incidence data (e.g., co-occurrence frameworks). These methods have received criticism despite their recent resurgence in the literature. However, co-occurrence frameworks for identifying nonrandomly associated species pairs (e.g., aggregated or segregated pairs) have value as heuristic tools for sharpening hypotheses concerning fish ecology. This paper provides a case study examining species co-occurrence across 33 stream fish assemblages in southeastern Oklahoma, USA, which were sampled twice (1974 and 2014). This study sought to determine (a) which species were nonrandomly associated, (b) what processes might have driven these associations and (c) how consistent patterns were across time. Associations among most pairs of species (24 species, 276 unique pairs) were not significantly different from random (>80%). Among all significant, nonrandomly associated species pairs (54 unique pairs), 78% (42 pairs) were aggregated and 22% (12 pairs) segregated. Most of these (28 pairs, 52%) were hypothesized to be driven by nonbiotic mechanisms: habitat filtering (20 pairs, 37%), dispersal limitation (two pairs, 0.4%) or both (six pairs, 11%). The remaining 26 nonrandomly associated pairs (48%) had no detectable signal of spatial or environmental factors involved with the association, therefore the potential for biotic interaction was not refuted. Only five species pairs were consistently associated across both sampling periods: stonerollers Campostoma spp. and orangebelly darter Etheostoma radiosum; red shiner Cyprinella lutrensis and bullhead minnow Pimephales vigilax; bluegill sunfish Lepomis macrochirus and redear sunfish Lepomis microlophus; redfin shiner Lythrurus umbratilis and bluntnose minnow Pimephales notatus; and bigeye shiner Notropis boops and golden shiner Notemigonus crysoleucas. Frameworks for identifying nonrandomly associated species pairs can provide insight into broader mechanisms of species assembly and point to potentially interesting species interactions (out of many possible pairs). However, this approach is best applied as a tool for sharpening hypotheses to be investigated further. Rather than a weakness, the heuristic nature is the strength of such methods, and can help guide biologists toward better questions by employing relatively cheap diversity survey data, which are often already in hand, to reduce complex interaction networks down to their nonstochastic parts which warrant further investigation.

Addressing the Eltonian shortfall with trait-based interaction models

We have very limited knowledge of how species interact in most communities and ecosystems despite trophic relationships being fundamental for linking biodiversity to ecosystem functioning. A promising approach to fill this gap is to predict interactions based on functional traits, but many questions remain about how well we can predict interactions for different taxa, ecosystems and amounts of input data. Here, we built a new traits-based model of trophic interactions for European vertebrates and found that even models calibrated with 0.1% of the interactions (100 out of 71 k) estimated the full European vertebrate food web reasonably well. However, predators were easier to predict than prey, especially for some clades (e.g. fowl and storks) and local food web connectance was consistently overestimated. Our results demonstrate the ability to rapidly generate food webs when empirical data are lacking-an important step towards a more complete and spatially explicit description of food webs.

The effects of defaunation on plants' capacity to track climate change

Half of all plant species rely on animals to disperse their seeds. Seed dispersal interactions lost through defaunation and gained during novel community assembly influence whether plants can adapt to climate change through migration. We develop trait-based models to predict pairwise interactions and dispersal function for fleshy-fruited plants globally. Using interactions with introduced species as an observable proxy for interactions in future novel seed dispersal networks, we find strong potential to forecast their assembly and functioning. We conservatively estimate that mammal and bird defaunation has already reduced the capacity of plants to track climate change by 60% globally. This strong reduction in the ability of plants to adapt to climate change through range shifts shows a synergy between defaunation and climate change that undermines vegetation resilience.

Ecological dependencies make remote reef fish communities most vulnerable to coral loss

Ecosystems face both local hazards, such as over-exploitation, and global hazards, such as climate change. Since the impact of local hazards attenuates with distance from humans, local extinction risk should decrease with remoteness, making faraway areas safe havens for biodiversity. However, isolation and reduced anthropogenic disturbance may increase ecological specialization in remote communities, and hence their vulnerability to secondary effects of diversity loss propagating through networks of interacting species. We show this to be true for reef fish communities across the globe. An increase in fish-coral dependency with the distance of coral reefs from human settlements, paired with the far-reaching impacts of global hazards, increases the risk of fish species loss, counteracting the benefits of remoteness. Hotspots of fish risk from fish-coral dependency are distinct from those caused by direct human impacts, increasing the number of risk hotspots by ~30% globally. These findings might apply to other ecosystems on Earth and depict a world where no place, no matter how remote, is safe for biodiversity, calling for a reconsideration of global conservation priorities.

Forecasting parasite sharing under climate change

Species are shifting their distributions in response to climate change. This geographic reshuffling may result in novel co-occurrences among species, which could lead to unseen biotic interactions, including the exchange of parasites between previously isolated hosts. Identifying potential new host-parasite interactions would improve forecasting of disease emergence and inform proactive disease surveillance. However, accurate predictions of future cross-species disease transmission have been hampered by the lack of a generalized approach and data availability. Here, we propose a framework to predict novel host-parasite interactions based on a combination of niche modelling of future host distributions and parasite sharing models. Using the North American ungulates as a proof of concept, we show this approach has high cross-validation accuracy in over 85% of modelled parasites and find that more than 34% of the host-parasite associations forecasted by our models have already been recorded in the literature. We discuss potential sources of uncertainty and bias that may affect our results and similar forecasting approaches, and propose pathways to generate increasingly accurate predictions. Our results indicate that forecasting parasite sharing in response to shifts in host geographic distributions allow for the identification of regions and taxa most susceptible to emergent pathogens under climate change. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

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'.

Inferring species interactions from ecological survey data: A mechanistic approach to predict quantitative food webs of seed feeding by carabid beetles

Ecological networks are valuable for ecosystem analysis but their use is often limited by a lack of data because many types of ecological interaction, for example, predation, are short-lived and difficult to observe or detect. While there are different methods for inferring the presence of interactions, they have rarely been used to predict the interaction strengths that are required to construct weighted, or quantitative, ecological networks.Here, we develop a trait-based approach suitable for inferring weighted networks, that is, with varying interaction strengths. We developed the method for seed-feeding carabid ground beetles (Coleoptera: Carabidae) although the principles can be applied to other species and types of interaction.Using existing literature data from experimental seed-feeding trials, we predicted a per-individual interaction cost index based on carabid and seed size. This was scaled up to the population level to create inferred weighted networks using the abundance of carabids and seeds from empirical samples and energetic intake rates of carabids from the literature. From these weighted networks, we also derived a novel measure of expected predation pressure per seed type per network.This method was applied to existing ecological survey data from 255 arable fields with carabid data from pitfall traps and plant seeds from seed rain traps. Analysis of these inferred networks led to testable hypotheses about how network structure and predation pressure varied among fields.Inferred networks are valuable because (a) they provide null models for the structuring of food webs to test against empirical species interaction data, for example, DNA analysis of carabid gut regurgitates and (b) they allow weighted networks to be constructed whenever we can estimate interactions between species and have ecological census data available. This permits ecological network analysis even at times and in places when interactions were not directly assessed.

Intraspecific variation in plant-associated herbivore communities is phylogenetically structured in Brassicaceae

As a result of co‐evolution between plants and herbivores, related plants often interact with similar herbivore communities. Variation in plant–herbivore interactions is determined by variation in underlying functional traits and by ecological and stochastic processes. Hence, typically, only a subset of possible interactions is realised on individual plants. We show that insect herbivore communities assembling on individual plants are structured by plant phylogeny among 12 species in two phylogenetic lineages of Brassicaceae. This community sorting to plant phylogeny was retained when splitting the community according to herbivore feeding guilds. Relative abundance of herbivores as well as the size of the community structured community dissimilarity among plant species. Importantly, the amount of intraspecific variation in realised plant–herbivore interactions is also phylogenetically structured. We argue that variability in realised interactions that are not directly structured by plant traits is ecologically relevant and must be considered in the evolution of plant defences.

Molecular assays of pollen use consistently reflect pollinator visitation patterns in a system of flowering plants

Determining how pollinators visit plants vs. how they carry and transfer pollen is an ongoing project in pollination ecology. The current tools for identifying the pollens that bees carry have different strengths and weaknesses when used for ecological inference. In this study we use three methods to better understand a system of congeneric, coflowering plants in the genus Clarkia and their bee pollinators: observations of plant-pollinator contact in the field, and two different molecular methods to estimate the relative abundance of each Clarkia pollen in samples collected from pollinators. We use these methods to investigate if observations of plant-pollinator contact in the field correspond to the pollen bees carry; if individual bees carry Clarkia pollens in predictable ways, based on previous knowledge of their foraging behaviors; and how the three approaches differ for understanding plant-pollinator interactions. We find that observations of plant-pollinator contact are generally predictive of the pollens that bees carry while foraging, and network topologies using the three different methods are statistically indistinguishable from each other. Results from molecular pollen analysis also show that while bees can carry multiple species of Clarkia at the same time, they often carry one species of pollen. Our work contributes to the growing body of literature aimed at resolving how pollinators use floral resources. We suggest our novel relative amplicon quantification method as another tool in the developing molecular ecology and pollination biology toolbox.

Integration of remote sensing in spatial ecology: assessing the interspecific interactions of two plant species in a semi-arid woodland using unmanned aerial vehicle (UAV) photogrammetric data

The spatial structure of plant communities in semi-arid regions is mostly derived by plant-plant interactions and environmental heterogeneity. In this study, we investigated the intra- and interspecific interactions and their contribution to growth inhibition in the patches of Pistacia trees and Amygdalus shrubs in semi-arid woodland communities through the implementation of photogrammetric data provided by unmanned aerial vehicle (UAV). This study was conducted in a part of Wild Pistachio Natural Reserve covered by Pistacia-Amygdalus stands in Zagros Mountains, western Iran. We used univariate and bivariate forms of pair- and mark correlation functions and Analytical Global Envelopes under inhomogeneous Poisson process which allow detection of the interactions of the species within the 45-ha study area. Our results indicated that the UAV-derived photogrammetric data proved to be efficient in identification of the plant individuals (F-score ≈ 0.92 for both species). Additionally, strong coefficients of determination (R² = 0.98 and 0.94 for Pistacia and Amygdalus, respectively) supported prediction of crown area. We observed the aggregation of the species individuals in clusters of conspecifics and heterospecifics at small spatial scales, most likely as a result of aggregation in favourable parts of the study area. The aggregation of the species within patches had a marked effect on their size (i.e., crown area, height) inferred as growth inhibition, probably due to intra- and interspecific competition. Our findings demonstrated that promising UAV photogrammetric data can be effectively utilized by ecologists for investigation of plant associations, hence increasing the potentiality of remote sensing in spatial ecology of vegetation patches in semi-arid environments.

The spatial scaling of food web structure across European biogeographical regions

The species-area relationship (SAR) is one of the most well-established scaling patterns in ecology. Its implications for understanding how communities change across spatial gradients are numerous, including the effects of habitat loss on biodiversity. However, ecological communities are not mere collections of species. They are the result of interactions between these species forming complex networks that tie them together. Should we aim to grasp the spatial scaling of biodiversity as a whole, it is fundamental to understand the changes in the structure of interaction networks with area. In spite of a few empirical and theoretical studies that address this challenge, we still do not know much about how network structure changes with area, or what are the main environmental drivers of these changes. Here, using the meta-network of potential interactions between all terrestrial vertebrates in Europe (1140 species and 67 201 feeding interactions), we analysed network-area relationships (NARs) that summarize how network properties scale with area. We do this across ten biogeographical regions, which differ in environmental characteristics. We found that the spatial scaling of network complexity strongly varied across biogeographical regions. However, once the variation in SARs was accounted for, differences in the shape of NARs vanished. On the other hand, the proportion of species across trophic levels remained remarkably constant across biogeographical regions and spatial scales, despite the great variation in species richness. Spatial variation in mean annual temperature and habitat clustering were the main environmental determinants of the shape of both SARs and NARs across Europe. Our results suggest new avenues in the exploration of the effects of environmental factors on the spatial scaling of biodiversity. We argue that NARs can provide new insights to analyse and understand ecological communities.