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Predicting the outcome of interactions between species is central to our current understanding of diversity maintenance. However, we have limited information about the robustness of many model-based predictions of species coexistence. This limitation is partly because several sources of uncertainty are often ignored when making predictions. Here, we introduce a framework to simultaneously explore how different mathematical models, different environmental contexts, and parameter uncertainty impact the probability of predicting species coexistence. Using a set of pairwise competition experiments on annual plants, we provide direct evidence that subtle differences between models lead to contrasting predictions of both coexistence and competitive exclusion. We also show that the effects of environmental context-dependency and parameter uncertainty on predictions of species coexistence are not independent of the model used to describe population dynamics. Our work suggests that predictions of species coexistence and extrapolations thereof may be particularly vulnerable to these underappreciated founts of uncertainty.

Functional responses relate a consumer’s feeding rates to variation in its abiotic and biotic environment, providing insight into consumer behavior and fitness, and underpinning population and food-web dynamics. Despite their broad relevance and long-standing history, we show here that the types of density dependence found in classic resource- and consumer-dependent functional-response models equate to strong and often untenable assumptions about the independence of processes underlying feeding rates. We first demonstrate mathematically how to quantify non-independence between feeding and consumer interference and between feeding on multiple resources. We then analyze two large collections of functional-response datasets to show that non-independence is pervasive and borne out in previously-hidden forms of density dependence. Our results provide a new lens through which to view variation in consumer feeding rates and disentangle the biological underpinnings of species interactions in multi-species contexts.

Niche packing is one of the prevailing mechanisms underlying the increase in the number of co-occurring species and the extraordinary diversity of tropical ecosystems. However, it is not yet understood whether niche packing is facilitated by higher specialization and reduced niche overlap or, rather, by diffuse competition and increased niche overlap. We combined highly resolved bird-plant interaction networks, bird phylogenies, and plant functional traits to compare dietary niche overlap and foraging frequencies among frugivorous birds at seven sites in the tropical Andes. We quantified niche overlap on the basis of the traits of the plants used by each bird and related it to the degree of niche packing at the different sites. Niche complementarity decreased with increasing niche packing, suggesting that increasingly dense niche packing is facilitated by increased niche overlap. Pairwise niche overlap was mediated by shifts in foraging frequencies away from shared resources, and it decreased with decreasing phylogenetic relatedness and increasing dependence on fruit as resource. Our findings suggest that foraging choices are a key axis of diversification in frugivorous birds and that differences in resource use frequencies are already sufficient to reduce potential competition between ecologically similar species and facilitate niche packing, especially if species differ in their dependence on particular resources.

All organisms must simultaneously tolerate the environment and access limiting resources if they are to persist. Otherwise they go extinct. Approaches to understanding environmental tolerance and resource competition have generally been developed independently. Consequently, integrating the factors that determine abiotic tolerance with those that affect competitive interactions to model species abundances and community structure remains an unresolved challenge. This is likely the reason why current models of community assembly do not accurately predict species abundances and dynamics. Here, we introduce a new synthetic framework that models both abiotic tolerance and biotic competition by using functional traits, which are phenotypic attributes that influence organism fitness. First, our framework estimates species carrying capacities that vary along abiotic gradients based on whether the phenotype tolerates the local environment. Second, it estimates pairwise competitive interactions as a function of multidimensional trait differences between species and determines which trait combinations produce the most competitive phenotypes. We demonstrate that our combined approach more than doubles the explained variance of species covers in a wetland community compared to the model of abiotic tolerances alone. Trait-based integration of competitive interactions and abiotic filtering improves our ability to predict species abundances across space, bringing us closer to more accurate predictions of biodiversity structure in a changing world.

Parasites are often key players in biological invasions since they can mediate the impact of host invasions or can themselves become invasive species. However, the nature and extent of parasite‐mediated invasions are often difficult to delineate. Here, we used individual‐based, weighted bipartite networks to study the roles (degrees of interactions of individuals in a modular network according to their within‐ and among‐module connections) played by native and invasive host individuals to their parasite communities. We studied two phylogenetically and ecologically close fish species, Mugil cephalus s.l. and Planiliza haematocheilus (Teleostei: Mugilidae). Planiliza haematocheilus is native to the Sea of Japan and invasive in the Sea of Azov whereas, M. cephalus s.l. is native to both seas. Based on the common evolutionary history that drives native host–parasite networks, we hypothesised that 1) native networks have higher modularity than invaded ones; and 2) invasive hosts in the invaded area play a peripheral role to structure parasite communities. We analysed the whole parasite community and subsets based on transmission strategy and host specificity of the parasite species to establish whether modularity and host roles are related to these features in the native and invaded areas. All networks were found to be modular. However, modularity tended to be higher in networks of the native area rather than those of the invaded area. Host individuals of both fish species played similar roles in the native area, whereas invasive hosts played a peripheral role in the networks of the invaded area. We propose that long‐term monitoring of the roles of invasive hosts in parasite communities can be a useful proxy for estimating the maturity of the establishment of the invasive hosts in an ecosystem.

Extinctions stemming from environmental change often trigger trophic cascades and coextinctions. Bottom-up cascades occur when changes in the primary producers in a network elicit flow-on effects to higher trophic levels. However, it remains unclear what determines a species' vulnerability to bottom-up cascades and whether such cascades were a large contributor to the megafauna extinctions that swept across several continents in the Late Pleistocene. The pathways to megafauna extinctions are particularly unclear for Sahul (landmass comprising Australia and New Guinea), where extinctions happened earlier than on other continents. We investigated the potential role of bottom-up trophic cascades in the megafauna extinctions in Late Pleistocene Sahul by first developing synthetic networks that varied in topology to identify how network position (trophic level, diet breadth, basal connections) influences vulnerability to bottom-up cascades. We then constructed pre-extinction (-80 ka) network models of the ecological community of Naracoorte, south-eastern Sahul, to assess whether the observed megafauna extinctions could be explained by bottom-up cascades. Synthetic networks showed that node vulnerability to bottom-up cascades decreased with increasing trophic level, diet breadth and basal connections. Extinct species in the Naracoorte community were more vulnerable overall to these cascades than were species that survived. The position of extinct species in the network - tending to be of low trophic level and therefore having relatively narrow diet breadths and fewer connections to plants - made them vulnerable. However, these species also tended to have few or no predators, a network-position attribute that suggests they might have been particularly vulnerable to new predators. Together, these results suggest that trophic cascades and naivety to predators could have contributed to the megafauna extinction event in Sahul.

Animals often change their behaviour in the presence of other species and the environmental context they experience, and these changes can substantially modify the course their populations follow. In the case of animals involved in mutualistic interactions, it is still unclear how to incorporate the effects of these behavioural changes into population dynamics. We propose a framework for using pollinator functional responses to examine the roles of pollinator-pollinator interactions and abiotic conditions in altering the times between floral visits of a focal pollinator. We then apply this framework to a unique foraging experiment with different models that allow resource availability and sublethal exposure to a neonicotinoid pesticide to modify how pollinators forage alone and with co-foragers. We found that all co-foragers interfere with the focal pollinator under at least one set of abiotic conditions; for most species, interference was strongest at higher levels of resource availability and with pesticide exposure. Overall our results highlight that density-dependent responses are often context-dependent themselves.

Growth in individual size or biomass is a key demographic component in population models, with wide-ranging applications from quantifying species performance across abiotic or biotic conditions to assessing landscape-level dynamics under global change. In forest ecology, the responses of tree growth to biotic interactions are widely held to be crucial for understanding forest diversity, function, and structure. To date, most studies on plant–plant interactions only examine the additive competitive or facilitative interactions between species pairs; however, there is increasing evidence of non-additive, higher-order interactions (HOIs) impacting species demographic rates. When HOIs are present, the dynamics of a multi-species community cannot be fully understood or accurately predicted solely from pairwise outcomes because of how additional species ‘interfere’ with the direct, pairwise interactions. Such HOIs should be particularly prevalent where species show nonlinear functional responses to resource availability and resource-acquisition traits themselves are density dependent. With this in mind, we used data from a tropical secondary forest—a system that fulfills both of these conditions—to build a ontogenetic diameter-growth model for individuals across ten woody-plant species. We allowed both direct and indirect interactions within communities to influence the species-specific growth parameters in a generalized Lotka–Volterra model. Specifically, indirect interactions entered the model as higher-order quadratic terms, i.e. non-additive effects of conspecific and heterospecific neighbour size on the focal individual’s growth. For the whole community and for four out of ten focal species, the model that included HOIs had more statistical support than the model that included only direct interactions, despite the former containing a far greater number of parameters. HOIs had comparable effect sizes to direct interactions, and tended to further reduce the diameter growth rates of most species beyond what direct interactions had already reduced. In a simulation of successional stand dynamics, the inclusion of HOIs lead to rank swaps in species’ diameter hierarchies, even when community-level size distributions remained qualitatively similar. Our study highlights the implications, and discusses possible mechanisms, of non-additive density dependence in highly diverse and light-competitive tropical forests.

To avoid extinction, every species must be able to exploit available resources at least as well as the other species in its community. All else being equal, theory predicts that the more distinct the niches of such co-occurring and competing species, the more species that can persist in the long run. However, both theoretical and experimental studies define a priori the nature and number of resources over which species compete. It therefore remains unclear whether or not species in empirically realistic contexts are actually exploiting all or some of the niches available to them. Here we provide a mathematical solution to this long-standing problem. Specifically, we show how to use the interactions between sets of co-occurring plant species to quantify their implied “niche dimensionality”: the effective number of resources over which those species appear to be competing. We then apply this approach to quantify the niche dimensionality of 12 plant assemblages distributed across the globe. Contrary to conventional wisdom, we found that the niche dimensionality in these systems was much lower than the number of competing species. However, two high-resolution experiments also show that changes in the local environment induce a reshuffling of plant’s competitive roles and hence act to increase the assemblages’ effective niche dimensionality. Our results therefore indicate that homogeneous environments are unlikely to be able to maintain high diversity and also shows how environmental variation impacts species’ niches and hence their opportunities for long-term survival.

The assessment of relative model performance using information criteria like AIC and BIC has become routine among functional-response studies, reflecting trends in the broader ecological literature. Such information criteria allow comparison across diverse models because they penalize each model’s fit by its parametric complexity — in terms of their number of free parameters — which allows simpler models to outperform similarly fitting models of higher parametric complexity. However, criteria like AIC and BIC do not consider an additional form of model complexity, referred to as geometric complexity, which relates specifically to the mathematical form of the model. Models of equivalent parametric complexity can differ in their geometric complexity and thereby in their ability to flexibly fit data. Here we use the Fisher Information Approximation to compare, explain, and contextualize how geometric complexity varies across a large compilation of single-prey functional-response models — including prey-, ratio-, and predator-dependent formulations — reflecting varying apparent degrees and forms of non-linearity. Because a model’s geometric complexity varies with the data’s underlying experimental design, we also sought to determine which designs are best at leveling the playing field among functional-response models. Our analyses illustrate (1) the large differences in geometric complexity that exist among functional-response models, (2) there is no experimental design that can minimize these differences across all models, and (3) even the qualitative nature by which some models are more or less flexible than others is reversed by changes in experimental design. Failure to appreciate model flexibility in the empirical evaluation of functional-response models may therefore lead to biased inferences for predator–prey ecology, particularly at low experimental sample sizes where its impact is strongest. We conclude by discussing the statistical and epistemological challenges that model flexibility poses for the study of functional responses as it relates to the attainment of biological truth and predictive ability.Contribution to Field StatementThe use of criteria like AIC and BIC for selecting among functional-response models is now standard, well-accepted practice, just as it is in the ecological literature as a whole. The generic desire underlying the use of these criteria is to make the comparison of model performance an unbiased and equitable process by penalizing each model’s fit to data by itsparametric complexity(relating to its number of free parameters). Here we introduce the Fisher Information Approximation to the ecological literature and use it to understand how thegeometric complexityof models — a form of model complexity relating to a model’s functional flexibility that is not considered by criteria like AIC and BIC — varies across a large compilation of 40 different single-prey functional-response models. Our results add caution against the simplistic use and interpretation of information-theoretic model comparisons for functional-response experiments, showing just how large an effect that model flexibility can have on inferences of model performance. We therefore use our work to help clarify the challenges that ecologists studying functional responses must face in the attainment of biological truth and predictive ability.

Species interactions can propagate disturbances across space, though ecological and biogeographic boundaries may limit this spread. We tested whether large-scale ecological boundaries (ecoregions and biomes) and human disturbance gradients increase dissimilarity among ecological networks, while accounting for background spatial and elevational effects and differences in network sampling. We assessed network dissimilarity patterns over a broad spatial scale, using 196 quantitative avian frugivory networks (encompassing 1,496 plant and 1,003 bird species) distributed across 67 ecoregions and 11 biomes. Dissimilarity in species and interactions, but not in network structure, increased significantly across ecoregion and biome boundaries and along human disturbance gradients. Our findings suggest that ecological boundaries contribute to maintaining the world’s biodiversity of interactions and mitigating the propagation of disturbances at large spatial scales.One-Sentence SummaryEcoregions and biomes delineate the large-scale distribution of plant-frugivore interactions.

Resilience has emerged as a key concept in ecology and conservation biology to understand and predict ecosystem responses to global change. In its broadest sense, resilience describes the ability of an ecosystem to resist, and recover from, a disturbance. However, the application of such a concept in different subdisciplines of ecology and in different study systems has resulted in a wide disparity of definitions and ways of quantifying resilience.\ud This Special Feature, which spans the Journal of Ecology, Journal of Animal Ecology and Functional Ecology, provides an overview of how ecologists define, quantify, compare and predict resilience across different study systems.\ud The 29 contributions to this Special Feature show the broad range of approaches used by ecologists to study resilience. Almost half of the contributions (48%) study resilience at the community level, with a 30% of them studying resilience at multiple levels of biological organisation. A large proportion of these articles are observational (42%), experimental (14%) or a combination of both (17%) while a 17% utilise theoretical or computational approaches. Although 38%, 21% and 14% of the studies were based solely on plants, animals or micro-organisms, respectively, 17% of them incorporated these multiple trophic levels.\ud Synthesis. A unified ecological understanding of resilience across systems and taxa requires a trans-disciplinary consensus on what resilience actually is and how to best measure it. Here, we provide an overview of how ecologists define, quantify, compare and predict resilience across different ecological systems and subdisciplines, with reference to the diverse approaches used by contributions to this Special Feature. We identify four key recommendations to harmonise future efforts in resilience research: (a) define resilience using existing theoretical frameworks; (b) use common and comparable metrics to measure resilience; (c) clearly contextualise and define the pre- and post-disturbance state of the ecological system and (d) consider explicitly the disturbance type and regime impacting the system.

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes’ metadata. Graph embedding methods project the nodes of a network onto a—low dimensional—latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes’ metadata to their position in a latent feature space corresponds to a classic—and low dimensional—machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes’ meta-data. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

Author(s): Baiser, B; Gravel, D; Cirtwill, AR; Dunne, JA; Fahimipour, AK; Gilarranz, LJ; Grochow, JA; Li, D; Martinez, ND; McGrew, A; Poisot, T; Romanuk, TN; Stouffer, DB; Trotta, LB; Valdovinos, FS; Williams, RJ; Wood, SA; Yeakel, JD | Abstract: Aim: How do factors such as space, time, climate and other ecological drivers influence food web structure and dynamics? Collections of well-studied food webs and replicate food webs from the same system that span biogeographical and ecological gradients now enable detailed, quantitative investigation of such questions and help integrate food web ecology and macroecology. Here, we integrate macroecology and food web ecology by focusing on how ecogeographical rules [the latitudinal diversity gradient (LDG), Bergmann's rule, the island rule and Rapoport's rule] are associated with the architecture of food webs. Location: Global. Time period: Current. Major taxa studied: All taxa. Methods: We discuss the implications of each ecogeographical rule for food webs, present predictions for how food web structure will vary with each rule, assess empirical support where available, and discuss how food webs may influence ecogeographical rules. Finally, we recommend systems and approaches for further advancing this research agenda. Results: We derived testable predictions for some ecogeographical rules (e.g. LDG, Rapoport's rule), while for others (e.g., Bergmann's and island rules) it is less clear how we would expect food webs to change over macroecological scales. Based on the LDG, we found weak support for both positive and negative relationships between food chain length and latitude and for increased generality and linkage density at higher latitudes. Based on Rapoport's rule, we found support for the prediction that species turnover in food webs is inversely related to latitude. Main conclusions: The macroecology of food webs goes beyond traditional approaches to biodiversity at macroecological scales by focusing on trophic interactions among species. The collection of food web data for different types of ecosystems across biogeographical gradients is key to advance this research agenda. Further, considering food web interactions as a selection pressure that drives or disrupts ecogeographical rules has the potential to address both mechanisms of and deviations from these macroecological relationships. For these reasons, further integration of macroecology and food webs will help ecologists better understand the assembly, maintenance and change of ecosystems across space and time.

An important dimension of a species' role is its ability to alter the state and maintain the diversity of its community. Centrality metrics have often been used to identify these species, which are sometimes referred as “keystone” species. However, the relationship between centrality and keystoneness is largely phenomenological and based mostly on our intuition regarding what constitutes an important species. While centrality is useful when predicting which species' extinctions could cause the largest change in a community, it says little about how these species could be used to attain or preserve a particular community state. Here we introduce structural controllability, an approach that allows us to quantify the extent to which network topology can be harnessed to achieve a desired state. It also allows us to quantify a species' control capacity—its relative importance—and identify the set of species that are critical in this context because they have the largest possible control capacity. We illustrate the application of structural controllability with ten pairs of uninvaded and invaded plant‐pollinator communities. We found that the controllability of a community is not dependent on its invasion status, but on the asymmetric nature of its mutual dependences. While central species were also likely to have a large control capacity, centrality fails to identify species that, despite being less connected, were critical in their communities. Interestingly, this set of critical species was mostly composed of plants and included every invasive species in our dataset. We also found that species with high control capacity, and in particular critical species, contribute the most to the stable coexistence of their community. This result was true, even when controlling for the species' degree, abundance/interaction strength, and the relative dependence of their partners. Synthesis. Structural controllability is strongly related to the stability of a network and measures the difficulty of managing an ecological community. It also identifies species that are critical to sustain biodiversity and to change or maintain the state of their community and are therefore likely to be very relevant for management and conservation.

Motivated by both analytical tractability and empirical practicality, community ecologists have long treated the species pair as the fundamental unit of study. This notwithstanding, the challenge of understanding more complex systems has repeatedly generated interest in the role of so-called higher-order interactions (HOIs) imposed by species beyond the focal pair. Here we argue that HOIs - defined as non-additive effects of density on per capita growth - are best interpreted as emergent properties of phenomenological models (e.g. Lotka-Volterra competition) rather than as distinct 'ecological processes' in their own right. Using simulations of consumer-resource models, we explore the mechanisms and system properties that give rise to HOIs in observational data. We demonstrate that HOIs emerge under all but the most restrictive of assumptions, and that incorporating non-additivity into phenomenological models improves the quantitative and qualitative accuracy of model predictions. Notably, we also observe that HOIs derive primarily from mechanisms and system properties that apply equally to single-species or pairwise systems as they do to more diverse communities. Consequently, there exists a strong mandate for further recognition of non-additive effects in both theoretical and empirical research.

It has long been anticipated that relating functional traits to species demography would be a cornerstone for achieving large-scale predictability of ecological systems. If such a relationship existed, species demography could be modeled only by measuring functional traits, transforming our ability to predict states and dynamics of species-rich communities with process-based community models. Here, we introduce a new method that links empirical functional traits with the demographic parameters of a process-based model by calibrating a transfer function through inverse modeling. As a case study, we parameterize a modified Lotka–Volterra model of a high-diversity mountain grassland with static plant community and functional trait data only. The calibrated trait–demography relationships are amenable to ecological interpretation, and lead to species abundances that fit well to the observed community structure. We conclude that our new method offers a general solution to bridge the divide between trait data and process-based models in species-rich ecosystems. Advances in process-based community ecology models are hindered by the challenge of linking functional traits to demography in species-rich systems, where a high number of parameters need to be estimated from limited data. Here the authors propose a new Bayesian framework to calibrate community models via functional traits, and validate it in a species-rich plant community.

Direct species interactions are commonly included in individual fitness models used for coexistence and local diversity modeling. Though widely considered important for such models, direct interactions alone are often insufficient for accurately predicting fitness, coexistence, or diversity outcomes. Incorporating higher-order interactions (HOIs) can lead to more accurate individual fitness models but also adds many model terms, which can quickly result in model overfitting. We explore approaches for balancing the trade-off between tractability and model accuracy that occurs when HOIs are added to individual fitness models. To do this, we compare models parameterized with data from annual plant communities in Australia and Spain, varying in the extent of information included about the focal and neighbor species. The best-performing models for both data sets were those that grouped neighbors based on origin status and life form, a grouping approach that reduced the number of model parameters substantially while retaining important ecological information about direct interactions and HOIs. Results suggest that the specific identity of focal or neighbor species is not necessary for building well-performing fitness models that include HOIs. In fact, grouping neighbors by even basic functional information seems sufficient to maximize model accuracy, an important outcome for the practical use of HOI-inclusive fitness models.

In ecological networks, neutral predictions suggest that species’ interaction frequencies are proportional to their relative abundances. Deviations from neutral predictions thus correspond to interaction preferences (when positive) or avoidances (when negative), driven by nonneutral (e.g., niche-based) processes. Exotic species interact with many partners with which they have not coevolved, and it remains unclear whether this systematically influences the strength of neutral processes on interactions, and how these interaction-level differences scale up to entire networks. To fill this gap, we compared interactions between plants and frugivorous birds at nine forest sites in New Zealand varying in the relative abundance and composition of native and exotic species, with independently sampled data on bird and plant abundances from the same sites. We tested if the strength and direction of interaction preferences differed between native and exotic species. We further evaluated whether the performance of neutral predictions at the site level was predicted by the proportion of exotic interactions in each network from both bird and plant perspectives, and the species composition in each site. We found that interactions involving native plants deviated more strongly from neutral predictions than did interactions involving exotics. This “pickiness” of native plants could be detrimental in a context of global biotic homogenization where they could be increasingly exposed to novel interactions with neutrally interacting mutualists. However, the realization of only a subset of interactions in different sites compensated for the neutrality of interactions involving exotics, so that neutral predictions for whole networks did not change systematically with the proportion of exotic species or species composition. Therefore, the neutral and niche processes that underpin individual interactions may not scale up to entire networks. This shows that seemingly simplistic neutral assumptions entail complex processes and can provide valuable understanding of community assembly or invasion dynamics., JMT is funded by the Bioprotection Centre. ID was funded by the FPI Program-European Social Fund BES2012-052863 and the Mobility Grant EEBB-I-14-08279 within the MinECo/FEDER projects CGL2015-68963-C2-2-R, and PRI-AIBNZ2011-0863 granted to DG. ID is funded by the Alexander von Humboldt Foundation. DBS was funded by a Marsden Fast-Start Grant and a Rutherford Discovery Fellowship (11-UOC-1101 and RDF-13-UOC-003), administered by the Royal Society of New Zealand Te Apārangi.

Pollinator foraging behavior determines floral visitation rates, an important proxy to the strength of mutual- istic interactions. Although there is evidence that pollinators modify their behavior in the presence of other foragers, there are equivocal findings regarding whether or not pollinators interfere with one another. We employ a functional-response framework to analyse experimental data of times between floral visits made by a focal pollinator and to estimate pollinator interference by conspecifics and three other species. Additionally we develop and compare models that allow different levels of resource availability and the sub-lethal exposure to a neonicotinoid pesticide to modify how pollinators forage alone and with co-foragers. We found that all co-foragers interfere with a focal pollinator under at least one set of abiotic conditions; for most species, interference was strongest at higher levels of resource availability and with pesticide exposure. Overall our results highlight that density-dependent responses are often context dependent themselves.

Functional responses are a cornerstone to our understanding of consumer-resource interactions, so how to best describe them using models has been actively debated. Here we focus on the consumer dependence of functional responses to evidence systematic bias in the statistical comparison of functional-response models and the estimation of their parameters. Both forms of bias are universal to nonlinear models (irrespective of consumer dependence) and are rooted in a lack of sufficient replication. Using a large compilation of published datasets, we show that – due to the prevalence of low sample size studies – neither the overall frequency by which alternative models achieve top rank nor the frequency distribution of parameter point estimates should be treated as providing insight into the general form or central tendency of consumer interference. We call for renewed clarity in the varied purposes that motivate the study of functional responses, purposes that can compete with each other in dictating the design, analysis, and interpretation of functional-response experiments.

In a rapidly changing world, the composition, diversity and structure of ecological communities face many threats. Biodiversity-Ecosystem Functioning (BEF) and community food-chain analyses have focused on investigating the consequences of these changes on ecosystem processes and the resulting functions. These different and diverging conceptual frameworks have each produced important results and identified a set of important mechanisms, that shape ecosystem functions. But the disconnection between these frameworks, and the various simplifications of the study systems are not representative of the complexity of real-world communities. Here we use food webs as a more realistic depiction of communities, and use a bioenergetic model to simulate their biomass dynamics and quantify the resulting flows and stocks of biomass. We use tools from food web analysis to investigate how the predictions from BEF and food-chain analyses fit together, how they correlate to food-web structure and how it might help us understand the interplay between various drivers of ecosystem functioning. We show that food web structure is correlated to the community’s efficiency in storing the captured biomass, which may explain the distribution of biomass (top heaviness) across the different trophic compartments (producers, primary and secondary consumers). While we know that ecological network structure is important in shaping ecosystem dynamics, identifying structural attributes important in shaping ecosystem processes and synthesizing how it affects various underpinning mechanisms may help prioritize key conservation targets to protect not only biodiversity but also its structure and the resulting services.

Different modelling approaches have been used to relate the structure of mutualistic interactions with the stability of communities. However, inconsistencies arise when we compare modelling outcomes with the patterns of interactions observed in empirical studies. To shed light on these inconsistencies, we explored the network structure–stability relationship by incorporating the cost of mutualistic interactions, a long ignored feature of mutualisms, into population dynamics models. We assessed the changes in the relationship between network structure (species richness, connectance, modularity) and community stability (species persistence, resilience), and between network structure and community structural attributes (average abundance), using models with increasing levels of cost for mutualistic communities. We found that adding the potential cost of mutualistic interactions affected the strength of the network structure–stability relationship. Our results revive the question of whether the structure of mutualistic networks determines community stability. Fil: Peralta, Guadalupe. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina Fil: Stouffer, Daniel B.. University of Canterbury; Nueva Zelanda Fil: Bringa, Eduardo Marcial. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina Fil: Vazquez, Diego P.. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina

Ecological communities often show changes in populations and their interactions over time. To date, however, it has been challenging to effectively untangle the mechanisms shaping such dynamics. One approach that has yet to be fully explored is to treat the varying structure of empirical communities—i.e. their network of interactions—as time series. Here, we follow this approach by applying a network-comparison technique to study the seasonal dynamics of plant-pollinator networks. We find that the structure of these networks is extremely variable, where species constantly change how they interact with each other within seasons. Most importantly, we find the holistic dynamic of plants and pollinators to be remarkably coherent across years, allowing us to reveal general rules by which species first enter, then change their roles, and finally leave the networks. Overall, our results disentangle key aspects of species’ interaction turnover, phenology, and seasonal assembly/disassembly processes in empirical plant-pollinator communities., Nature Communications, 11 (1), ISSN:2041-1723

What determines whether or not a species is a generalist or a specialist? Evidence that the environment can influence species interactions is rapidly accumulating. However, a systematic link between environment and the number of partners a species interacts with has been elusive so far. Presumably, because environmental gradients appear to have contrasting effects on species depending on the environmental variable. Here, we test for a relationship between the stresses imposed by the environment, instead of environmental gradients directly, and species specialisation using a global dataset of plant-pollinator interactions. We found that the environment can play a significant effect on specialisation, even when accounting for community composition, likely by interacting with species’ traits and evolutionary history. Species that have a large number of interactions are more likely to focus on a smaller number of, presumably higher-quality, interactions under stressful environmental conditions. Contrastingly, the specialists present in multiple locations are more likely to broaden their niche, presumably engaging in opportunistic interactions to cope with increased environmental stress. Indeed, many apparent specialists effectively behave as facultative generalists. Overall, many of the species we analysed are not inherently generalist or specialist. Instead, species’ level of specialisation should be considered on a relative scale depending on where they are found and the environmental conditions at that location.

Coexistence between plant species is well known to depend on the outcomes of species interactions within an environmental context. The incorporation of environmental variation into empirical studies of coexistence are rare, however, due to the complex experiments needed to do so and the lack of feasible modelling approaches for determining how environmental factors alter specific coexistence mechanisms. In this article, we present a simple modelling framework for assessing how variation in species interactions across environmental gradients impact on niche overlap and fitness differences, two core determinants of coexistence. We use a novel formulation of an annual plant population dynamics model that allows for competitive and facilitative species interactions and for variation in the strength and direction of these interactions across environmental gradients. Using this framework, we examine outcomes of plant–plant interactions between four commonly co-occurring annual plant species from Western Australian woodlands. We then determine how niche overlap and fitness differences between these species vary across three environmental gradients previously identified as important for structuring diversity patterns in this system: soil phosphorus, shade and water. We found facilitation to be a widespread phenomenon and that interactions between most species pairs shift between competitive and facilitative across multiple environmental gradients. Environmental conditions also altered the strength, direction and relative variation of both niche overlap and fitness differences in nonlinear and unpredictable ways. Synthesis. We provide a simple framework for incorporating environmental heterogeneity into explorations of coexistence mechanisms. Our findings highlight the importance of the environment in determining the outcome of species interactions and the potential for pairwise coexistence between species. The prevalence of facilitation in our system indicates a need to improve current theoretical frameworks of coexistence to include noncompetitive interactions and ways of translating these effects into explicit predictions of coexistence. Our study also suggests a need for further research into determining which factors result in consistent responses of niche overlap and fitness differences to environmental variation. Such information will improve our ability to predict outcomes of coexistence, invasion events and responses of whole communities to future environmental change.

Although the structure of empirical food webs can differ between ecosystems, there is growing evidence of multiple ways in which they also exhibit common topological properties. To reconcile these contrasting observations, we postulate the existence of a backbone of interactions underlying all ecological networks—a common substructure within every network comprised of species playing similar ecological roles—and a periphery of species whose idiosyncrasies help explain the differences between networks. To test this conjecture, we introduce a new approach to investigate the structural similarity of 411 food webs from multiple environments and biomes. We first find significant differences in the way species in different ecosystems interact with each other. Despite these differences, we then show that there is compelling evidence of a common backbone of interactions underpinning all food webs. We expect that identifying a backbone of interactions will shed light on the rules driving assembly of different ecological communities., The structure of ecological networks can vary dramatically, yet there may be common features across networks from different ecosystem types. Here, Bramon Mora et al. use network alignment to demonstrate that there is a common backbone of interactions underlying empirical food webs.

Recent studies have brought renewed attention to the importance of complex species interactions—notably intransitive interactions—to patterns of plant community diversity. One underappreciated avenue through which intransitivity can occur is through cyclic population dynamics. Although such cyclic intransitive relationships have been extensively studied in predator–prey systems, evidence of their importance in competitive communities, notably plant communities, is more limited. Most studies of coexistence in plant communities assume fixed-point coexistence even while utilising models that allow for cyclic population dynamics. In this paper, we explore the potential for density-dependent, cyclic population dynamics and intransitivity in a model for annual plants. We then examine how these density-dependent cycles impact mutual invasibility and ultimately stable coexistence between plant species pairs. We do this using data collected from four co-occurring annual plant species living in natural wildflower communities in SW Western Australia. To maximise the number of biologically plausible pathways by which coexistence mediated by density-dependent cyclic intransitivity can occur, we use an annual plant model that allows for competitive direct interactions, facilitative direct interactions and higher-order interactions between species. Results from our empirically parameterised model suggest that monocultures of all four focal species can have cyclic solutions with periodicity

Despite the fact that natural selection underlies both traits and interactions, evolutionary models often neglect that ecological interactions may, and in many cases do, influence the evolution of traits. Herein, we explore the interdependence of ecological interactions and functional traits in the pollination associations of hawkmoths and flowering plants. Specifically, we develop an adaptation of the Ornstein-Uhlenbeck model of trait evolution that allows us to study the influence of plant corolla depth and observed hawkmoth-plant interactions on the evolution of hawkmoth proboscis length. Across diverse modelling scenarios, we find that the inclusion of contemporary interactions can provide a better description of trait evolution than the null expectation. Moreover, we show that the pollination interactions provide more-likely models of hawkmoth trait evolution when interactions are considered at increasingly fine-scale groups of hawkmoths. Finally, we demonstrate how the results of best-fit modeling approaches can implicitly support the association between interactions and trait evolution that our method explicitly examines. In showing that contemporary interactions can provide insight into the historical evolution of hawkmoth proboscis length, we demonstrate the clear utility of incorporating additional ecological information to models designed to study past trait evolution.