Experimental evidence of the importance of multitrophic structure for species persistence

Pollinator-mediated effects of landscape-scale land use on grassland plant community composition and ecosystem functioning - seven hypotheses

Environmental change is disrupting mutualisms between organisms worldwide. Reported declines in insect populations and changes in pollinator community compositions in response to land use and other environmental drivers have put the spotlight on the need to conserve pollinators. While this is often motivated by their role in supporting crop yields, the role of pollinators for reproduction and resulting taxonomic and functional assembly in wild plant communities has received less attention. Recent findings suggest that observed and experimental gradients in pollinator availability can affect plant community composition, but we know little about when such shifts are to be expected, or the impact they have on ecosystem functioning. Correlations between plant traits related to pollination and plant traits related to other important ecosystem functions, such as productivity, nitrogen uptake or palatability to herbivores, lead us to expect non-random shifts in ecosystem functioning in response to changes in pollinator communities. At the same time, ecological and evolutionary processes may counteract these effects of pollinator declines, limiting changes in plant community composition, and in ecosystem functioning. Despite calls to investigate community- and ecosystem-level impacts of reduced pollination, the study of pollinator effects on plants has largely been confined to impacts on plant individuals or single-species populations. With this review we aim to break new ground by bringing together aspects of landscape ecology, ecological and evolutionary plant-insect interactions, and biodiversity-ecosystem functioning research, to generate new ideas and hypotheses about the ecosystem-level consequences of pollinator declines in response to land-use change, using grasslands as a focal system. Based on an integrated set of seven hypotheses, we call for more research investigating the putative pollinator-mediated links between landscape-scale land use and ecosystem functioning. In particular, future research should use combinations of experimental and observational approaches to assess the effects of changes in pollinator communities over multiple years and across species on plant communities and on trait distributions both within and among species.

Multitrophic Higher-Order Interactions Modulate Species Persistence

AbstractEcologists increasingly recognize that interactions between two species can be affected by the density of a third species. How these higher-order interactions (HOIs) affect species persistence remains poorly understood. To explore the effect of HOIs stemming from multiple trophic layers on a plant community composition, we experimentally built a mesocosm with three plants and three pollinator species arranged in a fully nested and modified network structure. We estimated pairwise interactions among plants and between plants and pollinators, as well as HOIs initiated by a plant or a pollinator affecting plant species pairs. Using a structuralist approach, we evaluated the consequences of the statistically supported HOIs on the persistence probability of each of the three competing plant species and their combinations. HOIs substantially redistribute the strength and sign of pairwise interactions between plant species, promoting the opportunities for multispecies communities to persist compared with a non-HOI scenario. However, the physical elimination of a plant-pollinator link in the modified network structure promotes changes in per capita pairwise interactions and HOIs, resulting in a single-species community. Our study provides empirical evidence of the joint importance of HOIs and network structure in determining species persistence within diverse communities.

Trophic tug-of-war: Coexistence mechanisms within and across trophic levels

Ecological communities encompass rich diversity across multiple trophic levels. While modern coexistence theory has been widely applied to understand community assembly, its traditional formalism only allows assembly within a single trophic level. Here, using an expanded definition of niche and fitness differences applicable to multitrophic communities, we study how diversity within and across trophic levels affects species coexistence. If each trophic level is analysed separately, both lower- and higher trophic levels are governed by the same coexistence mechanisms. In contrast, if the multitrophic community is analysed as a whole, different trophic levels are governed by different coexistence mechanisms: coexistence at lower trophic levels is predominantly limited by fitness differences, whereas coexistence at higher trophic levels is predominantly limited by niche differences. This dichotomy in coexistence mechanisms is supported by theoretical derivations, simulations of phenomenological and trait-based models, and a case study of a primeval forest ecosystem. Our work provides a general and testable prediction of coexistence mechanism operating in multitrophic communities.

Interaction network structure explains species' temporal persistence in empirical plant-pollinator communities

Despite clear evidence that some pollinator populations are declining, our ability to predict pollinator communities prone to collapse or species at risk of local extinction is remarkably poor. Here, we develop a model grounded in the structuralist approach that allows us to draw sound predictions regarding the temporal persistence of species in mutualistic networks. Using high-resolution data from a six-year study following 12 independent plant-pollinator communities, we confirm that pollinator species with more persistent populations in the field are theoretically predicted to tolerate a larger range of environmental changes. Persistent communities are not necessarily more diverse, but are generally located in larger habitat patches, and present a distinctive combination of generalist and specialist species resulting in a more nested structure, as predicted by previous theoretical work. Hence, pollinator interactions directly inform about their ability to persist, opening the door to use theoretically informed models to predict species' fate within the ongoing global change.

On the limits to invasion prediction using coexistence outcomes

The dynamics of ecological communities in nature are typically characterized by probabilistic processes involving invasion dynamics. Because of technical challenges, however, the majority of theoretical and experimental studies have focused on coexistence dynamics. Therefore, it has become central to understand the extent to which coexistence outcomes can be used to predict analogous invasion outcomes relevant to systems in nature. Here, we study the limits to this predictability under a geometric and probabilistic Lotka-Volterra framework. We show that while individual survival probability in coexistence dynamics can be fairly closely translated into invader colonization probability in invasion dynamics, the translation is less precise between community persistence and community augmentation, and worse between exclusion probability and replacement probability. These results provide a guiding and testable theoretical framework regarding the translatability of outcomes between coexistence and invasion outcomes when communities are represented by Lotka-Volterra dynamics under environmental uncertainty.

Soil organic carbon loss decreases biodiversity but stimulates multitrophic interactions that promote belowground metabolism

Soil organic carbon (SOC) plays an essential role in mediating community structure and metabolic activities of belowground biota. Unraveling the evolution of belowground communities and their feedback mechanisms on SOC dynamics helps embed the ecology of soil microbiome into carbon cycling, which serves to improve biodiversity conservation and carbon management strategy under global change. Here, croplands with a SOC gradient were used to understand how belowground metabolisms and SOC decomposition were linked to the diversity, composition, and co-occurrence networks of belowground communities encompassing archaea, bacteria, fungi, protists, and invertebrates. As SOC decreased, the diversity of prokaryotes and eukaryotes also decreased, but their network complexity showed contrasting patterns: prokaryotes increased due to intensified niche overlap, while that of eukaryotes decreased possibly because of greater dispersal limitation owing to the breakdown of macroaggregates. Despite the decrease in biodiversity and SOC stocks, the belowground metabolic capacity was enhanced as indicated by increased enzyme activity and decreased enzymatic stoichiometric imbalance. This could, in turn, expedite carbon loss through respiration, particularly in the slow-cycling pool. The enhanced belowground metabolic capacity was dominantly driven by greater multitrophic network complexity and particularly negative (competitive and predator-prey) associations, which fostered the stability of the belowground metacommunity. Interestingly, soil abiotic conditions including pH, aeration, and nutrient stocks, exhibited a less significant role. Overall, this study reveals a greater need for soil C resources across multitrophic levels to maintain metabolic functionality as declining SOC results in biodiversity loss. Our researchers highlight the importance of integrating belowground biological processes into models of SOC turnover, to improve agroecosystem functioning and carbon management in face of intensifying anthropogenic land-use and climate change.

Structural asymmetry in biotic interactions as a tool to understand and predict ecological persistence

A universal feature of ecological systems is that species do not interact with others with the same sign and strength. Yet, the consequences of this asymmetry in biotic interactions for the short- and long-term persistence of individual species and entire communities remains unclear. Here, we develop a set of metrics to evaluate how asymmetric interactions among species translate to asymmetries in their individual vulnerability to extinction under changing environmental conditions. These metrics, which solve previous limitations of how to independently quantify the size from the shape of the so-called feasibility domain, provide rigorous advances to understand simultaneously why some species and communities present more opportunities to persist than others. We further demonstrate that our shape-related metrics are useful to predict short-term changes in species' relative abundances during 7 years in a Mediterranean grassland. Our approach is designed to be applied to any ecological system regardless of the number of species and type of interactions. With it, we show that is possible to obtain both mechanistic and predictive information on ecological persistence for individual species and entire communities, paving the way for a stronger integration of theoretical and empirical research.

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.

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.

The Resilience of Plant-Pollinator Networks

There is growing awareness of pollinator declines worldwide. Conservation efforts have mainly focused on finding the direct causes, while paying less attention to building a systemic understanding of the fragility of these communities of pollinators. To fill this gap, we need operational measures of network resilience that integrate two different approaches in theoretical ecology. First, we should consider the range of conditions compatible with the stable coexistence of all of the species in a community. Second, we should address the rate and shape of network collapse once this safe operational space is exited. In this review, we describe this integrative approach and consider several mechanisms that may enhance the resilience of pollinator communities, chiefly rewiring the network of interactions, increasing heterogeneity, allowing variance, and enhancing coevolution. The most pressing need is to develop ways to reduce the gap between these theoretical recommendations and practical applications. This perspective shifts the emphasis from traditional approaches focusing on the equilibrium states to strategies that allow pollination networks to cope with global environmental change.

Ranking species based on sensitivity to perturbations under non-equilibrium community dynamics

Managing ecological communities requires fast detection of species that are sensitive to perturbations. Yet, the focus on recovery to equilibrium has prevented us from assessing species responses to perturbations when abundances fluctuate over time. Here, we introduce two data-driven approaches (expected sensitivity and eigenvector rankings) based on the time-varying Jacobian matrix to rank species over time according to their sensitivity to perturbations on abundances. Using several population dynamics models, we demonstrate that we can infer these rankings from time-series data to predict the order of species sensitivities. We find that the most sensitive species are not always the ones with the most rapidly changing or lowest abundance, which are typical criteria used to monitor populations. Finally, using two empirical time series, we show that sensitive species tend to be harder to forecast. Our results suggest that incorporating information on species interactions can improve how we manage communities out of equilibrium.

Seasonal structure of interactions enhances multidimensional stability of mutualistic networks

Community ecologists have made great advances in understanding how natural communities can be both diverse and stable by studying communities as interaction networks. However, focus has been on interaction networks aggregated over time, neglecting the consequences of the seasonal organization of interactions (hereafter 'seasonal structure') for community stability. Here, we extended previous theoretical findings on the topic in two ways: (i) by integrating empirical seasonal structure of 11 plant–hummingbird communities into dynamic models, and (ii) by tackling multiple facets of network stability together. We show that, in a competition context, seasonal structure enhances community stability by allowing diverse and resilient communities while preserving their robustness to species extinctions. The positive effects of empirical seasonal structure on network stability vanished when using randomized seasonal structures, suggesting that eco-evolutionary dynamics produce stabilizing seasonal structures. We also show that the effects of seasonal structure on community stability are mainly mediated by changes in network structure and productivity, suggesting that the seasonal structure of a community is an important and yet neglected aspect in the diversity–stability and diversity–productivity debates.

Competition for pollinators destabilizes plant coexistence

Mounting concern over the global decline of pollinators has fuelled calls for investigating their role in maintaining plant diversity^1,2. Theory predicts that competition for pollinators can stabilize interactions between plant species by providing opportunities for niche differentiation³, while at the same time can drive competitive imbalances that favour exclusion⁴. Here we empirically tested these contrasting effects by manipulating competition for pollinators in a way that predicts its long-term implications for plant coexistence. We subjected annual plant individuals situated across experimentally imposed gradients in neighbour density to either ambient insect pollination or a pollen supplementation treatment alleviating competition for pollinators. The vital rates of these individuals informed plant population dynamic models predicting the key theoretical metrics of species coexistence. Competition for pollinators generally destabilized the interactions between plant species, reducing the proportion of pairs expected to coexist. Interactions with pollinators also influenced the competitive imbalances between plant species, effects that are expected to strengthen with pollinator decline, potentially disrupting plant coexistence. Indeed, results from an experiment simulating pollinator decline showed that plant species experiencing greater reductions in floral visitation also suffered greater declines in population growth rate. Our results reveal that competition for pollinators may weaken plant coexistence by destabilizing interactions and contributing to competitive imbalances, information critical for interpreting the impacts of pollinator decline.

Towards a system-level causative knowledge of pollinator communities

Pollination plays a central role in both crop production and maintaining biodiversity. However, habitat loss, pesticides, invasive species and larger environmental fluctuations are contributing to a dramatic decline of pollinators worldwide. Different management solutions require knowledge of how ecological communities will respond following interventions. Yet, anticipating the response of these systems to interventions remains extremely challenging due to the unpredictable nature of ecological communities, whose nonlinear behaviour depends on the specific details of species interactions and the various unknown or unmeasured confounding factors. Here, we propose that this knowledge can be derived by following a probabilistic systems analysis rooted on non-parametric causal inference. The main outcome of this analysis is to estimate the extent to which a hypothesized cause can increase or decrease the probability that a given effect happens without making assumptions about the form of the cause-effect relationship. We discuss a road map for how this analysis can be accomplished with the aim of increasing our system-level causative knowledge of natural communities. This article is part of the theme issue 'Natural processes influencing pollinator health: from chemistry to landscapes'.

Future Climate Change Conditions May Compromise Metabolic Performance in Juveniles of the Mud Crab Scylla serrata

Research characterising the effects of future climate change on the marine environment remains heavily focussed on that of temperate regions and organisms. Furthermore, little is known of these effects on the early life stages of many marine species. Tropical regions are already experiencing an increase in sea surface temperature and decrease in sea surface salinity, conditions favoured by pathogenic bacteria such as Vibrio spp. The early life stages of crabs are known to be particularly vulnerable to both the direct physiological effects of climate change and exposure to harmful microorganisms, yet there are limited data on these effects on juveniles of many tropical crustacean species. This study assessed the metabolic responses of mud crab (Scylla serrata) juveniles to warming and/or freshening in the presence or absence of pathogenic bacteria in southwest India. Juvenile crabs were exposed to either ambient (28 °C/30 PSU) or one of three projected climate change regimes (28 °C/20 PSU (freshening), 32 °C/30 PSU (warming), 32 °C/20 PSU (warming + freshening)) for 10 days, in either the presence or absence of the pathogenic bacteria Vibrio parahaemolyticus. Results show that simulated climate change conditions, especially freshening, caused a significant increase in oxygen consumption rates (MO2), and that these were further increased when juveniles were exposed to V. parahaemolyticus. These results suggest that the effects of future climate change conditions could have significant implications for the conservation of wild stocks and commercial farming of this species in South Asia.

Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble

Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand, there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models.