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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’.

The realization that evolutionary feedbacks need to be considered to fully grasp ecological dynamics has sparked recent interest in the effect of evolution on community properties like coexistence and productivity. However, modern coexistence theory being limited to pairwise interactions, little is known about coevolution and diversification in rich communities. We leverage the recent multidimensional co-existence theory metrics, together with a structural community robustness metric, to study such properties in a general trait-based model of competition on a niche axis. We show that the effects of coevolution on coexistence are two-fold. In the short-term our results show synergies emerging between increasing productivity and reinforcing coexistence, while in the long-term, diversification and niche-packing destabilize communities, thus inducing a long-term trade-off between productivity and coexistence. In light of classical and recent work, our findings help advance understanding of evolutionary effects in high-dimensional systems. We illustrate how our theoretical predictions echo in observed empirical patterns. Finally, we discuss their implications and provide testable hypotheses.

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'. ISSN:0962-8436 ISSN:1471-2970 ISSN:0080-4622

Two contradictory hypotheses have been put forth to forecast alien invasiveness: being either functionally similar, or dissimilar, to resident natives along environmental gradients. The ‘try-harder’ hypothesis predicts that alien plants will be functionally dissimilar to natives and should thus exhibit exaggerated trait values when compared to natives in respect to resource extraction or stress tolerance. In contrast, the ‘join-the-locals’ hypothesis, which is compatible with “environmental filtering”, predicts functional similarities among alien and native species in richer, but not in resource-limited environments. Here, we propose a framework that links the successful alien plant strategy, i.e. being functionally similar or dissimilar to natives, to the harshness of the environment and the availability of resources. We tested these two hypotheses using a trait-environment dataset of 33 alien and 130 native plants in 96 sites covering a gradient of soil resources (organic matter, nitrogen, soil moisture) in Saint-Katherine, an arid protected area in Egypt. We estimated 18 interaction coefficients between three candidate traits (specific leaf area, above-ground biomass, height) of alien and native plants as well as soil resources using linear mixed-effects models. Additionally, we calculated the mean and the hierarchical functional distance among aliens and natives along soil gradients. Our results revealed that in extreme resource-limited environments, aliens and natives were functionally similar and had relatively equal trait values consistent with environmental filtering, thus supporting the ‘join-the-locals’ hypothesis. However, in environments richer in resources, aliens and natives were functionally dissimilar with aliens exhibiting more exploitative trait values (taller, higher SLA and biomass) than natives, providing support for the ‘try-harder’ hypothesis. While demonstrated in only one arid system, results suggest that linking soil resource availability with functional divergence and convergence among native and alien plants could be used as an informative strategy to predict alien invasiveness. Hence, future studies should investigate the functional response of alien and native plants in different environments against different resource gradients to test for the generality of the patterns we found.

International audience; We discuss the interpretation and dimensions of the population dynamic parameters that are commonly used to quantify the strength of intraspecific and interspecific interactions. The concept of "interaction strength" is not unequivocal. Its theoretical formalization relies on the generalized Lotka-Volterra model. However, four different ways of parameterizing the model have been proposed in the literature, leading to four different definitions of the term "interaction strength." In particular, the dimensions of these four definitions are not identical, some of these incorporating explicitly the dimension used to measure the population size. Using an individual-based simulation model as an illustration, we show that, in the latter case, the interaction strength depends implicitly on the habitat size. As a consequence, it is of crucial importance to quantify the population as a density rather than an absolute population abundance. We insist that the dimension of the interaction strength coefficient should not be overlooked and any quantitative estimation must be given with explicit units.

Pollination plays a central role both in the maintenance of biodiversity and in crop production. However, habitat loss, pesticides, invasive species, and larger environmental fluctuations are contributing to a dramatic decline of numerous pollinators world-wide. This has increased the need for interventions to protect the composition, functioning, and dynamics of pollinator communities. Yet, how to make these interventions successful at the system level remains extremely challenging due to the complex nature of species interactions and the various unknown or unmeasured confounding ecological factors. Here, we propose that this knowledge can be derived by following a probabilistic causal analysis of pollinator communities. This analysis implies the inference of interventional expectations from the integration of observational and synthetic data. We propose that such synthetic data can be generated using theoretical models that can enable the tractability and scalability of unseen confounding ecological factors affecting the behavior of pollinator communities. We discuss a road map for how this probabilistic causal analysis can be accomplished to increase our system-level causative knowledge of natural communities.

Gaining knowledge of how ecosystems provide essential services to humans is of primary importance, especially with the current threat of climate change. Yet little is known about how increased temperature will impact the biodiversity–ecosystem functioning (BEF) relationship. We tackled this subject theoretically and experimentally. We developed a BEF theory based on mechanistic population dynamic models, which allows the inclusion of the effect of temperature. Using experimentally established relationships between attack rate and temperature, the model predicts that temperature increase will intensify competition, and consequently the BEF relationship will flatten or even become negative. We conducted a laboratory experiment with natural microbial microcosms, and the results were in agreement with the model predictions. The experimental results also revealed that an increase in both temperature average and variation had a more intense effect than an increase in temperature average alone. Our results indicate that under climate change, high diversity may not guarantee high ecosystem functioning.

Ecological theory predicts that species interactions embedded in multitrophic networks shape the opportunities for species to persist. However, the lack of experimental support of this prediction has limited our understanding of how species interactions occurring within and across trophic levels simultaneously regulate the maintenance of biodiversity. Here, we integrate a mathematical approach and detailed experiments in plant-pollinator communities to demonstrate the need to jointly account for species interactions within and across trophic levels when estimating the ability of species to persist. Within the plant trophic level, we show that the persistence probability of plant species increases when introducing the effects of plant-pollinator interactions. Across trophic levels, we show that the persistence probabilities of both plants and pollinators exhibit idiosyncratic changes when experimentally manipulating the multitrophic structure. Importantly, these idiosyncratic effects are not recovered by traditional simulations. Our work provides tractable experimental and theoretical platforms upon which it is possible to investigate the multitrophic factors affecting species persistence in ecological communities.

A major challenge in ecological research is to identify the tolerance of ecological communities to external perturbations. Modern coexistence theory (MCT) has been widely adopted as a framework to investigate the tolerance to perturbations in relative reductions of per capita growth rates, often using metrics that explicitly eliminate the independent role of intrinsic growth rates. More recently, the structural approach (SA) was introduced to investigate the tolerance of communities to perturbations in intrinsic growth rates as a function of the strength of intraspecific and interspecific competition. Because the external perturbations are likely to happen in both intrinsic growth rates and competition strengths, no framework alone can fully disentangle the effects of external perturbations.Here we combine MCT and SA to disentangle the tolerance in coexistence and priority effects of a pair of competing species when subject to perturbations in intrinsic growth rates and competition strengths. Through this combination, we reveal the emergence of a key trade‐off: increasing the tolerance to perturbations in intrinsic growth rates typically decreases the tolerance in competition strengths, and vice versa. Furthermore, this trade‐off is stronger under coexistence than under priority effects.We test this combined framework on competing pairs of 18 California annual plant species. For both coexistence and priority effects, we find that the tolerance to perturbations in intrinsic growth rates is maximized instead of that to perturbations in competition strengths in the studied annual plant communities.Synthesis. Our combined framework of modern coexistence theory and structural approach illustrates that it is possible to disentangle the impact of different external perturbations on the persistence of species. Importantly, our findings show that species interactions may reveal whether communities are dominated either by changes in intrinsic growth rates or by competition strengths. Overall, this combined framework can open a new perspective to understand and predict the response of populations to changing environmental conditions.

Observational and experimental studies have shown that an interaction class between two species (be it mutualistic, competitive, antagonistic, or neutral) may switch to a different class, depending on the biotic and abiotic factors within which species are observed. This complexity arising from the evidence of context-dependencies has underscored a difficulty in establishing a systematic analysis about the extent to which species interactions are expected to switch in nature and experiments. Here, we propose an overarching theoretical framework, by integrating probabilistic and structural approaches, to establish null expectations about switches of interaction classes across environmental contexts. This integration provides a systematic platform upon which it is possible to establish new hypotheses, clear predictions, and quantifiable expectations about the context-dependency of species interactions.

Understanding the effects of evolution on emergent population properties such as intrinsic growth rate, species abundance, or dynamical resilience is not only a key theoretical question, but has major empirical implications for conservation, agroecology, invasion ecology among others. In particular, could we classify evolutionary scenarios leading to optimisation of those properties, from the ones who do not. First, we uncover two classes of invasion fitness functions, only the first one allowing optimization of some (but typically not all) population properties. Second, we showed that our two classes are also strongly linked to niche displacement and emergence of polymorphism. Our results indicate that optimization is, in general, incompatible with niche differentiation and, therefore, with emergence of polymorphism through evolutionary branching. Actually, niche displacement between resident and mutant morphs, and potentially polymorphism, only arise when we do not expect optimality to hold. We extensively discuss which biological traits can fall into which class of invasion fitness. Although, it is possible to find traits for which optimality is expected, we argue that for the majority of the cases it does not hold. Finally, we provide practical applications of our results in conservation, agroecology, harvesting and invasion ecology.

Pollen beetles (Brassicogethes spp.) are a major pest in colza (Brassica napus L., also known as oilseed rape) and show increasing resistance to commonly used insecticides. Entomopathogenic fungi (EPF) such as Beauveria bassiana have potential as a sustainable alternative means of control. However, field applications of fungal spores targeting the immigrated beetles on the crop have not been efficient to date. To enhance the effectiveness of fungal spores in insect pest control, this study focused on co-formulations of B. bassiana blastospores with vegetable oil and stone dust, which have already shown effects against pollen beetles as single treatments in previous studies. To estimate synergistic effects of these combinations, we developed a novel statistical approach based on a generalized linear mixed effect model and a parametric bootstrapping method. The combination of B. bassiana and colza oil increased pollen beetle mortality much more than expected from the additive effect of single applications, indicating a strong synergistic effect. In contrast, the combination of B. bassiana and stone dust showed an antagonistic effect, i.e. mortality was lower than expected from the additive effect of single substances. Our study confirms the suitability of a combination of entomopathogenic fungi with vegetable oil and demonstrates that combinations may exhibit a higher level of pest control than expected from single substances., Biological Control, 140, ISSN:1049-9644, ISSN:1090-2112

For plants, the advantages of associating with beneficial bacteria include plant growth promotion, reduction of abiotic and biotic stresses and enhanced protection against various pests and diseases. Beneficial bacteria rightly equipped for successful plant colonization and showing antagonistic activity toward plant pathogens seem to be actively recruited by plants. To gain more insights into the genetic determinants responsible for plant colonization and antagonistic activities, we first sequenced and de novo assembled the complete genomes of nine Pseudomonas strains that had exhibited varying antagonistic potential against the notorious oomycete Phytophthora infestans, placed them into the phylogenomic context of known Pseudomonas biocontrol strains and carried out a comparative genomic analysis to define core, accessory (i.e., genes found in two or more, but not all strains) and unique genes. Next, we assessed the colonizing abilities of these strains and used bioassays to characterize their inhibitory effects against different stages of P. infestans’ lifecycle. The phenotype data were then correlated with genotype information, assessing over three hundred genes encoding known factors for plant colonization and antimicrobial activity as well as secondary metabolite biosynthesis clusters predicted by antiSMASH. All strains harbored genes required for successful plant colonization but also distinct arsenals of antimicrobial compounds. We identified genes coding for phenazine, hydrogen cyanide, 2-hexyl, 5-propyl resorcinol and pyrrolnitrin synthesis, as well as various siderophores, pyocins and type VI secretion systems. Additionally, the comparative genomic analysis revealed about a hundred accessory genes putatively involved in anti-Phytophthora activity, including a type II secretion system (T2SS), several peptidases and a toxin. Transcriptomic studies and mutagenesis are needed to further investigate the putative involvement of the novel candidate genes and to identify the various mechanisms involved in the inhibition of P. infestans by different Pseudomonas strains.

The feasibility domain of an ecological community can be described by the set of environmental abiotic and biotic conditions under which all co-occurring and interacting species in a given site and time can have positive abundances. Mathematically, the feasibility domain corresponds to the parameter space compatible with positive (feasible) solutions at equilibrium for all the state variables in a system under a given model of population dynamics. Under specific dynamics, the existence of a feasible equilibrium is a necessary condition for species persistence regardless of whether the feasible equilibrium is dynamically stable or not. Thus, the size of the feasibility domain can also be used as an indicator of the tolerance of a community to random environmental variations. This has motivated a rich research agenda to estimate the feasibility domain of ecological communities. However, these methodologies typically assume that species interactions are static, or that input and output energy flows on each trophic level are unconstrained. Yet, this is different to how communities behave in nature. Here, we present a step-by-step quantitative guideline providing illustrative examples, computational code, and mathematical proofs to study systematically the feasibility domain of ecological communities under changes of interspecific interactions and subject to different constraints on the trophic energy flows. This guideline covers multi-trophic communities that can be formed by any type of interspecific interactions. Importantly, we show that the relative size of the feasibility domain can significantly change as a function of the biological information taken into consideration. We believe that the availability of these methods can allow us to increase our understanding about the limits at which ecological communities may no longer tolerate further environmental perturbations, and can facilitate a stronger integration of theoretical and empirical research.

Knowledge of species composition and their interactions, in the form of interaction networks, is required to understand processes shaping their distribution over time and space. As such, comparing ecological networks along environmental gradients represents a promising new research avenue to understand the organization of life. Variation in the position and intensity of links within networks along environmental gradients may be driven by turnover in species composition, by variation in species abundances and by abiotic influences on species interactions. While investigating changes in species composition has a long tradition, so far only a limited number of studies have examined changes in species interactions between networks, often with differing approaches. Here, we review studies investigating variation in network structures along environmental gradients, highlighting how methodological decisions about standardization can influence their conclusions. Due to their complexity, variation among ecological networks is frequently studied using properties that summarize the distribution or topology of interactions such as number of links, connectance, or modularity. These properties can either be compared directly or using a procedure of standardization. While measures of network structure can be directly related to changes along environmental gradients, standardization is frequently used to facilitate interpretation of variation in network properties by controlling for some co-variables, or via null models. Null models allow comparing the deviation of empirical networks from random expectations and are expected to provide a more mechanistic understanding of the factors shaping ecological networks when they are coupled with functional traits. As an illustration, we compare approaches to quantify the role of trait matching in driving the structure of plant-hummingbird mutualistic networks, i.e. a direct comparison, standardized by null models and hypothesis-based metaweb. Overall, our analysis warns against a comparison of studies that rely on distinct forms of standardization, as they are likely to highlight different signals. Fostering a better understanding of the analytical tools available and the signal they detect will help produce deeper insights into how and why ecological networks vary along environmental gradients.

With current environmental changes, evolution can rescue declining populations, but what happens to their interacting species? Mutualistic interactions can help species sustain each other when their environment worsens. However, mutualism is often costly to maintain, and evolution might counter-select it when not profitable enough. We investigate how evolution of mutualism affects the coexistence of two mutualistic species, e.g. a plant-pollinator or plant-fungi system. Specifically, using eco-evolutionary dynamics, we study the evolution of the focal species investment in the mutualistic interaction of a focal species (e.g. plant attractiveness via flower or nectar production for pollinators or carbon exudate for mycorrhizal fungi), and how it is affected by the decline of the partner population with which it is interacting. We assume an allocation trade-off so that investment in the mutualistic interaction reduces the species intrinsic growth rate. First, we investigate how evolution changes species persistence, biomass production, and the intensity of the mutualistic interaction. We show that concave trade-offs allow evolutionary convergence to stable coexistence. We next assume an external disturbance that decreases the partner population by lowering its intrinsic growth rate. Such declines result in the evolution of lower investment of the focal species in the mutualistic interaction, which eventually leads to the extinction of the partner species. With asymmetric mutualism favouring the partner, the evolutionary disappearance of the mutualistic interaction is delayed. Our results suggest that evolution may account for the current collapse of some mutualistic system like plant-pollinator ones, and that restoration attempts should be enforced early enough to prevent potential negative effects driven by evolution.

Song, Rohr, and Saavedra (2017) have proposed a methodology to compare network properties across systems with different sizes and constraints, in response to the fact that z‐scores cannot be used for such purposes. Simmons, Hoeppke, and Sutherland (2019) have shown that part of the methodology can be improved. Here, we show that all previous results hold and are strengthened by the new methodology.

Studies on biodiversity–ecosystem functioning (BEF) in highly controlled experiments often yield results incompatible with observations from natural systems: experimental results often reveal positive relationships between diversity and productivity, while for natural systems, zero or even negative relationships have been reported. The discrepancy may arise due to a limited or closed local species pool in experiments, while natural systems in meta-community contexts experience dynamic processes, i.e., colonization and extinctions. In our study, we analysed plant community properties and above-ground biomass within a semi-natural (i.e., not weeded) experiment in an agricultural landscape. Eleven replicates with four different diversity levels were created from a species pool of 20 wildflower species. We found an overall significant negative relationship between total diversity and productivity. This relationship likely resulted from invasion resistance: in plots sown with low species numbers, we observed colonization by low-performing species; colonization increased species richness but did not contribute substantially to productivity. Interestingly, when analysing the biomass of the sown and the colonizer species separately, we observed in both cases positive BEF relationships, while this relationship was negative for the whole system. A structural equation modelling approach revealed that higher biomass of the sown species was linked to higher species richness, while the positive BEF relationship of the colonizers was indirect and constrained by the sown species biomass. Our results suggest that, in semi-natural conditions common in extensive agroecosystems, the negative BEF relationship results from the interplay between local dominant species and colonization from the regional species pool by subordinate species.

14 páginas.-- 4 figuras.-- 1 tabla.-- 100 referencias.-- Supplemental information associated with this article can be found, in the online version, at https://doi.org/10.1016/j.tree.2018.01.007, We propose to integrate niche and network theories into a single framework. This integration is possible through the strong parallelisms of concepts and methodologies of both theories. Feasibility and stability conditions are the key concepts that can allow the integration. How species interactions across trophic levels fulfill feasibility and stability conditions is yet to be discovered. The quest for understanding how species interactions modulate diversity has progressed by theoretical and empirical advances following niche and network theories. Yet, niche studies have been limited to describe coexistence within tropic levels despite incorporating information about multi-trophic interactions. Network approaches could address this limitation, but they have ignored the structure of species interactions within trophic levels. Here we call for the integration of niche and network theories to reach new frontiers of knowledge exploring how interactions within and across trophic levels promote species coexistence. This integration is possible due to the strong parallelisms in the historical development, ecological concepts, and associated mathematical tools of both theories. We provide a guideline to integrate this framework with observational and experimental studies., Comments from three anonymous reviewers greatly improve the quality of this paper, which is the result of a collaborative project funded by the Spanish Ministry of Economy and Competitiveness (LINCX, CGL2014-61590-EXP). OG acknowledges postdoctoral financial support provided by the Spanish Ministry of Economy and Competitiveness (JCI-2012-12061) and by the European Union Horizon 2020 research and innovation program under the Marie Sklodowska- Curie grant agreement No 661118-BioFUNC.SS alsoacknowledges support from MIT Research Committee Funds and the Mitsui Chair. Wefinally thank Phylopic.com and Melissa Broussard for providing silhouette images of plants and pollinators.