A conceptual framework for studying the strength of plant-animal mutualistic interactions

A Continuum From Positive to Negative Interactions Drives Plant Species' Performance in a Diverse Community

With many species interacting in nature, determining which interactions describe community dynamics is nontrivial. By applying a computational modeling approach to an extensive field survey, we assessed the importance of interactions from plants (both inter- and intra-specific), pollinators and insect herbivores on plant performance (i.e., viable seed production). We compared the inclusion of interaction effects as aggregate guild-level terms versus terms specific to taxonomic groups. We found that a continuum from positive to negative interactions, containing mostly guild-level effects and a few strong taxonomic-specific effects, was sufficient to describe plant performance. While interactions with herbivores and intraspecific plants varied from weakly negative to weakly positive, heterospecific plants mainly promoted competition and pollinators facilitated plants. The consistency of these empirical findings over 3 years suggests that including the guild-level effects and a few taxonomic-specific groups rather than all pairwise and high-order interactions, can be sufficient for accurately describing species variation in plant performance across natural communities.

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.

Ecological theory of mutualism: Robust patterns of stability and thresholds in two-species population models

Mutualisms are ubiquitous in nature, provide important ecosystem services, and involve many species of interest for conservation. Theoretical progress on the population dynamics of mutualistic interactions, however, comparatively lagged behind that of trophic and competitive interactions, leading to the impression that ecologists still lack a generalized framework to investigate the population dynamics of mutualisms. Yet, over the last 90 years, abundant theoretical work has accumulated, ranging from abstract to detailed. Here, we review and synthesize historical models of two-species mutualisms. We find that population dynamics of mutualisms are qualitatively robust across derivations, including levels of detail, types of benefit, and inspiring systems. Specifically, mutualisms tend to exhibit stable coexistence at high density and destabilizing thresholds at low density. These dynamics emerge when benefits of mutualism saturate, whether due to intrinsic or extrinsic density dependence in intraspecific processes, interspecific processes, or both. We distinguish between thresholds resulting from Allee effects, low partner density, and high partner density, and their mathematical and conceptual causes. Our synthesis suggests that there exists a robust population dynamic theory of mutualism that can make general predictions.

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

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

Avoiding artifacts when varying the number of species in ecological models

Ecological theory recognizes the importance of the variety of species for maintaining the functioning of ecosystems and their derived services. We assert that when studying the effects of shifts in biodiversity levels using mathematical models, their dynamics must be sensitive to the variety of species traits but not to raw species numbers, a property that we call order-invariance. We present a testing procedure for verifying order-invariance of ecological network models -with or without trait adaptation- expressed as ODEs. Furthermore, we applied our test to several influential models used for evaluating biodiversity effects on ecosystem functioning. In most of the surveyed studies the equations failed our test. This raises doubts about the validity of previous results and calls for revisiting the theory derived from these studies. Our results foster the creation of artifact-free models, a necessary step towards building a more robust theory of biodiversity-driven ecosystem functioning.

Hidden layers of density dependence in consumer feeding rates

Functional responses relate a consumer's feeding rates to variation in its abiotic and biotic environment, providing insight into consumer behaviour 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 analyse two large collections of functional-response data sets 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.

Effects of community composition on plant-pollinator interaction networks across a spatial gradient of oak-savanna habitats

Distance between habitats may impact the composition and corresponding interactions between trophic levels. Mutualistic networks, such as those of plants and pollinators tend to have a core set of properties that often relate to the resilience of the community, or the ability of the community to retain function and structure after a disturbance. Furthermore, network structure is highly dependent on the number of specialists and generalists; however, it is unclear how different groups of species with various life-history strategies influence network structure. In this study, we evaluated how the composition of plants and pollinators within 16 oak-savanna sites changed across a latitudinal gradient. In addition, we evaluated how the abundance of different groups of plants and pollinators affected network metrics related to resilience. We found that the composition of plants and pollinators varied between ecoregions, while pollinator composition further varied with habitat characteristics. Network metrics displayed no spatial pattern but were related to the abundance of several pollinator groups. Above-ground nesting insects had a positive relationship with nestedness and a negative relationship with modularity, while predatory larvae had a negative relationship with modularity. Thus, above-ground nesting insects and predatory larvae could be expected to increase network resilience. This study emphasizes how spatial scales can influence species compositions, which in turn affects the structure of interactions in the community with implications for resilience.

Opportunities for improving cancer treatment using systems biology

Current cancer therapies target a limited set of tumor features, rather than considering the tumor as a whole. Systems biology aims to reveal therapeutic targets associated with a variety of facets in an individual's tumor, such as genetic heterogeneity and its evolution, cancer cell-autonomous phenotypes, and microenvironmental signaling. These disparate characteristics can be reconciled using mathematical modeling that incorporates concepts from ecology and evolution. This provides an opportunity to predict tumor growth and response to therapy, to tailor patient-specific approaches in real time or even prospectively. Importantly, as data regarding patient tumors is often available from only limited time points during treatment, systems-based approaches can address this limitation by interpolating longitudinal events within a principled framework. This review outlines areas in medicine that could benefit from systems biology approaches to deconvolve the complexity of cancer.

Pollination effectiveness in a generalist plant: adding the genetic component

The pollination effectiveness of a flower visitor has traditionally been measured as the product of a quantity component that depends on the frequency of interaction and a quality component that measures the per-visit effects on plant reproduction. We propose that this could be complemented with a genetic component informing about each pollinator's contribution to the genetic diversity and composition of the plant progeny. We measured the quantity and quality components of effectiveness of most pollinator functional groups of the generalist herb Erysimum mediohispanicum. We used 10 microsatellite markers to calculate the genetic component as the diversity of sires among siblings and included it into the calculation of the pollination effectiveness. Functional groups varied in the quantity and quality components, which were shown to be decoupled. Functional groups also differed in the genetic component. This component changed the estimates of pollination effectiveness, increasing the differences between some functional groups and modifying the pollination effectiveness landscape. We demonstrate that including the genetic component in the calculation of the pollination effectiveness may allow a more complete quantification of the contribution of each pollinator to the reproductive success of a plant, providing information on its mating patterns and long-term fitness.

How to Invade an Ecological Network

Invasion science is in a state of paradox, having low predictability despite strong, identifiable covariates of invasion performance. We propose shifting the foundation metaphor of biological invasions from a linear filtering scheme to one that invokes complex adaptive networks. We link invasion performance and invasibility directly to the loss of network stability and indirectly to network topology through constraints from the emergence of the stability criterion in complex systems. We propose the wind vane of an invaded network - the major axis of its adjacency matrix - which reveals how species respond dynamically to invasions. We suggest that invasion ecology should steer away from comparative macroecological studies, to rather explore the ecological network centred on the focal species.

Phenology determines the robustness of plant-pollinator networks

Plant-pollinator systems are essential for ecosystem functioning, which calls for an understanding of the determinants of their robustness to environmental threats. Previous studies considering such robustness have focused mostly on species' connectivity properties, particularly their degree. We hypothesized that species' phenological attributes are at least as important as degree as determinants of network robustness. To test this, we combined dynamic modeling, computer simulation and analysis of data from 12 plant-pollinator networks with detailed information of topology of interactions as well as species' phenology of plant flowering and pollinator emergence. We found that phenological attributes are strong determinants of network robustness, a result consistent across the networks studied. Plant species persistence was most sensitive to increased larval mortality of pollinators that start earlier or finish later in the season. Pollinator persistence was especially sensitive to decreased visitation rates and increased larval mortality of specialists. Our findings suggest that seasonality of climatic events and anthropic impacts such as the release of pollutants is critical for the future integrity of terrestrial biodiversity.

On the use of functional responses to quantify emergent multiple predator effects

Non-independent interactions among predators can have important consequences for the structure and dynamics of ecological communities by enhancing or reducing prey mortality rate through, e.g., predator facilitation or interference. The multiplicative risk model, traditionally used to detect these emergent multiple predator effects (MPEs), is biased because it assumes linear functional response (FR) and no prey depletion. To rectify these biases, two approaches based on FR modelling have recently been proposed: the direct FR approach and the population-dynamic approach. Here we compare the strengths, limitations and predictions of the three approaches using simulated data sets. We found that the predictions of the direct FR and the multiplicative risk models are very similar and underestimate predation rates when prey density is high or prey depletion is substantial. As a consequence, these two approaches often fail in detecting risk reduction. Finally, parameters estimated with the direct FR approach lack mechanistic interpretation, which limits the understanding of the mechanisms driving multiple predator interactions and potential extension of this approach to more complex food webs. We thus strongly recommend using the population-dynamic approach because it is robust, precise, and provides a scalable mechanistic framework to detect and quantify MPEs.

Towards a predictive model of species interaction beta diversity

Species interactions are fundamental to community dynamics and ecosystem processes. Despite significant progress in describing species interactions, we lack the ability to predict changes in interactions across space and time. We outline a Bayesian approach to separate the probability of species co-occurrence, interaction and detectability in influencing interaction betadiversity. We use a multi-year hummingbird-plant time series, divided into training and testing data, to show that including models of detectability and occurrence improves forecasts of mutualistic interactions. We then extend our model to explore interaction betadiversity across two distinct seasons. Despite differences in the observed interactions among seasons, there was no significant change in hummingbird occurrence or interaction frequency between hummingbirds and plants. These results highlight the challenge of inferring the causes of interaction betadiversity when interaction detectability is low. Finally, we highlight potential applications of our model for integrating observations of local interactions with biogeographic and evolutionary histories of co-occurring species. These advances will provide new insight into the mechanisms that drive variation in patterns of biodiversity.

Global stability of the coexistence equilibrium for a general class of models of facultative mutualism

Many models of mutualism have been proposed and studied individually. In this paper, we develop a general class of models of facultative mutualism that covers many of such published models. Using mild assumptions on the growth and self-limiting functions, we establish necessary and sufficient conditions on the boundedness of model solutions and prove the global stability of a unique coexistence equilibrium whenever it exists. These results allow for a greater flexibility in the way each mutualist species can be modelled and avoid the need to analyse any single model of mutualism in isolation. Our generalization also allows each of the mutualists to be subject to a weak Allee effect. Moreover, we find that if one of the interacting species is subject to a strong Allee effect, then the mutualism can overcome it and cause a unique coexistence equilibrium to be globally stable.

Identifying Causes of Patterns in Ecological Networks: Opportunities and Limitations

Ecological networks depict the interactions between species, mainly based on observations in the field. The information contained in such interaction matrices depends on the sampling design, and typically, compounds preferences (specialization) and abundances (activity). Null models are the primary vehicles to disentangle the effects of specialization from those of sampling and abundance, but they ignore the feedback of network structure on abundances. Hence, network structure, as exemplified here by modularity, is difficult to link to specific causes. Indeed, various processes lead to modularity and to specific interaction patterns more generally. Inferring (co)evolutionary dynamics is even more challenging, as competition and trait matching yield identical patterns of interactions. A satisfactory resolution of the underlying factors determining network structure will require substantial additional information, not only on independently assessed abundances, but also on traits, and ideally on fitness consequences as measured in experimental setups.

Identifying species at coextinction risk when detection is imperfect: Model evaluation and case study

Losing a species from a community can cause further extinctions, a process also known as coextinction. The risk of being extirpated with an interaction partner is commonly inferred from a species’ host-breadth, derived from observing interactions between species. But observational data suffers from imperfect detection, making coextinction estimates highly unreliable. To address this issue and to account for data uncertainty, we fit a hierarchical N-mixture model to individual-level interaction data from a mutualistic network. We predict (1) with how many interaction partners each species interacts (to indicate their coextinction risk) and (2) how completely the community was sampled. We fit the model to simulated interactions to investigate how variation in sampling effort, interaction probability, and animal abundances influence model accuracy and apply it to an empirical dataset of flowering plants and their insect visitors. The model performed well in predicting the number of interaction partners for scenarios with high abundances, but indicated high parameter uncertainty for networks with many rare species. Yet, model predictions were generally closer to the true value than the observations. Our mutualistic plant-insect community most closely resembled the scenario of high interaction rates with low abundances. Median estimates of interaction partners were frequently much higher than the empirical data indicate, but uncertainty was high. Our analysis suggested that we only detected 14-59% of the flower-visiting insect species, indicating that our study design, which is common for pollinator studies, was inadequate to detect many species. Imperfect detection strongly affects the inferences from observed interaction networks and hence, host specificity, specialisation estimates and network metrics from observational data may be highly misleading for assessing a species’ coextinction risks. Our study shows how models can help to estimate coextinction risk, but also indicates the need for better data (i.e., intensified sampling and individual-level observations) to reduce uncertainty.

Niche partitioning due to adaptive foraging reverses effects of nestedness and connectance on pollination network stability

Much research debates whether properties of ecological networks such as nestedness and connectance stabilise biological communities while ignoring key behavioural aspects of organisms within these networks. Here, we computationally assess how adaptive foraging (AF) behaviour interacts with network architecture to determine the stability of plant-pollinator networks. We find that AF reverses negative effects of nestedness and positive effects of connectance on the stability of the networks by partitioning the niches among species within guilds. This behaviour enables generalist pollinators to preferentially forage on the most specialised of their plant partners which increases the pollination services to specialist plants and cedes the resources of generalist plants to specialist pollinators. We corroborate these behavioural preferences with intensive field observations of bee foraging. Our results show that incorporating key organismal behaviours with well-known biological mechanisms such as consumer-resource interactions into the analysis of ecological networks may greatly improve our understanding of complex ecosystems.

Predator diversity and environmental change modify the strengths of trophic and nontrophic interactions

Understanding the dependence of species interaction strengths on environmental factors and species diversity is crucial to predict community dynamics and persistence in a rapidly changing world. Nontrophic (e.g. predator interference) and trophic components together determine species interaction strengths, but the effects of environmental factors on these two components remain largely unknown. This impedes our ability to fully understand the links between environmental drivers and species interactions. Here, we used a dynamical modelling framework based on measured predator functional responses to investigate the effects of predator diversity, prey density, and temperature on trophic and nontrophic interaction strengths within a freshwater food web. We found that (i) species interaction strengths cannot be predicted from trophic interactions alone, (ii) nontrophic interaction strengths vary strongly among predator assemblages, (iii) temperature has opposite effects on trophic and nontrophic interaction strengths, and (iv) trophic interaction strengths decrease with prey density, whereas the dependence of nontrophic interaction strengths on prey density is concave up. Interestingly, the qualitative impacts of temperature and prey density on the strengths of trophic and nontrophic interactions were independent of predator identity, suggesting a general pattern. Our results indicate that taking multiple environmental factors and the nonlinearity of density-dependent species interactions into account is an important step towards a better understanding of the effects of environmental variations on complex ecological communities. The functional response approach used in this study opens new avenues for (i) the quantification of the relative importance of the trophic and nontrophic components in species interactions and (ii) a better understanding how environmental factors affect these interactions and the dynamics of ecological communities.

Mutualistic strategies minimize coextinction in plant-disperser networks

The global decline of mutualists such as pollinators and seed dispersers may cause negative direct and indirect impacts on biodiversity. Mutualistic network models used to understand the stability of mutualistic systems indicate that species with low partner diversity are most vulnerable to coextinction following mutualism disruption. However, existing models have not considered how species vary in their dependence on mutualistic interactions for reproduction or survival, overlooking the potential influence of this variation on species' coextinction vulnerability and on network stability. Using global databases and field experiments focused on the seed dispersal mutualism, we found that plants and animals that depend heavily on mutualistic interactions have higher partner diversity. Under simulated network disruption, this empirical relationship strongly reduced coextinction because the species most likely to lose mutualists depend least on their mutualists. The pattern also reduced the importance of network structure for stability; nested network structure had little effect on coextinction after simulations incorporated the empirically derived relationship between partner diversity and mutualistic dependence. Our results highlight a previously unknown source of stability in mutualistic networks and suggest that differences among species in their mutualistic strategy, rather than network structure, primarily accounts for stability in mutualistic communities.

Positive feedback between chironomids and algae creates net mutualism between benthic primary consumers and producers

The chironomids of Lake Mývatn show extreme population fluctuations that affect most aspects of the lake ecosystem. During periods of high chironomid densities, chironomid larvae comprise over 90% of aquatic secondary production. Here, we show that chironomid larvae substantially stimulate benthic gross primary production (GPP) and net primary production (NPP), despite consuming benthic algae. Benthic GPP in experimental mesocosms with 140,000 larvae/m² was 71% higher than in mesocosms with no larvae. Similarly, chlorophyll a concentrations in mesocosms increased significantly over the range of larval densities. Furthermore, larvae showed increased growth rates at higher densities, possibly due to greater benthic algal availability in these treatments. We investigated the hypothesis that larvae promote benthic algal growth by alleviating nutrient limitation, and found that (1) larvae have the potential to cycle the entire yearly external loadings of nitrogen and phosphorus during the growing season, and (2) chlorophyll a concentrations were significantly greater in close proximity to larvae (on larval tubes). The positive feedback between chironomid larvae and benthic algae generated a net mutualism between the primary consumer and primary producer trophic levels in the benthic ecosystem. Thus, our results give an example in which unexpected positive feedbacks can lead to both high primary and high secondary production.

Characterizing Species Interactions to Understand Press Perturbations: What Is the Community Matrix?

The community matrix is among ecology's most important mathematical abstractions, formally encapsulating the interconnected network of effects that species have on one another's populations. Despite its importance, the term “community matrix” has been applied to multiple types of matrices that have differing interpretations. This has hindered the application of theory for understanding community structure and perturbation responses. Here, we clarify the correspondence and distinctions among the Interaction matrix, the Alpha matrix, and the Jacobian matrix, terms that are frequently used interchangeably as well as synonymously with the term “community matrix.” We illustrate how these matrices correspond to different ways of characterizing interaction strengths, how they permit insights regarding different types of press perturbations, and how these are related by a simple scaling relationship. Connections to additional interaction strength characterizations encapsulated by the Beta matrix, the Gamma matrix, and the Removal matrix are also discussed. Our synthesis highlights the empirical challenges that remain in using these tools to understand actual communities.