Climate change: A rewired food web

Hysteretic and asynchronous regime shifts of bacterial and micro-eukaryotic communities driven by nutrient loading

Nutrient pollution is pervasive in many urban rivers, while restoration measures that reduce nutrient loading but fail to improve biological communities often lack effectiveness due to the indispensable role of biota, especially multi-taxa, in enhancing ecosystem stability and function. The investigation of the response patterns of multi-taxa to the nutrient loading in urban rivers is important for the recovery of biota structure and thus ecosystem function. However, little is known about the response patterns of multi-taxa and their impact on ecosystem structure and function in urban rivers. Here, the study, from the perspective of alternative stable states theory, showed the hysteretic response of both bacterial and micro-eukaryotic communities to nutrient loading based on the field investigation and environmental DNA metabarcoding. Bistability was shown to exist in both bacterial and micro-eukaryotic communities, demonstrating that the response of microbiota to nutrient loading was a regime shifts with hysteresis. Potential analysis then indicated that the increased nutrient loading drove regime shifts in the bacterial community and the micro-eukaryotic community towards a state dominated by anaerobic bacteria and benthic Bacillariophyta, respectively. High nutrient loading was found to reduce the relative abundance of metazoan, but increase that of eukaryotic algae, which made the trophic pyramid top-lighter and bottom-heavier, probably exacerbating the degradation of ecosystem function. It should be noted that, in response to the reduced nutrient loading, the recovery threshold of micro-eukaryotic communities (nutrient loading = ∼0.5) was lower than that of bacterial communities (nutrient loading = ∼1.2), demonstrating longer hysteresis of micro-eukaryotic communities. In addition, the markedly positive correlation between the status of microbial communities and N-related enzyme activities suggested the recovery of microbial communities probably will benefit the improvement of N-cycling functionality. The obtained results provide a deep insight into the collapse and recovery trajectories of multi-trophic microbiota to the nutrient loading gradient and their impact on the N transformation potential, therefore benefiting the restoration and management of urban rivers.

Predator-driven behavioural shifts in a common lizard shape resource-flow from marine to terrestrial ecosystems

Foraging decisions shape the structure of food webs. Therefore, a behavioural shift in a single species can potentially modify resource-flow dynamics of entire ecosystems. To examine this, we conducted a field experiment to assess foraging niche dynamics of semi-arboreal brown anole lizards in the presence/absence of predatory ground-dwelling curly-tailed lizards in a replicated set of island ecosystems. One year after experimental translocation, brown anoles exposed to these predators had drastically increased perch height and reduced consumption of marine-derived food resources. This foraging niche shift altered marine-to-terrestrial resource-flow dynamics and persisted in the diets of the first-generation offspring. Furthermore, female lizards that displayed more risk-taking behaviours consumed more marine prey on islands with predators present. Our results show how predator-driven rapid behavioural shifts can alter food-web connectivity between oceanic and terrestrial ecosystems and underscore the importance of studying behaviour-mediated niche shifts to understand ecosystem functioning in rapidly changing environments.

Stable diverse food webs become more common when interactions are more biologically constrained

Ecologists have long sought to understand how diversity and structure mediate the stability of whole ecosystems. For high-diversity food webs, the interactions between species are typically represented using matrices with randomly chosen interaction strengths. Unfortunately, this procedure tends to produce ecological systems with no underlying equilibrium solution, and so ecological inferences from this approach may be biased by nonbiological outcomes. Using recent computationally efficient methodological advances from metabolic networks, we employ for the first time an inverse approach to diversity-stability research. We compare classical random interaction matrices of realistic food web topology (hereafter the classical model) to feasible, biologically constrained, webs produced using the inverse approach. We show that an energetically constrained feasible model yields a far higher proportion of stable high-diversity webs than the classical random matrix approach. When we examine the energetically constrained interaction strength distributions of these matrix models, we find that although these diverse webs have consistent negative self-regulation, they do not require strong self-regulation to persist. These energetically constrained diverse webs instead show an increasing preponderance of weak interactions that are known to increase local stability. Further examination shows that some of these weak interactions naturally appear to arise in the model food webs from a constraint-generated realistic generalist-specialist trade-off, whereby generalist predators have weaker interactions than more specialized species. Additionally, the inverse technique we present here has enormous promise for understanding the role of the biological structure behind stable high-diversity webs and for linking empirical data to the theory.

Estimating comparable distances to tipping points across mutualistic systems by scaled recovery rates

Mutualistic systems can experience abrupt and irreversible regime shifts caused by local or global stressors. Despite decades of efforts to understand ecosystem dynamics and determine whether a tipping point could occur, there are no current approaches to estimate distances (in state/parameter space) to tipping points and compare the distances across various mutualistic systems. Here we develop a general dimension-reduction approach that simultaneously compresses the natural control and state parameters of high-dimensional complex systems and introduces a scaling factor for recovery rates. Our theoretical framework places various systems with entirely different dynamical parameters, network structure and state perturbations on the same scale. More importantly, it compares distances to tipping points across different systems on the basis of data on abundance and topology. By applying the method to 54 real-world mutualistic networks, our analytical results unveil the network characteristics and system parameters that control a system's resilience. We contribute to the ongoing efforts in developing a general framework for mapping and predicting distance to tipping points of ecological and potentially other systems.

Food web rewiring drives long-term compositional differences and late-disturbance interactions at the community level

Multiple anthropogenic disturbances affect the structure and functioning of communities. Recent evidence highlighted that, after pulse disturbance, the functioning a community performs may be recovered fast due to functional redundancy, whereas community multivariate composition needs a longer time. Yet, the mechanisms that drive the different community recovery times have not been quantified empirically. We use quantitative food-web analysis to assess the influence of species interactions on community recovery. We found species-interactions strength to be the main mechanism driving differences between structural and functional recovery. Additionally, we show that interactions between multiple disturbances appear in the long term only when both species-interaction strength and food-web architecture change significantly.

Ecological communities are constantly exposed to multiple natural and anthropogenic disturbances. Multivariate composition (if recovered) has been found to need significantly more time to be regained after pulsed disturbance compared to univariate diversity metrics and functional endpoints. However, the mechanisms driving the different recovery times of communities to single and multiple disturbances remain unexplored. Here, we apply quantitative ecological network analyses to try to elucidate the mechanisms driving long-term community-composition dissimilarity and late-stage disturbance interactions at the community level. For this, we evaluate the effects of two pesticides, nutrient enrichment, and their interactions in outdoor mesocosms containing a complex freshwater community. We found changes in interactions strength to be strongly related to compositional changes and identified postdisturbance interaction-strength rewiring to be responsible for most of the observed compositional changes. Additionally, we found pesticide interactions to be significant in the long term only when both interaction strength and food-web architecture are reshaped by the disturbances. We suggest that quantitative network analysis has the potential to unveil ecological processes that prevent long-term community recovery.

Riparian buffers maintain aquatic trophic structure in agricultural landscapes

Local and regional habitat conditions associated with agricultural activity can fundamentally alter aquatic ecosystems. Increased nutrient inputs, channelization and reduced riparian habitat both upstream and locally contribute to the degradation of stream ecosystems and their function. Here, we examine stream food webs in watersheds that feed into Lake Erie to determine the effects of agricultural land cover on major food web energy pathways and trophic structure. Given that higher agricultural intensity can increase nutrient runoff and reduce the riparian zone and litter in-fall into streams, we predicted that generalist fish would derive less energy from the terrestrial pathway and become more omnivorous. Consistent with these predictions, we show that both mean terrestrial energy use and trophic position of the resident top consumer, creek chub (Semotilus atromaculatus), decrease with local agricultural intensity but not with watershed-level agriculture intensity. These findings suggest that local riparian buffers can maintain trophic structure even in the face of high whole-watershed agricultural intensity.

Refocusing multiple stressor research around the targets and scales of ecological impacts

Ecological communities face a variety of environmental and anthropogenic stressors acting simultaneously. Stressor impacts can combine additively or can interact, causing synergistic or antagonistic effects. Our knowledge of when and how interactions arise is limited, as most models and experiments only consider the effect of a small number of non-interacting stressors at one or few scales of ecological organization. This is concerning because it could lead to significant underestimations or overestimations of threats to biodiversity. Furthermore, stressors have been largely classified by their source rather than by the mechanisms and ecological scales at which they act (the target). Here, we argue, first, that a more nuanced classification of stressors by target and ecological scale can generate valuable new insights and hypotheses about stressor interactions. Second, that the predictability of multiple stressor effects, and consistent patterns in their impacts, can be evaluated by examining the distribution of stressor effects across targets and ecological scales. Third, that a variety of existing mechanistic and statistical modelling tools can play an important role in our framework and advance multiple stressor research.

On the sensitivity of food webs to multiple stressors

Evaluating the effects of multiple stressors on ecosystems is becoming increasingly vital with global changes. The role of species interactions in propagating the effects of stressors, although widely acknowledged, has yet to be formally explored. Here, we conceptualise how stressors propagate through food webs and explore how they affect simulated three-species motifs and food webs of the Canadian St. Lawrence System. We find that overlooking species interactions invariably underestimate the effects of stressors, and that synergistic and antagonistic effects through food webs are prevalent. We also find that interaction type influences a species' susceptibility to stressors; species in omnivory and tri-trophic food chain interactions in particular are sensitive and prone to synergistic and antagonistic effects. Finally, we find that apex predators were negatively affected and mesopredators benefited from the effects of stressors due to their trophic position in the St. Lawrence System, but that species sensitivity is dependent on food web structure. In conceptualising the effects of multiple stressors on food webs, we bring theory closer to practice and show that considering the intricacies of ecological communities is key to assess the net effects of stressors on species.

Co-adaptation enhances the resilience of mutualistic networks

Mutualistic networks, which describe the ecological interactions between multiple types of species such as plants and pollinators, play a paramount role in the generation of Earth's biodiversity. The resilience of a mutualistic network denotes its ability to retain basic functionality when errors and failures threaten the persistence of the community. Under the disturbances of mass extinctions and human-induced disasters, it is crucial to understand how mutualistic networks respond to changes, which enables the system to increase resilience and tolerate further damages. Despite recent advances in the modelling of the structure-based adaptation, we lack mathematical and computational models to describe and capture the co-adaptation between the structure and dynamics of mutualistic networks. In this paper, we incorporate dynamic features into the adaptation of structure and propose a co-adaptation model that drastically enhances the resilience of non-adaptive and structure-based adaptation models. Surprisingly, the reason for the enhancement is that the co-adaptation mechanism simultaneously increases the heterogeneity of the mutualistic network significantly without changing its connectance. Owing to the broad applications of mutualistic networks, our findings offer new ways to design mechanisms that enhance the resilience of many other systems, such as smart infrastructures and social-economical systems.

Measuring network rewiring over time

Recent years have seen tremendous advances in the scientific study of networks, as more and larger data sets of relationships among nodes have become available in many different fields. This has led to pathbreaking discoveries of near-universal network behavior over time, including the principle of preferential attachment and the emergence of scaling in complex networks. Missing from the set of network analysis methods to date is a measure that describes for each node how its relationship (or links) with other nodes changes from one period to the next. Conventional measures of network change for the most part show how the degrees of a node change; these are scalar comparisons. Our contribution is to use, for the first time, the cosine similarity to capture not just the change in degrees of a node but its relationship to other nodes. These are vector (or matrix)-based comparisons, rather than scalar, and we refer to them as “rewiring” coefficients. We apply this measure to three different networks over time to show the differences in the two types of measures. In general, bigger increases in our rewiring measure are associated with larger increases in network density, but this is not always the case.

Accumulation of Plastic Debris and Associated Contaminants in Aquatic Food Webs

We present a generic theoretical model (MICROWEB) that simulates the transfer of microplastics and hydrophobic organic chemicals (HOC) in food webs. We implemented the model for an Arctic case comprised of nine species including Atlantic cod and polar bear as top predator. We used the model to examine the effect of plastic ingestion on trophic transfer of microplastics and persistent HOCs (PCBs) and metabolizable HOCs (PAHs), spanning a wide range of hydrophobicities. In a scenario where HOCs in plastic and water are in equilibrium, PCBs biomagnify less when more microplastic is ingested, because PCBs biomagnify less well from ingested plastic than from regular food. In contrast, PAHs biomagnify more when more microplastic is ingested, because plastic reduces the fraction of PAHs available for metabolization. We also explore nonequilibrium scenarios representative of additives that are leaching out, as well as sorbing HOCs, quantitatively showing how the above trends are strengthened and weakened, respectively. The observed patterns were not very sensitive to modifications in the structure of the food web. The model can be used as a tool to assess prospective risks of exposure to microplastics and complex HOC mixtures for any food web, including those with relevance for human health.

The influence of ontogenetic diet variation on consumption rate estimates: a marine example

Consumption rates are the foundation of trophic ecology, yet bioenergetics models used to estimate these rates can lack realism by not incorporating the ontogeny of diet. We constructed a bioenergetics model of a marine predatory fish (tailor, Pomatomus saltatrix) that incorporated high-resolution ontogenetic diet variation, and compared consumption estimates to those derived from typical bioenergetics models that do not consider ontogenetic diet variation. We found tailor consumption was over- or under-estimated by ~5–25% when only including the most common prey item. This error was due to a positive relationship between mean prey energy density and predator body size. Since high-resolution diet data isn’t always available, we also simulated how increasing dietary information progressively influenced consumption rate estimates. The greatest improvement in consumption rate estimates occurred when diet variation of 2–3 stanzas (1–2 juvenile stanzas, and adults) was included, with at least 5–6 most common prey types per stanza. We recommend increased emphasis on incorporating the ontogeny of diet and prey energy density in consumption rate estimates, especially for species with spatially segregated life stages or variable diets. A small-moderate increase in the resolution of dietary information can greatly benefit the accuracy of estimated consumption rates. We present a method of incorporating variable prey energy density into bioenergetics models.

Potential oscillators and keystone modules in food webs

Food web theory suggests that the placement of a weak interaction is critical such that under some conditions even one well-placed weak interaction can stabilise multiple strong interactions. This theory suggests that complex stable webs may be built from pivotal weak interactions such that the removal of even one to a few keystone interactions can have significant cascading impacts on whole system diversity and structure. However, the connection between weak interactions, derived from the theory of modular food web components, and keystone species, derived from empirical results, is not yet well understood. Here, we develop numerical techniques to detect potential oscillators hidden in complex food webs, and show that, both in random and real food webs, keystone consumer-resource interactions often operate to stabilise them. Alarmingly, this result suggests that nature frequently may be dangerously close to precipitous change with even the loss of one or a few weakly interacting species.

Next-Generation Global Biomonitoring: Large-scale, Automated Reconstruction of Ecological Networks

We foresee a new global-scale, ecological approach to biomonitoring emerging within the next decade that can detect ecosystem change accurately, cheaply, and generically. Next-generation sequencing of DNA sampled from the Earth's environments would provide data for the relative abundance of operational taxonomic units or ecological functions. Machine-learning methods would then be used to reconstruct the ecological networks of interactions implicit in the raw NGS data. Ultimately, we envision the development of autonomous samplers that would sample nucleic acids and upload NGS sequence data to the cloud for network reconstruction. Large numbers of these samplers, in a global array, would allow sensitive automated biomonitoring of the Earth's major ecosystems at high spatial and temporal resolution, revolutionising our understanding of ecosystem change.

Non-random food-web assembly at habitat edges increases connectivity and functional redundancy

Habitat fragmentation dramatically alters the spatial configuration of landscapes, with the creation of artificial edges affecting community structure and dynamics. Despite this, it is not known how the different food webs in adjacent habitats assemble at their boundaries. Here we demonstrate that the composition and structure of herbivore-parasitoid food webs across edges between native and plantation forests are not randomly assembled from those of the adjacent communities. Rather, elevated proportions of abundant, interaction-generalist parasitoid species at habitat edges allowed considerable interaction rewiring, which led to higher linkage density and less modular networks, with higher parasitoid functional redundancy. This was despite high overlap in host composition between edges and interiors. We also provide testable hypotheses for how food webs may assemble between habitats with lower species overlap. In an increasingly fragmented world, non-random assembly of food webs at edges may increasingly affect community dynamics at the landscape level.

Multiplex social ecological network analysis reveals how social changes affect community robustness more than resource depletion

Network analysis provides a powerful tool to analyze complex influences of social and ecological structures on community and household dynamics. Most network studies of social-ecological systems use simple, undirected, unweighted networks. We analyze multiplex, directed, and weighted networks of subsistence food flows collected in three small indigenous communities in Arctic Alaska potentially facing substantial economic and ecological changes. Our analysis of plausible future scenarios suggests that changes to social relations and key households have greater effects on community robustness than changes to specific wild food resources.