Interaction strengths in food webs: issues and opportunities

The environment to the rescue: can physics help predict predator-prey interactions?

Understanding the factors that determine the occurrence and strength of ecological interactions under specific abiotic and biotic conditions is fundamental since many aspects of ecological community stability and ecosystem functioning depend on patterns of interactions among species. Current approaches to mapping food webs are mostly based on traits, expert knowledge, experiments, and/or statistical inference. However, they do not offer clear mechanisms explaining how trophic interactions are affected by the interplay between organism characteristics and aspects of the physical environment, such as temperature, light intensity or viscosity. Hence, they cannot yet predict accurately how local food webs will respond to anthropogenic pressures, notably to climate change and species invasions. Herein, we propose a framework that synthesises recent developments in food-web theory, integrating body size and metabolism with the physical properties of ecosystems. We advocate for combination of the movement paradigm with a modular definition of the predation sequence, because movement is central to predator-prey interactions, and a generic, modular model is needed to describe all the possible variation in predator-prey interactions. Pending sufficient empirical and theoretical knowledge, our framework will help predict the food-web impacts of well-studied physical factors, such as temperature and oxygen availability, as well as less commonly considered variables such as wind, turbidity or electrical conductivity. An improved predictive capability will facilitate a better understanding of ecosystem responses to a changing world.

Unraveling emergent network indeterminacy in complex ecosystems: A random matrix approach

Indeterminacy of ecological networks-the unpredictability of ecosystem responses to persistent perturbations-is an emergent property of indirect effects a species has on another through interaction chains. Thus, numerous indirect pathways in large, complex ecological communities could make forecasting the long-term outcomes of environmental changes challenging. However, a comprehensive understanding of ecological structures causing indeterminacy has not yet been reached. Here, using random matrix theory (RMT), we provide mathematical criteria determining whether network indeterminacy emerges across various ecological communities. Our analytical and simulation results show that indeterminacy intricately depends on the characteristics of species interaction. Specifically, contrary to conventional wisdom, network indeterminacy is unlikely to emerge in large competitive and mutualistic communities, while it is a common feature in top-down regulated food webs. Furthermore, we found that predictable and unpredictable perturbations can coexist in the same community and that indeterminate responses to environmental changes arise more frequently in networks where predator-prey relationships predominate than competitive and mutualistic ones. These findings highlight the importance of elucidating direct species relationships and analyzing them with an RMT perspective on two fronts: It aids in 1) determining whether the network's responses to environmental changes are ultimately indeterminate and 2) identifying the types of perturbations causing less predictable outcomes in a complex ecosystem. In addition, our framework should apply to the inverse problem of network identification, i.e., determining whether observed responses to sustained perturbations can reconstruct their proximate causalities, potentially impacting other fields such as microbial and medical sciences.

Local-manifold-distance-based regression: an estimation method for quantifying dynamic biological interactions with empirical time series

Quantifying species interactions based on empirical observations is crucial for ecological studies. Advancements in nonlinear time-series analyses, particularly S-maps, are promising for high-dimensional and non-equilibrium ecosystems. S-maps sequentially perform a local linear model fitting to the time evolution of neighbouring points on the reconstructed attractor manifold, and the coefficients can approximate the Jacobian elements corresponding to interaction effects. However, despite that the advantages in nonlinear forecasting with noise-contaminated data, these methodologies have a limitation in the Jacobian estimation accuracy owing to non-equidistantly stretched local manifolds in the state space. Herein, we therefore introduced a local manifold distance (LMD) concept, a non-equidistant measure based on the multi-faceted state dependency. By integrating LMD with advanced computation techniques, we presented a robust and efficient analytical method, LMD-based regression (LMDr). To validate its advantages in prediction and Jacobian estimation, we analysed synthetic time series of model ecosystems with different noise levels and applied it to an experimental protozoan predator-prey system with established biological information. The robustness to noise was the highest for LMDr, which also showed a better correspondence to expected predator-prey interactions in the protozoan system. Thus, LMDr can be applied to study complex ecological networks under dynamic conditions.

Time is of the essence: A general framework for uncovering temporal structures of communities

Ecological communities are inherently dynamic: species constantly turn over within years, months, weeks or even days. These temporal shifts in community composition determine essential aspects of species interactions and how energy, nutrients, information, diseases and perturbations 'flow' through systems. Yet, our understanding of community structure has relied heavily on static analyses not designed to capture critical features of this dynamic temporal dimension of communities. Here, we propose a conceptual and methodological framework for quantifying and analysing this temporal dimension. Conceptually, we split the temporal structure into two definitive features, sequence and duration, and review how they are linked to key concepts in ecology. We then outline how we can capture these definitive features using perspectives and tools from temporal graph theory. We demonstrate how we can easily integrate ongoing research on phenology into this framework and highlight what new opportunities arise from this approach to answer fundamental questions in community ecology. As climate change reshuffles ecological communities worldwide, quantifying the temporal organization of communities is imperative to resolve the fundamental processes that shape natural ecosystems and predict how these systems may change in the future.

Food web context modifies predator foraging and weakens trophic interaction strength

Trophic interaction modifications (TIM) are widespread in natural systems and occur when a third species indirectly alters the strength of a trophic interaction. Past studies have focused on documenting the existence and magnitude of TIMs; however, the underlying processes and long-term consequences remain elusive. To address this gap, we experimentally quantified the density-dependent effect of a third species on a predator's functional response. We conducted short-term experiments with ciliate communities composed of a predator, prey and non-consumable 'modifier' species. In both communities, increasing modifier density weakened the trophic interaction strength, due to a negative effect on the predator's space clearance rate. Simulated long-term dynamics indicate quantitative differences between models that account for TIMs or include only pairwise interactions. Our study demonstrates that TIMs are important to understand and predict community dynamics and highlights the need to move beyond focal species pairs to understand the consequences of species interactions in communities.

Inferring stochastic group interactions within structured populations via coupled autoregression

The internal behaviour of a population is an important feature to take account of when modelling its dynamics. In line with kin selection theory, many social species tend to cluster into distinct groups in order to enhance their overall population fitness. Temporal interactions between populations are often modelled using classical mathematical models, but these sometimes fail to delve deeper into the, often uncertain, relationships within populations. Here, we introduce a stochastic framework that aims to capture the interactions of animal groups and an auxiliary population over time. We demonstrate the model's capabilities, from a Bayesian perspective, through simulation studies and by fitting it to predator-prey count time series data. We then derive an approximation to the group correlation structure within such a population, while also taking account of the effect of the auxiliary population. We finally discuss how this approximation can lead to ecologically realistic interpretations in a predator-prey context. This approximation also serves as verification to whether the population in question satisfies our various assumptions. Our modelling approach will be useful for empiricists for monitoring groups within a conservation framework and also theoreticians wanting to quantify interactions, to study cooperation and other phenomena within social populations.

Potential extinction cascades in a desert ecosystem: Linking food web interactions to community viability

Desert communities are threatened with species loss due to climate change, and their resistance to such losses is unknown. We constructed a food web of the Mojave Desert terrestrial community (300 nodes, 4080 edges) to empirically examine the potential cascading effects of bird extinctions on this desert network, compared to losses of mammals and lizards. We focused on birds because they are already disappearing from the Mojave, and their relative thermal vulnerabilities are known. We quantified bottom-up secondary extinctions and evaluated the relative resistance of the community to losses of each vertebrate group. The impact of random bird species loss was relatively low compared to the consequences of mammal (causing the greatest number of cascading losses) or reptile loss, and birds were relatively less likely to be in trophic positions that could drive top-down effects in apparent competition and tri-tropic cascade motifs. An avian extinction cascade with year-long resident birds caused more secondary extinctions than the cascade involving all bird species for randomized ordered extinctions. Notably, we also found that relatively high interconnectivity among avian species has formed a subweb, enhancing network resistance to bird losses.

How collectively integrated are ecological communities?

Beyond abiotic conditions, do population dynamics mostly depend on a species' direct predators, preys and conspecifics? Or can indirect feedback that ripples across the whole community be equally important? Determining where ecological communities sit on the spectrum between these two characterizations requires a metric able to capture the difference between them. Here we show that the spectral radius of a community's interaction matrix provides such a metric, thus a measure of ecological collectivity, which is accessible from imperfect knowledge of biotic interactions and related to observable signatures. This measure of collectivity integrates existing approaches to complexity, interaction structure and indirect interactions. Our work thus provides an original perspective on the question of to what degree communities are more than loose collections of species or simple interaction motifs and explains when pragmatic reductionist approaches ought to suffice or fail when applied to ecological communities.

Community ecological response to polycyclic aromatic hydrocarbons in Baiyangdian Lake based on an ecological model

The dynamic response of a single population to chemicals can be represented by a Weibull function. However, it is unclear whether the overall response can still be represented in this manner when scaled up to the community level. In this study, we investigated the responses of biological communities to polycyclic aromatic hydrocarbons by using an ecological model of Baiyangdian Lake in northern China. The community dynamics process was divided into the following three stages. In the first stage, toxicity, played a dominant role and strong, medium, and weak species responses were observed according to the toxicity sensitivity. In the second stage, the dynamic process was dominated by the interaction strength with three alternative dynamic pathways comprising of direct response, no response, or inverse response. In the third stage, the toxicity was again dominant, and the biomasses of all species decreased to extinction. The toxicological dynamics were far more complex at the community level than those at the single species level and they were also influenced by the interaction strength as well as toxicity. The toxicological dynamic process in the community was constantly driven by the competing effects of these two forces. In addition to the total biomass, the interaction strength was identified as a suitable community-level signal because it exhibited good indicator properties regarding ecosystem steady-state transitions. However, we found that food web stability indicators were not suitable for use as community-level signals because they were not sensitive to changes in the ecosystem state. Some ecological management suggestions have been proposed, including medium to long-term monitoring, and reduction of external pollution loads and bioindicators. The results obtained in this study increase our understanding of how chemicals interfere with community dynamics, and the interaction strength and total biomass were identified as useful holistic indicators.

A framework for untangling the consequences of artificial light at night on species interactions

Although much evidence exists showing organismal consequences from artificial light at night (ALAN), large knowledge gaps remain regarding ALAN affecting species interactions. Species interactions occur via shared spatio-temporal niches among species, which may be determined by natural light levels. We review how ALAN is altering these spatio-temporal niches through expanding twilight or full Moon conditions and constricting nocturnal conditions as well as creating patches of bright and dark. We review literature from a database to determine if ALAN is affecting species interactions via spatio-temporal dynamics. The literature indicates a growing interest in ALAN and species interactions: 58% of the studies we analysed have been published since 2020. Seventy-five of 79 studies found ALAN altered species interactions. Enhancements and reductions of species interactions were equally documented. Many studies revealed ALAN affecting species interactions spatially, but few revealed temporal alterations. There are biases regarding species interactions and ALAN-most studies investigated predator-prey interactions with vertebrates as predators and invertebrates as prey. Following this literature review, we suggest avenues, such as remote sensing and animal tracking, that can guide future research on the consequences of ALAN on species interactions across spatial and temporal axes. This article is part of the theme issue 'Light pollution in complex ecological systems'.

Atlantic cod individual spatial behaviour and stable isotope associations in a no-take marine reserve

Foraging is a behavioural process and, therefore, individual behaviour and diet are theorized to covary. However, few comparisons of individual behaviour type and diet exist in the wild. We tested whether behaviour type and diet covary in a protected population of Atlantic cod, Gadus morhua. Working in a no-take marine reserve, we could collect data on natural behavioural variation and diet choice with minimal anthropogenic disturbance. We inferred behaviour using acoustic telemetry and diet from stable isotope compositions (expressed as δ13 C and δ15 N values). We further investigated whether behaviour and diet could have survival costs. We found cod with shorter diel vertical migration distances fed at higher trophic levels. Cod δ13 C and δ15 N values scaled positively with body size. Neither behaviour nor diet predicted survival, indicating phenotypic diversity is maintained without survival costs for cod in a protected ecosystem. The links between diet and diel vertical migration highlight that future work is needed to understand whether the shifts in this behaviour during environmental change (e.g. fishing or climate), could lead to trophic cascades.

Arsenic pollution from human activities drives changes in soil microbial community characteristics

Soil arsenic (As) pollution not only decreases plant productivity but also soil quality, in turn hampering sustainable agricultural development. Despite the negative effects of As contamination on rice yield and quality being reported widely, the responses of microbial communities and co-occurrence networks in paddy soil to As pollution have not been explored. Here, based on high-throughput sequencing technologies, we investigated bacterial abundance and diversity in paddy soils with different levels of As contamination, and constructed associated microbial co-occurrence networks. As pollution reduced soil bacterial diversity significantly (p < 0.001). In addition, bioavailable As concentrations were negatively correlated with Actinobacteria and Acidobacteria relative abundance (p < 0.05). Conversely, As pollution had a positive relationship with Chloroflexi, Betaproteobacteria, and Bacteroidetes relative abundance (p < 0.05). Firmicutes relative abundance decreased with an increase in total As concentration. The ecological clusters and key groups in bacterial co-occurrence networks exhibited distinct trends with an increase in As pollution. Notably, Acidobacteria play an important role in maintaining microbial networks in As contaminated soils. Overall, we provide empirical evidence that As contamination influences soil microbial community structure, posing a threat to soil ecosystem health and sustainable agriculture.

Landscape diversity promotes stable food-web architectures in large rivers

Uncovering relationships between landscape diversity and species interactions is crucial for predicting how ongoing land-use change and homogenization will impact the stability and persistence of communities. However, such connections have rarely been quantified in nature. We coupled high-resolution river sonar imaging with annualized energetic food webs to quantify relationships among habitat diversity, energy flux, and trophic interaction strengths in large-river food-web modules that support the endangered Pallid Sturgeon. Our results demonstrate a clear relationship between habitat diversity and species interaction strengths, with more diverse foraging landscapes containing higher production of prey and a greater proportion of weak and potentially stabilizing interactions. Additionally, rare patches of large and relatively stable river sediments intensified these effects and further reduced interaction strengths by increasing prey diversity. Our findings highlight the importance of landscape characteristics in promoting stabilizing food-web architectures and provide direct relevance for future management of imperilled species in a simplified and rapidly changing world.

Changes in vertical and horizontal diversities mediated by the size structure of introduced fish collectively shape food-web stability

Species introductions can alter local food-web structure by changing the vertical or horizontal diversity within communities, largely driven by their body size distributions. Increasing vertical and horizontal diversities is predicted to have opposing effects on stability. However, their interactive effects remain largely overlooked. We investigated the independent and collective effects of vertical and horizontal diversities on food-web stability in alpine lakes stocked with variable body size distributions of introduced fish species. Introduced predators destabilize food-webs by increasing vertical diversity through food chain lengthening. Alternatively, increasing horizontal diversity results in more stable food-web topologies. A non-linear interaction between vertical and horizontal diversities suggests that increasing vertical diversity is most destabilizing when horizontal diversity is low. Our findings suggest that the size structure of introduced predators drives their impacts on stability by modifying the structure of food-webs, and highlights the interactive effects of vertical and horizontal diversities on stability.

Microbial interactions in theory and practice: when are measurements compatible with models?

Most predictive models of ecosystem dynamics are based on interactions between organisms: their influence on each other's growth and death. We review here how theoretical approaches are used to extract interaction measurements from experimental data in microbiology, particularly focusing on the generalised Lotka-Volterra (gLV) framework. Though widely used, we argue that the gLV model should be avoided for estimating interactions in batch culture - the most common, simplest and cheapest in vitro approach to culturing microbes. Fortunately, alternative approaches offer a way out of this conundrum. Firstly, on the experimental side, alternatives such as the serial-transfer and chemostat systems more closely match the theoretical assumptions of the gLV model. Secondly, on the theoretical side, explicit organism-environment interaction models can be used to study the dynamics of batch-culture systems. We hope that our recommendations will increase the tractability of microbial model systems for experimentalists and theoreticians alike.

Spatial variability of the Po River food web and its comparison with the Danube River food web

Freshwater ecosystems are experiencing unprecedented pressure globally. To address environmental challenges, systematic and comparative studies on ecosystems are needed, though mostly lacking, especially for rivers. Here, we describe the food web of the Po River (as integrated from the white literature and monitoring data), describe the three river sections using network analysis, and compare our results with the previously compiled Danube River food web. The Po River food web was taxonomically aggregated in five consecutive steps (T1-T5) and it was also analyzed using the regular equivalence (REGE) algorithm to identify structurally similar nodes in the most aggregated T5 model. In total, the two river food webs shared 30 nodes. Two network metrics (normalized degree centrality [nDC]) and normalized betweenness centrality [nBC]) were compared using Mann-Whitney tests in the two rivers. On average, the Po River nodes have larger nDC values than in the Danube, meaning that neighboring connections are better mapped. Regarding nBC, there were no significant differences between the two rivers. Finally, based on both centrality indices, Carassius auratus is the most important node in the Po River food web, whereas phytoplankton and detritus are most important in the Danube River. Using network analysis and comparative methods, it is possible to draw attention to important trophic groups and knowledge gaps, which can guide future research. These simple models for the Po River food web can pave the way for more advanced models, supporting quantitative and predictive-as well as more functional-descriptions of ecosystems.

Colonization-competition dynamics of basal species shape food web complexity in island metacommunities

Exploring how food web complexity emerges and evolves in island ecosystems remains a major challenge in ecology. Food webs assembled from multiple islands are commonly recognized as highly complex trophic networks that are dynamic in both space and time. In the context of global climate change, it remains unclear whether food web complexity will decrease in a monotonic fashion when undergoing habitat destruction (e.g., the inundation of islands due to sea-level rise). Here, we develop a simple yet comprehensive patch-dynamic framework for complex food web metacommunities subject to the competition-colonization tradeoff between basal species. We found that oscillations in food web topological complexity (characterized by species diversity, mean food chain length and the degree of omnivory) emerge along the habitat destruction gradient. This outcome is robust to changing parameters or relaxing the assumption of a strict competitive hierarchy. Having oscillations in food web complexity indicates that small habitat changes could have disproportionate negative effects on species diversity, thus the success of conservation actions should be evaluated not only on changes in biodiversity, but also on system robustness to habitat alteration. Overall, this study provides a parsimonious mechanistic explanation for the emergence of food web complexity in island ecosystems, further enriching our understanding of metacommunity assembly.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42995-023-00167-0.

Mismatches in thermal performance between ectothermic predators and prey alter interaction strength and top-down control

Climate change can alter predator-prey interactions when predators and prey have different thermal preferences as temperature change can exacerbate thermal mismatches (also called thermal asymmetry) with population-level consequences. We tested this using micro-arthropod predators (Stratiolaelaps scimitus) and prey (Folsomia candida) that differ in their temperature optima to examine predator-prey interactions across two temperature ranges, a cool (12 and 20 °C) and warm (20 and 26 °C) range. We predict that the lower thermal preference and optimum in F. candida will alter top-down control (i.e., interaction strength) by predators with interaction strength being strongest at intermediate temperatures, coinciding with F. candida thermal optimum. Predators and prey were placed in mesocosms, whereafter we measured population (predator and prey abundance), trait-based (average predator and prey body mass, and prey body length distribution), and predator-prey indices (predator-prey mass ratio (PPMR), Dynamic Index, and Log Response Ratio) to determine how temperature affected their interactions. Prey populations were the highest at intermediate temperatures (average temperature exposure: 16-23 °C) but declined at warmer temperatures (average temperature exposure: 24.5-26 °C). Predators consistently lowered prey abundances and average prey mass increased when predators were added. Top-down control was the greatest at intermediate temperatures (indicated by Log Response Ratio) when temperatures were near or below the thermal optimum for both species. Temperature-related prey declines negated top-down control under the warmest conditions suggesting that mismatches in thermal performance between predators and their prey will alter the strength and dominance of top-down or bottom-up forces of predator-prey interactions in a warmer world.

DNA High-Throughput Sequencing for Arthropod Gut Content Analysis to Evaluate Effectiveness and Safety of Biological Control Agents

The search for effective biological control agents without harmful non-target effects has been constrained by the use of impractical (field direct observation) or imprecise (cage experiments) methods. While advances in the DNA sequencing methods, more specifically the development of high-throughput sequencing (HTS), have been quickly incorporated in biodiversity surveys, they have been slow to be adopted to determine arthropod prey range, predation rate and food web structure, and critical information to evaluate the effectiveness and safety of a biological control agent candidate. The lack of knowledge on how HTS methods could be applied by ecological entomologists constitutes part of the problem, although the lack of expertise and the high cost of the analysis also are important limiting factors. In this review, we describe how the latest HTS methods of metabarcoding and Lazaro, a method to identify prey by mapping unassembled shotgun reads, can serve biological control research, showing both their power and limitations. We explain how they work to determine prey range and also how their data can be used to estimate predation rates and subsequently be translated into food webs of natural enemy and prey populations helping to elucidate their role in the community. We present a brief history of prey detection through molecular gut content analysis and also the attempts to develop a more precise formula to estimate predation rates, a problem that still remains. We focused on arthropods in agricultural ecosystems, but most of what is covered here can be applied to natural systems and non-arthropod biological control candidates as well.

The Effects of Smoking on Human Pharynx Microbiota Composition and Stability

The oral microbiota is essential to the health of the host, yet little is known about how it responds to disturbances. We examined the oropharyngeal microbiota of 30 individuals over 40 weeks. As the oropharynx is an important gateway to pathogens, and as smoking is associated with increased incidence and severity of respiratory infections, we compared the microbiota of smokers and nonsmokers to shed light on its potential for facilitating infections. We hypothesized that decreased species diversity, decreased community stability, or increased differences in community structure could facilitate invading pathogens. We found that smoking is associated with reduced alpha diversity, greater differences in community structure, and increased environmental filtering. The effects of short-term perturbations (antibiotic use and participants exhibiting cold symptoms) were also investigated. Antibiotic use had a negative effect on alpha diversity, irrespective of smoking status, and both antibiotic use and cold symptoms were associated with highly unique bacterial communities. A stability analysis of models built from the data indicated that there were no differences in local or global stability in the microbial communities of smokers, compared to nonsmokers, and that their microbiota are equally resistant to species invasions. Results from these models suggest that smoker microbiota are perturbed but characterized by alternative stable states that are as stable and invasion-resistant as are the microbiota of nonsmokers. Smoking is unlikely to increase the risk of infectious disease through the altered composition and ecological function of the microbiota; this is more likely due to the effects of smoking on the local and systemic immune system. IMPORTANCE Smoking is associated with an increased risk of respiratory infections. Hypothetically, the altered community diversity of smokers' pharyngeal microbiota, together with changes in their ecological stability properties, could facilitate their invasion by pathogens. To address this question, we analyzed longitudinal microbiota data of baseline healthy individuals who were either smokers or nonsmokers. While the results indicate reduced biodiversity and increased species turnover in the smokers' pharyngeal microbiota, their ecological stability properties were not different from those of the microbiota of nonsmokers, implying, in ecological terms, that the smokers' microbial communities are not less resistant to invasions. Therefore, the study suggests that the increased propensity of respiratory infections that is seen in smokers is more likely associated with changes in the local and systemic immune system than with ecological changes in the microbial communities.

Generalized Lotka-Volterra model with hierarchical interactions

In the analysis of complex ecosystems it is common to use random interaction coefficients, which are often assumed to be such that all species are statistically equivalent. In this work we relax this assumption by imposing hierarchical interspecies interactions. These are incorporated into a generalized Lotka-Volterra dynamical system. In a hierarchical community species benefit more, on average, from interactions with species further below them in the hierarchy than from interactions with those above. Using dynamic mean-field theory, we demonstrate that a strong hierarchical structure is stabilizing, but that it reduces the number of species in the surviving community, as well as their abundances. Additionally, we show that increased heterogeneity in the variances of the interaction coefficients across positions in the hierarchy is destabilizing. We also comment on the structure of the surviving community and demonstrate that the abundance and probability of survival of a species are dependent on its position in the hierarchy.

Impacts of extreme climatic events on trophic network complexity and multidimensional stability

Untangling the relationship between network complexity and ecological stability under climate change is an arduous challenge for theoretical and empirical ecology. Even more so, when considering extreme climatic events. Here, we studied the effects of extreme climatic events (heatwaves) on the complexity of realistic freshwater ecosystems using topological and quantitative trophic network metrics. Next, we linked changes in network complexity with the investigation of four stability components (temporal stability, resistance, resilience, and recovery) of community's functional, compositional, and energy flux stability. We found reduction in topological network complexity to be correlated with reduction of functional and compositional resistance. However, temperature-driven increase in link-weighted network complexity increased functional and energy flux recovery and resilience, but at the cost of increased compositional instability. Overall, we propose an overarching approach to elucidate the effects of climate change on multidimensional stability through the lens of network complexity, providing helpful insights for preserving ecosystems stability under climate change.

The Stability of Phyto-Zooplanktonic Networks Varied with Zooplanktonic Sizes in Chinese Coastal Ecosystem

The linkages between phytoplankton and zooplankton are crucial for the stability of complex food webs and the flow of energy within the marine ecosystem. Despite body size exhibiting multiple effects on the planktonic community assembly and the dispersal scale, its role in determining the stability of phyto-zooplanktonic co-occurrence patterns remains unclear. Here, we focused on more than 13,000 kilometers of the Chinese coast to study the diatom-dominated plankton ecosystem and to report the significant negative effects of zooplanktonic body sizes on the topological properties of phyto-zooplanktonic networks (PZNs) by using more than 500 species from 251 samples taken along the coastline. PZNs tended to be more complex and stable when phytoplankton associated with smaller zooplankton. Particularly, the subnetworks of dominant phytoplankton displayed differences with different zooplanktonic body sizes. The zooplankton with larger and smaller body sizes tended to interact with dinoflagellates and diatoms, respectively. Additionally, abiotic factors (i.e., water temperature, pH, salinity, and metal concentrations) displayed significant effects on PZNs via the shifting of zooplanktonic composition, and the zooplanktonic body sizes altered the network modules' associations with different environmental factors. Our study elucidated the general relationship between zooplanktonic body sizes and the stability of PZNs, which provides new insights into marine food webs. IMPORTANCE Body size is a key life trait of aquatic plankton that affects organisms' metabolic rates and ecological functions; however, its specific effects on interactions between phytoplankton and zooplankton are poorly understood. We collected planktonic species and their body size data along more than 13,000 kilometers of coastline to explore the role of zooplanktonic body size in maintaining the stability of phyto-zooplanktonic networks (PZNs). We found that zooplankton play a more important role in maintaining PZN stability than do phytoplankton as well as that the PZN would be more complex and stable with smaller zooplankton. Furthermore, this work revealed that body size significantly determined the relationships between environmental factors and network structure. Overall, these findings lay a general relationship between zooplanktonic body sizes and the stability of PZNs, which helps us further explore the micro food web of coastal ecosystems.

Comparison of three modelling frameworks for aquatic ecosystems: practical aspects and applicability

Freshwater ecosystems are under multiple stressors and it is crucial to find methods to better describe, manage, and sustain aquatic ecosystems. Ecosystem modelling has become an important tool in integrating trophic relationships into food webs, assessing important nodes using network analysis, and making predictions via simulations. Fortunately, several modelling techniques exist, but the question is which approach is relevant and applicable when? In this study, we compare three modelling frameworks (Ecopath, Loop Analysis in R, STELLA software) using a case study of a small aquatic network (8 nodes). The choice of framework depends on the research question and data availability. We approach this topic from a methodological aspect by describing the data requirements and by comparing the applicability and limitations of each modelling approach. Each modelling framework has its specific focus, but some functionalities and outcomes can be compared. The predictions of Loop Analysis as compared to Ecopath’s Mixed Trophic Impact plot are in good agreement at the top and bottom trophic levels, but the middle trophic levels are less similar. This suggests that further comparisons are needed of networks of varying resolution and size. Generally, when data are limiting, Loop Analysis can provide qualitative predictions, while the other two methods provide quantitative results, yet rely on more data.

Extinction of biotic interactions due to habitat loss could accelerate the current biodiversity crisis

Habitat loss disrupts species interactions through local extinctions, potentially orphaning species that depend on interacting partners, via mutualisms or commensalisms, and increasing secondary extinction risk. Orphaned species may become functionally or secondarily extinct, increasing the severity of the current biodiversity crisis. While habitat destruction is a major cause of biodiversity loss, the number of secondary extinctions is largely unknown. We investigate the relationship between habitat loss, orphaned species, and bipartite network properties. Using a real seed dispersal network, we simulate habitat loss to estimate the rate at which species are orphaned. To be able to draw general conclusions, we also simulate habitat loss in synthetic networks to quantify how changes in network properties affect orphan rates across broader parameter space. Both real and synthetic network simulations show that even small amounts of habitat loss can cause up to 10% of species to be orphaned. More area loss, less connected networks, and a greater disparity in the species richness of the network's trophic levels generally result in more orphaned species. As habitat is lost to land-use conversion and climate change, more orphaned species increase the loss of community-level and ecosystem functions. However, the potential severity of repercussions ranges from minimal (no species orphaned) to catastrophic (up to 60% of species within a network orphaned). Severity of repercussions also depends on how much the interaction richness and intactness of the community affects the degree of redundancy within networks. Orphaned species could add substantially to the loss of ecosystem function and secondary extinction worldwide.

Forecasting in the face of ecological complexity: Number and strength of species interactions determine forecast skill in ecological communities

The potential for forecasting the dynamics of ecological systems is currently unclear, with contrasting opinions regarding its feasibility due to ecological complexity. To investigate forecast skill within and across systems, we monitored a microbial system exposed to either constant or fluctuating temperatures in a 5-month-long laboratory experiment. We tested how forecasting of species abundances depends on the number and strength of interactions and on model size (number of predictors). We also tested how greater system complexity (i.e. the fluctuating temperatures) impacted these relations. We found that the more interactions a species had, the weaker these interactions were and the better its abundance was predicted. Forecast skill increased with model size. Greater system complexity decreased forecast skill for three out of eight species. These insights into how abundance prediction depends on the connectedness of the species within the system and on overall system complexity could improve species forecasting and monitoring.

Food webs for three burn severities after wildfire in the Eldorado National Forest, California

Wildfire dynamics are changing around the world and understanding their effects on ecological communities and landscapes is urgent and important. We report detailed food webs for unburned, low-to-moderate and high severity burned habitats three years post-fire in the Eldorado National Forest, California. The cumulative cross-habitat food web contains 3,084 ontogenetic stages (nodes) or plant parts comprising 849 species (including 107 primary producers, 634 invertebrates, 94 vertebrates). There were 178,655 trophic interactions between these nodes. We provide information on taxonomy, body size, biomass density and trophic interactions under each of the three burn conditions. We detail 19 sampling methods deployed across 27 sites (nine in each burn condition) used to estimate the richness, body size, abundance and biomass density estimates in the node lists. We provide the R code and raw data to estimate summarized node densities and assign trophic links.

Functional Responses Shape Node and Network Level Properties of a Simplified Boreal Food Web

Ecological communities are fundamentally connected through a network of trophic interactions that are often complex and difficult to model. Substantial variation exists in the nature and magnitude of these interactions across various predators and prey and through time. However, the empirical data needed to characterize these relationships are difficult to obtain in natural systems, even for relatively simple food webs. Consequently, prey-dependent relationships and specifically the hyperbolic form (Holling’s Type II), in which prey consumption increases with prey density but ultimately becomes saturated or limited by the time spent handling prey, are most widely used albeit often without knowledge of their appropriateness. Here, we investigate the sensitivity of a simplified food web model for a natural, boreal system in the Kluane region of the Yukon, Canada to the type of functional response used. Intensive study of this community has permitted best-fit functional response relationships to be determined, which comprise linear (type I), hyperbolic (type II), sigmoidal (type III), prey- and ratio-dependent relationships, and inverse relationships where kill rates of alternate prey are driven by densities of the focal prey. We compare node- and network-level properties for a food web where interaction strengths are estimated using best-fit functional responses to one where interaction strengths are estimated exclusively using prey-dependent hyperbolic functional responses. We show that hyperbolic functional responses alone fail to capture important ecological interactions such as prey switching, surplus killing and caching, and predator interference, that in turn affect estimates of cumulative kill rates, vulnerability of prey, generality of predators, and connectance. Exclusive use of hyperbolic functional responses also affected trends observed in these metrics over time and underestimated annual variation in several metrics, which is important given that interaction strengths are typically estimated over relatively short time periods. Our findings highlight the need for more comprehensive research aimed at characterizing functional response relationships when modeling predator-prey interactions and food web structure and function, as we work toward a mechanistic understanding linking food web structure and community dynamics in natural systems.

Nested structure is dependent on visitor sex in the flower‒visitor networks in Kyoto, Japan

The characteristics of flower‒visitor networks, comprised of multiple species interacting with each other, predict ecological and evolutionary processes. Intraspecific and interspecific variations in interaction patterns should affect network structures. Because female and male visitors usually differ in flower-visiting patterns due to mating strategy, visitor sex should affect nestedness, in which specialist species interact with a subset of species that interact with generalist species. I hypothesized that a network of male visitors and flowering plants would be more nested than a female network because males are less picky about which flowers they visit. To examine the effect of visitor sex on nestedness, I used museum specimens of insects and built 11 flower-visitor species networks, each composed of female and male subnetworks, and compared the strength of nestedness and related network metrics between the subnetworks. I found that male subnetworks were significantly more nested than female ones, and species networks were less nested than male or female subnetworks. The result may be attributable to the by-chance selection of flowers by males. Because a nested structure is predicted to promote community stability in mutualistic flower-visitor networks, the greater nestedness of male subnetworks may suggest a positive effect of male visitors on pollination community stability.

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.

The Dimensions and Units of the Population Interaction Coefficients

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.

Impacts of environmental factors on the food web structure, energy flows, and system attributes along a subtropical urban river in southern China

Tropical and subtropical rivers are being subjected to multiple stressors from human disturbance (e.g., water pollution and habitat degradation). Understanding the relationship between environmental conditions and the river ecosystem is important for improving river management. We built 14 Ecopath models composed of 28 functional groups (trophic levels [TLs] of 1.0-3.8) along a subtropical urban river to explore the influence of environmental changes on system attributes. From headwaters to downstream, the model outputs showed that the transfer efficiency (TE), energy flow parameters, and ecosystem theory indices exhibited significant (P < 0.05) differences across a longitudinal gradient of disturbance, indicating heterogeneous attributes of local river segments. The high TE values of TLs I, II, and III separated the upper, middle, and lower reaches, respectively, which could be attributed to the shift in dominant consumption flows from upstream 'periphyton - aquatic insects - insectivorous fish' to midstream 'detritus - shrimp - crustaceavorous fish' and to downstream 'phytoplankton - filter-feeding invertebrates/fish'. Structural equation modelling was used to test the causal relationships among environmental variables and demonstrated that abiotic factors directly influenced biomass composition and indirectly influenced trophic networks. Water quality, including dissolved oxygen and flow velocity; habitat characteristics, such as riffles, cobble-gravel substrate, and seasonal floodplain; and biological indicators, including the relative contributions (%) of decapods, insectivorous fish, and insect scrapers to biomass composition, had significant (P < 0.05) positive impacts on system maturity (evaluated by omnivory, connectance, and cycling indices). In the future, it will be possible to evaluate the health of river ecosystems by monitoring representative environmental factors, which could be a cost-effective approach to system-level improvement.

A roadmap towards predicting species interaction networks (across space and time)

Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species-and to describe the structure, variation, and change of the ecological networks they form-we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Predators balance consequences of climate-change-induced habitat shifts for range-shifting and resident species

While many species distributions are shifting poleward or up in elevation in response to a changing climate, others are shifting their habitats along localized gradients in environmental conditions as abiotic conditions become more stressful. Whether species are moving across regional or local environmental gradients in response to climate change, range-shifting species become embedded in established communities of competitors and predators. The consequences of these shifts for both resident and shifting species are often unknown, as it can be difficult to isolate the effects of multiple species interactions. Using a model system of insects in high-elevation ponds in the Rocky Mountains of Colorado, we sought to disentangle the effects of predation and intraguild interactions on the survival and development of a semi-permanent pond resident caddisfly Limnephilus externus and the habitat-shifting caddis Asynarchus nigriculus that is being forced into semi-permanent ponds as temporary ponds dry too quickly to complete development. We conducted a manipulative in-situ pond cage experiment in which L. externus and A. nigriculus caddisfly larvae in single-species treatments and together were exposed to the presence/absence of predatory Dytiscus diving beetle larvae. This approach allowed us to isolate the effects of intraguild interactions and predation on the survival and development of both the resident and habitat-shifting species. We found that intraguild interactions had strong negative effects on the resident and habitat-shifting species. Intraguild interactions reduced the survival of the resident L. externus and increased the variation in survival of the shifting A. nigriculus. However, Dytiscus predators reduced these negative effects, stabilizing the community by increasing L. externus survival and reducing variation in A. nigriculus survival. We also found that intraguild interactions reduced L. externus biomass but resulted in increased A. nigriculus development. A. nigriculus development was also increased by predation. Our results show that strong intraguild interactions between resident and shifting species are likely to have negative consequences for both species. However, the presence of predators reduces these negative consequences of the habitat shift on both the resident and the shifting.

Interaction networks between solitary hymenopterans and their natural enemies in different restoration areas

The diversity of species and their interactions have been positively related with environmental complexity. Therefore, highly anthropized environments have their integrity under serious threat. These effects may last for years compromising the dynamics of natural communities, such as antagonistic and mutualistic interactions, including host-natural enemy interactions. To investigate these effects, trap nest methodology was used to assess the diversity of solitary bees, wasps and their natural enemies in three fragmented environments with different degree of anthropic perturbation, composed of a Eucalyptus plantation (considered here as higher perturbation), a Cerrado area (medium perturbation) and a Riparian forest (lesser perturbation). Then, host-natural enemies associations were analysed to verify the size, specialization degree and modularity of interaction network. The gradient from highest to lowest degree of anthropic perturbation was evidenced in the species diversity index, the size of the interaction network and the specialization indexes of the host-natural enemy network. The environment with Eucalyptus plantation showed higher values of diversity of natural enemies, greater number of species in the interaction network, lesser degree of specialization in the interaction and lesser modularity, than Cerrado and Riparian forest environments, respectively. The low degree of nestedness and lack of significance of this index to all sampled areas are indicative of a specialized pattern of networks. The results corroborate the notion that human impact may affect interaction networks, this being an important tool for checking the degree of anthropic alteration.

Reconstructing large interaction networks from empirical time series data

Reconstructing interactions from observational data is a critical need for investigating natural biological networks, wherein network dimensionality is usually high. However, these pose a challenge to existing methods that can quantify only small interaction networks. Here, we proposed a novel approach to reconstruct high-dimensional interaction Jacobian networks using empirical time series without specific model assumptions. This method, named "multiview distance regularised S-map," generalised the state space reconstruction to accommodate high dimensionality and overcome difficulties in quantifying massive interactions with limited data. When evaluating this method using time series generated from theoretical models involving hundreds of interacting species, estimated strengths of interaction Jacobians were in good agreement with theoretical expectations. Applying this method to a natural bacterial community helped identify important species from the interaction network and revealed mechanisms governing the dynamical stability of a bacterial community. The proposed method overcame the challenge of high dimensionality in large natural dynamical systems.

Inferring species interactions from ecological survey data: A mechanistic approach to predict quantitative food webs of seed feeding by carabid beetles

Ecological networks are valuable for ecosystem analysis but their use is often limited by a lack of data because many types of ecological interaction, for example, predation, are short-lived and difficult to observe or detect. While there are different methods for inferring the presence of interactions, they have rarely been used to predict the interaction strengths that are required to construct weighted, or quantitative, ecological networks.Here, we develop a trait-based approach suitable for inferring weighted networks, that is, with varying interaction strengths. We developed the method for seed-feeding carabid ground beetles (Coleoptera: Carabidae) although the principles can be applied to other species and types of interaction.Using existing literature data from experimental seed-feeding trials, we predicted a per-individual interaction cost index based on carabid and seed size. This was scaled up to the population level to create inferred weighted networks using the abundance of carabids and seeds from empirical samples and energetic intake rates of carabids from the literature. From these weighted networks, we also derived a novel measure of expected predation pressure per seed type per network.This method was applied to existing ecological survey data from 255 arable fields with carabid data from pitfall traps and plant seeds from seed rain traps. Analysis of these inferred networks led to testable hypotheses about how network structure and predation pressure varied among fields.Inferred networks are valuable because (a) they provide null models for the structuring of food webs to test against empirical species interaction data, for example, DNA analysis of carabid gut regurgitates and (b) they allow weighted networks to be constructed whenever we can estimate interactions between species and have ecological census data available. This permits ecological network analysis even at times and in places when interactions were not directly assessed.

Theory of temperature-dependent consumer-resource interactions

Changes in temperature affect consumer-resource interactions, which underpin the functioning of ecosystems. However, existing studies report contrasting predictions regarding the impacts of warming on biological rates and community dynamics. To improve prediction accuracy and comparability, we develop an approach that combines sensitivity analysis and aggregate parameters. The former determines which biological parameters impact the community most strongly. The use of aggregate parameters (i.e., maximal energetic efficiency, ρ, and interaction strength, κ), that combine multiple biological parameters, increases explanatory power and reduces the complexity of theoretical analyses. We illustrate the approach using empirically derived thermal dependence curves of biological rates and applying it to consumer-resource biomass ratio and community stability. Based on our analyses, we generate four predictions: (1) resource growth rate regulates biomass distributions at mild temperatures, (2) interaction strength alone determines the thermal boundaries of the community, (3) warming destabilises dynamics at low and mild temperatures only and (4) interactions strength must decrease faster than maximal energetic efficiency for warming to stabilise dynamics. We argue for the potential benefits of directly working with the aggregate parameters to increase the accuracy of predictions on warming impacts on food webs and promote cross-system comparisons.

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.

Phenological traits foster persistence of mutualistic networks by promoting facilitation

Morphological and phenological traits are key determinants of the structure of mutualistic networks. Both traits create forbidden links, but phenological traits can also decouple interaction in time. While such difference likely affects the indirect effects among species and consequently network persistence, it remains overlooked. Here, using a dynamic model, we show that networks structured by phenology favour facilitation over competition within guilds of pollinators and plants, thereby increasing network persistence, while the contrary holds for networks structured by morphology. We further show that such buffering of competition by phenological traits mostly beneficiate to specialists, the most vulnerable species otherwise, which propagate the most positive effects within guilds and promote nestedness. Our results indicate that beyond trophic mismatch, phenological shifts such as those induced by climate change are likely to affect indirect effects within mutualistic assemblages, with consequences for biodiversity.