Ecosystem function in predator-prey food webs-confronting dynamic models with empirical data

A new mechanistic model for individual growth applied to insects under ad libitum conditions

Metabolic theories in ecology interpret ecological patterns at different levels through the lens of metabolism, typically applying allometric power scaling laws to describe rates of energy use. This requires a sound theory for metabolism at the individual level. Commonly used mechanistic growth models lack some potentially important aspects and fail to accurately capture a growth pattern often observed in insects. Recently, a new model (MGM-the Maintenance-Growth Model) was developed for ontogenetic and post-mature growth, based on an energy balance that expresses growth as the net result of assimilation and metabolic costs for maintenance and feeding. The most important contributions of MGM are: 1) the division of maintenance costs into a non-negotiable and a negotiable part, potentially resulting in maintenance costs that increase faster than linearly with mass and are regulated in response to food restriction; 2) differentiated energy allocation strategies between sexes and 3) explicit description of costs for finding and processing food. MGM may also account for effects of body composition and type of growth at the cellular level. The model was here calibrated and evaluated using empirical data from an experiment on house crickets growing under ad libitum conditions. The procedure involved parameter estimations from the literature and collected data, using statistical models to account for individual variation in parameter values. It was found that ingestion rate cannot be generally described by a simple allometry, here requiring a more complex description after maturity. Neither could feeding costs be related to ingestion rate in a simplistic manner. By the unusual feature of maintenance costs increasing faster than linearly with body mass, MGM could well capture the differentiated growth patterns of male and female crickets. Some other mechanistic growth models have been able to provide good predictions of insect growth during early ontogeny, but MGM may accurately describe the trajectory until terminated growth.

Different patterns of coupled predator-prey dynamics when the same species interact in different locations

Individual predator and prey species exhibit coupled population dynamics in simple laboratory systems and simple natural communities. It is unclear how often such pairwise coupling occurs in more complex communities, in which an individual predator species might feed on several prey species and an individual prey species might be attacked by several predators. To examine this problem, we applied multivariate autoregressive state-space (MARSS) models to 5-year time-series of monthly surveys of a predatory fish, the eastern mosquitofish (Gambusia holbrooki), and its littoral zone prey species, the least killifish (Heterandria formosa), in three locations in north Florida. The MARSS models were consistent with coupled predator-prey dynamics at two of the three locations. In one of these two locations, the estimated densities of the two species displayed classic predator-prey oscillations. In the third location, there was a positive effect of killifish density on mosquitofish density but no detectable effect of mosquitofish density on killifish density. In all three locations, increased submergent vegetation cover was associated with increased prey density but not increased predator density. Eigenvalues analyses for the joint predator-prey dynamics indicated that one of the cyclic locations had more stable dynamics than the other locations. The three different patterns demonstrate that the dynamics of a pairwise predator-prey interaction emerge not only from the characteristics of the prey and the predator, but also those of the habitat and trophic web in which the predator and prey are embedded.

Flexible foraging behaviour increases predator vulnerability to climate change

Higher temperatures are expected to reduce species coexistence by increasing energetic demands. However, flexible foraging behaviour could balance this effect by allowing predators to target specific prey species to maximize their energy intake, according to principles of optimal foraging theory. Here we test these assumptions using a large dataset comprising 2,487 stomach contents from six fish species with different feeding strategies, sampled across environments with varying prey availability over 12 years in Kiel Bay (Baltic Sea). Our results show that foraging shifts from trait- to density-dependent prey selectivity in warmer and more productive environments. This behavioural change leads to lower consumption efficiency at higher temperature as fish select more abundant but less energetically rewarding prey, thereby undermining species persistence and biodiversity. By integrating this behaviour into dynamic food web models, our study reveals that flexible foraging leads to lower species coexistence and biodiversity in communities under global warming.

Impact of the Parasitoids Anisopteromalus calandrae (Howard) and Lariophagus distinguendus (Förster) on Three Pests of Stored Rice

This study evaluated the ability of pteromalid parasitoids Anisopteromalus calandrae and Lariophagus distinguendus reared on Sitophilus zeamais to control stored product coleopteran pests Sitophilus oryzae, Rhyzopertha dominica and Lasioderma serricorne. In trials of parasitoid treatment with A. calandrae, fewer pests (S. oryzae and R. dominica) emerged than in the control. Parasitoid reproduction was highest with S. oryzae as a host, followed by R. dominica and L. serricorne. In trials of parasitoid treatment with L. distinguendus, fewer pests (S. oryzae, R. dominica and L. serricorne) emerged than in the control treatment. Sitophilus oryzae was the host with the highest rate of parasitoid reproduction, although the greatest level of reduction was seen in R. dominica (i.e., host feeding levels were higher for this host species). For L. serricorne, no L. distinguendus progeny was produced. For both species, parasitoids with significantly longer bodies and tibiae emerged from S. oryzae. These results suggest that both parasitoids have potential for use as biocontrol agents for different coleopteran species that attack stored rice.

A bioenergetic framework for aboveground terrestrial food webs

Bioenergetic approaches have been greatly influential for understanding community functioning and stability and predicting effects of environmental changes on biodiversity. These approaches use allometric relationships to establish species' trophic interactions and consumption rates and have been successfully applied to aquatic ecosystems. Terrestrial ecosystems, where body mass is less predictive of plant-consumer interactions, present inherent challenges that these models have yet to meet. Here, we discuss the processes governing terrestrial plant-consumer interactions and develop a bioenergetic framework integrating those processes. Our framework integrates bioenergetics specific to terrestrial plants and their consumers within a food web approach while also considering mutualistic interactions. Such a framework is poised to advance our understanding of terrestrial food webs and to predict their responses to environmental changes.

Temperature fluctuation alters optimal predator community composition for anticipated biological control

Alongside pesticides and specialist predators, natural communities of generalist beetle and spider predators play an important role in suppressing agricultural pests. However, the predation pressure of natural communities can be unpredictable. Overall predation pressure is influenced by a dense network of potential intraguild interactions, which are further shaped by species traits and environmental factors. Understanding how these different influences combine to impact pest control is especially important in the context of changing global temperatures. Recent empirical studies have demonstrated that the foraging behavior of arthropod predators is influenced by an interaction between temperature and predator body size. To explore the consequences of these findings for intraguild interactions and pest control, we expand a previously published model describing interactions between arthropod predators and a pest population. The model assumed that interaction strengths are influenced by body size and habitat preference. In our updated model, we incorporate the effect of temperature on predator foraging activity. We parameterize the model to match empirically observed predator community composition in 10 agricultural fields and use simulations to demonstrate how temperature-dependent behaviors change the expected efficiency of the natural predator community. Then, we use an optimization approach to identify the most efficient composition of natural predators for pest control. We then evaluate whether the most efficient predator compositions would change with increasing average daily temperature and its variability, as is expected under future temperature change. We find that optimal communities often include predators with complementary foraging activity and that in 2 fields, the optimal community changes drastically under future temperatures. We also note that at some temperatures, foraging activity reduces the negative effects of intraguild interference on pest consumption. This work allows us to assess the effect of climate change on the efficiency of natural predator communities to control pest populations and provide guidance for farmers to design pest management strategies tailored to different climate scenarios.

Archetype models upscale understanding of natural pest control response to land-use change

Control of crop pests by shifting host plant availability and natural enemy activity at landscape scales has great potential to enhance the sustainability of agriculture. However, mainstreaming natural pest control requires improved understanding of how its benefits can be realized across a variety of agroecological contexts. Empirical studies suggest significant but highly variable responses of natural pest control to land-use change. Current ecological models are either too specific to provide insight across agroecosystems or too generic to guide management with actionable predictions. We suggest obtaining the full benefit of available empirical, theoretical, and methodological knowledge by combining trait-mediated understanding from correlative studies with the explicit representation of causal relationships achieved by mechanistic modeling. To link these frameworks, we adapt the concept of archetypes, or context-specific generalizations, from sustainability science. Similar responses of natural pest control to land-use gradients across cases that share key attributes, such as functional traits of focal organisms, indicate general processes that drive system behavior in a context-sensitive manner. Based on such observations of natural pest control, a systematic definition of archetypes can provide the basis for mechanistic models of intermediate generality that cover all major agroecosystems worldwide. Example applications demonstrate the potential for upscaling understanding and improving predictions of natural pest control, based on knowledge transfer and scientific synthesis. A broader application of this mechanistic archetype approach promises to enhance ecology's contribution to natural resource management across diverse regions and social-ecological contexts.

Beyond body size—new traits for new heights in trait-based modelling of predator-prey dynamics

Food webs map feeding interactions among species, providing a valuable tool for understanding and predicting community dynamics. Using species’ body sizes is a promising avenue for parameterizing food-web models, but such approaches have not yet been able to fully recover observed community dynamics. Such discrepancies suggest that traits other than body size also play important roles. For example, differences in species’ use of microhabitat or non-consumptive effects of intraguild predators may affect dynamics in ways not captured by body size. In Laubmeier et al. (2018), we developed a dynamic food-web model incorporating microhabitat and non-consumptive predator effects in addition to body size, and used simulations to suggest an optimal sampling design of a mesocosm experiment to test the model. Here, we perform the mesocosm experiment to generate empirical time-series of insect herbivore and predator abundance dynamics. We minimize least squares error between the model and time-series to determine parameter values of four alternative models, which differ in terms of including vs excluding microhabitat use and non-consumptive predator-predator effects. We use both statistical and expert-knowledge criteria to compare the models and find including both microhabitat use and non-consumptive predator-predator effects best explains observed aphid and predator population dynamics, followed by the model including microhabitat alone. This ranking suggests that microhabitat plays a larger role in driving population dynamics than non-consumptive predator-predator effects, although both are clearly important. Our results illustrate the importance of additional traits alongside body size in driving trophic interactions. They also point to the need to consider trophic interactions and population dynamics in a wider community context, where non-trophic impacts can dramatically modify the interplay between multiple predators and prey. Overall, we demonstrate the potential for utilizing traits beyond body size to improve trait-based models and the value of iterative cycling between theory, data and experiment to hone current insights into how traits affect food-web dynamics.

Towards Predictions of Interaction Dynamics between Cereal Aphids and Their Natural Enemies: A Review

(1) Although most past studies are based on static analyses of the pest regulation drivers, evidence shows that a greater focus on the temporal dynamics of these interactions is urgently required to develop more efficient strategies. (2) Focusing on aphids, we systematically reviewed (i) empirical knowledge on the drivers influencing the dynamics of aphid-natural enemy interactions and (ii) models developed to simulate temporal or spatio-temporal aphid dynamics. (3) Reviewed studies mainly focus on the abundance dynamics of aphids and their natural enemies, and on aphid population growth rates. The dynamics of parasitism and predation are rarely measured empirically, although it is often represented in models. Temperature is mostly positively correlated with aphid population growth rates. Plant phenology and landscape effects are poorly represented in models. (4) We propose a research agenda to progress towards models and empirical knowledge usable to design effective CBC strategies. We claim that crossover works between empirical and modeling community will help design new empirical settings based on simulation results and build more accurate and robust models integrating more key drivers of aphid dynamics. Such models, turned into decision support systems, are urgently needed by farmers and advisors in order to design effective integrated pest management.

Molecular assays to reliably detect and quantify predation on a forest pest in bats faeces

Targeted molecular methods such as conventional PCR (cPCR) and quantitative PCR (qPCR), combined with species-specific primers and probes, are widely applied for pest species detection. Besides, the potential of qPCR to quantify DNA in samples makes it an invaluable molecular tool to infer the predation levels on specific prey by analysing predators’ stools. Nevertheless, studies on the diet of bats failed to find any empirical relationship, and it remains to be evaluated. Thus, we developed and evaluated two species-specific PCR assays to detect and quantify DNA of a major forest pest, the pine processionary, Thaumetopoea pityocampa, in bats’ faeces. Further, we empirically compared a range of different known DNA concentrations (input) of the target species mixed with mocks and bat faecal samples against DNA abundances yielded by qPCR (output) for a quantitative assessment. Overall, cPCR showed a lower detection rate than qPCR, but augmenting the replicate effort from one to three replicates led to a greater increase in the detection rate of the cPCR (from 57 to 80%) than the qPCR (from 90 to 99%). The quantitative experiment results showed a highly significant correlation between the input and output DNA concentrations (t = 10.84, p < 0.001) with a mean slope value of 1.05, indicating the accuracy of our qPCR assay to estimate DNA abundance of T. pityocampa in bat faeces. The framework of this study can be taken as a model to design similar assays applicable to other species of interest, such as agricultural pests or insects of public health concern.

Wood ants as biological control of the forest pest beetles Ips spp

Climate change is one of the major threats to biodiversity, but its impact varies among the species. Bark beetles (Ips spp.), as well as other wood-boring pests of European forests, show escalating numbers in response to the changes driven by climate change and seriously affect the survival of the forests through the massive killing of trees. Many methods were developed to control these wood-boring beetles, however, their implementation can be detrimental for other forest specialists. Ants are widely used for biological pest-control, so in our study, we aimed to test the effect of Formica polyctena on the control of the wood-boring beetles. The results show that the proportion of infested trees is significantly reduced by the increase of the number of F. polyctena nests, with a strong effect on those infested by Ips species. We also show that the boring beetle community is shaped by different biotic and abiotic factors, including the presence of F. polyctena nests. However, the boring beetle infestation was not related to the latitude, altitude and age of the forests. Based on our results, we assert the effectiveness of the red wood ants as biological pest control and the importance of their conservation to keep the health of the forests.

Linking functional traits and demography to model species-rich communities

It has long been anticipated that relating functional traits to species demography would be a cornerstone for achieving large-scale predictability of ecological systems. If such a relationship existed, species demography could be modeled only by measuring functional traits, transforming our ability to predict states and dynamics of species-rich communities with process-based community models. Here, we introduce a new method that links empirical functional traits with the demographic parameters of a process-based model by calibrating a transfer function through inverse modeling. As a case study, we parameterize a modified Lotka–Volterra model of a high-diversity mountain grassland with static plant community and functional trait data only. The calibrated trait–demography relationships are amenable to ecological interpretation, and lead to species abundances that fit well to the observed community structure. We conclude that our new method offers a general solution to bridge the divide between trait data and process-based models in species-rich ecosystems.

Advances in process-based community ecology models are hindered by the challenge of linking functional traits to demography in species-rich systems, where a high number of parameters need to be estimated from limited data. Here the authors propose a new Bayesian framework to calibrate community models via functional traits, and validate it in a species-rich plant community.

Fitting stochastic predator-prey models using both population density and kill rate data

Most mechanistic predator-prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road: we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator-prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i.e., a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability. The Fisher Information Matrix suggests that models with and without kill rate data are all identifiable, although correlations remain between parameters that belong to the same functional form. However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood. Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data - including in small amounts - can make the estimates much more precise. Overall, we show that under process stochasticity in interaction rates, interaction data might be essential to obtain identifiable dynamical models for multiple species. These results may extend to other biotic interactions than predation, for which similar models combining interaction rates and population counts could be developed.

A guide to ecosystem models and their environmental applications

Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm - managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems.

From winter to summer and back: Lessons from the parameterization of a seasonal food web model for the Białowieża forest

Dynamic food web models describe how species abundances change over time as a function of trophic and life-history parameters. They are essential to predicting the response of ecosystems to perturbations. However, they are notoriously difficult to parameterize, so that most models rely heavily either on allometric scaling of parameters or inverse estimation of biomass flows. The allometric approach makes species of comparable body mass have near-identical parameters which can generate extinctions within a trophic level. The biomass flow approach is more precise, but is restricted to steady-states, which is not appropriate for time-varying environments. Adequately parameterizing large food webs of temperate and arctic environments requires dealing both with many species of similar sizes and a strongly seasonal environment. Inspired by the rich empirical knowledge on the vertebrate food web of the Białowieża forest, we parameterize a bipartite food web model comprising 21 predators and 124 prey species. Our model is a non-autonomous coupled ordinary differential equations system that allows for seasonality in life-history and predation parameters. Birth and death rates, seasonal descriptions of diet for each species, food requirements and biomass information are combined into a seasonal parameterization of a dynamic food web model. Food web seasonality is implemented with time-varying intrinsic growth rate and interaction parameters, while predation is modelled with both type I and type II functional responses. All our model variants allow for >80% persistence in spite of massive apparent competition, and a quantitative match to observed (seasonal) biomasses. We also identify trade-offs between maximizing persistence, reproducing observed biomasses, and ensuring model robustness to sampling errors. Although multi-annual cycles are expected with type II functional responses, they are here prevented by a strong predator self-regulation. We discuss these results and possible improvements on the model. We provide a general workflow to parameterize dynamic food web models in seasonal environments, based on a real case study. This may help to better predict how biodiverse food webs respond to changing environments.

Community dynamics and sensitivity to model structure: towards a probabilistic view of process-based model predictions

Statistical inference and mechanistic, process-based modelling represent two philosophically different streams of research whose primary goal is to make predictions. Here, we merge elements from both approaches to keep the theoretical power of process-based models while also considering their predictive uncertainty using Bayesian statistics. In environmental and biological sciences, the predictive uncertainty of process-based models is usually reduced to parametric uncertainty. Here, we propose a practical approach to tackle the added issue of structural sensitivity, the sensitivity of predictions to the choice between quantitatively close and biologically plausible models. In contrast to earlier studies that presented alternative predictions based on alternative models, we propose a probabilistic view of these predictions that include the uncertainty in model construction and the parametric uncertainty of each model. As a proof of concept, we apply this approach to a predator-prey system described by the classical Rosenzweig-MacArthur model, and we observe that parametric sensitivity is regularly overcome by structural sensitivity. In addition to tackling theoretical questions about model sensitivity, the proposed approach can also be extended to make probabilistic predictions based on more complex models in an operational context. Both perspectives represent important steps towards providing better model predictions in biology, and beyond.

Ecosystem function in predator-prey food webs-confronting dynamic models with empirical data

Most ecosystem functions and related services involve species interactions across trophic levels, for example, pollination and biological pest control. Despite this, our understanding of ecosystem function in multitrophic communities is poor, and research has been limited to either manipulation in small communities or statistical descriptions in larger ones. Recent advances in food web ecology may allow us to overcome the trade-off between mechanistic insight and ecological realism. Molecular tools now simplify the detection of feeding interactions, and trait-based approaches allow the application of dynamic food web models to real ecosystems. We performed the first test of an allometric food web model's ability to replicate temporally nonaggregated abundance data from the field and to provide mechanistic insight into the function of predation. We aimed to reproduce and explore the drivers of the population dynamics of the aphid herbivore Rhopalosiphum padi observed in ten Swedish barley fields. We used a dynamic food web model, taking observed interactions and abundances of predators and alternative prey as input data, allowing us to examine the role of predation in aphid population control. The inverse problem methods were used for simultaneous model fit optimization and model parameterization. The model captured >70% of the variation in aphid abundance in five of ten fields, supporting the model-embodied hypothesis that body size can be an important determinant of predation in the arthropod community. We further demonstrate how in-depth model analysis can disentangle the likely drivers of function, such as the community's abundance and trait composition. Analysing the variability in model performance revealed knowledge gaps, such as the source of episodic aphid mortality, and general method development needs that, if addressed, would further increase model success and enable stronger inference about ecosystem function. The results demonstrate that confronting dynamic food web models with abundance data from the field is a viable approach to evaluate ecological theory and to aid our understanding of function in real ecosystems. However, to realize the full potential of food web models, in ecosystem function research and beyond, trait-based parameterization must be refined and extended to include more traits than body size.