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Grazing is known as one of the key factors for diversity and community composition in grassland ecosystems, but the response of plant communities towards grazing varies remarkably between sites with different environmental conditions. It is generally accepted that grazing increases plant diversity in productive environments, while it tends to reduce diversity in unproductive habitats (grazing reversal hypothesis). Despite empirical evidence for this pattern the mechanistic link between modes of plant-plant competition and grazing response at the community level still remains poorly understood. Root-competition in particular has rarely been included in theoretical studies, although it has been hypothesized that variations in productivity and grazing regime can alter the relative importance of shoot- and root-competition. We therefore developed an individual-based model based on plant functional traits to investigate the response of a grassland community towards grazing. Models of different complexity, either incorporating only shoot competition or with distinct shoot- and root-competition, were used to study the interactive effects of grazing, resource availability, and the mode of competition (size-symmetric or asymmetric). The pattern predicted by the grazing reversal hypothesis (GRH) can only be explained by our model if shoot- and root-competition are explicitly considered and if size asymmetry of above- and symmetry of below-ground competition is assumed. For this scenario, the model additionally reproduced empirically observed plant trait responses: erect and large plant functional types (PFTs) dominated without grazing, while frequent grazing favoured small PFTs with a rosette growth form. We conclude that interactions between shoot- and root-competition and size symmetry/asymmetry of plant-plant interactions are crucial in order to understand grazing response under different habitat productivities. Our results suggest that future empirical trait surveys in grassland communities should include root traits, which have been largely ignored in previous studies, in order to improve predictions of plants" responses to grazing.
Facilitation (positive interaction) has received increasing attention in plant ecology over the last decade. Just as for competition, distinguishing different modes of facilitation (mutualistic, commensal or even antagonistic) may be crucial. We therefore introduce the new concept of symmetric versus asymmetric facilitation and present a generic individual-based zone-of-influence model. The model simultaneously implements different modes of both facilitation and competition among individual plants via their overlapping zone of influence. Because we consider facilitation modes as a continuum related to environmental context, we integrated this concept with the stress-gradient hypothesis (SGH) by exploring differences in spatial pattern formation in self-thinning plants along a stress gradient in our model. The interplay among modes of interaction creates distinctly varied spatial patterns along stress gradients. When competition was symmetric, symmetric facilitation (mutualism) consistently led to plant aggregation along stress gradients. However, asymmetric facilitation (commensalism) produces plant aggregation only under more benign conditions but tends to intensify local competition and spatial segregation when conditions are harsh. When competition was completely asymmetric, different modes of facilitation contributed little to spatial aggregation. Symmetric facilitation significantly increased survival at the severe end of the stress gradient, which supports the claim of the SGH that facilitation should have generally positive net effects on plants under high stress levels. Asymmetric facilitation, however, was found to increase survival only under intermediate stress conditions, which contradicts the current predictions of the SGH. Synthesis. Our modelling study demonstrates that the interplay between modes of facilitation and competition affects different aspects of plant populations and communities, implying context-dependent outcomes and consequences. The explicit consideration of the modes and mechanisms of interactions (both facilitation and competition) and the nature of stress factors will help to extend the framework of the SGH and foster research on facilitation in plant ecology.
Agent-based models (ABMs) are widely used to predict how populations respond to changing environments. As the availability of food varies in space and time, individuals should have their own energy budgets, but there is no consensus as to how these should be modelled. Here, we use knowledge of physiological ecology to identify major issues confronting the modeller and to make recommendations about how energy budgets for use in ABMs should be constructed. Our proposal is that modelled animals forage as necessary to supply their energy needs for maintenance, growth and reproduction. If there is sufficient energy intake, an animal allocates the energy obtained in the order: maintenance, growth, reproduction, energy storage, until its energy stores reach an optimal level. If there is a shortfall, the priorities for maintenance and growth/reproduction remain the same until reserves fall to a critical threshold below which all are allocated to maintenance. Rates of ingestion and allocation depend on body mass and temperature. We make suggestions for how each of these processes should be modelled mathematically. Mortality rates vary with body mass and temperature according to known relationships, and these can be used to obtain estimates of background mortality rate. If parameter values cannot be obtained directly, then values may provisionally be obtained by parameter borrowing, pattern-oriented modelling, artificial evolution or from allometric equations. The development of ABMs incorporating individual energy budgets is essential for realistic modelling of populations affected by food availability. Such ABMs are already being used to guide conservation planning of nature reserves and shell fisheries, to assess environmental impacts of building proposals including wind farms and highways and to assess the effects on nontarget organisms of chemicals for the control of agricultural pests.
Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of -4/3 between mean individual biomass and density during density-dependent mortality (self-thinning). Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms' internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than -4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive.
Individual-based models (IBMs) predict how dynamics at higher levels of biological organization emerge from individual-level processes. This makes them a particularly useful tool for ecotoxicology, where the effects of toxicants are measured at the individual level but protection goals are often aimed at the population level or higher. However, one drawback of IBMs is that they require significant effort and data to design for each species. A solution would be to develop IBMs for chemical risk assessment that are based on generic individual-level models and theory. Here we show how one generic theory, Dynamic Energy Budget (DEB) theory, can be used to extrapolate the effect of toxicants measured at the individual level to effects on population dynamics. DEB is based on first principles in bioenergetics and uses a common model structure to model all species. Parameterization for a certain species is done at the individual level and allows to predict population-level effects of toxicants for a wide range of environmental conditions and toxicant concentrations. We present the general approach, which in principle can be used for all animal species, and give an example using Daphnia magna exposed to 3,4-dichloroaniline. We conclude that our generic approach holds great potential for standardized ecological risk assessment based on ecological models. Currently, available data from standard tests can directly be used for parameterization under certain circumstances, but with limited extra effort standard tests at the individual would deliver data that could considerably improve the applicability and precision of extrapolation to the population level. Specifically, the measurement of a toxicant's effect on growth in addition to reproduction, and presenting data over time as opposed to reporting a single EC50 or dose response curve at one time point.
1. The health of managed and wild honeybee colonies appears to have declined substantially in Europe and the United States over the last decade. Sustainability of honeybee colonies is important not only for honey production, but also for pollination of crops and wild plants alongside other insect pollinators. A combination of causal factors, including parasites, pathogens, land use changes and pesticide usage, are cited as responsible for the increased colony mortality. 2. However, despite detailed knowledge of the behaviour of honeybees and their colonies, there are no suitable tools to explore the resilience mechanisms of this complex system under stress. Empirically testing all combinations of stressors in a systematic fashion is not feasible. We therefore suggest a cross-level systems approach, based on mechanistic modelling, to investigate the impacts of (and interactions between) colony and land management. 3. We review existing honeybee models that are relevant to examining the effects of different stressors on colony growth and survival. Most of these models describe honeybee colony dynamics, foraging behaviour or honeybee - varroa mite - virus interactions. 4. We found that many, but not all, processes within honeybee colonies, epidemiology and foraging are well understood and described in the models, but there is no model that couples in-hive dynamics and pathology with foraging dynamics in realistic landscapes. 5. Synthesis and applications. We describe how a new integrated model could be built to simulate multifactorial impacts on the honeybee colony system, using building blocks from the reviewed models. The development of such a tool would not only highlight empirical research priorities but also provide an important forecasting tool for policy makers and beekeepers, and we list examples of relevant applications to bee disease and landscape management decisions.
Individual-based models (IBMs) are increasingly used to link the dynamics of individuals to higher levels of biological organization. Still, many IBMs are data hungry, species specific, and time-consuming to develop and analyze. Many of these issues would be resolved by using general theories of individual dynamics as the basis for IBMs. While such theories have frequently been examined at the individual level, few cross-level tests exist that also try to predict population dynamics. Here we performed a cross-level test of dynamic energy budget (DEB) theory by parameterizing an individual-based model using individual-level data of the water flea, Daphnia magna, and comparing the emerging population dynamics to independent data from population experiments. We found that DEB theory successfully predicted population growth rates and peak densities but failed to capture the decline phase. Further assumptions on food-dependent mortality of juveniles were needed to capture the population dynamics after the initial population peak. The resulting model then predicted, without further calibration, characteristic switches between small-and large-amplitude cycles, which have been observed for Daphnia. We conclude that cross-level tests help detect gaps in current individual-level theories and ultimately will lead to theory development and the establishment of a generic basis for individual-based models and ecology.
When data are limited it is difficult for conservation managers to assess alternative management scenarios and make decisions. The natterjack toad (Bufo calamita) is declining at the edges of its distribution range in Europe and little is known about its current distribution and abundance in Poland. Although different landscape management plans for central Poland exist, it is unclear to what extent they impact this species. Based on these plans, we investigated how four alternative landscape development scenarios would affect the total carrying capacity and population dynamics of the natterjack toad. To facilitate decision-making, we first ranked the scenarios according to their total carrying capacity. We used the software RAMAS GIS to determine the size and location of habitat patches in the landscape. The estimated carrying capacities were very similar for each scenario, and clear ranking was not possible. Only the reforestation scenario showed a marked loss in carrying capacity. We therefore simulated metapopulation dynamics with RAMAS taking into account dynamical processes such as reproduction and dispersal and ranked the scenarios according to the resulting species abundance. In this case, we could clearly rank the development scenarios. We identified road mortality of adults as a key process governing the dynamics and separating the different scenarios. The renaturalisation scenario clearly ranked highest due to its decreased road mortality. Taken together our results suggest that road infrastructure development might be much more important for natterjack toad conservation than changes in the amount of habitat in the semi-natural river valley. We gained these insights by considering both the resulting metapopulation structure and dynamics in the form of a PVA. We conclude that the consideration of dynamic processes in amphibian conservation management may be indispensable for ranking management scenarios.
Contamination of soil with toxic heavy metals poses a major threat to the environment and human health. Anthropogenic sources include smelting of ores, municipal wastes, fertilizers, and pesticides. In assessing soil quality and the environmental and ecological risk of contamination with heavy metals, often homogeneous contamination of the soil is assumed. However, soils are very heterogeneous environments. Consequently, both contamination and the response of soil organisms can be assumed to be heterogeneous. This might have consequences for the exposure of soil organisms and for the extrapolation of risk from the individual to the population level. Therefore, to explore how soil contamination of different spatial heterogeneity affects population dynamics of soil invertebrates, we developed a spatially explicit individual-based model of the springtail, Folsomia candida, a standard test species for ecotoxicological risk assessment. In the model, individuals were assumed to sense and avoid contaminated habitat with a certain probability that depends on contamination level. Avoidance of contaminated areas thus influenced the individuals' movement and feeding, their exposure, and in turn all other biological processes underlying population dynamics. Model rules and parameters were based on data from the literature, or were determined via pattern-oriented modelling. The model correctly predicted several patterns that were not used for model design and calibration. Simulation results showed that the ability of the individuals to detect and avoid the toxicant, combined with the presence of clean habitat patches which act as "refuges", made equilibrium population size due to toxic effects less sensitive to increases in toxicant concentration. Additionally, the level of heterogeneity among patches of soil (i.e. the difference in concentration) was important: at the same average concentration, a homogeneously contaminated scenario was the least favourable habitat, while higher levels of heterogeneity corresponded to higher population growth rate and equilibrium size. Our model can thus be used as a tool for extrapolating from short-term effects at the individual level to long-term effects at the population level under more realistic conditions. It can thus be used to develop and extrapolate from standard ecotoxicological tests in the laboratory to ecological risk assessments.