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The concept that diversity promotes reliability of ecosystem function depends on the pattern that community-level biomass shows lower temporal variability than species-level biomasses. However, this pattern is not universal, as it relies on compensatory or independent species dynamics. When in contrast within--trophic level synchronization occurs, variability of community biomass will approach population-level variability. Current knowledge fails to integrate how species richness, functional distance between species, and the relative importance of predation and competition combine to drive synchronization at different trophic levels. Here we clarify these mechanisms. Intense competition promotes compensatory dynamics in prey, but predators may at the same time increasingly synchronize, under increasing species richness and functional similarity. In contrast, predators and prey both show perfect synchronization under strong top-down control, which is promoted by a combination of low functional distance and high net growth potential of predators. Under such conditions, community-level biomass variability peaks, with major negative consequences for reliability of ecosystem function.
Some like it hot
(2018)
Accumulating evidence has demonstrated considerable impact of climate change on biodiversity, with terrestrial ectotherms being particularly vulnerable. While climate-induced range shifts are often addressed in the literature, little is known about the underlying ecological responses at individual and population levels. Using a 30-yr monitoring study of the long-living nocturnal gecko Gehyra variegata in arid Australia, we determined the relative contribution of climatic factors acting locally (temperature, rainfall) or distantly (La Nina induced flooding) on ecological processes ranging from traits at the individual level (body condition, body growth) to the demography at population level (survival, sexual maturity, population sizes). We also investigated whether thermoregulatory activity during both active (night) and resting (daytime) periods of the day can explain these responses. Gehyra variegata responded to local and distant climatic effects. Both high temperatures and high water availability enhanced individual and demographic parameters. Moreover, the impact of water availability was scale independent as local rainfall and La Nina induced flooding compensated each other. When water availability was low, however, extremely high temperatures delayed body growth and sexual maturity while survival of individuals and population sizes remained stable. This suggests a trade-off with traits at the individual level that may potentially buffer the consequences of adverse climatic conditions at the population level. Moreover, hot temperatures did not impact nocturnal nor diurnal behavior. Instead, only cool temperatures induced diurnal thermoregulatory behavior with individuals moving to exposed hollow branches and even outside tree hollows for sun-basking during the day. Since diurnal behavioral thermoregulation likely induced costs on fitness, this could decrease performance at both individual and population level under cool temperatures. Our findings show that water availability rather than high temperature is the limiting factor in our focal population of G.variegata. In contrast to previous studies, we stress that drier rather than warmer conditions are expected to be detrimental for nocturnal desert reptiles. Identifying the actual limiting climatic factors at different scales and their functional interactions at different ecological levels is critical to be able to predict reliably future population dynamics and support conservation planning in arid ecosystems.
Myrmecochory, i.e. dispersal of seeds by ants towards and around their nests, plays an important role in temperate forests. Yet hardly any study has examined plant population spread over several years and the underlying joint contribution of a hierarchy of dispersal modes and plant demography. We used a seed-sowing approach with three replicates to examine colonization patterns of Melampyrum pratense, an annual myrmecochorous herb, in a mixed Scots pine forest in northeastern Germany. Using a spatially explicit individualbased (SEIB) model population patterns over 4 years were explained by short-distance transport of seeds by small ant species with high nest densities, resulting in random spread. However, plant distributions in the field after another 4 years were clearly deviating from model predictions. Mean annual spread rate increased from 0.9 m to 5.1 m per year, with a clear inhomogeneous component. Obviously, after a lag-phase of several years, non-random seed dispersal by large red wood ants (Formica rufa) was determining the species’ spread, thus resulting in stratified dispersal due to interactions with different-sized ant species. Hypotheses on stratified dispersal, on dispersal lag, and on non-random dispersal were verified using an extended SEIB model, by comparison of model outputs with field patterns (individual numbers, population areas, and maximum distances). Dispersal towards red wood ant nests together with seed loss during transport and redistribution around nests were essential features of the model extension. The observed lag-phase in the initiation of non-random, medium-distance transport was probably due to a change of ant behaviour towards a new food source of increasing importance, being a meaningful example for a lag-phase in local plant species invasion. The results demonstrate that field studies should check model predictions wherever possible. Future research will show whether or not the M. pratense–ant system is representative for migration patterns of similar animal dispersal systems after having crossed range edges by long-distance dispersal events.
BACKGROUND Central European outbreak populations of the bank vole (Myodes glareolus Schreber) are known to cause damage in forestry and to transmit the most common type of Hantavirus (Puumala virus, PUUV) to humans. A sound estimation of potential effects of future climate scenarios on population dynamics is a prerequisite for long-term management strategies. Historic abundance time series were used to identify the key weather conditions associated with bank vole abundance, and were extrapolated to future climate scenarios to derive potential long-term changes in bank vole abundance dynamics.
RESULTS Classification and regression tree analysis revealed the most relevant weather parameters associated with high and low bank vole abundances. Summer temperatures 2 years prior to trapping had the highest impact on abundance fluctuation. Extrapolation of the identified parameters to future climate conditions revealed an increase in years with high vole abundance.
CONCLUSION Key weather patterns associated with vole abundance reflect the importance of superabundant food supply through masting to the occurrence of bank vole outbreaks. Owing to changing climate, these outbreaks are predicted potentially to increase in frequency 3-4-fold by the end of this century. This may negatively affect damage patterns in forestry and the risk of human PUUV infection in the long term. (c) 2014 Society of Chemical Industry
Declines in breeding site fidelity in an increasing population of White Storks Ciconia ciconia
(2011)
Following a steep decline, White Stork Ciconia ciconia populations in Germany are currently increasing, allowing us to examine potential density-dependent effects on breeding dispersal. Our data suggest that the proportion of breeding dispersers has increased over time, indicating a density-dependent component in nest-site fidelity that may be linked to increased competition.
Species are adapted to the environment they live in. Today, most environments are subjected to rapid global changes induced by human activity, most prominently land cover and climate changes. Such transformations can cause adjustments or disruptions in various eco-evolutionary processes. The repercussions of this can appear at the population level as shifted ranges and altered abundance patterns. This is where global change effects on species are usually detected first.
To understand how eco-evolutionary processes act and interact to generate patterns of range and abundance and how these processes themselves are influenced by environmental conditions, spatially-explicit models provide effective tools. They estimate a species’ niche as the set of environmental conditions in which it can persist. However, the currently most commonly used models rely on static correlative associations that are established between a set of spatial predictors and observed species distributions. For this, they assume stationary conditions and are therefore unsuitable in contexts of global change. Better equipped are process-based models that explicitly implement algorithmic representations of eco-evolutionary mechanisms and evaluate their joint dynamics. These models have long been regarded as difficult to parameterise, but an increased data availability and improved methods for data integration lessen this challenge. Hence, the goal of this thesis is to further develop process-based models, integrate them into a complete modelling workflow, and provide the tools and guidance for their successful application.
With my thesis, I presented an integrated platform for spatially-explicit eco-evolutionary modelling and provided a workflow for their inverse calibration to observational data. In the first chapter, I introduced RangeShiftR, a software tool that implements an individual-based modelling platform for the statistical programming language R. Its open-source licensing, extensive help pages and available tutorials make it accessible to a wide audience. In the second chapter, I demonstrated a comprehensive workflow for the specification, calibration and validation of RangeShiftR by the example of the red kite in Switzerland. The integration of heterogeneous data sources, such as literature and monitoring data, allowed to successfully calibrate the model. It was then used to make validated, spatio-temporal predictions of future red kite abundance. The presented workflow can be adopted to any study species if data is available. In the third chapter, I extended RangeShiftR to directly link demographic processes to climatic predictors. This allowed me to explore the climate-change responses of eight Swiss breeding birds in more detail. Specifically, the model could identify the most influential climatic predictors, delineate areas of projected demographic suitability, and attribute current population trends to contemporary climate change.
My work shows that the application of complex, process-based models in conservation-relevant contexts is feasible, utilising available tools and data. Such models can be successfully calibrated and outperform other currently used modelling approaches in terms of predictive accuracy. Their projections can be used to predict future abundances or to assess alternative conservation scenarios. They further improve our mechanistic understanding of niche and range dynamics under climate change. However, only fully mechanistic models, that include all relevant processes, allow to precisely disentangle the effects of single processes on observed abundances. In this respect, the RangeShiftR model still has potential for further extensions that implement missing influential processes, such as species interactions.
Dynamic, process-based models are needed to adequately model a dynamic reality. My work contributes towards the advancement, integration and dissemination of such models. This will facilitate numeric, model-based approaches for species assessments, generate ecological insights and strengthen the reliability of predictions on large spatial scales under changing conditions.
Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.
RangeShiftR
(2021)
Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.
Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.
In the context of ecological risk assessment of chemicals, individual-based population models hold great potential to increase the ecological realism of current regulatory risk assessment procedures. However, developing and parameterizing such models is time-consuming and often ad hoc. Using standardized, tested submodels of individual organisms would make individual-based modelling more efficient and coherent. In this thesis, I explored whether Dynamic Energy Budget (DEB) theory is suitable for being used as a standard submodel in individual-based models, both for ecological risk assessment and theoretical population ecology. First, I developed a generic implementation of DEB theory in an individual-based modeling (IBM) context: DEB-IBM. Using the DEB-IBM framework I tested the ability of the DEB theory to predict population-level dynamics from the properties of individuals. We used Daphnia magna as a model species, where data at the individual level was available to parameterize the model, and population-level predictions were compared against independent data from controlled population experiments. We found that DEB theory successfully predicted population growth rates and peak densities of experimental Daphnia populations in multiple experimental settings, but failed to capture the decline phase, when the available food per Daphnia was low. 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 detecting 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. In addition to theoretical explorations, we tested the potential of DEB theory combined with IBMs to extrapolate effects of chemical stress from the individual to population level. For this we used information at the individual level on the effect of 3,4-dichloroanailine on Daphnia. The individual data suggested direct effects on reproduction but no significant effects on growth. Assuming such direct effects on reproduction, the model was able to accurately predict the population response to increasing concentrations of 3,4-dichloroaniline. We conclude that DEB theory combined with IBMs holds great potential for standardized ecological risk assessment based on ecological models.