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This is a publication-based dissertation comprising three original research stud-ies (one published, one submitted and one ready for submission; status March 2019). The dissertation introduces a generic computer model as a tool to investigate the behaviour and population dynamics of animals in cyclic environments. The model is further employed for analysing how migratory birds respond to various scenarios of altered food supply under global change. Here, ecological and evolutionary time-scales are considered, as well as the biological constraints and trade-offs the individual faces, which ultimately shape response dynamics at the population level. Further, the effect of fine-scale temporal patterns in re-source supply are studied, which is challenging to achieve experimentally. My findings predict population declines, altered behavioural timing and negative carry-over effects arising in migratory birds under global change. They thus stress the need for intensified research on how ecological mechanisms are affected by global change and for effective conservation measures for migratory birds. The open-source modelling software created for this dissertation can now be used for other taxa and related research questions. Overall, this thesis improves our mechanistic understanding of the impacts of global change on migratory birds as one prerequisite to comprehend ongoing global biodiversity loss. The research results are discussed in a broader ecological and scientific context in a concluding synthesis chapter.
The intrinsic predictability of ecological time series and its potential to guide forecasting
(2019)
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.
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.
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.
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.
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
Background
In Europe, bank voles (Myodes glareolus) are widely distributed and can transmit Puumala virus (PUUV) to humans, which causes a mild to moderate form of haemorrhagic fever with renal syndrome, called nephropathia epidemica. Uncovering the link between host and virus dynamics can help to prevent human PUUV infections in the future. Bank voles were live trapped three times a year in 2010–2013 in three woodland plots in each of four regions in Germany. Bank vole population density was estimated and blood samples collected to detect PUUV specific antibodies.
Results
We demonstrated that fluctuation of PUUV seroprevalence is dependent not only on multi-annual but also on seasonal dynamics of rodent host abundance. Moreover, PUUV infection might affect host fitness, because seropositive individuals survived better from spring to summer than uninfected bank voles. Individual space use was independent of PUUV infections.
Conclusions
Our study provides robust estimations of relevant patterns and processes of the dynamics of PUUV and its rodent host in Central Europe, which are highly important for the future development of predictive models for human hantavirus infection risk.
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.