@phdthesis{Malchow2023,
  author    = {Malchow, Anne-Kathleen},
  title     = {Developing an integrated platform for predicting niche and range dynamics},
  doi       = {10.25932/publishup-60273},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-602737},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiv, 169},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}