Analysing and modelling spatial patterns to infer the influence of environmental heterogeneity using point pattern analysis, individual-based simulation modelling and landscape metrics

Abstract

One of the main goals of ecology is to understand processes underlying patterns. Because presumably all ecological processes are spatially explicit, especially spatial patterns can contain a lot of information about the processes shaping them. Nevertheless, the pattern-process link can be ambiguous. Reasons for this include different processes that lead to similar patterns, interact-ing processes that lead to random patterns or patterns that lead to processes and not the other way around. However, many of these issues can be dealt with using appropriate analysis methods. This includes meaningful ecological hypotheses, precise descriptions of patterns in the data as well as null models and model simulations to strengthen the pattern-process link. Biotic and abiotic processes were shown to interact in plant populations and to result in similar clustered spatial patterns. The clustering can be introduced by limited seed dispersal as an example of biotic processes, but also by higher densities of individuals at suitable growing conditions as an example of abiotic processes. Even though challenging, analysing spatial patterns in the data can be a powerful tool to disentangle the importance and interactions of these two processes. The aim of this thesis was to present approaches that use the power of spatial patterns to study the role of environmental heterogeneity. The methods included spatial point pattern analysis, individual-based simulation models as well as tools to quantify environmental heterogeneity to link spatial patterns to underlying processes. To this end, field data from a temperate old-growth forest was used. Overall, the thesis demonstrated how spatial patterns can be used to explore underlying ecological processes, even if contrasting processes lead to similar patterns as for biotic and abiotic processes. To successfully link pattern to process, i) meaningful ecological hypotheses are needed, ii) patterns in the data need to be described precisely using suitable methods and iii) appropriate null models as well as model simulations need to be applied to link pattern to process as unambiguously as possible.

Publication
Dissertation
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Dr. Maximilian H.K. Hesselbarth
Postdoctoral Fellow

Ecological Modelling