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Biodiversity is threatened by a wide range of anthropogenic activities. Monitoring offers critical insights into how and why biodiversity is changing, helping to identify effective measures for maintaining biodiversity and its ecosystem services. However, conventional biodiversity monitoring methods are labor-intensive, and standardized long-term monitoring series are scarce. DNA-based approaches like metabarcoding environmental DNA (eDNA) promise rapid, cost-efficient, and highly resolved community data. At the same time, scientists are looking for alternative data sources that can compensate for the lack of long-term monitoring data to study past biodiversity changes. This work explores the potential of the German Environmental Specimen Bank (ESB), a pollution monitoring archive, which appears particularly promising for retrospective biodiversity monitoring. Biota samples from different ecosystems across the country are collected and archived at an exceptional level of standardization. Sampling species act as natural eDNA samplers, accumulating genetic traces from surrounding organisms. The cryogenic storage at the ESB preserves any eDNA in the samples in its original state. In this thesis, Chapter I serves as an introductory chapter, outlining the general chances and challenges of metabarcoding for assessing biodiversity. Chapter II focuses on primer design and testing the utility of ESB sampling species like mussels and macroalgae for characterizing the surrounding community. Both chapters form the basis for Chapters III to V, which report the use of ESB time series to uncover sample-associated communities and the changes they undergo. Chapter III illustrates the value of these time series by revealing the invasion trajectory of an alien barnacle into German coastal waters and linking the process to climate change. Chapter IV forms the core of this thesis by presenting an expanded measurement of biodiversity change in ESB time series across different taxonomic groups and ecosystem types. Here, a gradual compositional change (turnover) is reported from bacterial, fungal, microeukaryotic, and metazoan communities tending to either spatial homogenization or differentiation. Observed trends are tested for significance using a dynamic model of community ecology based on the equilibrium theory of island biogeography. The model reveals significantly accelerated turnover rates across all taxonomic groups and ecosystems investigated, suggesting a common, anthropogenically induced driver of biodiversity change. Since these analyses most likely include DNA derived from dead as well as from living organisms, Chapter V aims to separate both groups by metabarcoding both DNA and less stable ribosomal RNA from mussel samples. Contrary to the hypothesis, RNA is detectable from both living endobionts and dietary taxa. However, it outcompetes DNA in detecting microeukaryotic biodiversity. In summary, this thesis demonstrates the outstanding potential of archived ESB samples for retrospective biodiversity monitoring, a resource that offers many further untapped opportunities for future biodiversity research at multiple scales.
Biotic communities experienced significant changes in recent decades. Climate change, the overexploitation of natural resources and the immigration of invasive species are major drivers for this change and present unknown challenges for communities worldwide. To assess the impact of these drivers, standardised long-term studies are required, which are currently lacking for many species and ecosystems. Analysing environmental samples and the DNA of associated organisms using metabarcoding and high-throughput sequencing provides a cost-efficient and rapid way to generate the high-resolution biodiversity data which is so direly needed.
In this thesis, I demonstrate the great potential of using samples from the German Environ- mental Specimen Bank (ESB), a long-term monitoring archive that has been collecting and cryogenically storing highly standardised environmental samples since 1985. Modern analytical methods enable retrospective long-term biodiversity monitoring using these samples. In the first chapter, I illustrate metabarcoding as a central method, discussing its strengths and drawbacks, how to avoid them, and new application approaches. This chapter provides the methodological basis for the following studies.
In subsequent chapters, I present time series analyses of communities associated with these environmental samples. While for Chapter two the focus is on terrestrial arthropod communities, in Chapter three aquatic and terrestrial communities across the tree of life are analysed. A null model was developed for this survey for robust conclusions. The studies covered the last three decades and revealed substantial compositional changes across all ecosystems. These changes deviated significantly from the model, indicating that the changes are occurring faster than expected. Moreover, a trend toward homogenization in many terrestrial communities was uncovered. Climate change and the immigration of invasive species in combination with the loss of site-specific species are suspected to be the main drivers for this. In a follow-up study, changes of arthropod communities in German and South Korean terrestrial ecosystems were compared using ESB leaf samples from these two countries. Since both ESBs are harmonised in sample collection and processing, comparative analyses were applicable. This research covered the last decade and revealed substantial declines in species richness in Korea. Abiotic and biotic factors are discussed as potential drivers of these results.
Finally, the possibility of assessing tree health by analysing changes in functional fungal groups using German ESB samples was investigated. The results indicate that increasing infestation of specific functional groups is a proxy for declining tree health, with further analyses planned. In this dissertation, I present the great potential of samples from long-term monitoring archives to conduct retrospective biodiversity trend analyses across the tree of life. As technologies evolve, these samples will help to understand past and predict future ecosystem changes.
