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Statistical eco(-toxico)logy
(2017)
Freshwaters are of immense importance for human well-being.
Nevertheless, they are currently facing unprecedented levels of threat from habitat loss and degradation, overexploitation, invasive species and
pollution.
To prevent risks to aquatic ecosystems, chemical substances, like agricultural pesticides, have to pass environmental risk assessment (ERA) before entering the market.
Concurrently, large-scale environmental monitoring is used for surveillance of biological and chemical conditions in freshwaters.
This thesis examines statistical methods currently used in ERA.
Moreover, it presents a national-scale compilation of chemical monitoring data, an analysis of drivers and dynamics of chemical pollution in streams and, provides a large-scale risk assessment by combination with results from ERA.
Additionally, software tools have been developed to integrate different datasets used in ERA.
The thesis starts with a brief introduction to ERA and environmental monitoring and gives an overview of the objectives of the thesis.
Chapter 2 addresses experimental setups and their statistical analyses using simulations.
The results show that current designs exhibit unacceptably low statistical power, that statistical methods chosen to fit the type of data provide higher power and that statistical practices in ERA need to be revised.
In chapter 3 we compiled all available pesticide monitoring data from Germany.
Hereby, we focused on small streams, similar to those considered in ERA and used threshold concentrations derived during ERA for a large-scale assessment of threats to freshwaters from pesticides.
This compilation resulted in the most comprehensive dataset on pesticide exposure currently available for Germany.
Using state-of-the-art statistical techniques, that explicitly take the limits of quantification into account, we demonstrate that 25% of small streams are at threat from pesticides.
In particular neonicotinoid pesticides are responsible for these threats.
These are associated with agricultural intensity and can be detected even at low levels of agricultural use.
Moreover, our results indicated that current monitoring underestimates pesticide risks, because of a sampling decoupled from precipitation events.
Additionally, we provide a first large-scale study of annual pesticide exposure dynamics.
Chapters 4 and 5 describe software solutions to simplify and accelerate the integration of data from ERA, environmental monitoring and ecotoxicology that is indispensable for the development of landscape-level risk assessment.
Overall, this thesis contributes to the emerging discipline of statistical ecotoxicology and shows that pesticides pose a large-scale threat to small streams.
Environmental monitoring can provide a post-authorisation feedback to ERA.
However, to protect freshwater ecosystems ERA and environmental monitoring need to be further refined and we provide software solutions to utilise existing data for this purpose.
World’s ecosystems are under great pressure satisfying anthropogenic demands, with freshwaters being of central importance. The Millennium Ecosystem Assessment has identified anthropogenic land use and associated stressors as main drivers in jeopardizing stream ecosystem functions and the
biodiversity supported by freshwaters. Adverse effects on the biodiversity of freshwater organisms, such as macroinvertebrates, may propagate to fundamental ecosystem functions, such as organic matter breakdown (OMB) with potentially severe consequences for ecosystem services. In order to adequately protect and preserve freshwater ecosystems, investigations regarding potential and observed as well as direct and indirect effects of anthropogenic land use and associated stressors (e.g. nutrients, pesticides or heavy metals) on ecosystem functioning and stream biodiversity are needed. While greater species diversity most likely benefits ecosystem functions, the direction and magnitude of changes in ecosystem functioning depends primarily on species functional traits. In this context, the functional diversity of stream organisms has been suggested to be a more suitable predictor of changes in ecosystem functions than taxonomic diversity.
The thesis aims at investigating effects of anthropogenic land use on (i) three ecosystem functions by anthropogenic toxicants to identify effect thresholds (chapter 2), (ii) the organic matter breakdown by three land use categories to identify effects on the functional level (chapter 3) and (iii)on the stream community along an established land-use gradient to identify effects on the community level.
In chapter 2, I reviewed the literature regarding pesticide and heavy metal effects on OMB, primary production and community respiration. From each reviewed study that met inclusion criteria, the toxicant concentration resulting in a reduction of at least 20% in an ecosystem function was standardized based on laboratory toxicity data. Effect thresholds were based on the relationship between ecosystem functions and standardized concentration-effect relationships. The analysis revealed that more than one third of pesticide observations indicated reductions in ecosystem functions at concentrations that are assumed being protective in regulation. However, high variation within and between studies hampered the derivation of a concentration-effect relationship and thus effect thresholds.
In chapter 3, I conducted a field study to determine the microbial and invertebrate-mediated OMB by deploying fine and coarse mesh leaf bags in streams with forested, agricultural, vinicultural
and urban riparian land use. Additionally, physicochemical, geographical and habitat parameters were monitored to explain potential differences in OMB among land use types and sites. Regarding results, only microbial OMB differed between land use types. The microbial OMB showed a negative relationship with pH while the invertebrate-mediated OMB was positively related to tree cover. OMB responded to stressor gradients rather than directly to land use.
In chapter 4, macroinvertebrates were sampled in concert with leaf bag deployment and after species identification (i) the taxonomic diversity in terms of Simpson diversity and total taxonomic
richness (TTR) and (ii) the functional diversity in terms of bio-ecological traits and Rao’s quadratic entropy was determined for each community. Additionally, a land-use gradient was established and the response of the taxonomic and functional diversity of invertebrate communities along this gradient was investigated to examine whether these two metrics of biodiversity are predictive for the rate of OMB. Neither bio-ecological traits nor the functional diversity showed a significant relationship with
OMB. Although, TTR decreased with increasing anthropogenic stress and also the community structure and 26 % of bio-ecological traits were significantly related to the stress gradient, any of these shifts propagated to OMB.
Our results show that the complexity of real-world situations in freshwater ecosystems impedes the effect assessment of chemicals and land use for functional endpoints, and consequently our potential to predict changes. We conclude that current safety factors used in chemical risk assessment may not be sufficient for pesticides to protect functional endpoints. Furthermore, simplifying real-world stressor gradients into few land use categories was unsuitable to predict and quantify losses in OMB. Thus, the monitoring of specific stressors may be more relevant than crude land use categories to detect effects on ecosystem functions. This may, however, limit the large scale assessment of the status of OMB. Finally, despite several functional changes in the communities the functional diversity over several trait modalities remained similar. Neither taxonomic nor functional diversity were suitable predictors of OMB. Thus, when understanding anthropogenic impacts on the linkage between biodiversity and ecosystem functioning is of main interest, focusing on diversity metrics that are clearly linked to the stressor in question (Jackson et al. 2016) or integrating taxonomic and functional metrics (Mondy et al., 2012) might enhance our predictive capacity.