Department Systems Analysis, Integrated Assessment and Modelling

The combination of various nitrogen (N) transformation pathways (mineralization, nitrification, denitrification, DNRA, anammox) modulates the fixed-N availability in aquatic systems, with important environmental consequences. Several models have been developed to investigate specific processes and estimate their rates, especially in benthic habitats, known hotspots for N-transformation reactions. Constraints on the N cycle are often based on the isotopic composition of N species, which integrates signals from various reactions. However, a comprehensive benthic N-isotope model, encompassing all canonical pathways in a stepwise manner, and including nitrous oxide, was still lacking. Here, we introduce a new diagenetic N-isotope model to analyse benthic N processes and their N-isotopic signatures, validated using field data from the porewaters of the oligotrophic Lake Lucerne (Switzerland). As parameters in such a complex model cannot all uniquely be identified from sparse data alone, we employed Bayesian inference to integrate prior parameter knowledge with data-derived information. For parameters where marginal posterior distributions considerably deviated from prior expectations, we performed sensitivity analyses to assess the robustness of these findings. Alongside developing the model, we established a methodology for its effective application in scientific analysis. For Lake Lucerne, the model accurately replicated observed porewater N-isotope and concentration patterns. We identified aerobic mineralization, denitrification, and nitrification as dominant processes, whereas anammox and DNRA played a less important role in surface sediments. Among the estimated N isotope effects, the value for nitrate reduction during denitrification was unexpectedly low (2.8 ± 1.1 ‰). We identified the spatial overlap of multiple reactions to be influential for this result.

1. Animal geolocation is the core of movement ecology. In aquatic ecosystems, electronic tagging and tracking technologies, such as passive acoustic telemetry systems and biologging sensors, are widely deployed. However, statistical estimation of individual locations from these datasets can be challenging and computationally expensive.
2. Here, we introduce Wahoo.jl, a Julia package that fits state-space models to animal-tracking data via convolution algorithms. Wahoo.jl supports passive acoustic telemetry (detection/non-detection) and biologging (i.e. depth) datasets; implements grid-based filtering, smoothing and sampling of trajectories; and exploits GPU acceleration.
3. Using simulations, we illustrate how to use Wahoo.jl from Julia and R to reconstruct movements for an example individual tagged with an acoustic transmitter and an archival depth tag. We also provide validation and sensitivity analyses.
4. Wahoo.jl fills a key gap in the animal-tracking toolbox. The package provides an accessible, flexible and performant interface for an inference methodology that reliably handles multimodal inference problems that challenge other approaches. We discuss the approach's pros and cons and provide guidance to readers on when to reach for Wahoo.jl.

Large-sample hydrology datasets have advanced hydrological research, yet the impact of landscape map details on identifying dominant streamflow generation processes remains underexplored. This study investigates the role of geology using maps of increasing detail – global, continental, and regional – each reclassified into four permeability classes. These geological attributes were used along with topography, soil, land use, and climate attributes to identify dominant controls on streamflow signatures across 4469 European catchments. To distinguish landscape influences from the otherwise dominant influence of climate, we conducted separate analyses on nested basins. Three scales were considered to assess scale-dependent patterns: large (63 nested basins), intermediate (the Moselle nested basin), and small (five nested catchments within the Moselle). The large-scale study used geology information from global and continental maps, while the others also incorporated regional maps. At the large scale, dominant controls varied widely between nested basins, but landscape generally outweighed climate, highlighting the value of our nested basin design. At this scale, continental and global geology maps produced different correlation patterns, with neither consistently superior. At the intermediate scale, increased geological detail led geology to shift from the least to the most correlated variable for certain streamflow signatures. The small-scale experiment reinforced these findings, as the regional map highlighted controls more consistent with process understanding. This study underscores the benefit of integrating detailed, region-specific geological data into large sample hydrology studies, and demonstrates the utility of a nested basins design. These findings have important implications for hydrological regionalization and streamflow prediction in ungauged basins.

1. Passive acoustic telemetry systems are widely deployed to track animals in aquatic environments. However, investments in integrative methods of data analysis have remained comparatively limited, with current workflows typically considering individual movements separately from space use, home ranges and residency.
2. This review presents a unifying perspective that bridges this divide. We argue that the core of animal-tracking analyses lies in the estimation of individual locations based on probabilistic principles. We formalise a generic state-space model for individual movements and a set of targets for statistical inference, unifying existing literature in a common framework. We critically assess inference algorithms and connect model-based inference to downstream ecological analyses of individual centres of activity, occurrence, residency, home ranges, habitat selection and behaviour.
3. We provide guidance to practitioners on model formulation, algorithm choice and software suitability in different contexts and identify key avenues for future research.
4. This review provides a roadmap for integrative data analysis in passive acoustic telemetry systems that should support research into the ecology and conservation of many aquatic species.

Mesocosm studies are conducted in the context of higher-tier ecological risk assessments (ERAs) to integrate environmental conditions and study species interactions within waterbodies in agricultural landscapes. Aquatic system models (ASMs) could provide tools to extrapolate the dynamics and effects of chemicals observed in mesocosm studies to a wider range of environmental conditions and exposure scenarios. In this article, we present the methodology of a ring study with four ASMs (AQUATOX, CASM, StoLaM+, Streambugs) applied to data from mesocosm studies, while the results of the ring study are presented in a companion article to be published alongside this work. The ring study aimed to test the feasibility and capability of ASMs to represent mesocosm data and to evaluate if such models can be used as an extension of mesocosm experiments in ERAs. The ring study methodology allowed for model comparison and identified models' strengths and limitations in representing the ecosystem dynamics in control and treated mesocosm studies. Groups of species were defined to map the diversity of the mesocosm ecosystem in the taxa represented by the models, and a consensus trophic web was agreed on to ensure harmonization among the models. Control and effect calibration criteria were developed to evaluate and compare model performances for each species group, explicitly considering the variability in mesocosm data. Our proposed methodologies and the challenges in using mesocosm data to inform modeling approaches are discussed. Finally, we derived recommendations for future research to facilitate the development of models representing mesocosms to support ERAs.

Animal movements affect their exposure to threats and the efficacy of conservation measures, such as marine protected areas (MPAs). However, many species' movements are difficult to reconstruct from available datasets, hampering conservation efforts. This is especially the case for aquatic species that rarely surface, for which data are often limited to observations from acoustic telemetry (detections) and ancillary sensors. Here, we pioneer the use of state-of-the-art particle algorithms to model movements, integrate datasets, and assess MPA design, leveraging a case study of a Critically Endangered elasmobranch. Our algorithms led to 5-fold improvements in space-use maps and 30-fold improvements in residency estimates compared to prevailing methods. By integrating tracking datasets, we were uniquely able to examine movements beyond acoustic receivers, MPA-scale residency, and specific habitats beyond protected areas that warrant protection. This work reveals a modeling framework that enhances the conservation value of acoustic telemetry, supporting analyses of MPA efficacy worldwide.