Citizen science is an increasingly valuable source of information about biodiversity. It is challenging to use this information for analysis of distribution and trends. The lack of a protocol leads to observation bias due to unequal detection and reporting probabilities, caused by different preferences and habits of citizen scientists. With an immediate focus on occupancy, it is hard to extract value from citizen science data because of such observation bias. We propose to incorporate multiple characteristics of excursions in analyses of data collected by citizen scientists to improve estimates of the probability that a species is not detected and reported, even though it does occur. By limiting these models to areas that are known to be occupied, detection can be modeled separately without considering variation in occupancy. We apply this idea to 150 common species in the Southwest Delta of The Netherlands, and illustrate the data selection, modeling process and results using four species. The strongest features to predict detection are the number of species reported during a visit (list length), earlier observations of the target species by the same observer, and the day of year. We compare three approaches to predict the probability that a species is not detected during any of the visits to an area. Predictions based on only the number of visits were outperformed by predictions that also take the list length into account. Predictions based on all features combined consistently beat both other approaches, across all 10 species groups that were compared. We thus show that explicitly modelling the characteristics of all visits to an occupied area results in estimation of non-detection probabilities, while providing insight into the causes of detection and reporting bias. Like in occupancy models, predictions of our model can be combined per area to update occupancy estimates and thereby correct bias in citizen science data when aiming to map a species’ distribution.

• nTIS approach outperforms simple CSTR in reproducing wetland hydrodynamics. • 3P-TIS ( n = 4) model best represents and reproduces wetland hydraulic behaviour. • Water quality model simulated pollutant removal processes in CW under CSO events. • Simplified modelling provides a basis for future wetland design and management. Free water surface constructed wetlands (FWSCWs) replicate the ecological functions of natural wetlands, providing multiple ecosystem services, including water quality improvement. The Tancat de la Pipa constructed wetland (TPCW), located in Albufera Natural Park, Valencia, Spain, was designed to mitigate lake eutrophication. Its role in treating urban runoff, particularly from combined sewer overflows (CSOs), was recently assessed. Hydrodynamic modelling revealed that three-parallel lines with tanks-in-series configuration best represents the wetland’s flow dynamics. By applying the water quality model, the main mechanisms enabling the TPCW, originally not designed for runoff control, to mitigate pollution peaks from CSO discharges were identified. Ammonium removal was primarily driven by washout and nitrification, while sedimentation dominated TSS reduction. These findings emphasize the critical role of FWSCWs in managing diffuse pollution from CSOs and highlight the potential of predictive water quality models to optimize wetland performance under variable inflow conditions.

• Objective data-driven approach to test geomagnetic navigation strategies. • High-resolution GPS data matched in time and location with satellite geomagnetic data. • First empirical evidence for the effect of night-time on geomagnetic navigation. • Effect of the angle between the two navigational cues (intensity and inclination). • Taxis navigation based on the geomagnetic field intensity showed the best model fit. Geomagnetic navigation during long-distant bird migration is widely debated and it remains unclear which properties of the geomagnetic field birds use and how information might be used. This study is the first use of high-resolution GPS tracking data with co-located and contemporaneous satellite geomagnetic data applied in a step-selection analysis to objectively model geomagnetic navigational strategies in a data-driven approach. We use a day and night-time migrant, the Eurasian Curlew ( Numenius arquata ) as a model species. Additionally, we test the effect of additional factors (day vs night, spring vs autumn) which have been suggested in the literature or tested indirectly with behavioural experiments. To our knowledge, this study is the first empirical evidence for the effect of night-time and the effect of the angle between the two navigational cues involved (intensity and inclination), on geomagnetic navigation. Our data support previous findings that taxis cue use based on the geomagnetic field intensity showed the best model fit (adj. R² ≈ 0.82 at night) suggesting this as a potential geomagnetic cue use mechanism for animals. The use of a data-driven and step-selection analysis has important implications for future studies of navigational cue use. This can be used for studying interaction effects of different navigation mechanisms, the effect of environmental factors like wind, and the comparison of navigation mechanisms between different species and geographical areas.

This systematic review examines the use of the mizer R package, a tool for multi-species size-spectrum modeling of marine ecosystems, over the past decade. We analyzed 43 publications, including peer-reviewed articles and academic theses, to highlight its contributions, strengths and limitations across various research domains. We grouped studies into five categories: fisheries management and policy, ecosystem dynamics and species interactions, methodological advances, climate change projections, and broad-scale ecological studies. Geographically, the majority of studies were concentrated in marine ecosystems, particularly in the North Sea and Haizhou Bay, China. Our visualizations, including maps, timelines, Sankey diagrams, and a scientific collaboration network, revealed strong international collaboration, with the UK, Australia, and the USA emerging as central hubs in the global research network. The mizer package has evolved through various extensions such as therMizer, MizerShelf and MizerEvo , broadening its application in studying climate impacts and eco-evolutionary dynamics. Overall, mizer has proven to be a valuable tool in advancing our understanding of aquatic ecosystems and informing sustainable management practices. Despite its widespread use in theoretical and exploratory studies, direct applications of mizer-derived strategies in real-world fisheries management remain limited, underscoring the challenges of integrating complex models into decision-making frameworks. We identify several opportunities to enhance mizer’s practical relevance, including the development of validation datasets and benchmarking protocols, comparative evaluation with other ecosystem models, structured sensitivity and uncertainty analyses, and incorporation of socio-environmental feedbacks. We also highlight key technical limitations, such as the absence of automated parameter optimization and the reliance on equilibrium-based model structure, which currently constrain its use in dynamic or data-limited contexts. Addressing these challenges will be critical for advancing the integration of size-spectrum modeling into ecosystem-based management and policy.