Recreational fishing is practiced by thousands of people in European coastal waters and is steadily gaining popularity. Serving multiple purposes, recreational fishing provides fresh fish for meals, offers leisure, and contributes to traditional ecological knowledge, especially at the local level. Therefore, analyzing its dynamics and catch is a complex task, since they not only depend on the environmental features but also on the behavior of fishers. In coastal areas, however, most recreational fishers remain unmonitored, making it difficult to obtain data on their impact on fish stocks. This is particularly evident in the Venice lagoon, where we conducted a comprehensive study aiming to characterize recreational fishing dynamics. We collected data through interviews, online questionnaires, and remote sensing techniques, including satellite imagery photointerpretation and machine learning algorithms. Our findings reveal spatial and temporal variations in fishing activity, with certain areas and times experiencing higher fishing pressure. This highlights a seasonality in fishing activity and a pattern in fishers’ behaviors that are associated with fish migratory dynamics. Such an association demonstrates the local fishers’ understanding of the fish lifecycle phases and the environmental conditions of the lagoon. Regarding the catch, the most targeted species are seabream (Sparus aurata), seabass (Dicentrarchus labrax), and cuttlefish (Sepia officinalis), with estimated total catches of 18.65 t per year, 15.82 t per year, and 8.36 t per year, respectively. However, our results showed a significant disproportion between the biomass caught by two different groups of fishers that differ in terms of fishing trip frequency, success rate, and catch. While the average catch of the first group, representing most recreational fishers, might be considered of low impact, the catch of the second group, encompassing recreational fishers who fish with very high frequency and efficiency, is substantial in the context of the lagoon ecosystem. Indeed, even considering a conservative estimate, recreational fishing in the Venice lagoon accounted for approximately 2% of the catch of cuttlefish and 17% of the catch of seabream compared to commercial fishing catch, whereas the catch of seabass by recreational fishing approached that of commercial fishing. Therefore, the implementation of a periodic monitoring program utilizing methods such as machine learning algorithms and remote sensing technologies could support the management of recreational fishing dynamics. We also suggest that participatory processes involving both professional and recreational fishers may aid in defining shared approaches and bottom–up initiatives, ensuring enjoyment as well as sustainable uses of coastal areas.

We present WM-Classroom v1.0, a pedagogical multi-species agent-based model (ABM) designed for educational purposes in predator–prey–harvest systems. The model embeds a predator, two prey breeds, and human harvesters on a homogeneous 50 × 50 grid with weekly time steps, implementing random movement, abstract energetics, prey consumption, reproduction, legal harvest with species-specific cut-offs and seasons, optional predator control, and a poaching switch. After basic technical checks (energetic calibration, prey composition, herbivore viability), we explore the consistency of the model under illustrative scenarios including no hunting, single-prey harvest, hunter-density and season-length gradients, predator removal, and poaching. In the no-hunting baseline (n = 100), mean end-of-run abundances were 22 deer, 159 boar, and 45 wolves, with limited extinction events. Deer-only harvest often drove deer to very low end-of-run counts (mean 1–16) with extinctions in 2–7/10 replicates across cut-offs, whereas boar-only harvest showed higher persistence (mean 11–74) and boar extinctions occurred only at the lowest cut-off (3/10). Increasing hunter numbers or season length depressed prey and could indirectly reduce wolves via prey depletion. Legal predator control reduced predators as designed, while poaching had little effect under the implemented rules. Because interaction and prey-choice rules are simplified for transparency, outcomes should be interpreted as conditional on model assumptions. WM-Classroom v1.0 provides a didactic sandbox for courses, professional training, and outreach, with extensions (habitat heterogeneity, age/sex structure, probabilistic diet/kill success, and calibration/validation) outlined for future versions.