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Spanish mackerel species are migratory species, also are highly commercially valuable in Taiwan. However, in recent years, climate change and variability have influenced oceanographic conditions, substantially impacting the population dynamics of pelagic species. This study developed weighted habitat suitability index models for three crucial Scomberomorus species—Scomberomorus commerson, Scomberomorus niphonius, and Scomberomorus guttatus—in the waters off Taiwan. Remote sensing data including sea surface temperature, sea surface chlorophyll-a, sea surface salinity, and sea surface height, along with voyage data records that included detailed catch data from Taiwanese drift gillnet fisheries were collected during 2011–2019. The primary fishing grounds for S. commerson are located in the central to southwestern Taiwan Strait, while S. niphonius and S. guttatus are mainly found in the coastal waters off western Taiwan. In autumn, the oceanographic factor preferences are relatively similar among Scomberomorus species, with sea surface temperature being the primary influencing factor. Sea surface height turns to the main influencing factor in winter. Based on the results of the weighted habitat suitability model, we found seasonal differences in habitat preferences and ranges. There is more pronounced overlap in potential habitat in autumn and partial separate condition in potential habitat in winter among Scomberomorus species. Further exploration of the spatiotemporal distribution dynamics of Scomberomorus species under different ENSO periods revealed that during La Niña periods, the potential habitat ranges are broader and extend further south. In contrast, during El Niño periods, and the potential habitat ranges contract northward. Habitat suitable index model may be a useful approach for evaluating possible changes in habitat suitability resulting from climate change or other oceanographic phenomena and in offering suggestions for fishery scientific management.

This study aimed to improve the attendance of 36 Grade 8 Santan students in Values Education classes during Catch-Up Fridays through Collaborative Game-Based Learning. Using a mixed-method design, the study evaluated attendance trends before and after the intervention. Quantitative data were collected from School Form 2 (SF2) to measure attendance, and a paired sample t-test revealed a significant increase in mean attendance values, from 3.47 to 4.47, with a p-value of less than 0.05, confirming the intervention's effectiveness. Qualitative data were gathered through a focus group discussion (FGD) and a thematic analysis following Braun and Clarke's (2006) framework was used to examine how students find Collaborative Game-Based Learning as an intervention to improve attendance. Students’ responses from the FGD showed that Collaborative Game-Based fosters student engagement, enhances participation, and creates a fun and dynamic learning environment.

Tree-killing bark beetles are important pests severely affecting forests worldwide. An understanding of their spatio-temporal swarming intensity, typically assessed with pheromone traps, is crucial to guide management actions. While multiple factors have been shown to affect trap catches, we lack knowledge of the effects of inherent dispenser-dependent variations in pheromone release and of local trap position. In a laboratory experiment, we assessed the influence of filling level and temperature on the release rate (Rr) of three commonly used pheromone dispenser products for bark beetles (Pheroprax®, Chalcoprax®, Curviwit®). By conducting a complementary field study at two sites in Germany, we quantified the effect of varying Rr of Pheroprax® and trap position on the number of Ips typographus trapped. Rr of all three products correlated with temperature and strongly declined during the application period in Pheroprax® and Chalcoprax®. In the field, both the temporal variability in filling level and the ambient temperature similarly affected Rr, which in combination led to a fivefold change in trap catches. Additionally, catches varied by a similar magnitude due to local trap position, partly explained by the distance from the forest edge. The large uncertainties found in pheromone trap catches, which may also apply to other pest species, highlight the need for careful interpretation (or correction) of trap data. As a potential improvement of monitoring, we propose swarming models to facilitate more accurate predictions of infestation risk by (i) incorporating uncertainties arising from trap-related factors and (ii) providing continuous information on the spatio-temporal abundance of pest species.