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Simple Summary: Human–wildlife conflict refers to conflicts that arise from the occurrence or behavior of wildlife that poses a threat to humans directly or indirectly. The wild boar, as one of the most widely distributed ungulates in the world, is known to dominate the species in mountain ecosystems in China. Here, we integrated data collected from damage and camera trap surveys to understand the damage status, abundance and density and activity rhythms of wild boar in Zhejiang, Eastern China. The damage distribution, density and activity rhythms provide specific priority management regions and activity intensity peaks for conflict mitigation. We believe that these findings could provide a scientific basis for mitigation management. Human–wildlife conflicts are becoming increasingly common worldwide and are a challenge to biodiversity management. Compared with compensatory management, which often focuses on solving emergency conflicts, mitigation management allows decision-makers to better understand where the damage is distributed, how the species are distributed and when the species conduct their activity. Here, we integrated data collected from 90 districts/counties' damage surveys and 1271 camera traps to understand the damage status, abundance, density and activity rhythms of wild boar (Sus scrofa) in Zhejiang, Eastern China, from January 2019 to August 2023. We found that (1) wild boar–human conflicts were mainly distributed in the northwest and southwest mountainous regions of Zhejiang Province; (2) the total abundance of wild boar was 115,156 ± 24,072 individuals, indicating a growing trend over the past decade and a higher density in the western and southern regions; (3) wild boar exhibited different activity patterns across different damage regions, and the periods around 7:00, 11:00 and 16:00 represented activity peaks for wild boar in seriously damaged regions. The damage distribution, density, distribution and activity rhythms provide specific priority regions and activity intensity peaks for conflict mitigation. We believe that these findings based on the damage, distribution and activity could provide a scientific basis for mitigation management at the county level and enrich the framework of human–wildlife conflict mitigation. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we investigated how predators and conspecific density impact the growth of Chinhai spiny newt larvae. Using a two-factor experimental design, we manipulated both factors to explore their independent and interactive effects on larval development. Our findings demonstrate that both high and low conspecific densities constrain larval growth, while predators also limit growth. Interestingly, high conspecific density restricts predator-induced growth plasticity without interacting effects. This study provides valuable insights into the complex interplay between environmental factors and larval development in endangered Chinhai spiny newts. Understanding these dynamics is crucial for informing conservation strategies aimed at protecting this vulnerable species. Predators significantly influence amphibian larval development. Predator-induced plasticity is often studied independently from conspecific density effects, but these environmental factors may interact. We conducted two-factor factorial experimental design to manipulate conspecific density and predator cues, aiming to investigate the independently or interactive impacts of these two factors on the development of Chinhai spiny newt larvae (Echinotriton chinhaiensis). Our findings reveal that both high and low conspecific densities constrain spiny newt larval growth and predators also limit growth. Interestingly, high conspecific density restricts predator-induced growth plasticity without interacting effects. Only lower density groups exhibit slower growth responses to predators. Our study investigates how density mediates predator-induced plasticity in the endangered Chinhai spiny newt larvae, providing insights into their intricate life history. These results contribute to the understanding of predator-induced plasticity in amphibians and provide insights into the adaptive strategies of endangered species like Chinhai spiny newt. Such knowledge informs the development of effective conservation strategies for their protection. [ABSTRACT FROM AUTHOR]

Aim: An area threshold is usually considered as the point of abrupt change in the species–area relationship (SAR). Despite their widespread existence, few studies have considered the influence of data transformations on their presence or the mechanisms that generate them. The aims of this study were thus to test for the existence of an area threshold in butterfly assemblages and to compare trait–environment associations between small and large islands split by the threshold. Location: Zhoushan Archipelago, China. Taxon: Butterflies. Methods: We used the line‐transect method to survey butterflies on 42 islands from July to August 2014. We also collected data on three commonly used ecological traits of butterflies (wingspan, wing loading and body weight). We transformed the butterfly datasets into three types (log‐log, semi‐log and untransformed) and fitted each of them using a set of breakpoint regression models. We then split all islands into small and large islands based on the area threshold and analysed the trait–environment associations for each island group. Results: Two‐slope breakpoint models performed best for log‐log and semi‐log transformed datasets. However, the former exhibited a steep‐shallow shape with an area threshold of 0.820 km2 and the latter was in shallow‐steep shape with a threshold of 17.022 km2. By contrast, no threshold was detected when using the untransformed power model. Moreover, for small islands, small area and large isolation were positively correlated with species with large wing loading and body weight. For large islands, however, the perimeter to area ratio (PAR) was significantly and positively related to wing loading. Main conclusions: We found that data transformations may influence the detection of area thresholds. Trait–environment analyses can further provide novel and mechanistic insights into processes generating area thresholds. We urge that the influence of data transformations and ecological traits of species should be explicitly considered in future studies. [ABSTRACT FROM AUTHOR]