Individuals with abnormalities are key drivers of subtle stress changes in forest ecosystems. Although remote sensing monitoring and deep learning have been developed for forest ecosystems, they are faced with the complexity of forest landscapes, multiple sources of remote sensing data, high monitoring costs, and complex terrain, which pose significant challenges to automatic identification. Therefore, taking pine nematode disease as an example, this paper proposes D-SCNet, an intelligent monitoring network for abnormal individuals applicable to UAV visible images. In this method, the convolutional block attention model and simplified dense block are introduced to enhance the semantic analysis ability of abnormal individual identification, use multi-level information of abnormal individuals well, enhance feature transfer as well as feature weights between network layers, and selectively focus on abnormal features of individuals while reducing feature redundancy and parameter and improving monitoring accuracy and efficiency. This method uses lightweight deep learning models through weak information sources to achieve rapid monitoring of a large range of abnormal individuals in complex environments. With the advantages of low cost, high efficiency, and simple data sources, it is expected to further enhance the practicality and universality of intelligent monitoring of anomalous individuals by UAV remote sensing.

Floods can change the physicochemical factors of the water body and the zooplankton community. In the summer of 2020, Huayanghe Lake experienced floods. Here, eight cruises were conducted in Huayanghe Lake from 2020 to 2022 to study the response of environmental factors and the zooplankton community to the floods. The results demonstrated that floods increased the concentrations of total nitrogen, total phosphorus and chlorophyll a. In addition, during the floods, the number of rotifer species increased, while the number of cladoceran and copepod species decreased. Floods also reduced the average density and biomass of zooplankton. The results of Pearson correlation analysis and redundancy analysis showed that environmental factors, such as water depth, water temperature, transparency, nitrogen and phosphorus concentration, conductivity, coverage of aquatic vegetation and chlorophyll a, were closely related to the seasonal dynamics of zooplankton in Huayanghe Lake. Our research emphasizes that zooplankton can quickly respond to floods, providing data support for the ecological relationship between flooding and the zooplankton community, which is crucial for the preservation and restoration of the lake water ecosystem.

The zooplankton community composition in shallow lakes is influenced by numerous factors, such as environmental factors and the land use patterns around the lake. To investigate the interaction between the spatial differences in the zooplankton community structure, aquatic parameters, and land use patterns in the Lake Chen Yao complex (Lake Chen Yao and Lake Feng Sha), we assessed them in four seasons from October 2020 to August 2021. The results showed that the zooplankton density and biomass of Lake Chen Yao were higher than the latter. The results of Pearson correlation and RDA analysis revealed that electrical conductivity (EC), Chlorophyll a (Chl.a), dissolved oxygen (DO), and pH were the main environmental factors affecting the zooplankton community structure in the two lakes. The nutrient content of nitrogen (N) and phosphorus (P) were significantly higher in Lake Chen Yao, and there was a considerable relationship with the distribution of land use patterns around the two lakes. The land use patterns were the main reason for the difference in water quality and thus the spatial variation in the characteristics of the zooplankton communities in the two lakes.

Riparian vegetation is crucial for maintaining terrestrial and aquatic biodiversity, but it is threatened by land-use activities. To assess the ecological impacts of riparian vegetation conversion to an oil tea (Camellia oleifera) plantation, we quantified the responses of small mammals in two natural habitats (mature forest and flood-meadow) and in Camellia forests at the community, population, and individual level. We found that the community diversity was similar between Camellia forests and mature forests, but higher than the flood-meadow. Meanwhile, the community composition differed across three habitats, with Camellia forests favoring habitat generalist species. At the population level, Camellia forests and flood-meadow had a similar population density, which were higher than mature forests. At the individual level, Rattus nitidus was less sensitive to this conversion, but the body condition index of Niviventer confucianus was higher in Camellia forests than in mature forests, and Apodemus agrarius in Camellia forests had more ectoparasite load than in the flood-meadow, indicating a species-specific response to the impacts of oil tea plantation. Our study highlights that the occurrence of habitat generalist species and high ectoparasite loads may threaten regional biodiversity and increase the risk of parasite transmission with enlarging the oil tea plantation area within riparian zones.