Most butterfly larvae are agricultural pests and forest pests, but butterflies have important ornamental value and the ability to sense and respond to changes in the ecological environment. There are many types of butterflies, and the research on classification of butterfly species is of great significance in practical work such as environmental protection and control of agricultural and forest pests. Butterfly classification is a fine-grained image classification problem that is more difficult than generic image classification. Common butterfly photos are mostly specimen photos (indoor photos) and ecological photos (outdoor photos/natural images). At present, research on butterfly classification is more based on specimen photos. Compared with specimen photos, classification based on ecological photos is relatively difficult. This paper mainly takes ecological photos as the research object, and presents a new classification network that combines the dilated residual network, squeeze-and-excitation (SE) module, and spatial attention (SA) module. The SA module can make better use of the long-range dependencies in the images, while the SE module takes advantage of global information to enhance useful information features and suppress less useful features. The results show that the integrated model achieves higher recall, precision, accuracy, and f1-score than the state-of-the-art methods on the introduced butterfly dataset.

Ecological fire prevention forest belts can effectively alleviate the spread of forest fires and reduce the harm caused by forest fires. Exploring the distribution and changes in suitable growth areas for fire-resistant forest species under the effects of climate change can provide effective references for the introduction of ecological fire prevention and tree species preservation in the region. This study is based on the distribution data of six typical ecological fire prevention forest species in the subtropical regions of China. The maximum entropy model (MaxEnt), optimized by the ENMeval data package, was used to analyze the potential relationship between the ecological environment variables and fire prevention forest species. The potential distribution of certain tree species in the historical period and in future periods is simulated. In addition, the area changes, migration trends, and stable areas of tree species under climate change are also discussed. The research results indicated the following: (1) The AUC values of the optimized model are all higher than 0.9, indicating the optimal prediction results. (2) The climate variables that have the greatest impact on the suitable habitat of Schima superba were the annual mean temperature, precipitation of the driest month, and mean diurnal range. Quercus glauca was mainly influenced by the minimum temperature of the coldest month and the precipitation of the warmest quarter. Castanopsis eyrei was mainly influenced by the precipitation of the driest month and the annual precipitation. The distribution of suitable growth areas for Symplocos sumuntia is mainly influenced by the precipitation of the driest month. The distribution of Camellia oleifera was influenced by the minimum temperature of the coldest month. The potential habitat distribution of Photinia serratifolia was greatly influenced by annual precipitation. (3) Until 2090, the expansion degree of the suitable growth area will be Symplocos sumuntia (51.05%) > Schima superba (19.41%) > Camellia oleifera (10.14%) > Quercus glauca (6.80%) > Castanopsis eyrei (2.34%) > Photinia serratifolia (−6.97%). (4) The centroid of Schima superba will migrate northward. Quercus glauca will migrate northeast. The suitable areas for the migration of Symplocos sumuntia and Castanopsis eyrei will move in a northwest direction, with repeated changes in alum migration, as well as with the largest migration span for Castanopsis eyrei. In addition, Camellia oleifera will move southwest. The centroid of Photinia serratifolia will migrate to the southeast. (5) The six fire-resistant tree species in this study were noted to have excellent stability in Guizhou, Hunan, Jiangxi, Fujian, Guangdong, and Guangxi. This conclusion can provide an effective reference for the introduction of ecological fire prevention tree species and the protection of tree species under climate change in subtropical forest-fire-prone areas in China.

The forest mortality models developed so far have ignored the effects of spatial correlations and climate, which lead to the substantial bias in the mortality prediction. This study thus developed the tree mortality models for Prince Rupprecht larch (Larix gmelinii subsp. principis-rupprechtii), one of the most important tree species in northern China, by taking those effects into account. In addition to these factors, our models include both the tree—and stand—level variables, the information of which was collated from the temporary sample plots laid out across the larch forests. We applied the Bayesian modeling, which is the novel approach to build the multi-level tree mortality models. We compared the performance of the models constructed through the combination of selected predictor variables and explored their corresponding effects on the individual tree mortality. The models precisely predicted mortality at the three ecological scales (individual, stand, and region). The model at the levels of both the sample plot and stand with different site condition (block) outperformed the other model forms (model at block level alone and fixed effects model), describing significantly larger mortality variations, and accounted for multiple sources of the unobserved heterogeneities. Results showed that the sum of the squared diameter was larger than the estimated diameter, and the mean annual precipitation significantly positively correlated with tree mortality, while the ratio of the diameter to the average of the squared diameter, the stand arithmetic mean diameter, and the mean of the difference of temperature was significantly negatively correlated. Our results will have significant implications in identifying various factors, including climate, that could have large influence on tree mortality and precisely predict tree mortality at different scales.

[Objective] To quantify key ecosystem services within the Dongting Lake region, clarify the trade-off/synergy relationships, and detect the driving factors in order to support the ecological sustainable development of the Dongting Lake region. [Methods] Using the InVEST model, taking the area around Dongting Lake as the study area, four ecosystem services including water yield, carbon storage, soil conservation, and habitat quality were quantitatively assessed. Interdependencies between ecosystem services were assessed using correlation analysis to quantify trade-offs/synergies, and the geodetector model was used to detect their driving factors. [Results] (1) From 2000 to 2020, the soil retention service and water yield service in the Dongting Lake area showed an increasing trend over time. The total water yield increased from 4.93 × 10¹⁰ m³ to 6.71 × 10¹⁰ m³, while the total soil retention increased from 4.46 × 10⁹ t to 5.77 × 10⁹ t; habitat quality and total carbon storage continued to decline, with habitat quality decreasing from 0.6906 to 0.6785 and carbon storage decreasing from 1.480 × 10⁹ t to 1.476 × 10⁹ t. (2) In the study area, significant synergistic effects existed between carbon storage and habitat quality, carbon storage and soil retention, carbon storage and water yield, habitat quality and soil retention, and soil retention and water yield. However, there was a significant trade-off relationship between habitat quality and water yield. (3) During the study period, ecosystem service trade-offs and synergy relationships in the Dongting Lake area were jointly influenced by natural factors and human activities. Ranked by the magnitude of driving factor influence, they were land use type, land use intensity, vegetation coverage, temperature, and nighttime light. [Conclusions] Synergies dominated the ecosystem services in the research region, and the influence of natural factors behind them was greater than that of human activities. These research conclusions offer a scientific foundation for the institutional construction of the ecological compensation mechanism in the Dongting Lake basin.

Climate change and human disturbance are critical factors affecting the habitat distribution of wild animals, with implications for management strategies such as protecting migration corridors, habitat restoration, and species conservation. In the Hupingshan National Nature Reserve (NNR), Reeve’s muntjac (Muntiacus reevesi) is a key prey species for the South China tiger (Panthera tigris amoyensis), which is extinct in the wild and targeted for reintroduction by the Chinese government. Thus, understanding the habitat distribution and abundance of Reeve’s muntjac is essential to ensure the survival and sustainability of reintroduced tiger populations. Despite significant conservation efforts, the impacts of climate change and human disturbance on Reeve’s muntjac habitat distribution in Hupingshan NNR remain unclear, though these factors could necessitate adaptive tiger management strategies due to shifts in prey abundance. In this study, we employed an optimized MaxEnt model to assess current habitat distribution and identify key environmental variables influencing the habitat distribution of Reeve’s muntjac. Assuming non-climatic environmental factors will remain constant over the next century, we projected future habitat distribution under two shared socioeconomic pathways (SSP126 and SSP585) for the mid-century (2050s) and the late-century (2090s). Comparative analyses of current and projected habitat areas revealed potential impacts of climate change on this species. MaxEnt outputs classified habitat suitability into high, medium, and low levels. Results showed that climatic and human disturbance factors contributed 35.2% and 49.4% to habitat suitability, respectively. Under the SSP126 scenario, habitats with decreased suitability covered 0 km² in the 2050s, expanding slightly to 4.2 km² in the 2090s, while those with increased suitability spanned 491.1 km² (2050s) and 463.2 km² (2090s). Under the SSP585 scenario, habitats with decreased suitability covered 10.2 km² in the 2050s and 431.8 km² in the 2090s. Habitats with increased suitability were comparatively smaller under SSP585, covering 162.0 km² (2050s) and 1.1 km² (2090s). These findings suggest that while mid-century climate projections (SSP126 and SSP585) may support Reeve’s muntjac’s survival, habitat loss is projected by 2090s (SSP126). Thus, future climate change may lead to decreased habitat suitability and increased fragmentation, raising extinction risks for Reeves’s muntjac. Mitigating these effects could involve establishing migration corridors, minimizing human disturbances, and potentially supplementing prey populations with captive-bred prey. Such measures are essential to support the South China tiger reintroduction plan and help ensure that prey availability remains sufficient for sustaining reintroduced tiger populations.

Li, X.; Zhang, G., and Li, J., 2020. Spatial structure evaluation of natural secondary forest around Dongting Lake based on entropy weight - cloud model. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 484–489. Coconut Creek (Florida), ISSN 0749-0208.In order to provide theoretical support for the formulation of optimized measures of stand spatial structure, 15 sample plots are set up in Daweishan Nature Reserve, Longhushan national forest farm in Yuanjiang City and Lutou national forest farm in Pingjiang County to analyze and evaluate the stand spatial structure of natural secondary forest around Dongting Lake. After determining the spatial structure unit of trees by Voronoi diagram, the five indicators including mingling degree, neighborhood comparison, openness comparison index, neighborhood pattern and angle competition index are segmented to correspond to the five grades of stand spatial structure divided by the combination of qualitative and quantitative methods. Then through the comprehensive assessment on stand spatial structure by entropy weight-cloud model, the fifteen plots are evaluated as grade III and grade IV, respectively accounting for two-thirds and one-thirds of all plots, which indicates that the reasonable stand spatial structure of natural secondary forest around Dongting Lake is above the average. As a new evaluation method of stand spatial structure, the entropy weight-cloud model has solved the issue of qualitative and quantitative conversion, and objectively calculate the weights of indicators, so as to enrich the evaluation methods of stand spatial structure, overcome the one-way evaluation by single indicator, improve the objectivity and scientificity of evaluation results, and provide theoretical basis for the scientific guidance of stand spatial structure optimization.