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.

Maximizing the carbon sequestration of forested land is important for achieving carbon neutrality. Although some studies have discussed forest carbon sequestration efficiency (FCSE) from the perspective of total factor production, it is being increasingly recognized that forestland use regulates sequestration and emissions. When viewing forestland use as input and carbon emissions as output, there is a lack of empirical evidence on FCSE and its influencing factors. Here, a superefficiency slacks-based measurement model was applied to estimate FCSE for 66 counties in Zhejiang Province, China. The influencing factors and spatial spillover effects of FCSE were also analyzed using a spatial autocorrelation model. The findings showed that over the sample observation period, county FCSE ranged from 0.199 to 1.258, with considerable gaps. The global Moran’s I index showed that county-level FCSE was markedly spatially autocorrelated. Spatially, forestland use, cutting, pests, and diseases had negative spatial spillover effects on FCSE, whereas average annual temperature and precipitation displayed positive spillover effects. These findings suggest that the overall coordination of forest resource supervision and management among counties should be strengthened. The implementation of forestry management models aimed at consolidating or increasing forest carbon sequestration should be emphasized to improve forest quality, thereby promoting FCSE enhancement.

Natural broadleaf forests (NBFs) are the most abundant zonal vegetation type in subtropical regions. Understanding the mechanisms influencing stand productivity in NBFs is important for developing “nature-based” solutions for climate change mitigation. However, minimal research has captured the effects of nonlinearities and feature interactions that often have nonlinear impacts on stand productivity and influencing factors. To address this research gap, we used continuous forest inventory data, and a machine learning model for stand productivity of NBFs was constructed. Subsequently, through leveraging the interpretable machine learning framework of the SHapley Additive explanation (SHAP) and partial dependence plot, we determined global and local explanations of the influencing factors of stand productivity. Our findings indicate the following: (1) The Autogluon model performed the strongest based on R², RMSE, and rRMSE metrics. (2) The basal area (BA), neighborhood comparison of diameter at breast height (NC), and stand age (AGE) were the key influencing factors. Stand productivity increased with increasing BA and decreased with increasing NC and AGE. BA was maintained above 15 m²ha⁻¹ and NC was maintained below 0.45, which represent favorable conditions for NBFs to maintain optimal growth. (3) SHAP interaction values were calculated to determine the effects of the five major interactions on stand productivity. Our study provides a reference for the sustainable management of NBFs, thereby highlighting the important role of forests in mitigating climate change.

Ecological stoichiometry provides a useful lens for linking nutrient status to ecosystem functioning, but cross-compartment (green leaves, surface litter, and topsoil) evidence for subtropical secondary forests is still limited. In particular, it remains unclear how forest type regulates coupled carbon (C), nitrogen (N), and phosphorus (P) patterns in leaves, litter, and soils on P-retentive Acrisols and how these patterns can be used to infer nutrient limitations. We measured C, N, and P concentrations and stoichiometric ratios in leaves, surface litter, and topsoil (0–10 cm) from 38 plots representing four dominant forest types (shrub, coniferous, mixed coniferous–broadleaf, and broadleaf) in subtropical public welfare forests of eastern China. We compared elemental concentrations and ratios among forest types and compartments and examined cross-compartment associations. Forest-type differences in stoichiometric patterns were most pronounced for leaf and soil concentrations/ratios, whereas litter metrics were comparatively conservative. Coniferous stands had the highest leaf C concentration and the highest litter C:N and C:P ratios, together with relatively low soil N and P concentrations. Broadleaf stands had the highest soil C and N concentrations and the highest litter and soil N:P, suggesting a tendency toward P limitation under comparatively N-rich conditions. Shrub and mixed forests were intermediate, with shrubs exhibiting the lowest litter N:P. Leaf N:P averaged 7.5 in coniferous stands and 12.5–14.9 in mixed and broadleaf stands. Coherent correlations of C:P from leaves to litter and soils and a negative relationship between leaf N:P and soil C:N suggested coordinated stoichiometric linkages along the leaf–litter–soil continuum. Overall, the results show that forest type organizes plot-scale C:N:P coupling on Acrisols and that leaf–litter–soil stoichiometry can be used as a practical framework for identifying whether N- versus P-related constraints are more likely to dominate different subtropical forest types and for informing nutrient-aware restoration and management.