The Yanshan-Taihang Mountain forest area is located in the northern farming-pastoral and forestry-grassland transition zones of China. It is a critical ecological security barrier for North China and a key area for wildlife distribution. The topography of the study area consists primarily of mid-low mountains and hills, with elevations ranging between 500 and 2000 meters. The region exhibits complex landforms and diverse habitat units. It is characterized by a temperate monsoon climate, with an annual average temperature of approximately 4˚C - 10˚C and annual precipitation ranging from 400 to 700 mm, with uneven seasonal distribution.

The regional forest vegetation is dominated by mixed coniferous and broadleaf forests, with key species including Chinese pine (Pinus tabuliformis), North China larch (Larix principis-rupprechtii), oaks (Quercus spp.), and birch (Betula spp.). The forest structure exhibits significant variation due to historical management practices and human activity. The area has considerable research value for studying habitat quality improvements, with diverse management types and structural differences.

The study data were collected from three primary sources: 1) Forest Resource Data: Sourced from continuous forest surveys and plot investigations [26]-[28]. 2) Wildlife Data: Obtained from transect surveys, infrared camera monitoring, and historical records. 3) Management and Socio-Economic Data: Sourced from forestry management departments’ statistical records and field interviews.

A “Transect-Plot-Quadrat” nested survey and monitoring system is set up, where the transects cover the plots. In each experimental area, 9 to 12 transects are established, each ranging from 500 to 1000 meters in length and 5 meters in width. Each forest type is divided into three management treatments (control, conventional management, biodiversity-oriented management), with three replications per treatment, totaling nine plots. The size of each plot is either 20 m × 30 m or 30 m × 30 m, with the plot groups arranged along the vertical contour lines. A total of 27 to 36 plots are established. One infrared camera is deployed in the center or a typical location of each plot. In each plot, 1 to 3 shrub quadrats (5 m × 5 m) and 3 herb quadrats (1 m × 1 m) are set up to conduct plant diversity surveys.

Habitat quality is a multi-dimensional and scale-dependent concept, and a single indicator is insufficient to fully reflect its variation [13][21]. This study centers on improving wildlife habitat quality as the primary goal and adopts a layered construction approach that balances ecological significance and data accessibility. A multi-tiered evaluation system was developed, comprising a goal layer, a criterion layer, and an indicator layer [29][30].

3.1.1. Structure of the Indicator System

The evaluation system includes:

Goal Layer: Overall improvement of wildlife habitat quality through forest management.Criterion Layer: Comprising four main dimensions—biodiversity, forest structure, ecological functions, and socio-economic and management factors.Indicator Layer: Comprising 35 specific indicators across these dimensions.

This system systematically reflects how forest management measures, through structural adjustments, functional support, and management constraints, impact habitat quality. The goal layer reflects the comprehensive effect of forest management measures on improving habitat conditions, enhancing ecosystem support capacity, and stabilizing wildlife populations. The criterion layer systematically divides the key factors influencing habitat quality across biodiversity, ecological structure, ecological function, and socio-economic management.

3.1.2. Indicator Selection and Ecological Significance

The indicator system encompasses various factors influencing habitat quality:

Biodiversity: Includes plant and animal species richness, diversity indices, and the frequency of indicator species [31]-[35].Forest Structure: Includes indicators such as canopy closure, species mixture, and structural diversity (e.g., the proportion of large-diameter trees, standing deadwood density) [36]-[40].Ecological Functions: Includes indicators related to productivity, soil health, hydrological functions, and carbon storage [18][41].Socio-Economic and Management Factors: Includes the implementation of management measures, community participation, ecological compensation, and forest product utilization [42]-[45].

These indicators (Table 1) were selected based on a thorough literature review, expert consultations, and the ability to obtain reliable data from the study area. The selection ensures a comprehensive representation of both ecological and management dimensions.

Table 1. Comprehensive evaluation system for forest management oriented to wildlife habitat quality.

Note: Standing dead tree (SDT) and downed dead wood (DDW), as key structural components of forest ecosystems, provide nesting sites, shelter, and food resources for a wide range of birds, mammals, and invertebrates. Their abundance and spatial distribution are widely regarded as important indicators of forest habitat quality and the degree of near-naturalness [46][47]. The calculation methods for the evaluation indicators listed in Table 1 are derived from commonly accepted definitions in forestry and ecology, and they systematically characterize forest habitat quality from multiple dimensions, including biological composition, forest structure, ecological functions, and management practices. Each indicator is first calculated based on plot surveys, monitoring data, or statistical records, and then rendered dimensionless using the min-max normalization method to eliminate the effects of differences in units and orders of magnitude among indicators.

3.2.1. Weight Determination Using the Analytic Hierarchy Process (AHP)

The Analytic Hierarchy Process (AHP) was employed to determine the relative weights of the indicators in the evaluation system. A pairwise comparison matrix was constructed using the 1 - 9 scale method proposed by Saaty, and the consistency of the judgments was tested [48]. To ensure the scientific validity of the weight allocation, it is recommended to consult 15 experts, covering the fields of forest ecology, wildlife conservation, and forest management, including 8 professors/researchers and 7 senior engineers. The Analytic Hierarchy Process (AHP) will be employed to calculate the weights of the evaluation indicators, with consistency checks performed to ensure the reliability of the results, thereby guaranteeing the scientific and rational distribution of the weights. The resulting weight values (Table 2) reflect the relative importance of each indicator in the overall evaluation system [48][49].

Table 2.Weighting and direction of evaluation system indicators.

In this context, a i j represents the relative importance of the i-th indicator compared to the j-th indicator, satisfying the conditions: a i j > 0 , a j i = 1 / a i j , and a i i = 1 .

Once the judgment matrix is constructed, the weights of the indicators are calculated using the eigenvector method. Let λ max denote the maximum eigenvalue of the judgment matrix, and W be the corresponding eigenvector. After normalization, the indicator weight vector is obtained.

To ensure the logical consistency of the expert judgments, a consistency test is performed on the judgment matrix. The consistency index ( C I ) and consistency ratio ( C R ) are calculated using the following formulas:

where R I is the random consistency index. If C R < 0.10 , the judgment matrix passes the consistency test, indicating that the weight distribution is logically consistent and statistically reasonable. Otherwise, the judgment matrix needs to be revised. In this study, all hierarchical judgment matrices satisfy the consistency test requirements.

3.2.2. Standardization of Indicators

To ensure comparability between indicators with different units and scales, all data were standardized using an extreme value normalization method. This transformation ensures that each indicator contributes on a comparable scale, eliminating the effects of dimensional differences.

3.2.3. Composite Evaluation Index

The composite habitat quality index (HQI) was calculated by summing the weighted, standardized values of each indicator. This index provides a quantitative measure of habitat quality, with higher values indicating better habitat conditions. The formula for the composite evaluation index is as follows:

where:

w i is the weight of indicator i, x ′ i is the standardized value of indicator i,n is the total number of indicators.

The biodiversity dimension, which includes indicators such as plant and animal species richness, diversity indices, and indicator species frequency, played a significant role in the overall evaluation of habitat quality. The standardized values and contribution rates of these indicators emphasize the importance of species richness and diversity in driving habitat quality improvements (Figure 1). Figure 1 shows the normalized values and contribution values of various biodiversity indicators.

Figure 1. Graph of biodiversity indicators value.

Plant Species Richness: The standardized value for plant species richness was 0.786, indicating that plant species diversity plays a crucial role in improving habitat quality. It was one of the key drivers of biodiversity in the study area.Animal Species Richness: Similarly, the standardized value for animal species richness was 0.787, which reflects a high level of animal diversity in the study area. This indicator also demonstrated a significant impact on the overall habitat quality evaluation.Animal Diversity Index: The standardized value for the animal diversity index was 0.833, suggesting that the diversity of animal communities contributes to maintaining the complexity and functionality of the habitat. Though it slightly lagged behind plant species richness, it was still a critical factor for the habitat’s overall quality.Frequency of Indicator Species: The standardized value for the frequency of indicator species was 0.750, indicating that while this indicator positively influences habitat quality, its contribution was somewhat lower compared to species richness. This is likely due to the slower response of indicator species to ecological changes.

Overall, the biodiversity dimension made a significant contribution to the habitat quality index (contribution value: 0.2365), with plant and animal species richness being the dominant contributors. These results emphasize the core role of biodiversity in determining habitat quality in the study area.

The ecological structure dimension, which includes indicators such as canopy closure, species mixture, and landscape fragmentation, had a significant impact on habitat quality. This dimension accounted for 25.54% of the overall contribution to the habitat quality index (Figure 2). Figure 2 presents a graph displaying the normalized values and contribution values of ecological structure indicators.

Figure 2. Graph of ecological structure indicators.

Canopy Closure: The canopy closure index had a high contribution (0.750), reflecting the importance of forest cover in providing suitable shelter and regulating microhabitats for wildlife.Species Mixture: The species mixture index, with a standardized value of 1, highlighted the significant positive effect of mixed-species forests on habitat complexity and biodiversity. This indicator was essential in promoting habitat heterogeneity.Landscape Fragmentation: The landscape fragmentation index had a standardized value of 0.480, which was one of the few negative indicators. It reflects the degree of habitat fragmentation in the study area, which limits the movement and population stability of wildlife. This index emphasizes the need to improve habitat connectivity and reduce fragmentation.

The presence of large-diameter trees, standing dead tree, and downed dead wood also contributed to the habitat quality by providing critical resources for various species, including nesting sites, shelter, and food. These features play a vital role in maintaining ecological diversity.

The ecological function dimension, which evaluates the forest’s role in energy production, soil health, water retention, and carbon storage, contributed 16.22% to the overall habitat quality index (Figure 3). Figure 3 presents a graph comparing the normalized values and contribution values of ecological function indicators.

Figure 3. Graph of ecological function indicators.

Net Primary Productivity (NPP): The standardized value for NPP was 0.875, indicating the forest’s high capacity for energy production. This high productivity supports the entire forest ecosystem and is vital for sustaining biodiversity.Soil Organic Matter Content: The standardized value for soil organic matter content was 0.829, reflecting the forest’s ability to maintain soil fertility and support plant life, which in turn enhances the habitat quality for wildlife.Water Conservation Capacity: The standardized value for water conservation capacity was 0.806, highlighting the forest’s role in stabilizing water availability and supporting wildlife habitat during dry periods.

While these indicators had a moderate impact, the results demonstrate that ecological functions like water retention and soil quality are fundamental for sustaining habitat quality, particularly in maintaining long-term ecosystem stability.

The socio-economic and management dimension, which includes factors such as the implementation of management measures, community participation, and ecological compensation, contributed the least (14.87%) to the overall habitat quality evaluation (Figure 4). Figure 4 presents a graph illustrating the normalized values and contribution values of socio-economic and management indicators.

Figure 4. Graph of socio-economic and management indicators.

Implementation of Management Measures: The standardized value for the implementation rate of forest management measures was 0.857, indicating that effective management practices are vital for ensuring habitat quality.Community Participation: The standardized value for community participation was 0.813, emphasizing the role of local involvement in forest management and ecosystem restoration efforts.Ecological Compensation: The ecological compensation index, with a standardized value of 0.786, showed that while compensation measures are important, their effectiveness still requires improvement.

Despite their lower contribution, socio-economic and management indicators were essential for regulating human impacts and ensuring the long-term improvement of habitat quality. The results underscore the importance of sustainable management and the integration of local communities in the conservation process.

Using the composite HQI, the study area was classified into five habitat quality categories: high, relatively high, moderate, relatively low, and low (Table 4). The HQI values for the study area ranged between 0.80 and 0.89, with a mean value of approximately 0.803, indicating that the overall habitat quality in the area is relatively high.

Table 4. Classification of habitat quality based on HQI scores.

Notes: HQI is a composite metric ranging from 0 to 1, reflecting habitat integrity, biodiversity, ecosystem function, and resilience. Higher HQI values indicate higher-quality habitats closer to reference or climax conditions.

The results showed that the forest management practices in the study area have led to substantial improvements in habitat quality, especially in terms of biodiversity and ecological structure. However, challenges remain in enhancing the ecological functions and socio-economic factors that support long-term habitat improvement.

The results underscore the importance of biodiversity and forest structure in determining habitat quality. Biodiversity, particularly plant and animal species richness, was the dominant factor driving habitat quality variations in the study area. This finding aligns with previous studies that emphasize biodiversity as a fundamental driver of ecosystem functioning and resilience. Plant and animal diversity contribute significantly to the stability and adaptability of ecosystems, which are essential for maintaining the ecological services that support wildlife populations

The contribution of forest structure indicators—such as canopy closure, species mixture, and the proportion of large-diameter trees, standing dead tree, and downed dead wood—further reinforces the importance of habitat heterogeneity. These features create diverse microhabitats that support a wide range of species, improving ecosystem complexity and biodiversity. Our findings suggest that enhancing forest structural complexity should be a priority in forest management practices, especially in regions facing habitat degradation due to over-exploitation and fragmentation.

However, the landscape fragmentation indicator revealed a limitation in the study area’s ecological connectivity. The relatively low contribution of this negative indicator (landscape fragmentation index) suggests that despite improvements in forest structure, habitat connectivity remains a challenge. Fragmented habitats hinder the movement of species, affecting gene flow and the long-term survival of populations. This result is consistent with research highlighting the importance of ecological corridors and habitat connectivity in mitigating the effects of fragmentation. To address this issue, future management strategies should focus on creating ecological corridors and reducing human-induced disturbances, such as road construction and urban expansion.

The ecological function indicators, such as NPP, soil organic matter content, and water retention capacity, also played a crucial role in supporting habitat quality. NPP, which reflects the forest’s capacity for energy production, and soil organic matter, which indicates soil fertility, both had high standardized values. These findings highlight the forest’s critical role in sustaining primary productivity, which supports the entire food web, from plants to herbivores and predators.

Despite the positive results, water retention capacity and carbon storage indicators indicated areas for improvement. Water retention, essential for maintaining habitat stability during droughts, showed significant potential for improvement. Similarly, increasing the forest’s carbon storage capacity would enhance its ability to sequester carbon, contributing to climate change mitigation efforts. These results point to the need for forest management strategies that not only focus on biodiversity conservation but also address the forest’s broader ecological functions.

While the socio-economic and management indicators contributed less to the overall habitat quality index, they still play an essential role in regulating human impacts and ensuring the long-term sustainability of habitat improvements. The high contribution of community participation and the implementation of management measures suggests that local communities and effective forest management practices are crucial for ensuring that conservation and restoration efforts are successfully carried out. The positive effects of community participation in forest management have been well-documented, as local stakeholders are more likely to support and maintain management initiatives.

However, the relatively lower contribution of ecological compensation and forest product utilization indicates that further efforts are needed to ensure that these socio-economic mechanisms are effectively integrated into forest management practices. Ecological compensation policies should be strengthened to provide sustainable economic incentives for conservation and restoration, especially in regions where local livelihoods depend heavily on forest resources. Similarly, forest product utilization should be managed sustainably to prevent over-harvesting and ensure that forests continue to provide long-term ecological benefits.

The findings of this study have significant implications for forest management and policy, particularly in regions experiencing forest degradation and biodiversity loss. The proposed evaluation system emphasizes the importance of multi-dimensional forest management that integrates ecological, social, and economic factors. The focus on wildlife habitat quality as a core evaluation goal provides a more comprehensive and ecologically relevant framework for assessing forest management practices. By incorporating biodiversity, ecological structure, ecological functions, and socio-economic factors into the evaluation system, the study highlights the need for a holistic approach to forest management that considers the interdependence of ecosystem health, human activities, and biodiversity conservation.

The study also provides valuable insights into the role of near-natural forest management in enhancing habitat quality. The findings suggest that forest management practices that prioritize the restoration of native species, enhance forest structure, and reduce human-induced disturbances are likely to yield the greatest benefits for wildlife habitat quality. This approach aligns with the global trend towards rewilding and ecosystem-based management practices, which aim to restore ecological processes and enhance the resilience of ecosystems to climate change.