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Article

Predicting the Population Size and Potential Habitat Distribution of Moschus berezovskii in Chongqing Based on the MaxEnt Model

by
Qing Liu
1,
Huilin Liu
1,
Xiaojuan Cui
1,*,
Jianjun Peng
1,*,
Xia Wang
2,
Ling Shen
2,
Minqiang Zhang
2,
Lixia Chen
2 and
Xin Li
2
1
School of Life and Health Sciences, Hunan University of Science and Technology, Xiangtan 411201, China
2
Chongqing Jinfo Mountain National Nature Reserve Management Affairs Center, Chongqing 408400, China
*
Authors to whom correspondence should be addressed.
Forests 2024, 15(8), 1449; https://doi.org/10.3390/f15081449
Submission received: 10 July 2024 / Revised: 12 August 2024 / Accepted: 15 August 2024 / Published: 16 August 2024
(This article belongs to the Special Issue Biodiversity in Forests: Management, Monitoring for Conservation)
Browse Figures
Figure 1
<p>The filtered distribution points and study area of <span class="html-italic">Moschus berezovskii</span>.</p> "> Figure 2
<p>Spearman correlation test among the 12 selected environmental variables (the names of all the variables are summarized in <a href="#forests-15-01449-t001" class="html-table">Table 1</a>). Positive correlations are displayed in red and negative correlations in a blue color. The color intensity and the size of the circle are proportional to the correlation coefficients.</p> "> Figure 3
<p>Optimization results for the MaxEnt model using different parameters. (<b>a</b>) AUC.diff.Avg; (<b>b</b>) AUC.Val.Avg; (<b>c</b>) delta.AICc; and (<b>d</b>) Or.10p.Avg. L = linear; Q = quadratic; P = product; T = threshold; H = hinge.</p> "> Figure 4
<p>The 12 environmental variables evaluated by the Jackknife method.</p> "> Figure 5
<p>Response curves of habitat suitability for major environmental factors: (<b>a</b>) elevation; (<b>b</b>) NDVI; (<b>c</b>) slope; (<b>d</b>) land-use type. For (<b>d</b>), 10 = Cropland; 20 = Forest; 30 = Grassland; 40 = Shrub; 50 = Wetland; 60 = Waterbody; and 80 = Built-up land. The red line represents the average value of all candidate models, and the blue range indicates the standard deviation.</p> "> Figure 5 Cont.
<p>Response curves of habitat suitability for major environmental factors: (<b>a</b>) elevation; (<b>b</b>) NDVI; (<b>c</b>) slope; (<b>d</b>) land-use type. For (<b>d</b>), 10 = Cropland; 20 = Forest; 30 = Grassland; 40 = Shrub; 50 = Wetland; 60 = Waterbody; and 80 = Built-up land. The red line represents the average value of all candidate models, and the blue range indicates the standard deviation.</p> "> Figure 6
<p>Potential habitat distribution of <span class="html-italic">M. berezovskii</span> in the Chongqing area.</p> ">
Versions Notes

Abstract

:
The forest musk deer (Moschus berezovskii) is a national Class I protected wild animal in China, and the IUCN Red list classifies it as globally endangered. It has significant value in traditional Chinese medicine and spices. However, wild M. berezovskii has faced a severe population decline due to human hunting, habitat loss, and fragmentation. Thus, studying its population size and distribution pattern is of great importance to develop effective conservation measures. Here, we determined the optimal MaxEnt model and used stratified sampling and the fecal pile counting method to predict the population size and potential habitat distribution of wild M. berezovskii in Chongqing using 133 species distribution points and 28 environmental variables. The results were as follows: (1) When the optimal model parameters were RM = 3.5 and FC = LQHPT, it had high model prediction accuracy (AUC = 0.909 ± 0.010, TSS = 0.663). (2) Under various climatic, topographic, vegetation, and anthropogenic disturbance scenarios, M. berezovskii was primarily distributed in northern, eastern, southwestern regions of Chongqing, covering an area of approximately 5562.80 km2. (3) The key environmental factors affecting the potential habitat distribution of M. berezovskii were elevation (36.5%), normalized difference vegetation index (NDVI, 16.6%), slope (11.8%), and land-use type (7.6%), whereas climate and anthropogenic disturbance factors had relatively little influence. (4) A population estimation for M. berezovskii identified approximately 928 ± 109 individuals in Chongqing. We recommend prioritizing the preservation of high-altitude habitats and native vegetation to mitigate human interference and minimize road damage. In summary, our results can enhance the understanding of M. berezovskii distribution and provide a basis for effective conservation and management initiatives.

1. Introduction

Various regions of the world are currently facing biodiversity crises [1]. Climate change, accelerated urbanization, agricultural land expansion, and increasing infrastructure development have led to severe habitat fragmentation for many wild animals [2]. This fragmentation isolates populations and impedes gene exchange, exacerbating the loss of genetic diversity within species [3,4]. Determining the potential habitat distribution and population size of threatened species is crucial for effective conservation efforts [5].
Moschus berezovskii (M. berezovskii), a kind of forest-dwelling musk deer, mainly inhabits mixed coniferous broad-leaved forests and evergreen broad-leaved forests [6], and was once widely distributed in southwest China and northeast Vietnam [7,8]. M. berezovskii has high economic and medicinal values owing to the musk it secretes [9], and has been subjected to sustained large-scale hunting. Coupled with habitat loss and fragmentation, this has led to a rapid decline in the population and it is endangered in many areas [10]. Therefore, M. berezovskii is listed as a Class I protected wild animal in China, and the IUCN Red list classifies it as globally endangered [11]. Recently, wild M. berezovskii have been discovered in protected and forest areas in Chongqing. From 2020 to 2023, wild M. berezovskii activities were captured by infrared cameras in the Simian Mountain Nature Reserve in Jiangjin District, Wang Erbao Nature Reserve in Wanzhou District, Yintiaoling Nature Reserve in Wuxi Country, and Wulipo Nature Reserve in Wushan Country. Using field surveys [12], the population size of M. berezovskii in the Jinfoshan Nature Reserve was estimated to be approximately 95 individuals. However, the distribution and population size of wild M. berezovskii in the Chongqing area remain unclear.
Previous studies have widely utilized various models, such as mechanistic [13,14], regression [15,16], and niche models [17,18], to predict the spatial distribution patterns of the target species habitats. Among them, Maximum entropy (MaxEnt) modelling is a common approach taken when modelling species distributions. Compared with the traditional ecological modeling methods, the MaxEnt model only requires species distribution point data and can obtain accurate prediction results even with relatively few distribution points [19]. It has been widely utilized for habitat distribution prediction for endangered plants and animals [20,21,22].
In this study, we adopted the optimal MaxEnt model and ArcGIS 10.8 software to predict the potential habitat distribution of M. berezovskii in Chongqing and estimate its population size. We also explored the effects of various environmental variables factors, such as climate, topography, vegetation, and anthropogenic factors on the habitats’ suitability to provide a scientific reference for the habitat protection and management of M. berezovskii in Chongqing.

2. Materials and Methods

2.1. Study Area

Chongqing (105°11′ E–110°11′ E, 28°10′ N–32°13′ N) is located in the transition zone between the Qinghai–Tibet Plateau and the central and downstream areas of the Yangtze River Plain, and has a total area of 8.24 × 104 km2 [23,24]. The region contains 38 districts and counties and exhibits diverse topographic and geomorphic features that are mainly dominated by mountains and hills, with higher elevations in the southeastern and northeastern regions [25].
Chongqing experiences a subtropical humid monsoon climate, with average annual temperatures ranging from 13–19 °C. Low temperatures in the winter average 6–8 °C, whereas summer high temperatures average 27–29 °C; the cumulative average precipitation is 1000–1100 mm [26]. There is a dense network of waterways, including the Yangtze, Jialing, and Wujiang rivers, which provide abundant water resources for local flora and fauna.
The area is rich in biodiversity, with over 6000 species of vascular plants, including Ginkgo biloba, Cathaya argyrophylla, Metasequoia glyptostroboides, and Thuja sutchuenensis. In addition, there are more than 800 species of terrestrial wild vertebrates, including Neofelis nebulosa, Trachypithecus francoisi, Manis pentadactyla, Pardofelis temminckii, Macaca mulatta, Selenarctos thibetanus, and Naemorhedus griseus.

2.2. Species Distribution Data

Distribution data for M. berezovskii were obtained through two methods. First, field line transect surveys were conducted in various nature reserves and forest areas according to elevation and habitat type. The length of the line transects varied based on local conditions, with the width approximately 50 m and the spacing between line transects greater than 1 km. During these surveys, signs of M. berezovskii activities, such as feces and hair, were recorded as well as geographical locations and habitat characteristics. Additional distribution data were sourced from historical literature and news reports. To avoid the spatial autocorrelation of the species distribution points [27], the Spatially Rarefy Occurrence Data for Species Distribution Models (SDMs) tool [28] in ArcGIS 10.8 was used, resulting in 133 validated M. berezovskii distribution points (Figure 1). These filtered data were converted to the CSV format for subsequent model calculations.

2.3. Selection of Environmental Variable Data

After synthesizing the existing studies on M. berezovskii habitat, we identified 28 environmental factors for analysis, including climatic, topographic, vegetation, and anthropogenic disturbance factor variables. Nineteen climatic factor variables were obtained from the WorldClim Global Climate Database (version 2.1. https://worldclim.org/ (accessed on 25 November 2023)) [29], and three topographic factors variables (elevation, slope, and aspect) were obtained from the Geospatial Data Cloud website (https://www.gscloud.cn, accessed on 7 December 2023). Three vegetation factor variables, including vegetation type, normalized difference vegetation index (NDVI), and land-use type, were all obtained from the Resource and Environment Science Data Platform (http://www.resdc.cn, accessed on 22 November 2023). Three anthropogenic disturbance factor variables, including distance to the nearest road, residential area, and river were obtained from the National Center for Basic Geographic Information (http://www.webmap.cn, accessed on 28 December 2023) 1:1,000,000 geographic vector data of 2021. These anthropogenic disturbance factor variable data were processed using the Euclidean distance tool in ArcGIS 10.8 software to obtain a raster file of the distances between each pixel from the nearest residential area, road, and river in the study area.
All 28 environmental variables were converted to ASCII raster format with a 90 m resolution by resampling, and unified into the WGS_1984_UTM_Zone_49N projection coordinate system. To minimize the negative impact of the correlation between environmental variables on the prediction accuracy of the model [30], firstly, the species distribution data and environmental data were pre-simulated in the MaxEnt model to determine the contribution rate of these environmental variables. Subsequently, SPSS software (version 27.0) was used to calculate the Spearman correlation coefficients for all environmental variables (Figure 2).
Variables that were correlated with one another |r| > 0.80 were eliminated, and variables with large contribution values were retained [31], resulting in a final selection of 12 environmental variables for predicting the habitat distribution of M. berezovskii (Table 1).

2.4. MaxEnt Model Parameter Optimization

The ENMeval software package (R4.3.2) [32] was used to optimize the model parameters to avoid the influence of overfitting on the model’s migration ability. The regularization multiplier (RM) ranged from 0.5 to 4 in increments of 0.5, and the feature combinations (FC) included six different combinations (L, LQ, H, LQH, LQHP, and LQHPT) [33]. Optimal parameters were selected base on statistical significance, omission rates below 5%, and a delta AICc value of less than 2 that reflected the fit quality and complexity of the candidate model (a delta AICc value of 0 is preferred) [34].
The 133 species distribution points and 12 environmental variables were loaded into MaxEnt 3.4.4 [35]. Then, 75% of the occurrence data were randomly allocated for modeling, with the remaining 25% reserved for model validation [36]. The model parameters were set to FC and RM after optimization, and the bootstrap method was utilized with 10 repetitions. Model outputs were in logistic format, with default settings for other parameters. The accuracy of the model prediction results was assessed using receiver operating characteristic (ROC) curves and the True Skill Statistic (TSS) [37,38], with a higher area under the ROC curve (AUC) and the TSS value indicating a higher prediction accuracy [39]. Generally, an AUC value of >0.9 demonstrates excellent model performance [40]. TSS ranges from −1 to 1.
To determine the suitable habitat area and distribution position of M. berezovskii, the classification thresholds for suitable habitats were set using the Maximum Training Sensitivity Plus Specificity (MTSS) and balance Training omission, Predicted area, and Threshold value (TPT) [41,42]. The MTSS = 0.21 and the TPT = 0.49, and the suitable habitats of M. berezovskii were categorized into three levels: unsuitable habitat (<0.21), moderately suitable habitat (0.21–0.49), and highly suitable habitat (>0.49). Subsequently, the area of each habitat category was computed.

2.5. Integrating the Population Size Estimation with MaxEnt Model

Stratified sampling and fecal pile counting were used to estimate the population density of M. berezovskii in each study area [43]. To determine the cumulative excretion time of M. berezovskii, fresh feces that had been collected at various times and locations were observed to retain their shiny appearance for a period of up to 10 days. Due to challenges in directly measuring daily fecal discharge rates of M. berezovskii in the field, the daily fecal discharge rate of 4.91 ± 0.38 heaps/head measured by Yang Qisen et al. [44] in the Jintang rearing farm of Kangding County was used.
The average population density of M. berezovskii was calculated for the entire Chongqing area as well as each survey area. The population size was estimated based on the highly suitable habitat areas. The density calculation formula was:
D j = N j ε t A j ,
where Dj is the density of M. berezovskii in the jth sample strip; Nj is the number of fecal piles within 10 days in the jth sample strip; ɛ is the daily dung discharge rate (ɛ = 4.91 piles/head); t is the cumulative time of dung discharge (t = 10 days); and Aj is the area of the jth sample strip. The population size (N) was calculated as follows:
N = D × S,
where D is the average population density of M. berezovskii; and S is the area of highly suitable habitat for M. berezovskii based on the MaxEnt model.

3. Results

3.1. The Optimal Model Selection and Accuracy Evaluation

After optimizing and adjusting the model parameters for MaxEnt models of M. berezovskii, the model using default parameters (RM = 1, FC = LQHP) resulted in a delta AICc of 344.24, and the optimal model (delta AICc = 0) had the parameters RM = 3.5 and FC = LQPTH. The training AUC value of the optimized prediction model was 0.909 ± 0.010 (mean ± SD) and the mean TSS was 0.663, indicating excellent predictive performance for determining the potential habitat distribution of M. berezovskii in Chongqing (Figure 3).

3.2. Analysis of Critical Environmental Factor Variables

According to the contribution analysis of the MaxEnt model’s environment variables, the top four environmental variables influencing the distribution of M. berezovskii were elevation (36.5%), NDVI (16.6%), slope (11.8%), and land-use type (7.6%). Collectively, these variables accounted for 72.5% of the total contribution to the model. The cumulative contribution rates of the climate and anthropogenic disturbance factor variables were 15.8% and 6.7%, respectively. The aspect environmental variable had the lowest contribution (1.4%) (Table 2).
According to the Jackknife method testing results, the four environmental factor variables with the greatest influence on the regularized training gain of the MaxEnt model for M. berezovskii were elevation, NDVI, slope, and land-use type (Figure 4).
Based on both the comprehensive contribution rate and the result of the Jackknife method results, the primary factors affecting the potential suitable habitat distribution of M. berezovskii were topographic factors (elevation and slope) and vegetation factors (NDVI and land-use type), whereas the impact of the climate and anthropogenic disturbance factor variables was comparatively minor.
Single-factor modeling was performed using the four major environmental factors: elevation, slope, NDVI, and land-use type. The single-factor response curves clearly illustrate the correlation between the probability of M. berezovskii presence and these environmental factor variables. Generally, a value of ≥0.5 for the probability of species presence in the response curve indicates a suitable habitat condition for species growth [45]. Based on the single-factor modeling results, the optimal habitat conditions for M. berezovskii were identified as: elevation exceeding 1106.78 m, NDVI exceeding 0.88, slope exceeding 27.47°, and land-use type being forest habitat (Figure 5).

3.3. Potential Habitat Distribution of M. berezovskii

The potentially suitable habitat distribution for M. berezovskii was estimated using the MaxEnt model (Figure 6). In Chongqing, the areas of highly suitable habitat, moderately suitable habitat, and unsuitable habitat for wild M. berezovskii covered 5562.80 km2 (6.81% of the total study area), 16,402.63 km2 (20.07%), and 59,748.39 km2 (73.12%), respectively. The highly suitable habitats in Chongqing were primarily located in the northern regions (southeastern regions of Chengkou County, northeastern regions of Kaizhou District, northeastern and western regions of Wuxi County, northeastern regions of Wushan County, and southern regions of Fengjie County), the eastern regions (eastern and southeastern regions of Shizhu County, southeastern regions of Fengdu County, and northern and southwestern regions of Wulong District), and the southwestern regions (southeastern regions of Jiangjin District, eastern and western regions of Qijiang District, and southwestern and eastern regions of Nanchuan District). These habitats were notably concentrated in natural reserve areas, including the Simian Mountain Nature Reserve in Jiangjin District, Jinfosan Mountain Nature Reserve in Nanchuan District, Xuebao Mountain Nature Reserve in Kaizhou District, Wuli Po Nature Reserve in Wushan County, and other protected areas. The moderately suitable habitats were mainly distributed in areas surrounding the highly suitable habitats. The unsuitable habitats were mainly distributed in the western and southeastern regions of Chongqing.

3.4. Population Size Estimation

Based on the results of stratified sampling and fecal pile counting, and MaxEnt model predictions, the estimated population size of M. berezovskii in each region of Chongqing was determined (Table 3). The average population density of M. berezovskii across the entire Chongqing area was approximately 0.17 ± 0.02 individuals/km2, and the highly suitable habitat area of M. berezovskii was approximately 5461.40 km2. Using the population size estimation formula (2) the total population of M. berezovskii in the Chongqing area was calculated to be 928 ± 109 individuals. Among the regions studied, Nanchuan District had the largest population of M. berezovskii, followed by Wuxi County and Fengdu County. The smallest population of M. berezovskii was in Liangping District.

4. Discussion

4.1. The Major Environmental Factors Affecting M. berezovskii Distribution

Environmental variables influencing the habitat distribution of a species can vary significantly depending on the study area size [46]. Our results showed that the major factors limiting the potential suitable habitat distribution of M. berezovskii were elevation, NDVI, slope, and land-use type based on both the comprehensive contribution rate and the result of the Jackknife method results. Elevation had the greatest effect on the wild distribution of M. berezovskii in Chongqing. With the increase of elevation, vegetation types will also change correspondingly, which also impacts the food resources provided by the habitat, environmental temperature, and concealment [47]. The optimal habitats predicted by the model encompassed the higher elevation regions in the study area, often characterized by overhanging cliffs with a steep terrain where M. berezovskii can efficiently avoid predators. Gao et al. [48] evaluated the habitat suitability of M. berezovskii in the central region of the Qinling Mountains of in Shaanxi, China and obtained similar results, indicating that elevation and land-use type were the major factors limiting the geographical distribution of M. berezovskii.
In addition to elevation factors, NDVI also significantly influenced M. berezovskii distribution. The NDVI response curve indicated that the probability of M. berezovskii occurrence began to increase when the index reached approximately 0.6. The probability of M. berezovskii occurrence then increased rapidly, and plateaued around 0.9. The reason for this phenomenon may be related to the foraging choice of M. berezovskii. NDVI indirectly represents the abundance of food resources [49]. In the actual investigation, it was found that M. berezovskii often made use of the forest near the cliffs. This habitat was not only beneficial for M. berezovskii to replenish energy but was also conducive to predator avoidance [50]. Lin et al. [51] studied the suitable habitat of wild Moschus chrysogaster in autumn in the Xinglong Mountain Reserve and showed that NDVI was the dominant factor affecting its large-scale distribution, followed by distance from roads. Slope is also an important factor in habitat prediction of M. berezovskii, with the response curve illustrating an increase in habitat suitability with steeper terrains. Steep cliffs provide refuge from human activities and natural predators by leveraging the agility and adeptness of M. berezovskii due to its developed hind legs and side hooves [52]. This is similar to other Moschus spp., which prefer areas with steeper slopes [53,54]. The response curve of land-use type indicated that M. berezovskii was predominately active in forest habitats. M. berezovskii is a typical forest animal, mainly active in mixed coniferous broad-leaved forests and evergreen broad-leaved forests, which was consistent with previous studies [39,45]. These habitats offer abundant food resources such as various branches and young leaves of woody plants and herbaceous plants [55], and also provide a comfortable ambient temperature.
Climate change exerts both direct and indirect effects on temperature, precipitation, and related ecological factors within animal and plant habitat areas, thereby affecting food and water resources in the region [24]. We found that the distribution of M. berezovskii was primarily affected by the annual temperature range (bio7), precipitation in the wettest month (bio13), and precipitation in the driest month (bio14), with a cumulative contribution of 15.8%. Zhao et al. noted [56] that the high-suitability areas for M. berezovskii in Chongqing were located in the eastern regions, which is partially consistent with the predicted results of this study where precipitation factors played a significant role. Jiang et al. [57] projected the current and future distributions of Moschus spp. and emphasized that the temperature was the primary factor affecting their distribution.
The influence of human disturbance factors had a relatively small cumulative contribution rate of 6.7%. However, field investigations have revealed that M. berezovskii naturally avoids low-altitude areas with intense human activities when selecting habitat, with no observed activity found within 2 km.

4.2. Spatial Distribution and Population Size of M. berezovskii in Potential Suitable Areas

Reliable data are crucial for understanding the habitat and population dynamics of M. berezovskii. Owing to the timidity and alertness of M. berezovskii, direct observation in the wild is challenging [43]. Fecal counting is a useful method [58]. The results from model prediction indicated that the potential suitable habitat distribution of M. berezovskii was roughly consistent with its existing range and the distribution was fragmented. The highly suitable habitat area for M. berezovskii in Chongqing was approximately 5562.80 km2, constituting 8.91% of its mountainous area. This habitat was primarily distributed in the northern, eastern, and southwestern regions of Chongqing, and overlaps with the reported M. berezovskii distribution area. The northern regions of Chongqing are located in the Daba and Wushan Mountains, close to Shaanxi and Hubei Provinces. Some M. berezovskii may have migrated from Zhenping County in Shaanxi Province to the Shennongjia Forest Region in Hubei Province [10].
In this study, the population density of M. berezovskii in each study area was estimated by stratified sampling and the fecal pile counting method. Based on the highest population density 0.43 ± 0.12 individuals/km2, we could estimate that the carrying capacity for the region was approximately 2348 ± 655 individuals. The MaxEnt model actually estimated a total population size of approximately 928 ± 109 individuals, which was much smaller than the environmental capacity. The distribution pattern indicated higher population numbers in the northern and southern regions of Chongqing, with lower numbers in the central area. This phenomenon may be related to the geographical barriers formed between the distribution regions, which prevent the migration of M. berezovskii between different parts of Chongqing. Therefore, establishing biological corridors [59] among mountain ranges and expanding suitable habitats for M. berezovskii are recommended strategies to promote gene exchange between M. berezovskii populations.

4.3. Study Limitations and the Scope for Future Studies

Although the MaxEnt model can effectively predict the potential habitat distribution for species, there were still some errors due to the lack of species distribution points and a finite selection of environmental variables, which led to biases in the estimated population size [60]. In this study, only 28 environmental variables, including climate, vegetation, topography, and human disturbance, were used. However, the habitat distribution of M. berezovskii was also influenced by factors such as individual growth characteristics, interspecies interactions, migration abilities, and population diffusion, which are difficult to quantify [61]. Therefore, our research results should be further analyzed in conjunction with the actual local conditions based on the existing predictions. In addition, the study employed stratified sampling methods, coupled with the propensity of M. berezovskii for agile leaping and its preference for habitats along cliffs and precipices. It was difficult to cover the entire Chongqing area with field sampling, resulting in an insufficient sample size. To address this, future surveys should consider narrowing the scope of the study area to focus on specific protected zones and forest regions, thereby enabling a more comprehensive and in-depth survey, which will contribute to a more accurate assessment of the distribution and habitat preferences for species.

5. Conclusions

This study used the MaxEnt model and used stratified sampling and the fecal pile counting method to predict the potential suitable habitat distribution and estimate the population size of M. berezovskii in Chongqing, incorporating 133 species distribution points and 28 environmental variables. The findings revealed that under various climatic, topographic, vegetation, and anthropogenic factors, M. berezovskii was primarily distributed in northern, eastern, and southwestern regions of Chongqing, covering an approximate area of 5562.80 km2. The distribution of M. berezovskii was fragmented. Key environmental factors influencing the habitat suitability of M. berezovskii included elevation, NDVI, slope, and land-use type, whereas climatic and anthropogenic disturbance factors had relatively little influence. The population size of M. berezovskii in Chongqing was estimated to be approximately 928 ± 109 individuals using stratified sampling and the fecal pile counting method. Therefore, it is recommended that high-altitude habitats and original vegetation be protected to reduce human and road interference and damage. Given the habitat requirements and potential for population exchange among M. berezovskii and other rare and endangered species, systematic biodiversity protection planning should be conducted to realize the comprehensive protection of multiple species, and the protection of M. berezovskii should be integrated into the comprehensive biodiversity protection planning of various mountain systems in Chongqing.

Author Contributions

Conceptualization, Q.L. and H.L.; Methodology, Q.L. and H.L.; Software, Q.L., H.L. and X.W.; Validation, X.C., L.S. and M.Z.; Formal analysis, Q.L. and H.L.; Investigation, Q.L., H.L., X.W., L.S., M.Z., L.C. and X.L.; Resources, X.W., L.S. and L.C.; Data curation, J.P. and M.Z.; Writing—original draft, Q.L.; Writing—review & editing, X.C. and J.P.; Visualization, X.C. and X.L.; Supervision, X.C. and J.P.; Project administration, J.P.; Funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the People’s Republic of China Wildlife Protection Program of the Central Forestry Reform and Development Fund of the State Forestry Administration, and the National Natural Science Foundation of China (No. 31470570).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We sincerely thank the editor and anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper. We wish to convey our special gratitude to the Chongqing Forestry Bureau, the district forestry Bureau, and government personnel for their help and valuable advice in the field investigation work.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ceballos, G.; Ehrlich, P.R.; Dirzo, R. Biological Annihilation via the Ongoing Sixth Mass Extinction Signaled by Vertebrate Population Losses and Declines. Proc. Natl. Acad. Sci. USA 2017, 114, E6089–E6096. [Google Scholar] [CrossRef]
  2. Bellard, C.; Bertelsmeier, C.; Leadley, P.; Thuiller, W.; Courchamp, F. Impacts of Climate Change on the Future of Biodiversity. Ecol. Lett. 2012, 15, 365–377. [Google Scholar] [CrossRef]
  3. Fahrig, L. Effects of Habitat Fragmentation on Biodiversity. Annu. Rev. Ecol. Evol. Syst. 2003, 34, 487–515. [Google Scholar] [CrossRef]
  4. Lino, A.; Fonseca, C.; Rojas, D.; Fischer, E.; Pereira, M.J.R. A Meta-Analysis of the Effects of Habitat Loss and Fragmentation on Genetic Diversity in Mammals. Mamm. Biol. 2019, 94, 69–76. [Google Scholar] [CrossRef]
  5. Pearce, J.L.; Boyce, M.S. Modelling Distribution and Abundance with Presence-only Data. J. Appl. Ecol. 2006, 43, 405–412. [Google Scholar] [CrossRef]
  6. Fan, Z.; Li, W.; Jin, J.; Cui, K.; Yan, C.; Peng, C.; Jian, Z.; Bu, P.; Price, M.; Zhang, X.; et al. The Draft Genome Sequence of Forest Musk Deer (Moschus berezovskii). Gigascience 2018, 7, giy038. [Google Scholar] [CrossRef]
  7. Cai, R.; Shafer, A.B.A.; Laguardia, A.; Lin, Z.; Liu, S.; Hu, D. Recombination and Selection in the Major Histocompatibility Complex of the Endangered Forest Musk Deer (Moschus berezovskii). Sci. Rep. 2015, 5, 17285. [Google Scholar] [CrossRef] [PubMed]
  8. Tran, D.V.; Viet, D.P.; Tien, T.V.; Nguyen, A.; Van, C.P.; Tilker, A. New Records of the Forest Musk Deer Moschus berezovskii in Viet Nam Revealed by Camera Traps. Oryx 2021, 55, 494–495. [Google Scholar] [CrossRef]
  9. Green, M.J.B. The Distribution, Status and Conservation of the Himalayan Musk Deer Moschus chrysogaster. Biol. Conserv. 1986, 35, 347–375. [Google Scholar] [CrossRef]
  10. Feng, H.; Wang, L.; Cao, F.; Ma, J.; Tang, J.; Feng, C.; Su, Z. Forest Musk Deer (Moschus berezovskii) in China: Research and Protection. J. Vertebr. Biol. 2023, 72, 22067. [Google Scholar] [CrossRef]
  11. Li, N.; Pacheco-Fabig, M.; Arréllaga, M.M. 19. International Union for the Conservation of Nature (IUCN). Yearb. Int. Environ. Law 2014, 25, 585–606. [Google Scholar] [CrossRef]
  12. Peng, J.; Peng, J.; Deng, Y.; Huang, P.; Gao, H.; Sun, N.; Wang, X.; Yuan, X.; Zhou, H.; Zhang, C. Estimating Population Size of Forest Musk Deer (Moschus berezovskii) in Chongqing Jinfoshan National Nature Reserve. For. Sci. Technol. 2018, 3, 30–32. [Google Scholar] [CrossRef]
  13. Chapman, D.S.; Scalone, R.; Štefanić, E.; Bullock, J.M. Mechanistic Species Distribution Modeling Reveals a Niche Shift during Invasion. Ecology 2017, 98, 1671–1680. [Google Scholar] [CrossRef]
  14. Case, M.J.; Lawler, J.J. Integrating Mechanistic and Empirical Model Projections to Assess Climate Impacts on Tree Species Distributions in Northwestern North America. Glob. Chang. Biol. 2017, 23, 2005–2015. [Google Scholar] [CrossRef]
  15. Hao, M.; Aidoo, O.F.; Qian, Y.; Wang, D.; Ding, F.; Ma, T.; Tettey, E.; Ninsin, K.D.; Osabutey, A.F.; Borgemeister, C. Global Potential Distribution of Oryctes Rhinoceros, as Predicted by Boosted Regression Tree Model. Glob. Ecol. Conserv. 2022, 37, e02175. [Google Scholar] [CrossRef]
  16. Jȩdrzejewski, W.; Niedziałkowska, M.; Nowak, S.; Jȩdrzejewska, B. Habitat Variables Associated with Wolf (Canis Lupus) Distribution and Abundance in Northern Poland. Divers. Distrib. 2004, 10, 225–233. [Google Scholar] [CrossRef]
  17. Ramírez-León, M.R.; García-Aguilar, M.C.; Romo-Curiel, A.E.; Ramírez-Mendoza, Z.; Fajardo-Yamamoto, A.; Sosa-Nishizaki, O. Habitat Suitability of Cetaceans in the Gulf of Mexico Using an Ecological Niche Modeling Approach. PeerJ 2021, 9, e10834. [Google Scholar] [CrossRef]
  18. Blanco-Sacristán, J.; Johansen, K.; Duarte, C.M.; Daffonchio, D.; Hoteit, I.; McCabe, M.F. Mangrove Distribution and Afforestation Potential in the Red Sea. Sci. Total Environ. 2022, 843, 157098. [Google Scholar] [CrossRef]
  19. Yunsheng, W. Application of ROC Curve Analysis in Evaluating the Performance of Alien Species’ Potential Distribution Models. Biodivers. Sci. 2007, 15, 365. [Google Scholar] [CrossRef]
  20. Zeng, J.; Hu, J.; Shi, Y.; Li, Y.; Guo, Z.; Wang, S.; Song, S. Effects of Climate Change on the Habitat of the Leopard (Panthera pardus) in the Liupanshan National Nature Reserve of China. Animals 2022, 12, 1866. [Google Scholar] [CrossRef] [PubMed]
  21. Li, W.-N.; Zhao, Q.; Guo, M.-H.; Lu, C.; Huang, F.; Wang, Z.-Z.; Niu, J.-F. Predicting the Potential Distribution of the Endangered Plant Cremastra appendiculata (Orchidaceae) in China under Multiple Climate Change Scenarios. Forests 2022, 13, 1504. [Google Scholar] [CrossRef]
  22. Bang, M.; Sohn, D.; Kim, J.J.; Choi, W.; Jang, C.J.; Kim, C. Future Changes in the Seasonal Habitat Suitability for Anchovy (Engraulis japonicus) in Korean Waters Projected by a Maximum Entropy Model. Front. Mar. Sci. 2022, 9, 922020. [Google Scholar] [CrossRef]
  23. Zhou, L.; Wang, L.; Su, K.; Bi, G.; Chen, H.; Liu, X.; Yang, Q. Spatiotemporal Characteristics of Rural Restructuring Evolution and Driving Forces in Mountainous and Hilly Areas. Land 2022, 11, 848. [Google Scholar] [CrossRef]
  24. Ma, Q.; Wan, L.; Shi, S.; Wang, Z. Impact of Climate Change on the Distribution of Three Rare Salamanders (Liua shihi, Pseudohynobius jinfo, and Tylototriton wenxianensis) in Chongqing, China, and Their Conservation Implications. Animals 2024, 14, 672. [Google Scholar] [CrossRef]
  25. Jianfeng, W.U.; Cao, G.; Zhang, F.; Li, W.; Wang, H. Analysis of Vegetation Coverage Change Characteristics in Chongqing Based on MODIS—NDVI Data. IOP Conf. Ser. Earth Environ. Sci. 2017, 100, 012187. [Google Scholar] [CrossRef]
  26. Qi, Q.; Zhang, M.; Tong, S.; Liu, Y.; Zhang, D.; Zhu, G.; Lyu, X. Evolution of Potential Spatial Distribution Patterns of Carex Tussock Wetlands Under Climate Change Scenarios, Northeast China. Chin. Geogr. Sci. 2022, 32, 142–154. [Google Scholar] [CrossRef]
  27. Anderson, R.P.; Raza, A. The Effect of the Extent of the Study Region on GIS Models of Species Geographic Distributions and Estimates of Niche Evolution: Preliminary Tests with Montane Rodents (Genus nephelomys) in Venezuela. J. Biogeogr. 2010, 37, 1378–1393. [Google Scholar] [CrossRef]
  28. Brown, J.L.; Bennett, J.R.; French, C.M. SDMtoolbox 2.0: The next Generation Python-Based GIS Toolkit for Landscape Genetic, Biogeographic and Species Distribution Model Analyses. PeerJ 2017, 5, e4095. [Google Scholar] [CrossRef]
  29. Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km Spatial Resolution Climate Surfaces for Global Land Areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
  30. Merow, C.; Smith, M.J.; Silander Jr, J.A. A Practical Guide to MaxEnt for Modeling Species’ Distributions: What It Does, and Why Inputs and Settings Matter. Ecography 2013, 36, 1058–1069. [Google Scholar] [CrossRef]
  31. Xue, Y.; Lin, C.; Wang, Y.; Zhang, Y.; Ji, L. Ecological Niche Complexity of Invasive and Native Cryptic Species of the Bemisia Tabaci Species Complex in China. J. Pest. Sci. 2022, 95, 1245–1259. [Google Scholar] [CrossRef]
  32. Kass, J.M.; Muscarella, R.; Galante, P.J.; Bohl, C.L.; Pinilla-Buitrago, G.E.; Boria, R.A.; Soley-Guardia, M.; Anderson, R.P. ENMeval 2.0: Redesigned for Customizable and Reproducible Modeling of Species’ Niches and Distributions. Methods Ecol. Evol. 2021, 12, 1602–1608. [Google Scholar] [CrossRef]
  33. Muscarella, R.; Galante, P.J.; Soley-Guardia, M.; Boria, R.A.; Kass, J.M.; Uriarte, M.; Anderson, R.P. ENM Eval: An R Package for Conducting Spatially Independent Evaluations and Estimating Optimal Model Complexity for Maxent Ecological Niche Models. Methods Ecol. Evol. 2014, 5, 1198–1205. [Google Scholar] [CrossRef]
  34. Jiang, C.; Zhang, X.; Xie, W.; Wang, R.; Feng, C.; Ma, L.; Li, Q.; Yang, Q.; Wang, H. Predicting the Potential Distribution of the Fall Armyworm Spodoptera Frugiperda (J.E. Smith) under Climate Change in China. Glob. Ecol. Conserv. 2022, 33, e01994. [Google Scholar] [CrossRef]
  35. Phillips, S.J.; Anderson, R.P.; Dudík, M.; Schapire, R.E.; Blair, M.E. Opening the Black Box: An Open-Source Release of Maxent. Ecography 2017, 40, 887–893. [Google Scholar] [CrossRef]
  36. Fan, Z.-F.; Zhou, B.-J.; Ma, C.-L.; Gao, C.; Han, D.-N.; Chai, Y. Impacts of Climate Change on Species Distribution Patterns of Polyspora Sweet in China. Ecol. Evol. 2022, 12, e9516. [Google Scholar] [CrossRef]
  37. Wu, Y.; Yang, Y.; Liu, C.; Hou, Y.; Yang, S.; Wang, L.; Zhang, X. Potential Suitable Habitat of Two Economically Important Forest Trees (Acer truncatum and Xanthoceras sorbifolium) in East Asia under Current and Future Climate Scenarios. Forests 2021, 12, 1263. [Google Scholar] [CrossRef]
  38. Allouche, O.; Tsoar, A.; Kadmon, R. Assessing the Accuracy of Species Distribution Models: Prevalence, Kappa and the True Skill Statistic (TSS). J. Appl. Ecol. 2006, 43, 1223–1232. [Google Scholar] [CrossRef]
  39. Hernandez, P.A.; Graham, C.H.; Master, L.L.; Albert, D.L. The Effect of Sample Size and Species Characteristics on Performance of Different Species Distribution Modeling Methods. Ecography 2006, 29, 773–785. [Google Scholar] [CrossRef]
  40. Zhang, S.; Liu, B.; Liu, X.; Yuan, Q.; Xiao, X.; Zhou, T. Maximum Entropy Modeling for the Prediction of Potential Plantation Distribution of Arabica Coffee under the CMIP6 Mode in Yunnan, Southwest China. Atmosphere 2022, 13, 1773. [Google Scholar] [CrossRef]
  41. Clark, J.; Wang, Y.; August, P.V. Assessing Current and Projected Suitable Habitats for Tree-of-Heaven along the Appalachian Trail. Phil. Trans. R. Soc. B 2014, 369, 20130192. [Google Scholar] [CrossRef] [PubMed]
  42. Liu, C.; White, M.; Newell, G. Selecting Thresholds for the Prediction of Species Occurrence with Presence-Only Data. J. Biogeogr. 2013, 40, 778–789. [Google Scholar] [CrossRef]
  43. Hu, Z.; Wang, Y.; Xue, W.; Jiang, H.; Xu, H. Population density of Moschus berezovskii in Zibaishan Nature Reserve of Feng County, Shaanxi. J. Zhejiang For. Coll. 2006, 5, 70–74. [Google Scholar] [CrossRef]
  44. Yang, Q.; Hu, J.; Peng, J. Population Density Research on Forest Musk Deer (Moschus berezovskii) of Baiyu Country. J. Sichuan Teach Coll. 1989, 8, 329–336. [Google Scholar] [CrossRef]
  45. Li, Y.; Shao, W.; Huang, S.; Zhang, Y.; Fang, H.; Jiang, J. Prediction of Suitable Habitats for Sapindus Delavayi Based on the MaxEnt Model. Forests 2022, 13, 1611. [Google Scholar] [CrossRef]
  46. Burke, A. Classification and Ordination of Plant Communities of the Naukluft Mountains, Namibia. J. Veg. Sci. 2001, 12, 53–60. [Google Scholar] [CrossRef]
  47. Zhao, Y.; Tomita, M.; Hara, K.; Fujihara, M.; Yang, Y.; Da, L. Effects of Topography on Status and Changes in Land-Cover Patterns, Chongqing City, China. Landsc. Ecol. Eng. 2014, 10, 125–135. [Google Scholar] [CrossRef]
  48. Gao, X.; Bu, S.; Zheng, X. Integrating Species Distribution Models to Estimate the Population Size of Forest Musk Deer (Moschus berezovskii) in the Central Qinling Mountains of Shaanxi. Diversity 2023, 15, 1071. [Google Scholar] [CrossRef]
  49. Funghi, C.; Heim, R.H.J.; Schuett, W.; Griffith, S.C.; Oldeland, J. Estimating Food Resource Availability in Arid Environments with Sentinel 2 Satellite Imagery. PeerJ 2020, 8, e9209. [Google Scholar] [CrossRef]
  50. Li, Z.; Khattak, R.H.; Han, X.; Zhang, N.; Wu, J.; Liu, Z.; Teng, L. Distribution Update of Water Deer (Hydropotes inermis) and Prediction of Their Potential Distribution in Northeast China. Sci. Rep. 2023, 13, 5610. [Google Scholar] [CrossRef]
  51. Lin, H.; Zheng, Q.; Shen, L.; Wang, G.; Liu, R.; Zhang, X.; Qi, J.; Zhang, A.; Meng, X. Assessment of habitat suitability in autumn for wild alpine musk deer in Xinglongshan National Nature Reserve with MaxEnt model. Chin. J. Ecol. 2024, 43, 299–304. [Google Scholar] [CrossRef]
  52. Zhou, Y.; Meng, X.; Feng, J.; Yang, Q.; Feng, Z.; Xia, L.; Bartoš, L. Review of the Distribution, Status and Conservation of Musk Deer in China. Folia Zool. Praha 2004, 53, 129–140. [Google Scholar]
  53. Wu, J.; Zhou, L.; Mu, L. Summer habitat selection by Siberian musk deer (Moschus moschiferus) in Tonghe forest area in the Lesser Khingan Mountains. Acta Theriol. Sin. 2006, 26, 44–48. [Google Scholar] [CrossRef]
  54. Singh, P.B.; Saud, P.; Cram, D.; Mainali, K.; Thapa, A.; Chhetri, N.B.; Poudyal, L.P.; Baral, H.S.; Jiang, Z. Ecological Correlates of Himalayan Musk Deer Moschus Leucogaster. Ecol. Evol. 2019, 9, 4–18. [Google Scholar] [CrossRef]
  55. Wang, W.; He, L.; Liu, B.; Li, L.; Wei, N.; Zhou, R.; Qi, L.; Liu, S.; Hu, D. Feeding Performance and Preferences of Captive Forest Musk Deer While on a Cafeteria Diet. Folia Zool. 2015, 64, 151–160. [Google Scholar] [CrossRef]
  56. Zhao, J.; Wang, Q.; Zheng, C.; Hu, J.; Wang, R.; Jiang, G. Research on the suitable habitat for Moschus berezovskii based on maximum entropy model (MaxEnt) in climatic background. Hubei Agric. Sci. 2023, 62, 218–223. [Google Scholar] [CrossRef]
  57. Jiang, F.; Zhang, J.; Gao, H.; Cai, Z.; Zhou, X.; Li, S.; Zhang, T. Musk Deer (Moschus spp.) Face Redistribution to Higher Elevations and Latitudes under Climate Change in China. Sci. Total Environ. 2020, 704, 135335. [Google Scholar] [CrossRef]
  58. Pfeffer, S.E.; Spitzer, R.; Allen, A.M.; Hofmeester, T.R.; Ericsson, G.; Widemo, F.; Singh, N.J.; Cromsigt, J.P.G.M. Pictures or Pellets? Comparing Camera Trapping and Dung Counts as Methods for Estimating Population Densities of Ungulates. Remote Sens. Ecol. Conserv. 2018, 4, 173–183. [Google Scholar] [CrossRef]
  59. Falcy, M.R.; Estades, C.F. Effectiveness of Corridors Relative to Enlargement of Habitat Patches. Conserv. Biol. 2007, 21, 1341–1346. [Google Scholar] [CrossRef]
  60. Sun, S.; Zhang, Y.; Huang, D.; Wang, H.; Cao, Q.; Fan, P.; Yang, N.; Zheng, P.; Wang, R. The Effect of Climate Change on the Richness Distribution Pattern of Oaks (Quercus L.) in China. Sci. Total Environ. 2020, 744, 140786. [Google Scholar] [CrossRef]
  61. Boulangeat, I.; Gravel, D.; Thuiller, W. Accounting for Dispersal and Biotic Interactions to Disentangle the Drivers of Species Distributions and Their Abundances. Ecol. Lett. 2012, 15, 584–593. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. The filtered distribution points and study area of Moschus berezovskii.
Figure 1. The filtered distribution points and study area of Moschus berezovskii.
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Figure 2. Spearman correlation test among the 12 selected environmental variables (the names of all the variables are summarized in Table 1). Positive correlations are displayed in red and negative correlations in a blue color. The color intensity and the size of the circle are proportional to the correlation coefficients.
Figure 2. Spearman correlation test among the 12 selected environmental variables (the names of all the variables are summarized in Table 1). Positive correlations are displayed in red and negative correlations in a blue color. The color intensity and the size of the circle are proportional to the correlation coefficients.
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Figure 3. Optimization results for the MaxEnt model using different parameters. (a) AUC.diff.Avg; (b) AUC.Val.Avg; (c) delta.AICc; and (d) Or.10p.Avg. L = linear; Q = quadratic; P = product; T = threshold; H = hinge.
Figure 3. Optimization results for the MaxEnt model using different parameters. (a) AUC.diff.Avg; (b) AUC.Val.Avg; (c) delta.AICc; and (d) Or.10p.Avg. L = linear; Q = quadratic; P = product; T = threshold; H = hinge.
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Figure 4. The 12 environmental variables evaluated by the Jackknife method.
Figure 4. The 12 environmental variables evaluated by the Jackknife method.
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Figure 5. Response curves of habitat suitability for major environmental factors: (a) elevation; (b) NDVI; (c) slope; (d) land-use type. For (d), 10 = Cropland; 20 = Forest; 30 = Grassland; 40 = Shrub; 50 = Wetland; 60 = Waterbody; and 80 = Built-up land. The red line represents the average value of all candidate models, and the blue range indicates the standard deviation.
Figure 5. Response curves of habitat suitability for major environmental factors: (a) elevation; (b) NDVI; (c) slope; (d) land-use type. For (d), 10 = Cropland; 20 = Forest; 30 = Grassland; 40 = Shrub; 50 = Wetland; 60 = Waterbody; and 80 = Built-up land. The red line represents the average value of all candidate models, and the blue range indicates the standard deviation.
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Figure 6. Potential habitat distribution of M. berezovskii in the Chongqing area.
Figure 6. Potential habitat distribution of M. berezovskii in the Chongqing area.
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Table 1. Environmental variables used in the MaxEnt model for predicting the potential habitat distribution of M. berezovskii in Chongqing.
Table 1. Environmental variables used in the MaxEnt model for predicting the potential habitat distribution of M. berezovskii in Chongqing.
CodeDescriptionUnit
BIO7Annual temperature range (BIO5–BIO6)Degree Celsius (°C)
BIO13Precipitation of wettest monthMillimeter (mm)
BIO14Precipitation of driest month (coefficent of variation)Millimeter (mm)
EleElevation of the distribution points of M. berezovskiiMeter (m)
AspAspect of the distribution points of M. berezovskiiDegree (°)
SloSlope of the distribution points of M. berezovskiiDegree (°)
VegVegetation type of the distribution points of M. berezovskii-
NDVINormalized Difference Vegetation Index-
Land-useLand-use type-
Dis_riverDistance to the nearest riverMeter (m)
Dis_roadDistance to the nearest roadMeter (m)
Dis_residentDistance to the nearest residential areaMeter (m)
Table 2. Contribution and importance of 12 environmental factor variables.
Table 2. Contribution and importance of 12 environmental factor variables.
Environmental VariablesPercent Contribution (%)Permutation Importance (%)
elevation36.531
NDVI16.617.7
slope11.816.9
land-use7.61.7
bio76.10.3
bio144.97.1
bio134.86.2
vegetation3.71.5
dis_resident2.64.3
dis_river2.36.8
dis_road1.84.4
aspect1.42.1
Table 3. Population density and quantity of M. berezovskii in various regions of Chongqing.
Table 3. Population density and quantity of M. berezovskii in various regions of Chongqing.
RegionPopulation Density
(Individuals/km2)		Highly Suitable Habitat Area (km2)Population Quantity
(Individuals)
Chengkou0.15 ± 0.08454.8368 ± 36
Wuxi 0.24 ± 0.13651.04156 ± 84
Kaizhou 0.25 ± 0.09265.0166 ± 24
Nanchuan0.43 ± 0.12495.00213 ± 59
Jiangjin0.34 ± 0.09289.6098 ± 27
Qijiang0.08 ± 0.03238.7019 ± 7
Wushan0.22 ± 0.11250.8255 ± 29
Wanzhou0.23 ± 0.0755.8513 ± 4
Fuling0.04 ± 0.02115.835 ± 2
Fengdu0.18 ± 0.05450.0181 ± 24
Zhongxian0.19 ± 0.0423.274 ± 1
Shizhu0.04 ± 0.021084.8843 ± 22
Yunyang0.11 ± 0.0344.255 ± 1
Qianjiang0.09 ± 0.0490.438 ± 3
Fengjie0.19 ± 0.06237.8145 ± 15
Pengshui0.03 ± 0.01170.905 ± 2
Liangping0.17 ± 0.0614.082 ± 1
Wulong0.09 ± 0.02529.0950 ± 11
Total-5461.40-
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Liu, Q.; Liu, H.; Cui, X.; Peng, J.; Wang, X.; Shen, L.; Zhang, M.; Chen, L.; Li, X. Predicting the Population Size and Potential Habitat Distribution of Moschus berezovskii in Chongqing Based on the MaxEnt Model. Forests 2024, 15, 1449. https://doi.org/10.3390/f15081449

AMA Style

Liu Q, Liu H, Cui X, Peng J, Wang X, Shen L, Zhang M, Chen L, Li X. Predicting the Population Size and Potential Habitat Distribution of Moschus berezovskii in Chongqing Based on the MaxEnt Model. Forests. 2024; 15(8):1449. https://doi.org/10.3390/f15081449

Chicago/Turabian Style

Liu, Qing, Huilin Liu, Xiaojuan Cui, Jianjun Peng, Xia Wang, Ling Shen, Minqiang Zhang, Lixia Chen, and Xin Li. 2024. "Predicting the Population Size and Potential Habitat Distribution of Moschus berezovskii in Chongqing Based on the MaxEnt Model" Forests 15, no. 8: 1449. https://doi.org/10.3390/f15081449

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