5 Mingxi Forestry Bureau, Sanming, Fujian 365000
6 Fujian Junzifeng National Nature Reserve Management Bureau,
Sanming, Fujian 365000
7 College of Life Sciences, Huaibei Normal University, Huaibei,
Anhui 235000
* Author for correspondence. E-mail: 2019117@htu.edu.cn
Abstract Due to extensive poaching and habitat degradation, the
Chinese pangolin (Manis pentadactyla ) population had plummeted by
approximately 90%, leading the International Union for Conservation of
Nature (IUCN) to classify it as a Critically Endangered (CR) species.
The scarcity of up-to-date data on the species’ distribution and
dynamics presented a significant challenge in developing effective
conservation strategies and implementing protective measures within
China. Predominantly, China’s national-level nature reserve and
administrative departments operated at the county level, thereby
limiting the applicability of larger-scale analyses and studies,
especially those at the provincial level and above, for these
administrative entities. This study employed on eleven widely used
modeling techniques created within the BIOMOD2 framework to predict
suitable habitats for the pangolin at the county scale, while examining
the correlation between environmental variables and pangolin
distribution. The results revealed that in Mingxi County, situated in
the eastern sector of the Wuyi Mountains, the moderately suitable
habitat spanned 260 km², accounting for 15% of the total area, whereas
the highly suitable habitat encompassed only 49 km², constituting 3% of
the total area. Within the county-managed nature reserve, the proportion
of highly suitable habitats reached as high as 52%. However, nearly
half of these areas, both moderately and highly suitable, remained
inadequately addressed and conserved. The findings underscored the
inadequacy of existing protected areas in sustaining the current
pangolin population, leading to the identification of nine
administrative villages that necessitated prioritized conservation
efforts. The study anticipated an overall expansion in suitable habitats
over the ensuing two decades, likely associated with an increase in
precipitation, with significant growth projected in the eastern regions
of Xiayang Township and Hufang Town. This research offered a clear and
applicable research paradigm for the specific administrative level at
which China operates, particularly pertinent to county-level
jurisdictions with established nature reserve. Given the constraints of
the existing data and in order to more precisely evaluate the pangolin’s
situation at the county scale, the study underscored the paramount
importance of conducting field surveys, deemed as the most urgent task
at the time.
Keywords: Chinese Pangolin, County-Level Scale, Conservation, Population
Dynamics
INTRODUCTION
The Chinese pangolin (Manis pentadactyla ), an endemic scaly
mammal unique to Asia, has attracted significant global focus due to its
distinct biological attributes and the grave threats to its existence
(Wang et al., 2020; Yan et al., 2021; Zhang et al., 2021). Serving as a
myrmecophagous organism, it plays an integral role in the regulation of
termite and ant populations (Li et al., 2011). Nevertheless, this
species confronts substantial survival challenges, primarily attributed
to illicit poaching and habitat degradation (Challender et al., 2020;
Heinrich et al., 2016; Wu et al., 2002). Factors such as the illegal
trade (Gerard et al., 2023; Gu et al., 2023; Nash et al., 2018) and
local consumption of pangolin meat (Emogor et al., 2023) are posited as
the principal motivators behind its poaching. Presently, the population
of the Chinese pangolin has diminished by a staggering 90%, leading to
its classification as Critically Endangered (CR) by the International
Union for Conservation of Nature (IUCN) (Challender et al., 2019),
inclusion in Appendix 1 of the Convention on International Trade in
Endangered Species of Wild Fauna and Flora (CITES), and designation as a
first-class protected species under the national conservation laws of
China (Notice No. 3, 2021, National Forestry and Grassland
Administration, Ministry of Agriculture and Rural Affairs,http://www.forestry.gov.cn/ ). The prospects for this species are
rather bleak (Bashyal et al., 2021; Yang et al., 2018), and the
deficiency of contemporary data regarding its population distribution
and dynamics poses a pressing challenge in the formulation and execution
of conservation strategies and actions (Hu et al., 2010; Kong et al.,
2021; Sharma,Rimal, et al., 2020).
Recent studies have elucidated that the Chinese pangolin (Manis
pentadactyla) predominantly inhabits the southeastern territories of
China (Ta et al., 2021). The Wuyi Mountain region is identified as the
most pivotal habitat for this species in Eastern China (Peng, 2020; Yang
et al., 2018; Zhou, 2022). Furthermore, the distribution of the Chinese
pangolin is significantly influenced by human activities and variations
in precipitation (Ta et al., 2021; Yang et al., 2018), providing crucial
support for comprehending its current distributional status. However,
the predominance of county-level units in China’s national-level
protected areas presents a limitation in conducting analyses at larger
scales, notably at the provincial level and above, thus diminishing
their practical utility for administrative departments. Consequently,
meticulous analyses at the county level are imperative for formulating
viable and effective conservation strategies. A grave challenge
encountered globally is the dearth of dedicated protected areas with a
primary focus on pangolin conservation (Katuwal et al., 2017;
Sharma,Sharma, et al., 2020; Wei et al., 2022). The existing sanctuaries
lack targeted scope and specificity in policy development (Nash et al.,
2016; Sharma,Rimal, et al., 2020). Therefore, a detailed examination of
environmental influences such as climatic conditions, geological
factors, and anthropogenic disturbances on the Chinese pangolin at finer
scales, coupled with predictions of potential suitable habitats, is
essential. Such research will not only deepen our understanding of the
local population dynamics and distribution of the Chinese pangolin but
also furnish administrative entities with direct and efficacious
scientific underpinnings.
Sanming City, located in the eastern segment of the Wuyi Mountain Range
in southeastern China, with a total area of 1730 km2,
is distinguished for its abundant biodiversity and unique natural
habitat, historically constituting a critical distribution zone for the
Chinese pangolin (Zhou, 2022). Despite its ecological significance,
comprehensive scientific studies pertaining to the population
distribution and dynamics of the Chinese pangolin in this area are
markedly lacking. This investigation aims to forecast the potential
distribution zones of the Chinese pangolin in Mingxi County, Sanming
City, leveraging field survey data, Geographic Information Systems
(GIS), remote sensing technologies, and the Biomod2 model, for both the
present and the upcoming two decades. The varied topography and
extensive vegetation varieties in Mingxi County provide prospective
habitats for the pangolin. The objective of this study is to elucidate
the correlation between environmental variables and pangolin
distribution, and to predict potential suitable habitats. This is
intended to supply actionable scientific recommendations for local
policymakers and serve as a paradigm for formulating conservation
policies for endangered species, like the Chinese pangolin, in national,
provincial, and county-level protected areas throughout China. In light
of the global imperative for biodiversity conservation and the practical
demands of wildlife protection, this research emphasizes the
significance of engaging in detailed, scientific investigations at a
granular scale within the field of wildlife conservation.
This research is of paramount importance for county-level administrative
and management entities in China, particularly in the context of
developing conservation strategies for endangered species like the
Chinese pangolin. Our methodology encompassed a series of crucial steps
to fulfill the objectives: Initially, detailed location data for the
Chinese pangolin were amassed through extensive field expeditions.
Subsequently, environmental datasets were meticulously gathered and
rigorously corrected to assure their accuracy. Furthermore, an
assessment was conducted on the alterations in suitable habitats, both
in the current scenario and projected over the next two decades,
including an analysis of their potential influencing elements. Lastly,
with a consideration of the demarcations of protected zones and the
perimeters of administrative villages, tailored conservation proposals
were formulated for immediate and long-term implementation. These
strategic approaches substantially elevate the study’s reliability and
utility, solidifying its vital role in the formulation of local
conservation policies.
STUDY AREA
The Wuyi Mountain range, notably its eastern extension, Mingxi County,
is recognized as an ecologically significant potential habitat for Manis
pentadactyla (Chinese pangolin), underscoring its conservation value
(Peng, 2020; Yang et al., 2018; Zhou, 2022). Accordingly, this
investigation designates Mingxi County in Fujian Province (depicted in
Figure 1A and 1B) as the focal study locale. The county, typified by a
subtropical monsoonal ecosystem, averages an annual temperature near
18°C with mean precipitation around 2000 mm (Shi, 2021). An impressive
over 80% forest coverage (Zhang & Hunag, 2011) contributes to its
biodiverse landscape, previously a stronghold for the pangolin
population (Zhou, 1996). Mingxi’s encompassing nine townships and the
Junzifeng National Nature Reserve, devoted to the preservation of
subtropical evergreen broadleaf biomes and the safeguarding of endemic
fauna such as the Cabot’s Tragopan, delineate its ecological
significance. This research utilized vector data delineating the
townships and administrative village boundaries, sourced from the
county’s environmental governance agencies, to frame the geographical
scope of the habitat suitability analysis.
METHODS
Species Distribution Data
The elusive and nocturnally active Chinese pangolin (Manis
pentadactyla), characterized by its low population density, renders
direct observational studies logistically impractical (Macdonald, 2006).
Therefore, this research adopts an indirect approach, focusing on the
analysis of pangolin burrow systems. This methodology is instrumental in
elucidating the species’ habitat preferences and assessing the
environmental determinants influencing their burrow distribution
(Sharma,Sharma, et al., 2020; Thapa et al., 2014; Wu et al., 2002),
thereby providing critical insights for targeted conservation
interventions. In Mingxi County, a methodical stratified random sampling
framework was applied, deploying 90 transects across diverse ecological
niches: 21 in broadleaf forests, 16 in mixed coniferous-broadleaf
forests, 16 in coniferous forests, 17 in bamboo-dominated areas, and 20
within agricultural landscapes, each transect extending 1-2 kilometers
in length and encompassing a 5 to 10-meter width. Field expeditions were
conducted in distinct seasonal windows - November to December 2022,
February to March 2023, May to July 2023, and September to October 2023.
Geographic coordinates were meticulously documented upon burrow
discovery. Given the restricted spatial range of the species, typically
confined to less than 1 square kilometer (Sharma,Rimal, et al., 2020),
burrows spaced beyond 500 meters were selected for in-depth analysis.
The application of infrared camera traps validated the continued
occupancy of these burrows by pangolins, with Appendix 1 presenting
photographic evidence (Figure S1).
Environmental Data
In this study, an integrative modeling approach was applied to assess a
spectrum of environmental determinants, stratified into three primary
categories: (i) a suite of 19 bioclimatic variables, encompassing an
array of temperature and precipitation metrics for the period 1970-2000;
(ii) topographical and anthropogenic factors, including slope, aspect,
altitude, hydrological proximity, and infrastructural distance; and
(iii) temporal dynamics of vegetation health, quantified through the
analysis of Normalized Difference Vegetation Index (NDVI) across 23
temporal intervals in 2020. The compilation of these environmental
parameters (refer to Table S1 in Appendix 1) provided a robust
foundation for habitat suitability modeling.
The environmental variables utilized in this ecological analysis were
processed with a 2.5-minute spatial resolution, standardized to the
UTM-WGS1984 coordinate system. The selection criteria for environmental
factors involved a two-tiered process: preliminary individual factor
analysis using the Maxent model, identifying significant contributors
(AUC > 0.9, contribution rate > 10%),
followed by a comprehensive collinearity assessment, where one variable
from any highly correlated pair (|correlation|
> 0.8) was omitted (illustrated in Figure S2 in Appendix
1). This procedure culminated in the identification of nine pivotal
environmental factors: Bio03, Bio19, NDVI0321, NDVI0727, NDVI0913,
Aspect, Roads, Slope, and Waterway, each thoroughly defined in Table S2
in Appendix 1.
Species Distribution
Modeling
(1) Within the ambit of this study, the biomod2 software package
(Thuiller et al., 2023)was harnessed, amalgamating a cadre of 11
advanced modeling algorithms for a synergistic prediction of species
distribution. Utilizing the algorithms integral to Biomod2, a training
subset comprising 75% of extant species distribution data was deployed
for model calibration, reserving the remaining 25% for model validation
purposes. Each algorithm was iterated thrice to fortify the robustness
of the results. The pseudo-absence approach was employed to compensate
for the paucity of explicit absence data. Model efficacy was appraised
using True Skill Statistics (TSS) and the Area Under the Receiver
Operating Characteristic Curve (AUC) as metrics, evaluating the
precision of model fit. TSS amalgamates sensitivity and specificity,
with a score range from -1 to 1, where values between 0.8 to 1 signify
optimal model fidelity (Allouche et al., 2006). AUC values span from 0.5
to 1, with thresholds above 0.7 denoting reasonable predictive accuracy,
above 0.8 indicating satisfactory predictions, and values surpassing 0.9
reflecting high precision (Anderson, 2003). Models with TSS exceeding
0.7 were integrated to construct the ensemble model, leveraging the
EMwmean method, and AUC values were employed as the definitive standard
for prediction appraisal.
(2) A randomized sampling protocol was applied to ascertain Pearson
correlations among all predictive and evaluative variables (Guisan et
al., 2017; Thuiller et al., 2023), determining the relative import of
each variable in species distribution modeling. This non-model-dependent
approach allows for streamlined comparisons across different modeling
frameworks (Zanardo et al., 2017). Response curves were employed to
delineate the gradational changes in species occurrence probability with
pivotal predictive variables, elucidating the interplay between species
occurrence and environmental drivers, with ecological factors deemed
conducive for species survival when the occurrence probability exceeds
0.5.
(3) In this research, ArcGIS software was utilized for visual
representation of habitat suitability spatial distribution in TIFF
formats. Ensemble model outputs dictated the stratification of habitat
suitability into four discrete categories: 0-0.15 as unsuitable,
0.15-0.50 as lowly suitable, 0.50-0.75 as moderately suitable, and
0.75-1.00 as highly suitable. Further, an analysis incorporating the
perimeter of Fujian Junzifeng National Nature Reserve and current
administrative village delineations was conducted. This analysis was
pivotal in identifying key administrative villages on the periphery of
the reserve, earmarking them as primary zones for conservation and
monitoring initiatives. Distribution extents of diverse suitability
levels within all administrative villages were methodically ranked,
employing a weighted schema (score = highly suitable area × 0.7 +
moderately suitable area × 0.5). A conservation benchmark was set to
ensure no less than 75% of suitable habitats outside the reserve are
conserved, based on which, administrative villages necessitating
immediate conservation actions were identified. We conducted field
surveys in the selected administrative villages, establishing at least
one transect in each village to verify the presence of pangolin burrows
along the survey lines.
(4) For future projections, the BIOMOD_EnsembleForecasting function
within Biomod2 was deployed. Predictive variable binary transformation
was conducted using ArcGIS’s reclassification tool, setting a critical
threshold at 0.5, denoting values ≥0.5 as indicative of species
presence, and <0.5 as absence. Subsequent comparative analyses
of current and projected distributions under the SSP1-2.6 scenario
(similarly for SSP5-8.5) were facilitated using ArcGIS 10.2. Raster
layers were initially reclassified based on habitat suitability,
attributing new pixel values. Multiplicative raster calculations were
then employed, each pixel value acquiring a novel interpretation: ”3”
indicating absence, ”4” for expansion, ”6” for contraction, and ”8” for
stable regions (He et al., 2018; York et al., 2011). The final phase
involved ranking future suitable areas across townships to spotlight
regions meriting heightened conservation focus over the next two
decades.
All analytical processes were conducted in R software (version 4.3.1,
2023), with spatial analysis executed using ArcGIS (version 10.2; ESRI,
Inc., Redlands, CA, USA). Documentation and presentation tasks were
facilitated through WPS Office (Kingsoft Office Software,https://www.wps.com/office-free ). The integrated application of
these analytical tools endowed the study with robust data processing and
analytical prowess, ensuring the accuracy and reliability of the
results.
RESULTS
Model Performance Analysis
In this ecological study, a comprehensive dataset of 106 pangolin
burrows was collated, with a focus on 23 selected burrows for intensive
analysis (illustrated in Figures 1C and D). Within the scope of the
biomod2 framework, seven predictive models were meticulously chosen,
each surpassing the True Skill Statistics (TSS) benchmark of 0.7 (as
outlined in Table 1). Notably, the Random Forest (RF) and XGBOOST models
demonstrated superior Receiver Operating Characteristic (ROC) values of
1, eclipsing the ensemble model’s predictive accuracy. Conversely, the
Gradient Boosting Machine (GBM), Maximum Entropy (MAXENT), Generalized
Linear Model (GLM), Classification Tree Analysis (CTA), and Generalized
Additive Model (GAM) yielded ROC values marginally inferior to that of
the ensemble model (detailed in Table 1). This delineation of results
highlights the ensemble model’s exceptional proficiency in accurately
modeling the distribution patterns of Manis pentadactyla (Chinese
pangolin) in the near current historical window (1990-2000).