Spatiotemporal Evolution and Mechanisms of Habitat Quality in Nature Reserve Land: A Case Study of 18 Nature Reserves in Hubei Province

Abstract

The contribution of biodiversity to the global economy, human survival, and welfare has been significantly increasing. However, nature reserves have long been subject to a sequence of ecological environmental issues caused by human activities. Therefore, quantitatively assessing the spatiotemporal evolution characteristics of habitat quality due to land use changes and exploring the mechanisms of potential influencing factors can provide a scientific basis for the stable and sustainable development of natural ecosystems. This study aims to analyze 18 nature reserves in Hubei Province to identify the spatiotemporal evolution of habitat quality within these reserves and to explore the influence of multifactorial dynamics from nature, humanity, and policy on this evolution. Initially, the study utilizes land use transition matrices and land use dynamic degree methods to understand the spatiotemporal characteristics of land conversion within the study area. Subsequently, it analyzes the spatiotemporal changes in habitat quality from 2000–2020 based on the InVEST model and tools like spatial autocorrelation (Moran’s I) in ArcGIS. Finally, 14 potential influencing factors are selected from natural environment, socio-human, and policy regulation aspects and analyzed in the Geodetector software to understand the factors affecting the spatiotemporal evolution of habitat quality. The results show that, during the study period, the land area of 18 nature reserves in Hubei Province increased from 2000 to 2020, while the water area decreased. There were slight increases in farmland, construction land, and forest land, with significant decreases in grassland and water areas. This reveals the erosion of water bodies due to artificial lake filling during rapid urbanization, leading to a decline in overall habitat quality within the reserves and a gradual increase in spatial heterogeneity. Among the influencing factors, single-factor influences such as land use intensity and distance to county roads and slopes have a strong negative linear relationship with habitat quality, with land use intensity being the most significant human activity factor. The interaction strength among different types of influencing factors in the bivariate interaction detection results is ranked as follows: the interaction between natural geographical and socio-human factors > the interaction within socio-human factors > the interaction within natural geographical factors. This study has diverged from the past focus on the selection of a single continuous natural reserve as the empirical subject. Consequently, it allows for an integrated analysis of physical geographical dimensions such as locational topography with socio-cultural and policy elements including land use and transportation facilities, thereby facilitating a multifactorial assessment of the interactive impacts on habitat quality.

Full text

Citation: Lin, Y.; Zhang, X.; Zhu, H.;
Li, R. Spatiotemporal Evolution and
Mechanisms of Habitat Quality in
Nature Reserve Land: A Case Study
of 18 Nature Reserves in Hubei
Province. Land 2024,13, 363.
https://doi.org/10.3390/
land13030363
Academic Editor: Alexander
Khoroshev
Received: 30 January 2024
Revised: 8 March 2024
Accepted: 9 March 2024
Published: 13 March 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
land
Article
Spatiotemporal Evolution and Mechanisms of Habitat Quality in
Nature Reserve Land: A Case Study of 18 Nature Reserves in
Hubei Province
Ying Lin 1, Xian Zhang 1, He Zhu 2,* and Runtian Li 3
1Hubei Provincial Urbanization Engineering Technology Research Center, School of Architecture and Urban
Planning, Huazhong University of Science and Technology, Wuhan 430074, China; ianlin@hust.edu.cn (Y.L.);
m202273992@hust.edu.cn (X.Z.)
2School of Architecture and Urban Planning, Beijing University of Civil Engineering & Architecture,
Beijing 100044, China
3
Beijing Tsinghua Tongheng Planning and Design Institute Co., Ltd., Henan Branch, Zhengzhou 450000, China;
liruntian@thupdi.com
*Correspondence: zhuhe@bucea.edu.cn
Abstract: The contribution of biodiversity to the global economy, human survival, and welfare has
been significantly increasing. However, nature reserves have long been subject to a sequence of
ecological environmental issues caused by human activities. Therefore, quantitatively assessing the
spatiotemporal evolution characteristics of habitat quality due to land use changes and exploring
the mechanisms of potential influencing factors can provide a scientific basis for the stable and
sustainable development of natural ecosystems. This study aims to analyze 18 nature reserves in
Hubei Province to identify the spatiotemporal evolution of habitat quality within these reserves
and to explore the influence of multifactorial dynamics from nature, humanity, and policy on this
evolution. Initially, the study utilizes land use transition matrices and land use dynamic degree
methods to understand the spatiotemporal characteristics of land conversion within the study area.
Subsequently, it analyzes the spatiotemporal changes in habitat quality from 2000–2020 based on
the InVEST model and tools like spatial autocorrelation (Moran’s I) in ArcGIS. Finally, 14 potential
influencing factors are selected from natural environment, socio-human, and policy regulation aspects
and analyzed in the Geodetector software to understand the factors affecting the spatiotemporal
evolution of habitat quality. The results show that, during the study period, the land area of
18 nature
reserves in Hubei Province increased from 2000 to 2020, while the water area decreased. There
were slight increases in farmland, construction land, and forest land, with significant decreases in
grassland and water areas. This reveals the erosion of water bodies due to artificial lake filling during
rapid urbanization, leading to a decline in overall habitat quality within the reserves and a gradual
increase in spatial heterogeneity. Among the influencing factors, single-factor influences such as
land use intensity and distance to county roads and slopes have a strong negative linear relationship
with habitat quality, with land use intensity being the most significant human activity factor. The
interaction strength among different types of influencing factors in the bivariate interaction detection
results is ranked as follows: the interaction between natural geographical and socio-human factors
> the interaction within socio-human factors > the interaction within natural geographical factors.
This study has diverged from the past focus on the selection of a single continuous natural reserve
as the empirical subject. Consequently, it allows for an integrated analysis of physical geographical
dimensions such as locational topography with socio-cultural and policy elements including land use
and transportation facilities, thereby facilitating a multifactorial assessment of the interactive impacts
on habitat quality.
Keywords: land use change; habitat quality; InVEST model; Geodetector; driving factors; nature
reserves
Land 2024,13, 363. https://doi.org/10.3390/land13030363 https://www.mdpi.com/journal/land

Land 2024,13, 363 2 of 32
1. Introduction
Biodiversity is the foundation of human survival and development, with all other
ecosystem services relying on robust biodiversity [
1
,
2
]. Statistics indicate that biodiversity’s
contribution to the global economy, human survival, and welfare has increased in recent
years [
3
,
4
]. However, rapid urbanization and associated land use changes have led to
significant losses in biodiversity, massive declines in ecosystem services, and severe impacts
and alterations to the quality of biological habitats (i.e., ecological environmental quality),
presenting great challenges to biodiversity conservation [
5
,
6
]. Habitat quality (HQ) refers
to the ability of an ecosystem to provide resources and suitable conditions for the survival
and reproduction of individuals or populations, representing a crucial ecosystem service
function. It can characterize the region’s biodiversity and the degree of human disturbance
to ecosystems to some extent [
7
,
8
]. Land use dynamics, a primary activity through which
humans modify the natural environment and disturb habitat quality, reflect the intensity
of human activities [
9
,
10
]. Changes in land use have become one of the significant risk
factors affecting the natural environment and the quality of biological habitats. In particular,
the expansion of urban construction and agricultural lands exacerbates the destruction of
natural environments and biological habitats, hinders habitat connectivity, and intensifies
habitat fragmentation and degradation, leading to habitat loss [
11
–
14
]. Therefore, studying
the spatiotemporal evolution of land use changes and habitat quality, as well as exploring
the impact mechanisms of multifactorial dynamics from nature, humanity, and policy, can
provide a basis for analyzing the sustainable use of regional ecological environments.
Two conceptual methods exist for assessing habitat quality: The first is an indirect
measurement method, involving field surveys of population variables in different habitats
to reveal changes in habitat quality, which often requires substantial human and material
resources [
15
–
17
]. The second method is a direct measurement method, which consists
of building models to assess habitat quality. Some commonly used assessment models
include the InVEST model (Integrated Valuation of Ecosystem Services and Tradeoffs),
SoLVES model (Social Values for Ecosystem Services), and SDM (Species Distribution
Model) [
18
,
19
]. The second method is more efficient and convenient than the first, hence
its widespread application by scholars both domestically and internationally. The InVEST
model is extensively used due to its low data requirements, strong spatial visualization, and
high precision in computational results. The model can reflect habitat distribution under
various landscape patterns, of which the habitat quality module evaluates habitat quality by
analyzing land use/cover (LUCC) maps. Specifically, it can determine the degree of threat
from different land use types to biodiversity [
20
–
22
]. For instance, Sallustio et al. assessed
the habitat quality and degradation of current protected areas in Italy by InVEST [
23
].
Terrado et al. assessed terrestrial habitat quality and extended it to freshwater habitats in
different scenarios of river basins with the upgrading of InVEST [
24
]. Li et al. simulated
urban expansion in Changzhou City under scenarios preserving areas of high habitat
quality with integrated application of the InVEST and the SLEUTH models [
25
]. Chen et al.
assessed the evolution of habitat quality in China’s first batch of five national parks and
proposed management zones using InVEST [
26
]. Liu Chunfang et al. assessed the evolution
of habitat quality in Yuzhong County from 1995–2015 by InVEST and analyzed some
urbanization factor impact mechanisms on habitat quality with the
Geodetector model [27].
Overall, scholars both domestically and internationally have conducted multifaceted
and multi-scale research on the assessment of habitat quality at different scales. The method-
ology and indicator system of the INVEST model for habitat quality assessment has been
continuously improved through multiple validations. However, the previous studies still
suffer from the following limitations: (1) The selection of empirical objects often focuses
on continuous areas with typical geographical features, such as biological habitats [
14
,
23
],
watershed areas [
12
,
24
], urban agglomerations [
5
,
6
], national park areas, etc. [
26
]. These
areas have strong homogeneity and multiple human factors within them. Therefore, it is
difficult to draw scientific conclusions from continuous areas as a research object for ex-
ploring the sustainability of humans and nature. On the contrary, nature reserves have the

Land 2024,13, 363 3 of 32
following characteristics: diversity of protected objects, advantages of natural geographical
location, the integrity of the ecosystem, diversity of protective properties, and combination
of multiple functions, etc. These characteristics provide complete and direct support for
studying the sustainability and stability of natural ecosystems. (2) In the analysis of factors
affecting habitat quality, the models often incorporate single-factor dimensions such as
topographic gradients [
28
,
29
], soil and water conservation [
19
], urbanization [
30
], biolog-
ical communities [
31
], etc. Single-factor dimensions cannot comprehensively clarify the
spatiotemporal mechanisms of habitats because the evolution of habitats is the result of
multiple factors such as human participation, changes in natural conditions, policies, etc.
In addition, the single-factor dimension cannot comprehensively reflect the comprehensive
ability and specific perspective of the evaluated object. Furthermore, single-factor assess-
ment cannot detect the interaction between different factors. Therefore, the reliability of
conclusions drawn from examining a single factor is somewhat affected, and the inferential
nature is also greatly limited. (3) Application research on nature reserves mainly focuses on
the habitat quality assessment and influencing factors of individual reserves [
32
–
34
]. The
major concern brought by a single case is whether the research findings can be applied to a
broader range. The evaluation of a single area cannot explain the large-scale spatiotemporal
patterns because the spatiotemporal evolution mechanisms caused by a single protected
area are highly likely to be sporadic and have not been validated. Overall, these limitations
make it difficult for research findings to effectively identify the diverse impacts of human
activities on habitat quality. Moreover, whether historical research results can be fully
generalized to the habitat quality conservation practices of nature reserves with different
geographical characteristics is also debatable.
For this reason, this study intends to select multiple nature reserves within the same
administrative jurisdiction as empirical objects and apply the INVEST and geographical
detector models to explore the impact mechanisms on different types of nature reserves
from dimensions of physical geography, socio-culture, and policy regulation, aiming to ad-
dress the limitations of multi-factor impact analysis and multi-object comparative research
present in existing studies.
To better achieve the research objectives, this study selects all 18 terrestrial-dominated
national nature reserves in Hubei Province, China, as empirical objects. First, Hubei
Province features a diversity of natural elements such as rivers, forests, mountains, grass-
lands, wastelands, tidal flats, and wetlands, with a significant range of altitudes, and nature
reserves with various geographical characteristics, facilitating the identification of different
natural geographic influencing factors. Second, the national nature reserves in Hubei
Province adopt a hierarchical management policy of core areas, buffer zones, experimental
zones, and operational zones [
35
,
36
]. The experimental and operational zones allow for
business activities such as eco-tourism and visits under the management of regulatory
authorities, making the study of human activity factors on habitat quality in these areas
more identifiable. Third, Hubei Province spans approximately 740 km from east to west,
with the economic development of the eastern plain areas significantly higher than that of
the western mountainous regions. This results in greater pressure on the western counties
and cities in provincial GDP assessments, forcing most nature reserves located in western
Hubei to face more severe risks of economic development and policy regulation, such as
eco-tourism and ecological value products [
37
,
38
], further increasing the identifiability of
these two aspects of influencing factor analysis. Therefore, selecting the nature reserves
in Hubei Province as the empirical area, where the ecosystem is more typically affected
by socio-economic elements, is advantageous for better reflecting the impact effects of
multidimensional factors on habitat quality.
Currently, research on habitat quality in Hubei Province focuses on the impacts caused
by topography, land use, urban expansion, and other single elements in the continuous
complete region of the same class. For instance, Pengnan Xiao and others have evaluated
the spatiotemporal characteristics of habitat quality across Hubei Province and the impact
of topographic gradients on habitat quality [
39
]. Shuaipeng Chen has assessed the influence

Land 2024,13, 363 4 of 32
of the expansion of prefecture-level cities and counties on the quantity, area, and quality of
natural habitats, as well as the critical threshold distances affecting habitats [
40
]. Moreover,
Meng-yao Li and others have evaluated the effects of topographic gradient on habitat
quality in northwestern Hubei, using the city of Shiyan as a case study, by employing the
topographic position index [
41
]. However, there is a lack of research on the spatiotemporal
characteristics of habitat quality and the multi-factor influence mechanisms in nature
reserves across different geographical features of Hubei Province against the backdrop of
ecological civilization.
Therefore, our work proposes to further explore the 18 nature reserves in Hubei
Province as empirical subjects, aiming to achieve two research objectives: Firstly, to an-
alyze the spatiotemporal characteristics of land use and habitat quality within these 18
nature reserves from 2000 to 2020 based on remote sensing data, clarifying their consistent
characteristics and distinctive differences. Secondly, to establish the hypothesis that the
quality of habitat is influenced more by the interaction of multiple factors than by single
factors, employing the InVEST model and the Geodetector model to quantitatively assess
the impact of various factors from natural geography, socio-cultural, and policy regulation
dimensions on habitat quality. This involves further analyzing the degree of impact of
the interactions between various influencing factors on habitat quality, delving into the
research on the mechanisms affecting habitat quality in nature reserves, and providing
references for countries worldwide undertaking ecological transitions.
2. Overview of the Study Area and Research Methods
2.1. Overview of the Study Area
According to the Hubei Provincial Department of Ecology and Environment’s directory
of nature reserves in Hubei Province, as of 2020, there were a total of 22 national-level nature
reserves. Among these, the protection scope of aquatic wildlife reserves is delineated based
on the highest historical water levels of the river’s main channel and its tributaries, with a
disproportionately high ratio of water area within the reserves, making them unsuitable
for habitat quality estimation using this model. Fossil relic reserves contain many exposed
paleontological fossils, which have a high degree of similarity to bare ground in the
process of land use type extraction from remote sensing images, making them difficult
to distinguish and posing significant challenges to the interpretation of remote sensing
images. Therefore, excluding these two types of reserves unsuitable for habitat quality
estimation using the InVEST model’s Habitat Quality module, the study area includes
18 national nature reserves within Hubei Province (Table 1).
Hubei Province has a diverse array of protected natural areas, covering nearly 10%
of the province’s total land area. However, many national-level nature reserves in Hubei
are in impoverished regions such as the western Hubei mountain (Figure 1). These areas
face heavy burdens due to economic development, ecological protection, and poverty
alleviation tasks, leading to significant contradictions between regional ecological envi-
ronmental protection and poverty alleviation needs. In some places, blind development
and construction within the reserves have been pursued for economic gain, damaging
ecosystem integrity and causing issues such as habitat fragmentation and a sharp decrease
in species quantity and variety. For example, Xingdoushan National Nature Reserve houses
nearly 80,000 residents and includes the Fubaosi Development Zone within its boundaries.
Longgan Lake National Nature Reserve is home to many permanent basic farmlands, live-
stock farms, and enterprises, resulting in longstanding conflicts between humans, wetlands,
and migratory birds.

Land 2024,13, 363 5 of 32
Table 1. Directory of national nature reserves in Hubei Province.
Number Type Name of National Nature
Reserve in Hubei Area (ha)
Establishment
Year
Conservation
Targets Selected
1
Forest ecology
Shennongjia 70,467 1986
Forest
ecosystems, flora
and fauna Yes
2 Wufeng Houhe 10,340 2000
3 Xingdoushan 68,339 2003
4 Jiugongshan 16,608 2007
5 Qizimei Mountains 34,550 2008
6 Saiwudang 21,203 2011
7 Mulinzi 20,838 2012
8 Duheyuan National 47,173 2013
9 Shibalichangxia 25,605 2013
10 Nanhe 14,833 2014
11 Dabie Mountains 16,048 2014
12 Badong Golden Monkey 20,910 2016
13 Three Gorges Dalao Ridge 14,225 2017
14 Wudaoxia 20,860 2017
15 Changyang Bengjianzi 13,313 2017
16 Inland wetland Longgan Lake 22,322 2009 Wetland plants,
animals
17 Honghu 41,412 2014
18 Wildlife Shishou Milu Deer 1567 1998
Wildlife resources
19
Aquatic wildlife
Hubei Yangtze River Xintan
Baiji Dolphin 40,000 1992
Aquatic wildlife
and habitats
No
20 Hubei Yangtze River
Tian’ezhou Baiji Dolphin 15,250 1992
21 Hubei Zhongjian River
Giant Salamander 1043 2012
22 Paleontological
relics
Hubei Qinglongshan
Dinosaur Egg Fossil 455 2001 Paleontological
relics and fossils
Data Source: Compiled by the author based on the “Directory of Nature Reserves in Hubei Province”. Hubei
Provincial Department of Ecology and Environment. Directory of Nature Reserves in Hubei Province [EB/OL]
[19 November 2018].
Land2024,13,xFORPEERREVIEW6of34


Figure1.Locationmapofthe18nationalnaturereservesinHubeiProvince.
2.2.DataSourcesandProcessing
Intermsofdatasources,alldatawerederivedfromofficialplatforms.Specifically,
theboundarydataofthestudyareaweresourcedfromthefunctionalzoningmapofHu-
beiProvincenationalnaturereservesprovidedbytheHubeiProvincialDepartmentof
EcologyandEnvironment.Thevectorizedboundarieswereextractedusingthegeo-reg-
istrationtoolofArcGIS10.5.ThelandusedataforHubeiProvinceʹsnationalnaturere-
servesin2000,2010,and2020wereobtainedfromtheGlobeLand30globallandcover
datasetwitha30-meterresolution,usingtheWGS-84coordinatesystem.Roaddatawere
sourcedfromOpenStreetMap(OSM).TheDigitalElevationModel(DEM),annualaverage
temperature,annualaverageprecipitation,andPointofInformation(POI)datawereall
obtainedfromtheDataCenterforResourcesandEnvironmentalSciences,ChineseAcad-
emyofSciences.OtherdataweresourcedfromtheHubeiProvincialDepartmentofNat-
uralResourcesandtheofficialwebsitesofmunicipalnaturalresourcesandplanningbu-
reaus(Table2).Regardingdataquality,theGlobeLand30datasetemploysremotesensing
imagesfromLandsat’sTM5,ETM+,OLI,andGaofen-1(GF-1)multispectralimagery.The
selectionprinciplefortheseimagesistochoosethebestavailablemultispectralimages
fromthevegetationgrowingseasonwithin±2yearsofthebaselineorupdateyear,ensur-
ingminimaltonocloudcoverage.Forareaswhereacquisitionischallenging,the
timeframeforobtainingimagesmayberelaxedtoensurethecompletenessofglobalcov-
erage.TheaccuracyassessmentoftheGlobeLand30V2010data,ledbyTongjiUniversity,
achievedanoverallaccuracyof83.50%andaKappacoefficientof0.78.TheGlobeLand30
V2020dataaccuracyassessment,conductedbytheAerospaceInformationResearchInsti-
tuteoftheChineseAcademyofSciences,reachedanoverallaccuracyof85.72%anda
Kappacoefficientof0.82.Theremainingdataweresourcedfromopenstatisticalplatforms
andhaveundergonerigorousdatacollection,processing,andverificationprocesses,en-
suringhighaccuracyandreliability.Insummary,theresearchdatahavethefollowing
advantages:(1)Theintegrateduseofmulti-sourcedatanotonlyenhancesthemulti-di-
mensionalperspectiveofthedatabutalsoincreasesitsaccuracyandreliabilitythrough
cross-validationbetweendifferentdatasources.(2)Comprehensivegeographicinfor-
mationcoverage,encompassingvariousaspectsofthenaturalenvironmentandhuman
activities,providesaholisticviewoftheresearch.(3)Theuseofadvancedprocessingtools
Figure 1. Location map of the 18 national nature reserves in Hubei Province.

Land 2024,13, 363 6 of 32
2.2. Data Sources and Processing
In terms of data sources, all data were derived from official platforms. Specifically, the
boundary data of the study area were sourced from the functional zoning map of Hubei
Province national nature reserves provided by the Hubei Provincial Department of Ecology
and Environment. The vectorized boundaries were extracted using the geo-registration
tool of ArcGIS 10.5. The land use data for Hubei Province’s national nature reserves in
2000, 2010, and 2020 were obtained from the GlobeLand30 global land cover dataset with a
30-meter resolution, using the WGS-84 coordinate system. Road data were sourced from
OpenStreetMap (OSM). The Digital Elevation Model (DEM), annual average temperature,
annual average precipitation, and Point of Information (POI) data were all obtained from
the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences.
Other data were sourced from the Hubei Provincial Department of Natural Resources
and the official websites of municipal natural resources and planning bureaus (Table 2).
Regarding data quality, the GlobeLand30 dataset employs remote sensing images from
Landsat’s TM5, ETM+, OLI, and Gaofen-1 (GF-1) multispectral imagery. The selection
principle for these images is to choose the best available multispectral images from the veg-
etation growing season within
±
2 years of the baseline or update year, ensuring minimal
to no cloud coverage. For areas where acquisition is challenging, the timeframe for obtain-
ing images may be relaxed to ensure the completeness of global coverage. The accuracy
assessment of the GlobeLand30 V2010 data, led by Tongji University, achieved an overall
accuracy of 83.50% and a Kappa coefficient of 0.78. The GlobeLand30 V2020 data accuracy
assessment, conducted by the Aerospace Information Research Institute of the Chinese
Academy of Sciences, reached an overall accuracy of 85.72% and a Kappa coefficient of
0.82. The remaining data were sourced from open statistical platforms and have undergone
rigorous data collection, processing, and verification processes, ensuring high accuracy and
reliability. In summary, the research data have the following advantages: (1) The integrated
use of multi-source data not only enhances the multi-dimensional perspective of the data
but also increases its accuracy and reliability through cross-validation between different
data sources. (2) Comprehensive geographic information coverage, encompassing various
aspects of the natural environment and human activities, provides a holistic view of the
research. (3) The use of advanced processing tools and techniques, such as the ArcGIS
10.5 geo-registration tool and the WGS-84 coordinate system, ensures the high quality and
precision of the data.
Based on the classification system of the 30 m global land cover data GlobeLand30,
and considering the actual situation of land use/land cover in the national nature reserves
of Hubei Province, a reclassification was conducted. The 10 primary types of GlobeLand30
data were reclassified into six major categories, as shown in Table 3.
Table 2. Research data and sources.
Category Name Format Time Source
Land use data Land Use Data Raster data (30 m) 2000, 2010, 2020
30 Meters Global Land Cover Data
GlobeLobe30
(http://globeland30.org, (accessed
on 5 November 2022))
Basic geographic data
Road Data Vector data 2020 OpenStreetMap
(https://www.openstreetmap.org,
(accessed on 10 December 2022))
Water System Data Vector data 2020
Research Area
Boundary Image 2020
Hubei Provincial Department of
Ecology and Environment
(https://sthjt.hubei.gov.cn, (accessed
on 11 December 2022))

Land 2024,13, 363 7 of 32
Table 2. Cont.
Category Name Format Time Source
Basic geographic data
Digital Elevation
Model Raster data (30 m) -
Data Center for Resources and
Environmental Sciences, Chinese
Academy of Sciences
(http://www.resdc.cn, (accessed on
7 January 2023))
Natural environment
data
Annual Average
Temperature Raster data (1 km) 2000, 2010, 2020
Annual Average
Precipitation Raster data (1 km) 2000, 2010, 2020
Socio-cultural data POI Vector data 2000, 2010, 2020
Other
Hubei Province and
Municipal Land and
Space Planning
Document 2020
Hubei Provincial Department of
Natural Resources and municipal
natural resources and planning
bureaus official website
Table 3. Classification of land use/cover in national nature reserves of Hubei Province.
Land Type Name GlobeLand30 Land Types and Codes
01 Cultivated land 10 cultivated land
02 Forest land 20 forest land, 40 shrubland
03 Grassland 30 grassland
04 Water body 50 wetlands, 60 water bodies, 100 glaciers
and permanent snow
05 Urban, industrial, and residential
land (construction land) 80 artificial surfaces
06 Unused land 90 bare land, 70 tundra
Note: There is no tundra (70) or bare land (90) within the study area.
2.3. Research Methods
Our research methodology is divided into three stages encompassing five methods:
(1) analyzing the land use change conditions in 18 nature reserves in Hubei Province for the
years 2000, 2010, and 2020, including methods of land use transition matrix and land use
dynamic degree analysis; (2) investigating the spatiotemporal distribution characteristics
of habitat quality in the 18 nature reserves of Hubei Province for 2000, 2010, and 2020
using the INVEST model analysis and spatial autocorrelation analysis; and (3) examining
the contribution rates of various factors to changes in habitat quality and exploring the
main influencing factors and their evolutionary mechanisms of habitat quality evolution,
employing the geographic detector analysis method. Figure 2illustrates a schematic of our
research methods.
(1)
Land Use Transition Matrix
Before assessing the habitat quality index, this article first uses the land use transition
matrix to represent the changes in direction and area for various land uses during 2000–2020
and two sub-periods (2000–2010 and 2010–2020) [
42
,
43
]. The land use transition matrix is
not only a powerful analytical tool but also serves as a bridge between land change data
and land management policies. Through in-depth analysis of the land use transition matrix,
we can reveal with high precision the relationships and trends between different types
of land use in the nature reserves of Hubei Province during 2000, 2010, and 2020. This
analysis not only unveils the mutual conversions among various land use types within the
reserves, such as arable land, forest land, and construction land but also intricately displays
the specific conditions of these land use types after the implementation of certain policy

Land 2024,13, 363 8 of 32
measures, assisting in the analysis of habitat quality index results that follow. The formula
for the land use transition matrix is presented as Equation (1):
Sij =



S11 S12 . . . S1n
S21 S21 . . . S2n
. . .
Sn1
. . .
Sn2
. . . . . .
. . . Snn




(1)
In the equation,
Sij
represents the total area of the initial land use type ithat has
changed to land use type jby the study’s end, with ndenoting the count of land
use categories.
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
thecontributionratesofvariousfactorstochangesinhabitatqualityandexploringthe
maininfluencingfactorsandtheirevolutionarymechanismsofhabitatqualityevolution,
employingthegeographicdetectoranalysismethod.Figure2illustratesaschematicof
ourresearchmethods.

Figure2.Schematicdiagramofresearchmethods.
(1) LandUseTransitionMatrix
Beforeassessingthehabitatqualityindex,thisarticlefirstusesthelandusetransition
matrixtorepresentthechangesindirectionandareaforvariouslandusesduring2000–
2020andtwosub-periods(2000–2010and2010–2020)[42,43].Thelandusetransitionma-
trixisnotonlyapowerfulanalyticaltoolbutalsoservesasabridgebetweenlandchange
dataandlandmanagementpolicies.Throughin-depthanalysisofthelandusetransition
matrix,wecanrevealwithhighprecisiontherelationshipsandtrendsbetweendifferent
typesoflanduseinthenaturereservesofHubeiProvinceduring2000,2010,and2020.
Thisanalysisnotonlyunveilsthemutualconversionsamongvariouslandusetypes
withinthereserves,suchasarableland,forestland,andconstructionlandbutalsointri-
catelydisplaysthespecificconditionsoftheselandusetypesaftertheimplementationof
certainpolicymeasures,assistingintheanalysisofhabitatqualityindexresultsthatfol-
low.TheformulaforthelandusetransitionmatrixispresentedasEquation(1):
𝑆

𝑆

𝑆

…𝑆

𝑆

𝑆

…𝑆

…
𝑆

…
𝑆

……
…𝑆

(1)
Intheequation,𝑆

representsthetotalareaoftheinitiallandusetypeithathas
changedtolandusetypejbythestudy’send,withndenotingthecountoflandusecate-
gories.
(2) LandUseDynamicDegree
Followingthelandusetransitionmatrix,theanalysisoflandusedynamicdegree
becomesanimportantmeanstostudythecharacteristicsoflandusechange.Thelanduse
Figure 2. Schematic diagram of research methods.
(2)
Land Use Dynamic Degree
Following the land use transition matrix, the analysis of land use dynamic degree
becomes an important means to study the characteristics of land use change. The land use
dynamic degree refers to the rate of quantitative change in land use types over a certain
period, which can reflect the intensity and rate of land use changes within the nature
reserves of Hubei Province between 2000, 2010, and 2020. This facilitates the analysis of
the reasons behind subsequent changes in the habitat quality index within the reserve.
The land use dynamic degree is divided into the single land use dynamic degree and the
comprehensive land use dynamic degree [44–47]. The formulas are as follows:
A single index means the rate of change in a certain land use type in a specific time
frame in the study area, focusing on analyzing the changes in each land use type. The
calculation formula is shown as (2):
M=Ua−Ub
Ub
×1
T×100% (2)
In the formula,
M
represents the dynamic degree of land type Uwithin the study
duration; Trepresents the study duration;
Ua
is the initial area of land type U; and
Ub
is
the final area of land type Uat the study’s conclusion. Where
M
is positive, it indicates
an increase in the land type over the time
T
, and a negative
M
indicates a decrease. A

Land 2024,13, 363 9 of 32
higher absolute value of
M
indicates a faster rate of change, and a lower value suggests a
slower rate.
The comprehensive index describes the overall rate of change in land use for the entire
area, focusing on the study of regional variations in land use changes. The calculation
formula is shown as (3):
Lc =∑n
i=1∆LUi−j
2∑n
i=1∆LUi×1
T×100% (3)
In the formula,
Lc
represents the overall dynamic degree of land use in the study area,
∆LUi
is the initial area of the ith land use type,
∆LUi−j
is the absolute value of the area of
the ith land use type converted to the jth land use type during the study period, nis the
number of land types (n = 1, 2, 3, . . .), and Tdenotes the study period’s length.
(3)
InVEST Model
The InVEST model is used to assess the habitat quality index of national nature
reserves in Hubei Province. This model conducts an in-depth analysis of a key principle: by
meticulously assessing the sensitivity of various land use types within the nature reserves
of Hubei Province and the impact of various threat factors, it generates a detailed map of
habitat quality distribution across the reserves. This approach enables us to clearly see
the direct link between habitat quality and land use changes within the nature reserves,
demonstrating how human land use practices within these areas directly influence the
transformation of land use types and significantly impact habitat quality levels. Habitat
quality is closely related to land use changes; human utilization of land alters land use
types and affects habitat quality levels. The greater the intensity of human activities, the
greater the threat to the quality of regional habitats, resulting in lower habitat quality
and biodiversity levels. This model is a novel tool for assessing human-made threats to
habitat quality in natural ecological areas and has several advantages over other models:
(1) It has lower data intensity and relatively higher flexibility [
48
]. (2) It allows for data
adjustments based on actual conditions [
49
]. (3) It can analyze the spatial habitat quality
and connectivity of various land use types and quantify their sensitivity [50].
The model integrates the reclassification of land use data, combines land use data,
habitat threat sources, and the habitat’s response to threat factors to generate a habitat
quality index map. The calculations are as shown in Equations (4) and (5):
Qxj =Hj"1− Dz
xj
Dz
xj +Kz!# (4)
Dxj =
R
∑
r=1
Yr
∑
y=1 ωr/
R
∑
r=1
ωr!ryirxy βxSjr (5)
In the formula,
Qxj
represents the habitat quality index for the
xth
grid of the
jth
land
use type, with values from 0 to 1, where higher values signify superior habitat quality;
Hj
represents the habitat suitability of the
jth
land use type, with values from 0 to 1;
Dxj
refers
to the degradation score, indicating the level of degradation of the
xth
grid of the
jth
land
use type; Zis the scale constant, set by default to 2.5; and krepresents the half-saturation
constant, calibrated by first running the model once with the InVEST model’s default value
of 0.5, then using half of the maximum habitat degradation value as the model’s k value.
Following the above steps, the final kvalue was determined to be 0.02.
Rrepresents the count of threat factors;
ωr
is the relative importance value of each
threat factor; and Yis the total count of grid units for stressor r, with
Yr
being the count
of grid units affected by threat factor r.
ry
is the value of the threat factor of type r;
βx
is
the accessibility of various threat factors to the habitat grid, with values ranging from 0
to 1;
Sjr
measures the sensitivity of the
jth
habitat type to threat factor r, with the value

Land 2024,13, 363 10 of 32
interval located ranging from 0 to 1; and
irxy
refers to the distance between the threat and
the habitat grid, representing the threat’s impact on the habitat.
The influence of threat factors on habitat is categorized into linear (Equation (6)) and
exponential (Equation (7)) distance decay formulas, as follows:
irxy =1−dxy
drmax (Linear)(6)
irxy =exp−2.99
drmax dxy (Exponential)(7)
In the formula,
dxy
is the straight-line distance between habitat grid xand y;
dr
max
represents the maximum distance threat rcan impact.
To scientifically assess the habitat quality of the national nature reserves in Hubei
Province, in conjunction with the actual geographic conditions of the study area, farmland,
transportation land, residential areas, and bare land were selected as relevant threat factors
based on the InVEST model’s recommended values and previous research [
51
,
52
]. Con-
struction land, farmland, railways, expressways, and other roads were ultimately identified
as stress factors. Following expert recommendations, the relative sensitivity of each habitat
type to each threat factor, habitat suitability, maximum impact distance of threat sources,
and the weights of different threat sources were determined, with specific assignments as
shown in Tables 4and 5.
Table 4. Habitat quality threat factor attributes.
Threat Factor Maximum Impact Distance (Km) Weight Spatial Decay Type
Cultivated land 4 0.5 Exponential
Construction land 8 0.8 Exponential
Railway 2 0.6 Linear
Expressway 6 0.5 Linear
Other roads 5 0.65 Linear
Table 5. Relative sensitivity of each habitat type to each threat factor.
Land Use
Type
Habitat
Suitability
Threat Factor
Cultivated
Land
Construction
Land Railway
Expressway
Roads
Cultivated
land 0.3 0.3 0.4 0.35 0.35 0.3
Forest land 1 0.8 0.6 0.7 0.65 0.6
Grassland 0.7 0.5 0.6 0.7 0.65 0.6
Water body 0.9 0.65 0.7 0.6 0.6 0.65
Construction
land 0 0 0 0 0 0
The determination of the weights of stress factors on habitat types is usually quali-
tatively based on previous experiences, with quantitative improvements made using the
entropy weight method. Firstly, data on stress factors are recorded. Since stress factors lead
to a decrease in habitat quality, the relationship between stress factors and habitat quality
is negatively correlated. Subsequently, the data are standardized, and the information
entropy of each stress factor is calculated using a formula. Finally, the entropy weight of
each stress factor is calculated to serve as the weight of its impact on habitat quality.
(4)
Spatial Autocorrelation Analysis
This study utilizes ArcGIS software to analyze the spatial correlation of habitat quality
evolution among various grid units within the national nature reserves of Hubei Province.

Land 2024,13, 363 11 of 32
The analysis aims to uncover the fundamental patterns of habitat quality changes within the
reserves, particularly how they develop and change geospatially, thereby providing a solid
scientific basis for the formulation of conservation measures and the management of the
ecological environment. Moran’s I index is a powerful statistical tool used to measure the
spatial distribution patterns of specific characteristics (in this study, habitat quality) within
an area, to determine whether these characteristics exhibit significant clustering trends
or random distribution in space. Therefore, this paper adopts the global Moran’s I index
for spatial autocorrelation analysis to reflect whether clustering or outliers occur in space.
The hot spot analysis (Getis-Ord Gi*) can measure the spatial aggregation characteristics
of habitat quality. The advantages of spatial autocorrelation are as follows: Firstly, it can
calculate the similarity and correlation between neighboring areas to reveal the distribution
changes in habitat quality in space. Secondly, it can clearly identify the spatial aggregation,
which helps to determine the areas with high and low-quality habitats in the ecosystem,
and provides substantial direction for ecological resource protection and restoration [
53
].
The formula for calculation is as follows:
I=n∑n
i=1∑n
j=1wij (xi−¯
X)xj−¯
X
∑n
i=1(xi−¯
X)2∑n
i=1∑n
j=1wij (8)
G*
i=∑n
j=1wij xj−¯
X∑n
j=1wij
Srn∑n
j=1w2
ij −∑n
j=1wij )2
n−1
(9)
¯
X=∑n
j=1xj
n(10)
S=s∑n
j=1x2
j
n−¯
X2(11)
In the formula, Imeans the Moran’s I index; Gi* is the hot spot index;
wij
means the
spatial weight between the ith and jth spatial cells;
xi
and
xj
mean the values of the ith and
jth cells; and
¯
X
means the average value of the cells; n means the total number of cells in
the study area. The Moran’s I index ranges from [
−
1, 1], with higher values indicating
stronger spatial correlation, less than 0 representing negative correlation, and equal to 0
signifies a random distribution.
Subtract the habitat quality spatial distribution maps of 2020 from those of 2000,
then calculate the change in habitat quality using a 300 m
×
300 m grid, using the dif-
ference in habitat quality as the analysis variable for spatial autocorrelation (Moran I)
and hot spot analysis in ArcGIS. Hot spot analysis is divided into seven categories based
on Z-score values: Hot spot-99% confidence (Z-score
≥
2.56), Hot spot-95% confidence
(1.96 ≤Z-score < 2.56),
Hot spot-90% confidence (1.65
≤
Z-score < 1.96), Not significant
(Z-score
≤
1.65 or Z-score
≥ −
1.65), Cold spot-90% confidence (
−
1.96
≤
Z-score <
−
1.65),
Cold spot-95% confidence (
−
2.56
≤
Z-score <
−
1.96), and Cold spot-99% confidence (Z-
score
≤ −
2.56). For the period 2000–2020, hot spot and cold spot areas indicate changes in
habitat quality within nature reserves, where blue cold spots represent clusters of declining
habitat quality and red hot spots represent clusters of increasing habitat quality.
(5)
Geodetector
In the final phase of our study, which is also of paramount importance, we specifically
focused on identifying and assessing various potential driving factors that affect the habitat
quality index, for which we adopted the advanced statistical analysis method known as
the geographical detector. The geographical detector method comprises two key compo-
nents: the factor detector and the interaction detector. It aims to quantitatively assess the
contribution of different factors to habitat quality changes and to delve into the main influ-
encing factors behind habitat quality changes and their mechanisms of action. Compared

Land 2024,13, 363 12 of 32
with traditional statistical analysis methods, a significant advantage of the geographical
detector method is its less stringent requirement for hypothesis conditions, allowing for
more flexible application across various research domains. It is particularly suited for
studying spatial differentiation phenomena and their underlying influencing factors, as it
can effectively handle the complexity and variability of spatial data [
54
,
55
]. In this study,
through the application of the geographical detector, we were not only able to identify the
main factors affecting the habitat quality of national nature reserves in Hubei Province but
we were also able to gain a deep understanding of how these factors collectively influence
habitat quality evolution through various complex interaction mechanisms, providing a
scientific basis and guidance for future conservation and management strategies [56,57].
The factor detector primarily measures the explanatory capacity of different influ-
encing factors on the spatial heterogeneity of habitat quality evolution. The calculation
formula is as follows:
q=1−1
Nσ2
L
∑
h=1
Nhσ2
h(12)
In the formula, htakes values 1, 2, 3, . . ., L, where Lindicates the number of partitions
of variable Yor factor X;
Nh
and Nrepresent the number of units in layer hand the total
area, respectively; and qis the uniform driving explanatory power. For partition i, taking
values 1, 2, 3, . . . , L, where Lis the number of partition items; Nis the total number of
sample units in the area;
σ2
h
is the variance in layer h; and
σ2
is the variance of the Yvalues
in the entire area. Qrepresents the explanatory power of factor Xon variable Y, with q
ranging from [0, 1]. A higher qvalue indicates a stronger explanatory power of influencing
factor Xon habitat quality (variable Y) and vice versa.
The interaction detector assesses whether the combined effect of two influencing
factors increases or diminishes the explanatory capacity of the dependent variable Y. The
relationship between the two factors is shown in Table 6.
Table 6. Types of dual-factor interactions.
Judgment Type Type
q(X1∩X2) < min(q(X1),q(X2)) Nonlinear diminishing
min(q(X1),q(X2)) < q(X∩X2) < max(q(X1),q(X2)) Single-factor nonlinear diminishing
q(X1∩X2) > max(q(X1),q(X2)) Dual-factor enhancement
q(X1∩X2) = q(X1) + q(X2) Independent
q (X1∩X2) > q(X1) + q(X2) Nonlinear enhancement
3. Results and Analysis
3.1. Analysis of Land Use Change
3.1.1. Characteristics of Land Use Patterns
The land use distribution of the 18 national nature reserves in Hubei Province shows
significant regional differences and is relatively dispersed, primarily due to the influ-
ence of the region’s natural geographical foundation (Figure 3). Among them, forest
ecosystem-type national nature reserves are concentrated in the Daba Mountain foothills in
northwestern Hubei and the Wuling Mountain Range area in southwestern Hubei, while
wetland-type national nature reserves are located in the middle Hubei along with the
Yangtze River (Table 7and Figure 4).
From a spatial perspective (Figure 5), the main types of land use in the national nature
reserves of Hubei Province include forest land, grassland, cultivated land, water body, and
construction land. The areas of each land use type, in descending order, are forest land >
cultivated land > water body > grassland > construction land.

Land 2024,13, 363 13 of 32
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

Figure3.TopographicmapofHubeiProvince.

Figure4.The2020landusestatusmapofnationalnaturereservesinHubeiProvince.Note:The
abbreviationsstandforSWD—Saiwudang,NH—NanRiver,WDX—WudaoCanyon,SBL—Shibali,
DHY—Duheyuan,SNJ—Shennongjia,BDJSH—BadongGoldenMonkey,DLL—DalaoLing,XDS—
XingdouMountain,QJMS—QijiemeiMountain,MLZ—Mulinzi,WFHH—WufenghouRiver,
CYBJZ—ChangyangBengjianzi,SSML—ShishouElk,HH—HongLake,JGS—JiugongMountain,
LGH—LongganLake,DBS—DabieMountain.
Figure 3. Topographic map of Hubei Province.
Table 7. Classification of national nature reserves in Hubei Province.
Location Mountain Range or
River System
Name of National Nature Reserves in
Hubei Province
Western Hubei
(13)
Daba Mountains
Saiwudang, Nan River, Wudao Canyon,
Shibalili, Duheyuan, Shennongjia, Badong
Golden Monkey, Dalao Ling
Wuling Mountain Range
Xingdou Mountain, Qijiemei Mountain,
Mulinzi, Wufenghou River, Changyang
Bengjianzi
Central Hubei (2) Middle Yangtze River Shishou Elk, Honghu
Eastern Hubei
(3)
Dabie Mountains Dabie Mountain
Middle and Lower
Yangtze River Longgan Lake
Mufu Mountain Range Jiugong Mountain
3.1.2. Analysis of Land Use Transition
Looking at the overall land use transition map from 2000 to 2020 for the 18 nature
reserves in Hubei Province (Figure 6), overall land use changes show a stable trend. As
shown in Table 8, from 2000 to 2010, the largest land transfer area was from grassland
to forest land, followed by cultivated land to forest land, with a significant increase in
forest land area, possibly related to the implementation of the Grain for Green policy in
Hubei Province starting in 2000. Meanwhile, land transfers from forest land, grassland,
and water bodies to construction land indicate that small areas within the reserves were
encroached upon by human development activities during this period. From 2010 to 2020,
the largest land transfer was from water bodies to cultivated land, with a transfer rate of
31.3%, confirming that during the period of rapid urbanization, the study area experienced
extensive land reclamation from the lake, leading to significant erosion of ecological water
bodies by urban and rural development.

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
Figure3.TopographicmapofHubeiProvince.

Figure4.The2020landusestatusmapofnationalnaturereservesinHubeiProvince.Note:The
abbreviationsstandforSWD—Saiwudang,NH—NanRiver,WDX—WudaoCanyon,SBL—Shibali,
DHY—Duheyuan,SNJ—Shennongjia,BDJSH—BadongGoldenMonkey,DLL—DalaoLing,XDS—
XingdouMountain,QJMS—QijiemeiMountain,MLZ—Mulinzi,WFHH—WufenghouRiver,
CYBJZ—ChangyangBengjianzi,SSML—ShishouElk,HH—HongLake,JGS—JiugongMountain,
LGH—LongganLake,DBS—DabieMountain.
Figure 4. The 2020 land use status map of national nature reserves in Hubei Province. Note:
The abbreviations stand for SWD—Saiwudang, NH—Nan River, WDX—Wudao Canyon, SBL—
Shibali, DHY—Duheyuan, SNJ—Shennongjia, BDJSH—Badong Golden Monkey, DLL—Dalao Ling,
XDS—Xingdou Mountain, QJMS—Qijiemei Mountain, MLZ—Mulinzi, WFHH—Wufenghou River,
CYBJZ—Changyang Bengjianzi, SSML—Shishou Elk, HH—Hong Lake, JGS—Jiugong Mountain,
LGH—Longgan Lake, DBS—Dabie Mountain.
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

Figure5.The2020landusestructuremapofnationalnaturereservesinHubeiProvince.
3.1.2.AnalysisofLandUseTransition
Lookingattheoveralllandusetransitionmapfrom2000to2020forthe18nature
reservesinHubeiProvince(Figure6),overalllandusechangesshowastabletrend.As
showninTab l e 8,from2000to2010,thelargestlandtransferareawasfromgrasslandto
forestland,followedbycultivatedlandtoforestland,withasignificantincreaseinforest
landarea,possiblyrelatedtotheimplementationoftheGrainforGreenpolicyinHubei
Provincestartingin2000.Meanwhile,landtransfersfromforestland,grassland,andwa-
terbodiestoconstructionlandindicatethatsmallareaswithinthereserveswereen-
croacheduponbyhumandevelopmentactivitiesduringthisperiod.From2010to2020,
thelargestlandtransferwasfromwaterbodiestocultivatedland,withatransferrateof
31.3%,confirmingthatduringtheperiodofrapidurbanization,thestudyareaexperi-
encedextensivelandreclamationfromthelake,leadingtosignificanterosionofecological
waterbodiesbyurbanandruraldevelopment.
Overall,from2000to2020,thevarioustypesoflanduseinthenationalnaturere-
servesofHubeiProvinceweredynamicallychanging,withvariouslandtypesinterchang-
ing.Theoveralltrendshowssignificantencroachmentofcultivatedlandongrasslandand
waterbodies,andminorencroachmentofconstructionlandonotherlandtypes,indicat-
ingaclearimpactofrapidurbanizationonthenaturereserves.Aslightincreaseinforest
landareaindicatestheinitialeffectivenessofHubeiProvince’sGrainforGreenpolicyand
naturereserveprotectionpolicies,buttheprotectionofgrasslandandwaterbodyland
useareasisinsufficient.

Figure 5. The 2020 land use structure map of national nature reserves in Hubei Province.

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Tab l e 8.LandusetransitionmatrixfornationalnaturereservesinHubeiProvince,2000–2020(ha).
TimePe‐
riod
LandUse
Type
Cultivated
LandForestLandGrasslandWater
Body
Construction
LandTotal
2000–2010
Cultivatedland—4709.07432.993141.097.0249,780.62
Forestland5197.86—376.83148.681.53428,360.9
Grassland584.8214,452.56—53.014.8622,042.8
Waterbody492.93113.6744.28—1.4451,734.07
Constructionland9.002.700.090.00—139.59
Total47,775.06441,9147801.7454,424.53142.65552,058
2010–2020
Cultivatedland—5459.22270.9317.43141.2147,775.06
Forestland5932.71—4411.62587.0746.8441,914
Grassland675.991472.76—196.5628.717801.74
Waterbody17,055.54108.54257.85—9.9954,424.53
Constructionland95.944.050.630.99—142.65
Total65,346.48437,980.410,368.7238,094.66267.75552,058
2000–2020
Cultivatedland—5315.58506.16785.34145.5349,780.62
Forestland6440.67—3294.09572.7643.2428,203.5
Grassland853.0214,549.31—277.6540.1422,042.8
Waterbody14,921.19101.88245.07—7.4751,734.07
Constructionland103.593.420.720.45—139.59
Total65,346.48437,82310,368.7238,094.66267.755520.58

Figure6.ChorddiagramoflandusetransitioninnationalnaturereservesofHubeiProvince,2000–
2020.
3.1.3.AnalysisofLandUseDynamicDegree
AccordingtoTable 9,forthenationalnaturereservesinHubeiProvincefrom2000to
2020,theoveralldynamicdegreeoflanduseincreasedfrom0.27to0.34,indicatingan
Figure 6. Chord diagram of land use transition in national nature reserves of Hubei Province,
2000–2020.
Table 8. Land use transition matrix for national nature reserves in Hubei Province, 2000–2020 (ha).
Time
Period
Land Use
Type
Cultivated
Land Forest Land Grassland Water Body
Construction
Land Total
2000–2010
Cultivated land — 4709.07 432.99 3141.09 7.02 49,780.62
Forest land 5197.86 — 376.83 148.68 1.53 428,360.9
Grassland 584.82 14,452.56 — 53.01 4.86 22,042.8
Water body 492.93 113.67 44.28 — 1.44 51,734.07
Construction land 9.00 2.70 0.09 0.00 — 139.59
Total 47,775.06 441,914 7801.74 54,424.53 142.65 552,058
2010–2020
Cultivated land — 5459.22 270.9 317.43 141.21 47,775.06
Forest land 5932.71 — 4411.62 587.07 46.8 441,914
Grassland 675.99 1472.76 — 196.56 28.71 7801.74
Water body 17,055.54 108.54 257.85 — 9.99 54,424.53
Construction land 95.94 4.05 0.63 0.99 — 142.65
Total 65,346.48 437,980.4 10,368.72 38,094.66 267.75 552,058
2000–2020
Cultivated land — 5315.58 506.16 785.34 145.53 49,780.62
Forest land 6440.67 — 3294.09 572.76 43.2 428,203.5
Grassland 853.02 14,549.31 — 277.65 40.14 22,042.8
Water body 14,921.19 101.88 245.07 — 7.47 51,734.07
Construction land 103.59 3.42 0.72 0.45 — 139.59
Total 65,346.48 437,823 10,368.72 38,094.66 267.75 5520.58
Overall, from 2000 to 2020, the various types of land use in the national nature reserves
of Hubei Province were dynamically changing, with various land types interchanging. The
overall trend shows significant encroachment of cultivated land on grassland and water
bodies, and minor encroachment of construction land on other land types, indicating a clear
impact of rapid urbanization on the nature reserves. A slight increase in forest land area
indicates the initial effectiveness of Hubei Province’s Grain for Green policy and nature

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reserve protection policies, but the protection of grassland and water body land use areas
is insufficient.
3.1.3. Analysis of Land Use Dynamic Degree
According to Table 9, for the national nature reserves in Hubei Province from 2000
to 2020, the overall dynamic degree of land use increased from 0.27 to 0.34, indicating an
accelerated rate of land use change and relatively active land use between 2010 and 2020.
The dynamic degrees of construction land, water body, and cultivated land increased, while
those of forest land and grassland decreased. Combined with the land use transition matrix
analysis, this suggests that rapid urban development has accelerated the encroachment
of construction land on cultivated land and water bodies. The dynamic degree of forest
land increased from 2000 to 2010 but decreased from 2010 to 2020, with a gradual decrease
in area, indicating reduced protection and restoration efforts for forest land during this
period, consistent with the previous land use analysis.
Table 9. Land use dynamic degree of national nature reserves in Hubei Province, 2000–2020 (%).
Time
Period
Cultivated
Land
Forest
Land Grassland Water
Body
Construction
Land Total
2000–2010 −0.40 0.32 −6.40 0.50 −1.28 0.27
2010–2020 3.68 −0.09 3.25 −3.01 11.46 0.34
2000–2020 1.56 0.11 −2.62 −1.33 4.35 0.22
3.2. Spatiotemporal Distribution Characteristics of Habitat Quality
3.2.1. Temporal Evolution Analysis of Habitat Quality
The InVEST model was used to calculate the average habitat quality indices for Hubei
Province’s national nature reserves in 2000, 2010, and 2020, which were 0.8147, 0.8196, and
0.8016, respectively (Figure 7). The average habitat quality was 0.8120, which is generally at
a higher level. However, the standard deviations were 0.2520, 0.2516, and 0.2728, showing
a gradually increasing trend. This indicates that the spatial heterogeneity of habitat quality
within the nature reserves has been increasing, which aligns with the analysis of land use
dynamics discussed earlier.
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Tab l e 10.Areaproportionsofeachhabitatqualitylevel(%)

Figure7.HabitatqualityinnationalnaturereservesofHubeiProvincefrom2000to2020.Note:
Thereddoedlineindicatesanincreaseinhabitatqualityontheleftandadecreaseontheright

Yea r s200020102020
Habitat
Quality
Highest
Level
Higher
Level
Lower
Level
Lowest
Level
Highest
Level
Higher
Level
Lower
Level
Lowest
Level
Highest
Level
Higher
Level
Lower
Level
Lowest
Level
Shennongjia92.39 4.21 3.40 0.00 93.99 2.64 3.36 0.00 93.30 3.39 3.28 0.03
ShishouElk65.13 12.28 22.59 0.00 74.22 12.44 13.34 0.00 77.17 10.29 12.54 0.00
Wufenghou
River95.45 0.65 3.90 0.00 95.98 0.43 3.59 0.00 95.96 0.44 3.60 0.00
Xingdou
Mountain2.51 12.60 65.16 19.74 3.38 11.14 66.38 19.10 6.36 23.67 68.43 1.54
Jiugong
Mountain88.12 8.39 3.49 0.00 88.13 8.37 3.51 0.00 87.97 7.78 3.18 1.07
Qijiemei
Mountain86.90 5.07 8.03 0.00 88.31 3.68 8.02 0.00 91.66 0.52 7.82 0.00
Longgan
Lake0.26 23.22 42.18 34.35 0.28 23.31 42.48 33.93 0.12 17.75 41.47 40.66
Saiwudang95.67 2.16 1.78 0.38 97.57 0.06 1.99 0.38 97.00 0.72 2.28 0.00
Mulinzi86.06 4.28 9.66 0.00 86.49 4.10 9.40 0.00 89.77 0.89 9.35 0.00
Duheyuan92.51 1.50 5.69 0.31 92.92 1.06 5.71 0.31 92.85 1.05 6.03 0.06
ShibaliCan-
yon94.12 1.42 4.46 0.00 94.55 1.03 4.43 0.00 94.43 1.11 4.46 0.00
HongLake89.02 3.86 7.12 0.00 98.20 0.18 1.62 0.00 5.07 19.93 43.23 31.77
NanRiver92.24 3.78 3.88 0.10 95.45 0.00 4.41 0.14 95.46 0.00 4.50 0.04
Dabie
Mountain94.52 1.36 4.12 0.00 94.29 1.34 4.37 0.00 94.37 1.26 4.28 0.09
Badong
Golden
Monkey
93.08 3.36 3.56 0.00 94.88 1.33 3.80 0.00 93.12 3.13 3.76 0.00
DalaoLing97.58 0.43 1.99 0.00 97.26 0.13 2.61 0.00 97.28 0.14 2.59 0.00
Wudao
Canyon92.23 0.88 6.89 0.00 90.54 0.00 9.46 0.00 90.51 0.00 9.49 0.00
Changyang
Bengjianzi87.18 1.94 10.47 0.40 88.61 0.66 10.32 0.40 88.95 0.66 10.38 0.00
Total77.10 5.20 13.95 3.75 78.72 3.85 13.77 3.66 71.77 6.70 17.29 4.23
Figure 7. Habitat quality in national nature reserves of Hubei Province from 2000 to 2020. Note: The
red dotted line indicates an increase in habitat quality on the left and a decrease on the right.

Land 2024,13, 363 17 of 32
Using the equal interval method in ArcGIS, habitat quality was classified into four
levels, and a table showing their area proportions was created (Table 10). Analysis in
conjunction with Table 8reveals that although the overall habitat quality in the study area
is good, the distribution of high, medium, and low habitat quality areas within the nature
reserves is uneven. Taking 2010 as an example, high-quality habitat areas accounted for
78.72% while the combined proportion of the lower and lowest quality areas was 17.42%,
with a smaller proportion in the relatively high-quality category. From 2000 to 2020, the
proportion of high-quality habitat areas first increased and then decreased, showing an
overall downward trend. The proportion of relatively high-quality areas first decreased
and then increased, showing an overall upward trend. The areas of low and relatively
low-quality habitats continued to grow. The decrease in high-quality habitat areas, along
with the increase in relatively high, relatively low, and low-quality areas, is consistent with
the slightly declining trend in the habitat quality index mentioned earlier.
Table 10. Area proportions of each habitat quality level (%).
Years 2000 2010 2020
Habitat Quality
Highest
Level
Higher
Level
Lower
Level
Lowest
Level
Highest
Level
Higher
Level
Lower
Level
Lowest
Level
Highest
Level
Higher
Level
Lower
Level
Lowest
Level
Shennongjia 92.39 4.21 3.40 0.00 93.99 2.64 3.36 0.00 93.30 3.39 3.28 0.03
Shishou Elk 65.13 12.28 22.59 0.00 74.22 12.44 13.34 0.00 77.17 10.29 12.54 0.00
Wufenghou River 95.45 0.65 3.90 0.00 95.98 0.43 3.59 0.00 95.96 0.44 3.60 0.00
Xingdou Mountain 2.51 12.60 65.16 19.74 3.38 11.14 66.38 19.10 6.36 23.67 68.43 1.54
Jiugong Mountain 88.12 8.39 3.49 0.00 88.13 8.37 3.51 0.00 87.97 7.78 3.18 1.07
Qijiemei Mountain 86.90 5.07 8.03 0.00 88.31 3.68 8.02 0.00 91.66 0.52 7.82 0.00
Longgan Lake 0.26 23.22 42.18 34.35 0.28 23.31 42.48 33.93 0.12 17.75 41.47 40.66
Saiwudang 95.67 2.16 1.78 0.38 97.57 0.06 1.99 0.38 97.00 0.72 2.28 0.00
Mulinzi 86.06 4.28 9.66 0.00 86.49 4.10 9.40 0.00 89.77 0.89 9.35 0.00
Duheyuan 92.51 1.50 5.69 0.31 92.92 1.06 5.71 0.31 92.85 1.05 6.03 0.06
Shibali Canyon 94.12 1.42 4.46 0.00 94.55 1.03 4.43 0.00 94.43 1.11 4.46 0.00
Hong Lake 89.02 3.86 7.12 0.00 98.20 0.18 1.62 0.00 5.07 19.93 43.23 31.77
Nan River 92.24 3.78 3.88 0.10 95.45 0.00 4.41 0.14 95.46 0.00 4.50 0.04
Dabie Mountain 94.52 1.36 4.12 0.00 94.29 1.34 4.37 0.00 94.37 1.26 4.28 0.09
Badong Golden
Monkey 93.08 3.36 3.56 0.00 94.88 1.33 3.80 0.00 93.12 3.13 3.76 0.00
Dalao Ling 97.58 0.43 1.99 0.00 97.26 0.13 2.61 0.00 97.28 0.14 2.59 0.00
Wudao Canyon 92.23 0.88 6.89 0.00 90.54 0.00 9.46 0.00 90.51 0.00 9.49 0.00
Changyang
Bengjianzi 87.18 1.94 10.47 0.40 88.61 0.66 10.32 0.40 88.95 0.66 10.38 0.00
Total 77.10 5.20 13.95 3.75 78.72 3.85 13.77 3.66 71.77 6.70 17.29 4.23
Between 2000 and 2020, 14 national nature reserves in Hubei Province experienced
an improvement in habitat quality, while 4 saw a slight decline. The four reserves with
declining habitat quality were Longgan Lake, Sanxia Dalao Ridge, Honghu, and Wudaoxia,
with decreases of 0.0285, 0.0013, 0.4445, and 0.0167, respectively. Analysis of the land use
transition matrix shows that the decline in habitat quality in these reserves was due to
varying degrees of encroachment of farmland on forests, grasslands, and water bodies. This
is attributed to practices where farmers sacrifice natural environments, such as clearing
forests and grasslands, to obtain crop yields and economic benefits. This results in a
significant loss of living and breeding conditions for many species within the reserves,
posing a severe threat to biodiversity and leading to a decline in habitat quality.
3.2.2. Analysis of Spatial Evolution of Habitat Quality
Analysis of Changes in the Proportion of Cold and Hot Spots in Habitat Quality
Looking at the overall proportion of cold and hot spots in the study area (Figure 8),
from 2000 to 2020, in Hubei Province’s national nature reserves, areas of insignificant
change accounted for 90.2%, cold spot areas for 5.7%, and hot spot areas for 4.1%. The
proportion of hot spot areas being slightly less than that of cold spots indicates that the area
of habitat quality increase is smaller than that of decrease, aligning with the conclusion

Land 2024,13, 363 18 of 32
of habitat quality decline from 2000 to 2020. National nature reserves with more than
5% in cold spot areas include Honghu, Longgan Lake, Bengjianzi, Wudaoxia, Shishou
Milu, Jiugong Mountain, and Xingdou Mountain, totaling seven reserves. The significant
decline in habitat quality in these reserves is due to the conflict between poverty alleviation,
wealth creation, and ecological conservation, where the residents blindly develop the
reserves for economic benefits. As for the reserves with more than 5% in hot spot areas,
these include Shishou Milu, Bengjianzi, Xingdou Mountain, Jiugong Mountain, and Qijie
Mountain, totaling five reserves. Comparing these with the reserves having more than 5%
in cold spots, four of them also exceed 5% in cold spots. The overall habitat quality index
changes in these four reserves from 2000 to 2020 were 0.0692, 0.0314, 0.0813, and 0.0074,
respectively. This indicates that although the overall habitat quality indices of these four
reserves are stable, there exist drastic internal changes in habitat quality. The increases and
decreases in different areas offset each other, leading to an overall stable trend in the habitat
quality index.
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3.2.2.AnalysisofSpatialEvolutionofHabitatQuality
AnalysisofChangesintheProportionofColdandHotSpotsinHabitatQuality
Lookingattheoverallproportionofcoldandhotspotsinthestudyarea(Figure8),
from2000to2020,inHubeiProvince’snationalnaturereserves,areasofinsignificant
changeaccountedfor90.2%,coldspotareasfor5.7%,andhotspotareasfor4.1%.The
proportionofhotspotareasbeingslightlylessthanthatofcoldspotsindicatesthatthe
areaofhabitatqualityincreaseissmallerthanthatofdecrease,aligningwiththeconclu-
sionofhabitatqualitydeclinefrom2000to2020.Nationalnaturereserveswithmorethan
5%incoldspotareasincludeHonghu,LongganLake,Bengjianzi,Wudaoxia,Shishou
Milu,JiugongMountain,andXingdouMountain,totalingsevenreserves.Thesignificant
declineinhabitatqualityinthesereservesisduetotheconflictbetweenpovertyallevia-
tion,wealthcreation,andecologicalconservation,wheretheresidentsblindlydevelopthe
reservesforeconomicbenefits.Asforthereserveswithmorethan5%inhotspotareas,
theseincludeShishouMilu,Bengjianzi,XingdouMountain,JiugongMountain,andQijie
Mountain,totalingfivereserves.Comparingthesewiththereserveshavingmorethan5%
incoldspots,fourofthemalsoexceed5%incoldspots.Theoverallhabitatqualityindex
changesinthesefourreservesfrom2000to2020were0.0692,0.0314,0.0813,and0.0074,
respectively.Thisindicatesthatalthoughtheoverallhabitatqualityindicesofthesefour
reservesarestable,thereexistdrasticinternalchangesinhabitatquality.Theincreases
anddecreasesindifferentareasoffseteachother,leadingtoanoverallstabletrendinthe
habitatqualityindex.

Figure8.Percentageofareaincoldandhotspotsofhabitatqualitychangesfrom2000to2020.
AnalysisofSpatialDistributionCharacteristicsofColdandHotSpotAreas
ToinvestigatewhetherthehabitatqualitychangesinHubeiProvince’snationalna-
turereservesbetween2000and2020exhibitedspatialclustering,theArcGIS10.5software
wasusedforspatialautocorrelation(Moran’sI)analysis.Theresultsshowedthat13na-
turereservespassedthesignificancetestwithaglobalMoran’sIindexZ-scoreexceeding
2.58andp<0.01.Amongthem,Shennongjia,Wudaoxia,ShishouMilu,XingdouMoun-
tain,BadongGoldenMonkey,andLongganLakehadZ-scoresgreaterthan8withp<
0.01,indicatingpronouncedspatialclusteringinhabitatqualitychangesinthesereserves,
Figure 8. Percentage of area in cold and hot spots of habitat quality changes from 2000 to 2020.
Analysis of Spatial Distribution Characteristics of Cold and Hot Spot Areas
To investigate whether the habitat quality changes in Hubei Province’s national nature
reserves between 2000 and 2020 exhibited spatial clustering, the ArcGIS 10.5 software was
used for spatial autocorrelation (Moran’s I) analysis. The results showed that 13 nature
reserves passed the significance test with a global Moran’s I index Z-score exceeding 2.58
and p< 0.01. Among them, Shennongjia, Wudaoxia, Shishou Milu, Xingdou Mountain,
Badong Golden Monkey, and Longgan Lake had Z-scores greater than 8 with p< 0.01,
indicating pronounced spatial clustering in habitat quality changes in these reserves, sug-
gesting strong spatial aggregation. In contrast, Sai Wudang, Mulinzi, Shibalichangxia,
Dalao Ridge, and Wufeng Houhe nature reserves had global Moran’s I index Z-scores <1.68
and p> 0.1, indicating that habitat changes in these five reserves were randomly distributed
in space, without significant clustering.
Overall, the spatiotemporal evolution of habitat quality in the study area is character-
ized by an overall decline, phased changes, and spatial heterogeneity(Figure 9). The overall
habitat quality increased from 2000 to 2010 and declined from 2010 to 2020, possibly due to
the enhancement of habitat quality following the implementation of the Grain for Green

Land 2024,13, 363 19 of 32
policy in Hubei Province in 2000. However, post-2010, the indigenous people, driven by
economic pressures and national food security policies, resumed deforestation and grass
destruction for cultivation in other areas, leading to a decline in quality. Additionally, the
intermingling of hot and cold spots within nature reserves may result from some devel-
opment activities near major transport routes and tourist facilities, resulting in localized
sharp declines in quality. Consequently, in the following sections, an analysis is conducted
to detect the potential factors causing the aforementioned trends and to further discuss the
factors and mechanisms affecting the spatiotemporal evolution of habitat quality.
3.3. Analysis of Spatiotemporal Evolution Influencing Factors of Habitat Quality Based
on Geodetector
3.3.1. Construction of the Indicator System for Factors Influencing Habitat Quality
Establishing a habitat quality indicator system is fundamentally important. In light
of the actual situation of Hubei Province’s national nature reserves [
58
], this study selects
14 indicators from three dimensions: natural environment, socio-cultural, and policy
regulation, to explore the main factors affecting the habitat quality of national nature
reserves in Hubei Province and their mechanisms of evolution (Table 11).
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suggestingstrongspatialaggregation.Incontrast,SaiWudang,Mulinzi,Shibalichangxia,
DalaoRidge,andWufengHouhenaturereserveshadglobalMoran’sIindexZ-scores
<1.68andp>0.1,indicatingthathabitatchangesinthesefivereserveswererandomly
distributedinspace,withoutsignificantclustering.
Overall,thespatiotemporalevolutionofhabitatqualityinthestudyareaischarac-
terizedbyanoveralldecline,phasedchanges,andspatialheterogeneity(Figure9).The
overallhabitatqualityincreasedfrom2000to2010anddeclinedfrom2010to2020,possi-
blyduetotheenhancementofhabitatqualityfollowingtheimplementationoftheGrain
forGreenpolicyinHubeiProvincein2000.However,post-2010,theindigenouspeople,
drivenbyeconomicpressuresandnationalfoodsecuritypolicies,resumeddeforestation
andgrassdestructionforcultivationinotherareas,leadingtoadeclineinquality.Addi-
tionally,theinterminglingofhotandcoldspotswithinnaturereservesmayresultfrom
somedevelopmentactivitiesnearmajortransportroutesandtouristfacilities,resultingin
localizedsharpdeclinesinquality.Consequently,inthefollowingsections,ananalysisis
conductedtodetectthepotentialfactorscausingtheaforementionedtrendsandtofurther
discussthefactorsandmechanismsaffectingthespatiotemporalevolutionofhabitatqual-
ity.
(a)2000–2010(b)2010–2020(c)2000–2020

Shennongjia

ShishouElk

WufenghouRiver
Figure 9. Cont.

Land 2024,13, 363 20 of 32
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

XingdouMountain

JiugongMountain

QizimeiMountain

LongganLake

Saiwudang
Figure 9. Cont.

Land 2024,13, 363 21 of 32
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

Mulinzi

Duheyuan


ShibaliCanyon

HongLake

NanRiver
Figure 9. Cont.

Land 2024,13, 363 22 of 32
Land2024,13,xFORPEERREVIEW23of34


DabieMountain

BadongGoldenMonkey


DalaoLing

WudaoCanyon

ChangyangBengjianzi
Figure 9. Cont.

Land 2024,13, 363 23 of 32
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
Figure9.Spatialdistributionmapofhabitatqualitychangehotspotsfrom2000to2020.
3.3.AnalysisofSpatiotemporalEvolutionInfluencingFactorsofHabitatQuality
BasedonGeodetector
3.3.1.ConstructionoftheIndicatorSystemforFactorsInfluencingHabitatQuality
Establishingahabitatqualityindicatorsystemisfundamentallyimportant.Inlight
oftheactualsituationofHubeiProvince’snationalnaturereserves[58],thisstudyselects
14indicatorsfromthreedimensions:naturalenvironment,socio-cultural,andpolicyreg-
ulation,toexplorethemainfactorsaffectingthehabitatqualityofnationalnaturereserves
inHubeiProvinceandtheirmechanismsofevolution(Table11).
AsshowninTab le10,elevation,slope,andterrainruggednessareselectedtorepre-
senttopography,whichisanimportantcomponentofnaturalenvironmentalfactorsand
thebasisoftheformation,development,andevolutionofthegeographicalenvironment.
Thesearerepresentedbyelevation,thedegreeofsteepnessoftheunitlandsurface,and
thedifferencebetweenthemaximumandminimumelevationswithintheunitlandsur-
face.Distancefromwaterbodiesischosentorepresenthydrologicalfactors.Meanpatch
sizeandpatchdensityareselectedtorepresentlandscapepaernfactors.Higherpatch
densityandsmallermeanpatchsizeindicatemorelandscapefragmentationandhetero-
geneity,reflectinghumaninterferencewiththenaturallandscape,akeycauseofbiodi-
versitylossinnaturereserves.Annualaveragetemperatureandannualprecipitationare
selectedtorepresentclimaticconditions,whicharefundamentalfactorsaffectingplant
growthanddevelopment.Landuseintensityischosentorepresenttheactivitiesofindig-
enouspeople;itisclassifiedbasedontheimpactonthenaturalenvironment,withforest,
grassland,andwaterclassifiedas1,representinglanduseinarelativelynaturalstate.
Farmlandisclassifiedas2andconstructedlandas3.Bycomparison,farmlandandcon-
structedlandmayrepresentareasofmoreintensivehumanactivity,usuallyaccompanied
bylarge-scalelandtransformation,vegetationdestruction,andhabitatdegradation,with
significantimpactsonecosystems.Differenttypesoflandusehavevaryingeffectsonthe
qualityandfunctionofhabitats;forexample,farmlandandconstructedlandmayhavea
moresignificantimpactonhabitatdestruction,whileforestsandwaterbodiesmayoffer
beerprotectiontoecosystems.Thedistancefromtouristfacilitiesisselectedtorepresent
theimpactoftourism,withtheproximityofthesefacilitiesreflectingthelevelofdisturb-
ancetonaturereservesbytourism.Dataondining,culturalleisureandentertainment,
shopping,andhotelsfrom2020POI(PointsofInterest)withinthestudyareawereex-
tractedandanalyzedusingEuclideandistanceinArcGIStoindicatethedegreeoftourism
disturbancetonaturereserves.Closerproximitymayimplyhighervisitortraffic,disturb-
ance,andpressure,leadingtoecosystemdegradation,speciesdisturbance,andlandscape
changes,whichnegativelyaffectthespeciesandhabitatswithinthereserves.Thedistance
fromhighways,nationalandprovincialroads,andcountyandtownshiproadsischosen
torepresenttrafficlocationfactors,asaccessibilityisakeyfactorinhumanactivitiesim-
pactinghabitatquality.Thefactorofreturningfarmlandtoforestisselectedtorepresent
policycontrolfactorsinnaturereserves.Althoughtheremaybeotherfactorsassociated
withlandusedevelopmentpolicies,returningfarmlandtoforestisoftenconsideredthe
mostimportantregulatorymeasureinnaturereservepolicies.Thissignificantecological
engineeringprojectmeasuresitsregulatoryeffectsbythescaleoffarmlandconvertedto
forestland.

Figure 9. Spatial distribution map of habitat quality change hot spots from 2000 to 2020.
Table 11. Table of indicators for factors influencing habitat quality changes.
Category Element Code Factor Calculation Method and
Dimension
Dependent
variable
Habitat
quality Habitat quality
Y1 Habitat quality in 2000 —
Y2 Habitat quality in 2010 —
Y3 Habitat quality in 2020 —
Independent
variable
Natural
geography
Topography
X1 Elevation DEM data extraction (m)
X2 Slope Elevation Difference/water
Distance ×100% (%)
X3 Terrain Ruggedness Highest elevation—lowest
elevation (m)
Hydrology X4 Distance from water bodies Euclidean distance (m)
Landscape
pattern
X5 Average patch area Total patch area/number
of patches
X6 landscape fragmentation Number of patches/total
patch area
Climatic
conditions
X7 Annual average temperature ◦C
X8 Annual average precipitation mm
Socio-cultural
Indigenous
activities X9 Land use intensity
Forest, grassland, water = 1;
farmland = 2;
construction land = 3
Tourism facilities X10 Distance from tourist
service facilities Euclidean distance (m)
Transportation
location
X11 Distance from highways
Euclidean distance (m)
X12 Distance from national and
provincial roads
X13 Distance from county and
township roads
Policy
regulation
Returning
farmland to
forest
X14 Scale of returning farmland
to forest
Scale of returning farmland
to forest (ha)
As shown in Table 10, elevation, slope, and terrain ruggedness are selected to represent
topography, which is an important component of natural environmental factors and the
basis of the formation, development, and evolution of the geographical environment. These
are represented by elevation, the degree of steepness of the unit land surface, and the
difference between the maximum and minimum elevations within the unit land surface.
Distance from water bodies is chosen to represent hydrological factors. Mean patch size
and patch density are selected to represent landscape pattern factors. Higher patch density
and smaller mean patch size indicate more landscape fragmentation and heterogeneity,
reflecting human interference with the natural landscape, a key cause of biodiversity loss
in nature reserves. Annual average temperature and annual precipitation are selected to
represent climatic conditions, which are fundamental factors affecting plant growth and
development. Land use intensity is chosen to represent the activities of indigenous people;
it is classified based on the impact on the natural environment, with forest, grassland,
and water classified as 1, representing land use in a relatively natural state. Farmland
is classified as 2 and constructed land as 3. By comparison, farmland and constructed

Land 2024,13, 363 24 of 32
land may represent areas of more intensive human activity, usually accompanied by
large-scale land transformation, vegetation destruction, and habitat degradation, with
significant impacts on ecosystems. Different types of land use have varying effects on
the quality and function of habitats; for example, farmland and constructed land may
have a more significant impact on habitat destruction, while forests and water bodies
may offer better protection to ecosystems. The distance from tourist facilities is selected
to represent the impact of tourism, with the proximity of these facilities reflecting the
level of disturbance to nature reserves by tourism. Data on dining, cultural leisure and
entertainment, shopping, and hotels from 2020 POI (Points of Interest) within the study
area were extracted and analyzed using Euclidean distance in ArcGIS to indicate the degree
of tourism disturbance to nature reserves. Closer proximity may imply higher visitor traffic,
disturbance, and pressure, leading to ecosystem degradation, species disturbance, and
landscape changes, which negatively affect the species and habitats within the reserves. The
distance from highways, national and provincial roads, and county and township roads is
chosen to represent traffic location factors, as accessibility is a key factor in human activities
impacting habitat quality. The factor of returning farmland to forest is selected to represent
policy control factors in nature reserves. Although there may be other factors associated
with land use development policies, returning farmland to forest is often considered the
most important regulatory measure in nature reserve policies. This significant ecological
engineering project measures its regulatory effects by the scale of farmland converted to
forest land.
3.3.2. Detection and Analysis of Factors Influencing Habitat Quality
(1)
The Influence of Natural Geographical Factors
Natural geographical factors are the foundation of habitat quality evolution in Hubei
Province’s national nature reserves, generally promoting or constraining the evolution
of habitat quality. As the average values of factor contribution rates show (Figure 10),
topographical conditions make a prominent impact, with slope being the factor with the
largest contribution rate among natural geographical factors. The sum of the q-values
for each reserve over three periods in descending order is slope (0.296) > fragmentation
(0.658) > ruggedness (0.160) > mean patch area (0.153) > distance from water bodies
(0.096) > elevation (0.094) > annual mean temperature (0.045) > annual precipitation (0.041).
According to the data analysis of Figure 10, hydrological factors significantly influence
the habitat quality of wetland-type reserves. The landscape pattern factors widely affect
the habitat quality pattern. From 2000 to 2020, except for the contribution rate of terrain
ruggedness to habitat quality, which decreased, the contribution rates of all other factors to
habitat quality have risen to varying degrees, with annual average rainfall and landscape
fragmentation showing the most significant increases in their contribution rates, with
q-values rising from 0.031 and 0.203 to 0.060 and 0.227, respectively.
Land2024,13,xFORPEERREVIEW26of34
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increased,from0.623in2000to0.945in2020.Tourismservicefacilitiesareimportantfac-
torsinhabitatqualityevolution,withsignificantdifferencesamongdifferentreserves.
Over20years,theaverageq-valueofthetourismservicefactorhasbeenincreasing,with
14reservesshowinganincreaseand4adecreaseinq-values,indicatingtheeco-tourism
developmentinfluenceisgraduallyincreasing.Transportationlocationfactorsarekeyin
habitatqualityevolution,withlower-graderoadshavinghigherq-valuesthanhigher-
graderoads.Over20years,theaverageq-valuesofdistancetohighways,national/pro-
vincialroads,andcounty/townshiproadshaveshownarisingtrend,increasingby0.002,
0.031,and0.033,respectively,indicatingthattheimpactoftransportationlocationfactors
isgraduallystrengthening.
(3) TheInfluenceofPolicyRegulatoryFactors
Thepolicyofreturningfarmlandtoforestisaneffectivemeanstorestoredamaged
ecosystemsandakeyfactorinimprovinghabitatquality.Theq-valuesforthepolicyin
2000,2010,and2020were0.145,0.137,and0.124,respectively,showingagradualdecline
inexplanatorypower.Over20years,theq-valueofthepolicyincreasedin14national
naturereservesanddecreasedin4,buttheoverallq-valuestillslightlydeclined.This
alignswiththetrendofinitialincreaseandsubsequentdecreaseintheoverallhabitat
qualityofHubeiProvince’snationalnaturereservesfrom2000to2020.Thepolicy,initi-
atedin2000,hadasignificantoveralleffect,effectivelyimprovinghabitatquality,butthe
implementationeffectivenessworsenedduetoaweakeninginpolicyregulation,leading
toadecreaseinhabitatquality.

(a)Shennongjia

(b)ShishouElk

(c)WufenghouRiver

(d)XingdouMountain

(e)JiugongMountain

(f)QizimeiMountain

(g)LongganLake

(h)Saiwudang

(i)Mulinzi

(j)Duheyuan(k)ShibaliCanyon(l)HongLake
Figure 10. Cont.

Land 2024,13, 363 25 of 32
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increased,from0.623in2000to0.945in2020.Tourismservicefacilitiesareimportantfac-
torsinhabitatqualityevolution,withsignificantdifferencesamongdifferentreserves.
Over20years,theaverageq-valueofthetourismservicefactorhasbeenincreasing,with
14reservesshowinganincreaseand4adecreaseinq-values,indicatingtheeco-tourism
developmentinfluenceisgraduallyincreasing.Transportationlocationfactorsarekeyin
habitatqualityevolution,withlower-graderoadshavinghigherq-valuesthanhigher-
graderoads.Over20years,theaverageq-valuesofdistancetohighways,national/pro-
vincialroads,andcounty/townshiproadshaveshownarisingtrend,increasingby0.002,
0.031,and0.033,respectively,indicatingthattheimpactoftransportationlocationfactors
isgraduallystrengthening.
(3) TheInfluenceofPolicyRegulatoryFactors
Thepolicyofreturningfarmlandtoforestisaneffectivemeanstorestoredamaged
ecosystemsandakeyfactorinimprovinghabitatquality.Theq-valuesforthepolicyin
2000,2010,and2020were0.145,0.137,and0.124,respectively,showingagradualdecline
inexplanatorypower.Over20years,theq-valueofthepolicyincreasedin14national
naturereservesanddecreasedin4,buttheoverallq-valuestillslightlydeclined.This
alignswiththetrendofinitialincreaseandsubsequentdecreaseintheoverallhabitat
qualityofHubeiProvince’snationalnaturereservesfrom2000to2020.Thepolicy,initi-
atedin2000,hadasignificantoveralleffect,effectivelyimprovinghabitatquality,butthe
implementationeffectivenessworsenedduetoaweakeninginpolicyregulation,leading
toadecreaseinhabitatquality.

(a)Shennongjia

(b)ShishouElk

(c)WufenghouRiver

(d)XingdouMountain

(e)JiugongMountain

(f)QizimeiMountain

(g)LongganLake

(h)Saiwudang

(i)Mulinzi

(j)Duheyuan(k)ShibaliCanyon(l)HongLake
Land2024,13,xFORPEERREVIEW27of34


(m)NanRiver

(n)DabieMountain

(o)BadongGoldenMonkey

(p)
DalaoLing

(q)
WudaoCanyon

(r)
ChangyangBengjianzi

Figure10.Thecontributionrateoffactorsinfluencinghabitatqualityevolutionfrom2000to2020.
Note:X1:elevation;X2:slope;X3:terrainruggedness;X4:distancefromwatersystems;X5:average
patchdensity;X6:landscapefragmentation;X7:annualmeantemperaturechange;X8:annualmean
precipitationchange;X9:landuseintensity;X10:distancefromtouristservicefacilities;X11:dis-
tancefromhighways;X12:distancefromnationalandprovincialroads;X13:distancefromcounty
andtownshiproads;andX14:scaleofreturningfarmlandtoforest.
3.3.3.DetectionandAnalysisoftheEvolutionMechanismofHabitatQuality
Theprevioussectionanalyzedthecontributionsof14factorstotheevolutionofhab-
itatqualityinnationalnaturereservesinHubeiProvince.However,inpractice,thecom-
plexinteractionsamongdifferentinfluencingfactorsjointlypromoteorconstrainthe
changeinhabitatquality.Therefore,thissectionaimstousetheinteractivedetectormod-
uleoftheGeodetectortoanalyzetheinterrelationshipsbetweentheinteractionsofvarious
influencingfactorsandtheevolutionofhabitatquality.Thedetectionresultsshowthat
theinteractionsamongtheinfluencingfactorsoverthreeyearshaveasynergisticenhanc-
ingeffect(Table12,thisarticleonlyliststhetopfiveinteractionvaluesofthefactors).
Firstly,theinteractionbetweennaturalgeographicalfactorsandsocio-culturalfac-
torsisakeydriver.Overthepast20years,therehasbeenastronginteractionbetween
naturalgeographicalfactorsandsocio-culturalfactors,withtheinteractionmechanism
amongtheinfluencingfactorsshowingsynergisticenhancement.Notably,thenonlinear
enhancementeffectissignificantlygreaterthanthedual-factorenhancement.Thestrength
oftheinteractioneffectsofdifferenttypesofinfluencingfactorsisrankedasfollows:in-
teractionbetweennaturalgeographicalandsocio-culturalfactors>internalinteraction
withinsocio-culturalfactors>internalinteractionwithinnaturalgeographicalfactors.
Secondly,thelanduseintensityfactorhadthehighestexplanatorypoweramongall
influencingfactors,anditalsohadthestrongestinteractionwithbothnaturalenviron-
mentalandsocio-culturalfactors.Specifically,theinteractionofthelanduseintensityfac-
torwithnaturalenvironmentalfactorswasgreaterthanitsinteractionwithsocio-cultural
Figure 10. The contribution rate of factors influencing habitat quality evolution from 2000 to 2020.
Note: X1: elevation; X2: slope; X3: terrain ruggedness; X4: distance from water systems; X5: average
patch density; X6: landscape fragmentation; X7: annual mean temperature change; X8: annual mean
precipitation change; X9: land use intensity; X10: distance from tourist service facilities; X11: distance
from highways; X12: distance from national and provincial roads; X13: distance from county and
township roads; and X14: scale of returning farmland to forest.
(2)
The Influence of Socio-Cultural Factors
Socio-cultural factors are the fundamental drivers of habitat quality evolution in
Hubei Province’s national nature reserves (Figure 10). Indigenous activity factors are the
leading influencers of habitat quality evolution, with their explanatory power gradually

Land 2024,13, 363 26 of 32
increasing. Over 20 years, the overall q-value average of land use intensity has gradually
increased, from 0.623 in 2000 to 0.945 in 2020. Tourism service facilities are important
factors in habitat quality evolution, with significant differences among different reserves.
Over 20 years, the average q-value of the tourism service factor has been increasing, with
14 reserves showing an increase and 4 a decrease in q-values, indicating the eco-tourism
development influence is gradually increasing. Transportation location factors are key in
habitat quality evolution, with lower-grade roads having higher q-values than higher-grade
roads. Over 20 years, the average q-values of distance to highways, national/provincial
roads, and county/township roads have shown a rising trend, increasing by 0.002, 0.031,
and 0.033, respectively, indicating that the impact of transportation location factors is
gradually strengthening.
(3)
The Influence of Policy Regulatory Factors
The policy of returning farmland to forest is an effective means to restore damaged
ecosystems and a key factor in improving habitat quality. The q-values for the policy in
2000, 2010, and 2020 were 0.145, 0.137, and 0.124, respectively, showing a gradual decline in
explanatory power. Over 20 years, the q-value of the policy increased in 14 national nature
reserves and decreased in 4, but the overall q-value still slightly declined. This aligns with
the trend of initial increase and subsequent decrease in the overall habitat quality of Hubei
Province’s national nature reserves from 2000 to 2020. The policy, initiated in 2000, had
a significant overall effect, effectively improving habitat quality, but the implementation
effectiveness worsened due to a weakening in policy regulation, leading to a decrease in
habitat quality.
3.3.3. Detection and Analysis of the Evolution Mechanism of Habitat Quality
The previous section analyzed the contributions of 14 factors to the evolution of
habitat quality in national nature reserves in Hubei Province. However, in practice, the
complex interactions among different influencing factors jointly promote or constrain the
change in habitat quality. Therefore, this section aims to use the interactive detector module
of the Geodetector to analyze the interrelationships between the interactions of various
influencing factors and the evolution of habitat quality. The detection results show that the
interactions among the influencing factors over three years have a synergistic enhancing
effect (Table 12, this article only lists the top five interaction values of the factors).
Firstly, the interaction between natural geographical factors and socio-cultural factors
is a key driver. Over the past 20 years, there has been a strong interaction between
natural geographical factors and socio-cultural factors, with the interaction mechanism
among the influencing factors showing synergistic enhancement. Notably, the nonlinear
enhancement effect is significantly greater than the dual-factor enhancement. The strength
of the interaction effects of different types of influencing factors is ranked as follows:
interaction between natural geographical and socio-cultural factors > internal interaction
within socio-cultural factors > internal interaction within natural geographical factors.
Secondly, the land use intensity factor had the highest explanatory power among all in-
fluencing factors, and it also had the strongest interaction with both natural environmental
and socio-cultural factors. Specifically, the interaction of the land use intensity factor with
natural environmental factors was greater than its interaction with socio-cultural factors,
especially with topographical factors and transportation factors. Land use intensity reflects
the degree of human development and construction activities’ disturbance on habitat qual-
ity. Within national nature reserves, the strict protection of the ecological environment led
to the loss of economic sources for indigenous residents, intensifying the contradiction
between the need for poverty alleviation of the indigenous people and the conservation of
the ecological environment. Therefore, the impact of land use intensity in conjunction with
natural environmental and socio-cultural factors is the most complex.

Land 2024,13, 363 27 of 32
Table 12. The results of factor interactions in habitat quality evolution.
Years Shennongjia Shishou Elk Wufenghou
River
Xingdou
Mountain
Jiugong
Mountain
Qijiemei
Mountain Longgan Lake Saiwudang Mulinzi
2000 X9∩X12(0.69284)
X10
∩
X12(0.45401)
X9∩X12(0.89728) X9∩X13(0.72173) X6∩X9(0.71834) X9∩X13(0.74724) X9∩X13(0.62373) X9∩X14(0.73629) X9∩X13(0.83875)
X9∩X13(0.68929) X9∩X10(0.42885) X1∩X9(0.89455) X9∩X12(0.71407) X9∩X12(0.71684) X9∩X12(0.73822) X9∩X12(0.61644) X9∩X13(0.72731) X9∩X12(0.82016)
X6∩X9(0.64037) X9∩X12(0.41950) X4∩X9(0.89270) X9∩X14(0.68752) X9∩X13(0.71572) X9∩X14(0.72442) X6∩X9(0.59673) X6∩X9(0.72551) X9∩X14(0.81820)
X5∩X9(0.62275) X6∩X12(0.41668) X9∩X13(0.89241) X6∩X9(0.68518) X5∩X9(0.70724) X6∩X9(0.70839) X5∩X9(0.59470) X5∩X9(0.72529) X6∩X9(0.80644)
X9∩X14(0.61566) X2∩X12(0.40804) X9∩X10(0.89227) X4∩X9(0.67897) X7∩X9(0.70632) X5∩X9(0.89608) X4∩X9(0.59057) X2∩X5(0.71967) X7∩X9(0.90439)
2010 X9∩X13(0.83911) X9∩X10(0.64501) X9∩X13(0.96939) X9∩X13(0.81878) X9∩X12(0.87879) X9∩X13(0.83680) X9∩X12(0.63406) X9∩X13(0.81680) X9∩X13(0.79255)
X9∩X12(0.82957) X9∩X13(0.57896) X8∩X9(0.94814) X9∩X12(0.80917) X9∩X13(0.86913) X9∩X12(0.82933) X9∩X12(0.62736) X9∩X12(0.80933) X9∩X12(0.78181)
X6∩X9(0.82245) X9∩X12(0.56823) X9∩X12(0.94606) X6∩X9(0.72852) X2∩X12(0.73371) X9∩X14(0.74499) X2∩X9(0.59948) X9∩X14(0.74499) X9∩X14(0.76069)
X1∩X9(0.82239) X5∩X9(0.55707) X1∩X9(0.94375) X9∩X14(0.72674) X6∩X9(0.71462) X6∩X9(0.72829) X6∩X9(0.59899) X6∩X9(0.72829) X3∩X9(0.75468)
X5∩X9(0.81734) X2∩X12(0.53877) X4∩X9(0.93614) X5∩X9(0.71561) X2∩X13(0.73060) X3∩X9(0.71878) X5∩X9(0.59774) X3∩X9(0.72278) X6∩X9(0.75380)
2020 X9∩X13(0.98701) X9∩X13(0.93165) X2∩X9(0.81092) X9∩X12(0.73380) X9∩X13(0.91368) X9∩X13(0.96443) X9∩X13(0.72295) X1∩X9(0.98947) X9∩X13(0.98505)
X9∩X12(0.94731) X9∩X13(0.93091) X2∩X12(0.75188) X9∩X13(0.68441) X9∩X12(0.90447) X4∩X9(0.96197) X9∩X12(0.71187) X6∩X9(0.98370) X9∩X12(0.97423)
X9∩X10(0.84571) X1∩X9(0.91038) X3∩X12(0.73858) X4∩X9(0.59726) X6∩X9(0.86004) X9∩X12(0.95682)
X10
∩
X13(0.69542)
X5∩X9(0.98284) X9∩X10(0.96403)
X1∩X9(0.84439) X9∩X10(0.91006) X2∩X13(0.73634) X7∩X9(0.59535) X5∩X9(0.85328) X9∩X10(0.94953) X9∩X10(0.65182) X9∩X12(0.98162) X4∩X9(0.96247)
X5∩X9(0.84194) X6∩X9(0.90809) X9∩X13(0.73285)
X12
∩
X13(0.58457)
X1∩X9(0.85045) X1∩X9(0.94011)
X10
∩
X12(0.64741)
X9∩X13(0.98132) X8∩X9(0.96033)
Duheyuan Shibali Canyon Hong Lake Nan River Dabie Mountain Badong Golden
Monkey Dalao Ling Wudao Canyon Changyang
Bengjianzi
2000 X9∩X13(0.72497) X9∩X13(0.68024) X9∩X13(0.58189) X9∩X13(0.66915) X9∩X13(0.82347) X9∩X12(0.89805) X9∩X13(0.81997) X9∩X13(0.64312) X6∩X9(0.59884)
X9∩X12(0.71118) X9∩X12(0.67392)
X12
∩
X13(0.52352)
X1∩X9(0.64730) X9∩X12(0.81622) X9∩X13(0.88878) X9∩X12(0.80938) X9∩X12(0.62065) X1∩X9(0.58435)
X7∩X9(0.70103) X6∩X9(0.65879) X9∩X12(0.51559) X9∩X12(0.64002) X9∩X14(0.81403) X7∩X9(0.72928) X7∩X9(0.79232) X9∩X11(0.60312) X9∩X13(0.58429)
X6∩X9(0.70056) X2∩X9(0.66685) X2∩X13(0.61138) X4∩X9(0.63907) X6∩X9(0.79715) X1∩X9(0.72809) X3∩X9(0.78677) X1∩X9(0.57042) X9∩X10(0.58197
X9∩X14(0.70002) X9∩X14(0.63453) X8∩X13(0.50749) X9∩X10(0.63826) X8∩X9(0.79619) X8∩X9(0.71170) X5∩X9(0.77686) X7∩X9(0.56542) X5∩X9(0.57601)
2010 X9∩X13(0.81572) X9∩X13(0.99616) X9∩X13(0.67113) X9∩X13(0.64528) X9∩X13(0.86025) X9∩X13(0.64902) X9∩X13(0.91732) X9∩X13(0.57821) X9∩X13(0.77273)
X9∩X12(0.80788) X9∩X12(0.98664) X9∩X12(0.66411) X9∩X12(0.62937) X9∩X12(0.85861) X6∩X9(0.64189) X9∩X12(0.90545) X9∩X12(0.56664) X9∩X12(0.76301)
X1∩X9(0.72739) X6∩X9(0.96887) X6∩X9(0.53571) X4∩X9(0.62351) X6∩X9(0.83706) X9∩X14(0.63782) X5∩X9(0.87690) X6∩X9(0.53776) X6∩X9(0.74430)
X6∩X9(0.72641) X7∩X9(0.96788) X2∩X13(0.53559) X4∩X9(0.62351) X9∩X14(0.83218) X9∩X12(0.63539) X9∩X14(0.88156) X9∩X10(0.49617)
X9
∩
X13(0.741998
X9∩X14(0.72038) X3∩X9(0.96782) X9∩X10(0.53348) X9∩X10(0.62242) X2∩X9(0.82873) X1∩X9(0.62391) X1∩X9(0.88033) X7∩X9(0.49503) X7∩X9(0.73718)
2020 X9∩X12(0.97985) X8∩X9(0.94521) X9∩X13(0.76203) X9∩X13(0.99979) X9∩X12(0.96925) X9∩X13(0.96787) X9∩X13(1.00000) X9∩X13(0.99993) X9∩X11(0.98745)
X9∩X13(0.97692) X1∩X9(0.94282) X9∩X12(0.74298) X9∩X12(0.99979) X6∩X9(0.95779) X9∩X12(0.94340) X9∩X12(0.99992) X9∩X12(0.99561) X1∩X9(0.98410)
X9∩X10(0.96012) X7∩X9(0.84260) X8∩X9(0.72411) X6∩X9(0.99801) X4∩X9(0.95632) X1∩X9(0.93658) X1∩X9(0.99970) X9∩X11(0.98666) X4∩X9(0.98726)
X4∩X9(0.95917) X4∩X9(0.94023)
X12
∩
X13(0.64128)
X1∩X9(0.99798) X1∩X9(0.95550) X7∩X9(0.93393) X9∩X10(0.99963) X8∩X9(0.98648) X8∩X9(0.98478)
X6∩X9(0.95705) X9∩X13(0.93035) X8∩X13(0.60275) X3∩X9(0.99796) X5∩X9(0.95433) X8∩X9(0.91218) X4∩X9(0.99948) X4∩X9(0.98567) X8∩X9(0.98478)

Land 2024,13, 363 28 of 32
The influence of policy regulatory factors has gradually weakened, as indicated by
the diminishing interaction between the returning farmland to forest factor and natu-
ral geographical and socio-cultural factors, with the interaction between 2000 and 2010
being greater than between 2010 and 2020. However, the returning farmland to forest
factor still interacts strongly with the land use intensity, especially in national nature re-
serves like Xingdou Mountain, Qijie Mountain, Sai Wudang, Mulinzi, Duheyuan, and
Dabie Mountain.
4. Discussion
For nature reserves, frequent human activities inevitably lead to changes in land use.
Historical research has identified land use changes as significant risk factors affecting
habitat quality [
9
–
14
]. In recent years, numerous studies have further explored the impacts
of other factors such as location, topography, landscape patterns, and urbanization on
habitat quality. However, there are few studies on the comprehensive assessment and
interaction effects of the above natural geographic factors with human activity factors
such as socio-cultural aspects and policy implementation [
28
–
31
]. This study, focusing on
the 18 nature reserves in Hubei Province, innovatively employs the geographical detector
model and the InVEST evaluation model from three aspects: the natural environment,
socio-cultural factors, and policy regulation, to perform a quantitative assessment and
comparative analysis of the impacts on habitat quality in different types of nature reserves.
The research results make some theoretical contributions to the geographical study of
habitat quality, which can be reflected in the following two aspects:
Firstly, natural geographic factors are the foundation of habitat quality evolution in
nature reserves, having a stable and continuous impact. Socio-cultural factors are the most
significant dimension of influence, whereas policy elements have a smaller direct impact
but a significant temporal follow-up effect. From the single-factor detection results of
the natural geographic dimension, slope is the factor with the largest contribution rate
among natural geographic factors. Hydrology, water bodies, and landscape patterns also
broadly affect habitat quality changes in various types of nature reserves. From the single-
factor detection results of the socio-cultural dimension, although the study’s findings
on the explanatory power of single factors in land use are consistent with the existing
literature [
28
–
31
,
39
–
41
], this paper also makes further discoveries. Among subsequent
influencing factors, the impact of open, lower-grade roads exceeds that of closed, higher-
grade roads, showing an annually increasing trend, which reminds us to pay more attention
to the control intensity of development along roads. From the policy dimension detection
results, the effect of the Grain for Green policy implementation is consistent with the
spatiotemporal evolution of habitat quality, highlighting the need to consider the potential
impact of changes in policy implementation intensity on habitat quality.
Secondly, over the past 20 years, the interaction mechanism among influencing factors
has shown a synergistic enhancement, collectively driving changes in the habitat quality of
nature reserves. The strength of interactions is ranked as follows: the interaction between
natural geographic and socio-cultural factors > the internal interaction of socio-cultural
factors > the internal interaction of natural geographic factors. Among them, the interaction
between the land use intensity factor and natural environmental factors is greater than
that with socio-cultural factors; especially, the interaction with topographic features is the
strongest. It is evident that in nature reserves with significant topographical variations,
changes in land use intensity will further exacerbate the damage to habitat quality. This
impact may arise from the indigenous population’s high dependency on limited arable
land, where flat land as a means of production is scarcer in these areas, thereby mutually
amplifying the impact effect.
The practical application value of this article lies in providing evidence for the formu-
lation and management of territorial space development protection and land use planning.
Specifically, it applies to the establishment, adjustment, and optimization of regional nature
reserve systems. Based on the conclusions of this study, the interaction between physical

Land 2024,13, 363 29 of 32
geography and socio-cultural factors has a significant impact. Therefore, it is recommended
that various nature reserves within the same geographical unit, which are adjacent and
connected, could break through the unreasonable settings caused by administrative divi-
sions or resource classifications, and be reorganized according to principles of ecological
system integrity, species habitat connectivity, and unified protection management. The
2019 Chinese Government’s “Guidance on Establishing a Nature Re-serve System Centered
on National Parks” [
59
] provides policy support for this. In the ongoing compilation of
Hubei Province’s territorial space master plan, we have proposed corresponding adjust-
ment suggestions based on the results of this study. For instance, Shennongjia and the
Badong Golden Monkey national nature reserves, located in the Qinba Mountains and
belonging to the forest ecological type of national nature reserves, are spatially adjacent to
interconnected species habitats. The distribution of hot and cold spots of habitat quality
within them shows a unified trend, and the structure of influencing factor contributions is
similar. Therefore, they can be merged, with Shennongjia as the core, to establish a national
park, integrating surrounding nature reserves for unified management.
On the other hand, this article can offer recommendations for categorizing strategies
for the ecological restoration zoning, determination of ecological restoration units, and
generation of land use planning strategies for nature reserves and their surrounding
urbanized areas. Based on regions with significant spatiotemporal changes in habitat
quality and intense transitions between land use types identified in this article, these areas
should be classified as sensitive and key units for ecological restoration. Depending on
the strength of influencing factors such as land use, county and township roads, and
tourism service facilities within different nature reserves, a graded and differentiated
list of territorial space development control measures and policies should be established.
For example, due to the significantly higher impact of open county and township roads
compared with closed highways, it is necessary to strengthen the development control
along these roads to ensure regional ecological security.
Meanwhile, due to the selection of many study subjects and the difficulty in obtaining
uniform and comprehensive socio-economic data, the impact of development activities
with industrial characteristics has not been assessed. Further research could be conducted
on specific areas in the next step.
This study has some limitations. First, the introduction of the InVEST model provides
a feasible method for the quantitative assessment of habitat quality, but its results are based
on remote sensing images, meteorological data, and vegetation data, and the accuracy
of the habitat quality evaluation remains to be verified. Moreover, assessing habitat
quality in large-scale regions is a complex issue, which is a common challenge for scholars.
The future direction of quantitative habitat quality assessment will aim to establish a
connection between habitat quality evaluations based on remote sensing images and on-
the-ground habitat quality measurements. Second, in studying the factors affecting habitat
quality and their evolutionary mechanisms using the geographic detector model, the
author, drawing on previous research, discretized the independent variables using the
natural break method in ArcGIS. Due to the lack of clear classification standards, different
discretization methods may lead to different results. Future research needs to strengthen
the classification standards for the discretization of independent variables to improve the
reliability of detection results.
5. Conclusions
This study analyzed the spatiotemporal evolution characteristics of land use and
habitat quality within 18 nature reserves in Hubei Province from 2000 to 2020, employing
the INVEST model and the Geodetector model to quantitatively assess the extent of the
impact and the interaction mechanisms of various elements within natural geography,
socio-cultural, and policy regulation dimensions on habitat quality.
The results of this study indicate that the habitat quality of the 18 nature reserves in
Hubei Province is superior, with an average habitat quality reaching 0.8120. The density

Land 2024,13, 363 30 of 32
changes in habitat quality evolution from 2000 to 2020 exhibit characteristics of periodicity
and spatial heterogeneity. The spatiotemporal differentiation characteristics of habitat
quality in the study area are influenced by the combined effects of natural, cultural, and
policy factors, with the strength of their interactions ranked as follows: interaction between
natural geographical and socio-cultural factors > interaction within socio-cultural factors
> interaction within natural geographical factors. This finding is consistent with the
understanding that ecosystems are easily and rapidly degraded by human activities, while
natural restoration is difficult and slow. It reminds us of the need to pay more attention
to the destructive impact of human activities on nature reserves. In future research, we
must continue to focus on the complex process of how diverse elements related to human
activities intervene in habitat quality.
Author Contributions: Conceptualization, Y.L.; methodology, Y.L. and R.L.; formal analysis, Y.L.
and X.Z.; investigation, H.Z, R.L. and X.Z.; resources, Y.L., H.Z. and R.L.; writing—original draft
preparation, Y.L. and R.L.; writing—review and editing, Y.L., X.Z. and H.Z.; supervision, Y.L. and
H.Z. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by the National Natural Science Foundation of China’s Young
Scientists Fund, grant numbers 51908232 and 52108035; the Hubei Province Science and Technology
Innovation Talent and Services Special Soft Science Research Project, grant number 2022EDA052;
the Key project of National Social Science Foundation, grant number 21AZD048; and the Science
and Technology Plan Project of the Ministry of Housing and Urban-Rural Development of China,
Grant/Award Number: 2022-K-014.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The land use data for Hubei Province national nature reserves in 2000,
2010, and 2020 are derived from the 30 m Global Land Cover Data, GlobeLand30, a global geographic
information public product (www.globeland30.org, (accessed on 5 November2022)). The Digital
Elevation Model (DEM) is sourced from the Data Center for Resources and Environmental Sciences,
Chinese Academy of Sciences (http://www.resdc.cn, (accessed on 7 January 2023)).
Acknowledgments: The research idea is derived from the project “Territorial spatial changes research
in key protected areas” commissioned by Guangzhou City Planning and Design Institute. Thanks
for the support by Xu Lige, Zhu Hui, Xia Yuan, Liu Helin, Wang Zhiyong, Huang Yulin and all
colleagues in the project. Special thanks are given to the anonymous reviewers and editor for their
insightful suggestions.
Conflicts of Interest: Author Runtian Li was employed by the company Beijing Tsinghua Tongheng
Planning and Design Institute Co., Ltd. The remaining authors declare that the research was con-
ducted in the absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
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