Mapping transboundary ecological networks for conservation in the Altai Mountains

Full text

Mapping transboundary ecological networks for conservation in the
Altai Mountains
Jiali Han
a,b
, Fang Han
a,b,*
, Alexander Dunets
c
, Bayarkhuu Batbayar
d
a
State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and
Geography, Chinese Academy of Sciences, Urumqi 830011,China
b
University of Chinese Academy of Sciences, Beijing 100049, China
c
Altai State University, Barnaul 656038, Russia
d
Western Regional School of National University of Mongolia, Khovd 84140, Mongolia
ARTICLE INFO
Keywords:
Transboundary ecological networks for
conservation
Altai mountains
Core habitats
Ecological corridors
ABSTRACT
The Altai Mountains, spanning China, Russia, Kazakhstan, and Mongolia, are crucial habitats for many endemic,
rare, and endangered species and are a vital migration corridor. However, the standards for establishing pro-
tected areas (PAs) differ among the four countries, resulting in suboptimal spatial arrangements and protection
gaps in PAs. Therefore, here, by integrating the habitats of rare and endangered species and key ecosystem
service areas, we identied potential conservation areas in the Altai Mountains. And we overlaid them with
existing PAs in China, Russia, Kazakhstan, and Mongolia to determine the core habitats of the transboundary
ecological networks for conservation. The identied core habitats covered 168,729.00 km
2
, representing 50.63 %
of the Altai Mountains. Among these, potential conservation area not covered by existing PAs was approximately
82,833.50 km
2
(24.86%). Additionally, 116 ecological corridors were identied with an average length of 38.15
km, including 8 transboundary corridors that connect the core conservation areas across different countries.
Based on these ndings, new PAs and other effective conservation measures (OECMs) in the Altai Mountains
were proposed, along with a phased cooperation framework to gradually enhance the construction of trans-
boundary ecological networks for conservation. Establishing the Altai Mountains’ transboundary ecological
networks for conservation has the potential to become a model for transboundary conservation projects,
providing valuable insights and guidance for developing conservation and collaborative management strategies
in other transboundary regions.
1. Introduction
Establishing PAs is one of the most effective and crucial methods for
conserving biodiversity and natural ecosystems (Mi et al., 2023; Now-
akowski et al., 2023). However, despite the increasing size of PAs,
considerable challenges still impede their effectiveness (Geldmann
et al., 2015; Lee and Abdullah, 2019; Xu and Wu, 2024). Individual PAs
are insufcient for sustaining biodiversity and ecosystem services upon
which humans depend (Saura et al., 2019, 2018; Sreekar et al., 2022;
Ward et al., 2020; Williams et al., 2022). Small or poorly connected PAs
fail to provide adequate resilience for biodiversity, leaving them
vulnerable to threats such as climate change (Tabor et al., 2018) and
wildres (Ibanez et al., 2019). This may lead to species extinctions
(Lecl`
ere et al., 2020). Compared with isolated PAs, ecological networks
for conservation can play a more proactive role in achieving biodiversity
conservation goals (Zhang et al., 2020; Zhao et al., 2024). Therefore, we
need to shift our conservation approach from focusing solely on indi-
vidual PAs to viewing them as integral components of ecological net-
works and actively implementing this in conservation practice.
Ecological networks for conservation represent a concretization of
the concept of ecological networks within landscape ecology theory
(Qian et al., 2023) to emphasize the effective protection of biodiversity
and ecosystem functions. Ecological networks for conservation are sys-
tems of core habitats, that is, PAs, OECMs, and other intact natural areas
connected by ecological corridors that can be maintained, restored, and
established to protect biodiversity in fragmented systems (Hilty et al.,
* Corresponding author at: State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands,
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011,China (F. Han).
E-mail address: hanfang@ms.xjb.ac.cn (F. Han).
Contents lists available at ScienceDirect
Ecological Indicators
journal homepage: www.elsevier.com/locate/ecolind
https://doi.org/10.1016/j.ecolind.2024.112869
Received 23 September 2024; Received in revised form 11 November 2024; Accepted 17 November 2024
Ecological Indicators 169 (2024) 112869
Available online 21 November 2024
1470-160X/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (
http://creativecommons.org/licenses/by-
nc-nd/4.0/ ).

2020). Although ecological networks represent a broader concept, in
this study, we focused on ecological networks for conservation, with an
emphasis on protecting biodiversity and ecosystem functions.
Referred to as core habitats in this study, ecological sources play
crucial roles in providing essential ecological functions. Various
methods and indicator systems have been proposed to identify them,
which typically focus on the habitats of rare and endangered species,
specic ecosystems, and landscape connectivity (Luo, 2024; Neupane
et al., 2022; Peng, 2018; Wang et al., 2024; Zhuo et al., 2024). The core
habitats of the Altai Mountains primarily include existing PAs and pri-
ority conservation areas identied using the Marxan model. While
replacing “inefcient” PAs with lower-cost, higher-value priority con-
servation zones can enhance the effectiveness of ecological networks for
conservation, existing PAs have long beneted from international, na-
tional, and local support. The local expertise involved in their estab-
lishment underscores their conservation value, something that large-
scale conservation planning may struggle to replicate (Yang et al.,
2019). Therefore, efforts should be focused on expanding the coverage
of existing PAs and effectively integrating them into potential conser-
vation areas to expand the ecological network and achieve broader
ecosystem conservation goals.
Establishing ecological corridors connecting PAs, OECMs, and other
intact natural regions can promote ecological ow, safeguard ecosystem
functionality, and increase climate change resilience (Peng et al., 2018).
Currently, there are three main methods for constructing ecological
resistance surfaces. For the expert consultation method, resistance
values are assigned to land-use data based on expert opinions
(Gurrutxaga et al., 2011; Teng et al., 2011). The second method involves
selecting ecological factors such as land-use type, elevation, and slope to
construct a composite indicator framework (Chen et al., 2023; Gu et al.,
2023). The third is the adjusted valuation method, which takes land-use
type as the base resistance surface and uses factors such as nighttime
light data, elevation, and slope as correction variables to build a more
accurate ecological resistance surface (Peng et al., 2018). Owing to its
integration of multiple correction factors, the adjusted valuation method
reects ecological resistance more realistically and is widely used.
Common analytical methods for identifying ecological corridors include
Minimum Cumulative Resistance (MCR), the gravity model (Xu, 2023),
and circuit theory (Chen et al., 2023). In this study, we used circuit
theory to identify ecological corridors in the Altai Mountains. Circuit
theory combines the random-walk characteristics of electrons in a cir-
cuit with landscape connectivity to simulate the movement of organisms
across grid cells. Areas with higher current densities indicate a greater
likelihood of passage during ecological ow or suggest that these areas
are critical pathways with no viable alternative routes for species
movement (Men, 2024).
Border regions often overlap with biodiversity hotspots, with
approximately 55.6 % of mammals, 27.4 % of amphibians, and 68.6 % of
birds spanning international boundaries, and around 21 % of threatened
species distributed across borders (Liu et al., 2020; Mason et al., 2020).
National border fragmented ecosystems, with barriers such as fences and
walls impeding species migration, lead to population isolation and
habitat fragmentation (Dalui et al., 2024; Diniz et al., 2023; Liu et al.,
2020; Parks et al., 2022; Simkins et al., 2023; Zhuo et al., 2024). In the
absence of physical barriers, migratory species face challenges in
effective protection owing to management differences among national
protected area systems (Heywood, 2019; Thornton et al., 2018).
Although many countries have established PAs along their borders,
these areas are often too small, too few, or too dispersed to maintain
regional-scale biological processes.
Current transboundary research primarily focuses on areas such as
geopolitics (Fias and Stoffelen, 2024), transboundary species conserva-
tion (Li et al., 2020; Neupane et al., 2022; Yang et al., 2019), socio-
economic system interactions among neighboring countries (Khan
et al., 2019; Trogisch and Fletcher, 2022), boundary disputes and their
ecological impacts (Barquet et al., 2014; Kedem et al., 2024), and the
planning and adaptive governance of cross-border resources (Akamani
and Wilson, 2011; Li and Jay, 2023). Although transboundary conser-
vation research is increasingly shifting toward an integrated approach to
species, ecosystems, and landscape preservation (Heywood, 2019;
Taggart-Hodge and Schoon, 2016), signicant gaps remain in the con-
struction and governance of large-scale ecological networks for con-
servation, calling for further in-depth exploration (An et al., 2023; Wang
and Liu, 2020).
Located in central Asia and spanning China, Russia, Kazakhstan, and
Mongolia, the Altai Mountains are crucial habitats for many endemic,
rare, and endangered species and are a vital migration corridor. How-
ever, differences in protected area standards among the four countries
have led to suboptimal spatial congurations and protection gaps within
existing reserves, hindering the effective conservation of key species and
ecosystems in the region. The lack of ecological corridors in certain areas
compromises the integrity and connectivity of the protected area
network, affecting species migration, ecological processes, and ulti-
mately diminishing the effectiveness of biodiversity conservation.
In this study, we aimed to develop efcient transboundary ecological
networks for conservation to provide a scientic basis and management
strategies for the Altai Mountains and similar regions. The objectives
were as follows: (1) To identify protection gaps in existing PAs, deter-
mine core habitats by integrating the habitats of endangered species and
key ecosystem service areas from the four countries, and propose new
PAs and OECMs. (2) To identify ecological corridors and construct a
more precise ecological resistance surface using corrected land-use/land
cover data. (3) To propose construction and cooperation strategies for
the transboundary ecological networks for conservation based on his-
torical ecological protection cooperation experiences in the Altai
Mountains, promoting ecological protection coordination and coopera-
tion among the four countries.
2. Materials and methods
2.1. Study area
Situated in central Asia and spanning China, Russia, Kazakhstan, and
Mongolia, the Altai Mountains are key regions for global biodiversity.
This area is part of the Altai–Sayan Ecoregion and is listed as a Global
200 priority biodiversity conservation area. This region encompasses a
diverse range of ecosystems, including glaciers, mountain tundra, alpine
meadows, mountain forests, riparian ecosystems, steppes, deserts, and
semi-deserts. It is a crucial habitat for several rare and endangered
species such as Snow Leopard (Panthera uncia), Przawalski’s Horse
(Equus ferus), Asiatic Wild Ass (Equus hemionus), Argali (Ovis ammon),
Wild Camel (Camelus ferus), and Saiga (Saiga tatarica). Long-standing
economic and ecological protection collaborations have been in place
in regions such as Altay in Xinjiang, China, East Kazakhstan in
Kazakhstan, Altai Krai and the Altai Republic in Russia, and Khovd and
Bayan–
¨
Olgii provinces in Mongolia. To study the transboundary PAs of
the Altai Mountains in more detail, we used data from these six
administrative regions to clip the broad version of the GMBA V2.0
(Snethlage et al., 2022). This was used to extend and rene the study
area of the Altai Mountains and its surrounding landscapes (Fig. 1(a)).
The extent of the study area is shown in the context of the Altai–Sayan
region in Fig. 1(b). The details of existing PAs are provided in Table S1.
2.2. Data collection
Information on precipitation, evapotranspiration, land use/cover,
NDVI, DEM, landform, NPP, human footprint index, rivers, and glaciers
is provided in Table S2. The time range for precipitation, evapotrans-
piration, land cover, NDVI, and NPP data was 2001–2020, which aligns
with the temporal coverage of the human footprint index. Land cover
data were reclassied into categories including cropland, forest, grass-
land, wetland, built-up areas, shrubland, bare land, water bodies, ice,
J. Han et al.
Ecological Indicators 169 (2024) 112869
2

and snow. All the data were resampled to a resolution of 500 ×500 m
and projected using the Albers projection coordinate system.
Based on the Altai Mountains Regional Survey Report (Xiong, 2019)
and lists of wild fauna from protected area websites, along with queries
from the IUCN Red List (https://www.iucnredlist.org/), the National
Key Protected Wildlife List of China (https://www.gov.cn/xinwen/
2021-02/09/content_5586227.html), the Red Data Book of Russia
(https://ru.wikipedia.org/wiki/Кpacнaя_книгa_Poccии#Cпиcки_видoв),
the Red Data Book of Kazakhstan (https://redbook.kz/), and the Red
Data Book of Mongolia, a list of rare and endangered animals in the Altai
Mountain region was compiled. Based on the IUCN Red List and the rare
and endangered animal lists from the 4 countries, a total of 140 rare
species were selected for the Altai Mountains region. A species list is
provided in Table S7. The evaluation criteria are presented in Table 1
(Ma, 2021; Ma et al., 2022). Species point data were obtained from the
Global Biodiversity Information Facility (https://www.gbif.org/).
2.3. Methods
2.3.1. Marxan model input parameters
The Marxan model aims to create a reserve system that achieves the
minimum representation of biodiversity with the least cost (Stewart
et al., 2003). It uses a simulated annealing algorithm to nd the com-
bination of planning units that minimizes the objective function.
MarxanScore =
PUs
Cost +BLM
PUs
Boundary +
ConValue
SPF ×Penalty (1)
In Formula (1), PU represents the minimum planning unit, with the
smallest unit being a Level-12 catchment area from hydrosheds (https
://www.hydrosheds.org/), totaling 2765 units. The Boundary Length
Modier (BLM) value affects the compactness of the reserve system. A
higher BLM value results in a more compact system but increases the
cost. The Species Penalty Factor (SPF) is a multiplier used to determine
the intensity of penalties for failing to meet the conservation targets in
the current scheme. After multiple iterations, the BLM was set to 0.015
and SPF to 3.7 (Fig. S144).
In this study, conservation features encompassed rare species, key
ecosystem services, glaciers, and lake regions of the Altai Mountains.
Conservation targets are dened as the minimum protection proportion
for each conservation feature and relevant studies have generally set
these targets between 30 % and 50 % (Ma et al., 2022). We set the
conservation target for core habitats in the Altai Mountains to 50 %. The
conservation targets for water production services, water retention
services, soil retention services, and plant net primary productivity were
set at 0.5. Meanwhile, that for landscape aesthetic quality was set at 0.3.
The conservation targets for glaciers and major lake regions were set to
1. The fundamental conservation target for the species was set to 0.25,
Fig. 1. (a) The study area dened by the overlap of the Altai Mountains with the six provincial-level administrative regions (GS (2023)2762); (b) The location of the
study area relative to the Altai-Sayan Ecoregion;(c) Vegetation coverage within the study area.
Table 1
Detailed information of rare and endangered species.
Catalog Name Species Rarity Level Assigned
Value
The List of Key Wild Animals Under
State Protection (China)
I (First-class state protection) 1
II (Second-class state
protection)
0.6
The Red Data Book of the Russian
Federation
0; 1 1
2 0.6
3 0.3
The Red List of Republic of
Kazakhstan
I 1
II 0.6
III 0.3
Mongolian Red Book I(Hэн xoвop, yнaгaн зүйл；
Hэн xoвop зүйл)
1
II(Xoвop зүйл) 0.6
IUCN red list EXTINCT (EX) 1
EXTINCT IN THE WILD (EW) 1
CRITICALLY ENDANGERED
(CR)
1
ENDANGERED (EN) 0.8
VULNEABLE (VU) 0.6
NEAR THREATENED (NT) 0.4
LEAST CONCERN (LC) 0.2
J. Han et al.
Ecological Indicators 169 (2024) 112869
3

ensuring that the maximum conservation target did not exceed 0.5.
Specic species conservation targets are dened by (2) and (3):
Ri=0.6×RiIUCN +0.4×RiCN +RiRU +RiMG +RiKZ (2)
Ti=25% ×1+Ri−Rmin
Rmax −Rmin(3)
In Formulas (2) and (3), i denotes the species number, Ri represents the
species’ endangered status value, RiIUCN indicates the IUCN endangered
status value for the species and RiCN , RiRU , RiMG ,RiKZ represents the en-
dangered status value for species in different countries.Ti denotes the
conservation target value for a species. The Marxan model was run for
1,000 iterations, with the selection frequency of each planning unit
across all iterations as the irreplaceability value.
2.3.2. Determination of core habitats
2.3.2.1. Key cosystem services. Ecosystem services refer to the natural
environmental conditions and effects that ecosystems provide. These are
essential for human survival and well-being and encompass all the
benets humans obtain directly or indirectly from ecosystems (Costanza
et al., 1997). Ecosystem services include four key components—provi-
sioning, regulating, supporting, and cultural. Based on the specic
conditions of the Altai Mountain region (Ma et al., 2022; Wang et al.,
2024), we selected water yield as a provisioning service and water
conservation, soil retention, and net primary productivity as regulating
services. Landscape aesthetic quality values have been used to represent
cultural services (Hou et al., 2022; Xu et al., 2017). The value of
ecosystem services was divided into ve categories based on the natural
break method. In this study, areas with relatively high ecosystem service
values were considered areas that needed protection. The detailed
calculation process is provided in Supplementary information Appendix
D.
2.3.2.2. Species habitats. Maximum Entropy Modeling (MaxEnt) was
used to identify the habitat species. We selected 19 bioclimatic factors
and 3 topographic variables as fundamental environmental variables for
the MaxEnt model (Table S6) (Abolmaali et al., 2018). We randomly
selected 75 % of these distribution points as training data and used the
remaining 25 % as test data to validate the model accuracy. Model
performance was evaluated by calculating the Area Under the Curve
(AUC) of the Receiver Operating Characteristic (ROC). To mitigate the
effects of multicollinearity among environmental variables on model
interpretability and overall performance, we predicted species habitats
using all environmental variables to determine their importance
ranking. We then conducted Spearman’s correlation analysis on all
environmental variables at the species occurrence sites (Li et al., 2020;
Neupane et al., 2022). For variables with a correlation greater than 0.75,
we selected those with stronger explanatory power. These selected
variables were then used to conduct a secondary prediction of species
habitat suitability, resulting in the nal dataset. Following the previous
research (Feng et al., 2021; He et al., 2021), the suitability was cate-
gorized into four classes—unsuitable (0–0.2), low suitability (0.2–0.4),
moderate suitability (0.4–0.6), and high suitability (0.6–1). For this
study, habitats with moderate and high suitability were selected as the
species’ habitat ranges.
2.3.2.3. Spatial autocorrelation of irreplaceability values. When planning
new PAs, spatial aggregation and connectivity should be considered to
achieve optimal outcomes. By analyzing the spatial autocorrelation of
irreplaceability values, highly aggregated irreplaceable areas can be
identied and combined with potential and existing conservation areas
to dene the spatial extent of new PAs. It is recommended that potential
conservation areas not covered by existing PAs but overlapping with
H–H clusters be designated as new PAs or extensions of existing ones.
Potential conservation areas not covered by existing PAs and outside of
H–H clusters are suggested to be designated as new OECMs. The formula
for Global Moran’s I index is as follows (Moran, 1950):
I=N
WN
i=1N
j=1wij(xi−x)xj−x
N
i=1(xi−x)2(4)
In Formula (4), I represents the Global Moran’s I index, N denotes the
total number of planning units (PUs) in the study area, xi and xj refer to
the irreplaceability values of PUi, x indicates the average irreplaceability
value. wij represents the spatial weight between elements i and j, where
adjacent units are assigned a value of 1, and non-adjacent units are
assigned a value of 0. W is the sum of all wij .
The Local Moran’s I index can characterize whether clustering occurs
in a local space, and the formula is as follows (Anselin, 1995):
Ii=xi−x
Si2
n
j=1,j∕=i
wijxj−x(5)
In Formula (5), Ii represents the Local Moran’s I index for PUi, reecting
the spatial autocorrelation surrounding that unit. Si2 denotes the vari-
ance of i.
2.3.3. Construction of ecological corridors
2.3.3.1. Ecological resistance surface. The selected calibration factors
included elevation, slope, topographic roughness, vegetation cover,
distances to rivers, roads, and residential areas. The weights of these
calibration factors were determined using the Delphi method and their
scores and weights are listed in Table 2. Land-use types were used as the
base resistance surface, with the initial resistance values assigned as
follows: forest (10), shrubland (20), grassland (30), wetland (60), bare
land (60), cropland (100), ice/snow (200), and built-up areas (1000)
(Peng et al., 2018).
The correction formula for the ecological resistance surface is as
follows:
RESi=Di×wd+Si×ws+Ri×wr+Fi×wf+DRi×wdr +DDi
×wdd +DTi×wdt ×Li(6)
In Formula (6), i represents the grid ID, RESi is the ecological resistance
value of grid i,Di,Si,Ri,Fi,DRi,DDi,DTi denote the scores of cali-
bration factors for elevation, slope, terrain ruggedness, vegetation cover,
distance to rivers, distance to roads, and distance to residential areas,
respectively. wd,ws,wr,wf,wdr,wdd,wdt are the weights of the calibration
factors. Li represents the initial ecological resistance value for land-use
types.
2.3.3.2. Construction of ecological corridors. Ecological corridors consist
of a continuous set of pixels of a certain width. We determined the
corridor width by calculating the current density and identied the
critical ecological pinch points and barriers that play key roles in con-
servation efforts (Peng et al., 2018). In this study, we used the Linkage
Mapper tool (https://linkagemapper.org/) to extract ecological
corridors.
Ecological pinch points are critical areas within ecological corridors
identied using the PinchPoint Mapper module of the Linkage Mapper
tools, which leverages the Circuitscape program. We compared the CWD
cutoff distance thresholds ranging from 1,000 to 20,000 m to determine
the most suitable resistance threshold (Dutta et al., 2016). At an 8,000 m
width threshold, ecological corridors cover 5 % of the study area,
balancing ecological protection and economic development (Li et al.,
2023). The current density results for the pinch points were classied
into ve levels, with the highest density identied as the key area.
Ecological barriers impede the ow between core habitats. The removal
or restoration of these barriers can improve connectivity. Barrier points
J. Han et al.
Ecological Indicators 169 (2024) 112869
4

were identied using the Barrier Mapper module through cumulative
current recovery calculations with a minimum search radius of 500 m,
maximum search radius of 5000 m, and step size of 500 m.
3. Results
3.1. Core habitats
3.1.1. Identication of core habitats
The irreplaceability values, calculated using the Marxan model, are
shown in Fig. 2(a). When the irreplaceability value exceeded 700, the
core habitat area reached 168,729.00 km
2
. This constitutes approxi-
mately 50.63 % of the study area and meets the predetermined 50 %
conservation target. A total of 1,053 planning units had irreplaceability
values ranging from 701 to 1000, covering approximately 39.79 %
(132,596.00 km
2
) of the study area. In terms of core habitats, Russia
represents the largest proportion, accounting for 19.54 % (65,103.00
km
2
), followed by Mongolia at 16.15 % (53,832.50 km
2
), Kazakhstan at
8.48 % (28,246.75 km
2
), and China at 6.47 % (21,546.75 km
2
). The
overlapping areas between existing PAs and potential conservation
areas are predominantly situated along the border regions of China,
Russia, Mongolia, and Kazakhstan, covering 49,778.25 km
2
, represent-
ing 14.94 % of the total area. The potential conservation area not
covered by existing PAs was approximately 82,833.50 km
2
, representing
24.86 % of the total study area. This suggests that while existing PAs
encompass important species habitats and key ecosystem service zones,
substantial portions of core habitats still require further protection.
Overall, core habitats were predominantly located along national
borders, covering 50.63 % of the study area and demonstrating strong
spatial connectivity. Grasslands and forests were the most common land
cover types within these core habitats, accounting for 26.51 % and
14.24 % of the study area, respectively.
3.1.2. Location of newly established PAs
The results indicated that the Global Moran’s I value was 0.85>0,
with a Z-score of 57.73 >2.58 (p <0.01). This indicates that the spatial
distribution of the irreplaceability values is more clustered (positively
correlated) than expected from the random distribution. H–H clusters
were primarily concentrated in the northeastern and central parts of the
study area, with a signicant distribution along national borders,
forming several closely connected high-density areas (Fig. 3). The pro-
posed new or expanded PA is mainly located in the northern region,
particularly along the Russia–Kazakhstan border, where existing PAs are
insufcient to effectively safeguard key ecological features. The gap
between Russia’s Altai Nature Reserve and the Sumul Tinskij State
Natural Zakaznik has also been proposed for the creation and expansion
of PAs. The newly proposed OECMs were more dispersed and served as
supplementary areas primarily located along the edges of the core
habitats.
Table 2
Detail information of calibration factors for the ecological resistance surface.
Variables Weights Calibration Factor Scores
1 2 3 4 5
Elevation 0.0776699 <1000 m1000–1500 m1500–2000 m2000–3000 m>3000 m
Slope 0.1359223 <5◦5◦-15◦15◦-25◦25◦-35◦>35◦
Topographic roughness 0.1262136 0–25 25–50 50–70 70–100 >100
FVC 0.0485437 0.8–1 0.6–0.8 0.4–0.6 0.2–0.4 <0.2
Distance from the rivers 0.0873786 <300 m300–600 m600–1000 m1000–1500 m>1500 m
Distance from the roads 0.2524272 >2000 m1500–2000 m1000–1500 m500–1000 m<500 m
Distance from the residential areas 0.2718447 >1500 m1000–1500 m600–1000 m300–600 m<300 m
Fig. 2. (a) Irreplaceability values calculated by the Marxan model; (b) Spatial relationship between potential conservation areas and existing PAs; (c) Core habitats;
(d) Area of different types of PAs in the four countries. Note:PCA refers to Potential Conservation Areas; EPA refers to Existing PAs; CORE refers to core habitats; EPA
∩PCA refers to the overlapping areas between EPA and PCA; PCA-EPA refers to the areas belonging exclusively to PCA but not to EPA; EPA-PCA refers to the areas
belonging exclusively to EPA but not to PCA.
J. Han et al.
Ecological Indicators 169 (2024) 112869
5

3.2. Ecological corridors
In this study, we identied 116 ecological corridors with an average
length of 38.15 km and a total length of 4,425.78 km. Eight of these
corridors cross transboundary borders, primarily between China,
Kazakhstan, Russia, and Mongolia. The corridors primarily covered
grasslands (2.67 %), forests (0.81 %), water bodies (0.15 %), and
croplands (0.01 %). Mongolia has the highest number of ecological
corridors (59), whereas China has the longest average corridor length
(56.82 km). The longest corridor, measuring 215.01 km, connects the
Fuhai Jintasi Mountain Steppe Grassland State Nature Reserve with the
Wulunguhe National Wetland Park. Russia had the greatest number of
high-quality ecological corridors, whereas Mongolia had the lowest,
particularly near the Gobiin Ikh/B Strict Nature Reserve (Fig. 4).
Using the current density map generated by the PinchPoint Mapper
tool (Fig. 4(c)), 76 ecological pinchpoints cover an area of 59.50 km
2
.
The ecological pinch points primarily involve grasslands (42.75 km
2
),
bare land (6.75 km
2
), forests (6.25 km
2
), cropland (3.50 km
2
), and
shrubland (0.25 km
2
). Ecological pinch points were primarily distrib-
uted in northwest Kazakhstan, the southern part of the Katon–Karagay
National Park, and eastern Mongolia. A total of 28 ecological barrier
points were determined, covering an area of 513.25 km
2
(Fig. 4(d)). In
the northwestern part of the study area, ecological barriers were
concentrated near urban and cropland areas, likely due to urban
expansion and cropland development, which disrupted the previously
continuous corridors. In the southern region, particularly near Mongo-
lia’s Gobiin Ikh/B Strict Nature Reserve, barriers and pinch points are
found in barren or desert areas where fragile ecosystems and limited
resources pose considerable challenges to species migration.
4. Discussion
4.1. Cooperation and management of the Altai Mountains transboundary
ecological networks for conservation
The Altai region is located at the intersection of China, Russia,
Mongolia, and Kazakhstan and forms a unique biogeographical unit in
Eurasia rich in biodiverse ecosystems. Establishing the Altai trans-
boundary ecological protection network safeguards this biodiversity and
ecosystem service hotspot but also, due to its transboundary nature,
Fig. 3. (a) Local Moran’s I of irreplaceability values; (b) Recommended new or expanded PAs and OECMs.
Fig. 4. (a) Ratio of CWD to EucD for ecological corridors; (b) Ratio of CWD to LCP for ecological corridors; (c) Current density of ecological corridors; (d) Ecological
pinch points and barrier points.
J. Han et al.
Ecological Indicators 169 (2024) 112869
6

becomes a model for international cooperation and ecological gover-
nance. It offers valuable practical experience and case studies for
addressing global environmental challenges.
Establishing a transboundary ecological network for conservation is
a complex and gradual process that involves multiple countries and faces
challenges related to sovereignty, policy, and interest coordination
(Baghai et al., 2018; Miller, 2016). This process requires long-term
planning, from identifying key ecological areas to developing coordi-
nated management strategies for effective biodiversity and ecosystem
service protection (Fias and Stoffelen, 2024; Vasilijevi´
c et al., 2015).
Based on the transboundary ecological networks for conservation pro-
posed in this study and historical cooperation experiences in the study
area, a three-tier management cooperation framework centered on PAs
is suggested (Fig. 5, Table 3).
First-tier PAs are concentrated in the central part of the study area,
spanning the borders of China, Russia, Mongolia, and Kazakhstan, and
encompass several key existing PAs (Duan et al., 2024). To enhance
ecological connectivity in these regions and ll the gaps between the
current PAs, it is recommended to further expand the PAs, particularly
by establishing a new protected area between China’s Jiadengyu Na-
tional Forest Park and the Wuqilike National Park. This would
contribute to stabilizing the ecosystem, promoting biodiversity conser-
vation, and improving cross-border cooperation among the four coun-
tries. Building on the existing “Our Common Home – Altai” initiative,
unied management could be further promoted and aligned conserva-
tion strategies could help address the ecological challenges in this
region.
Second-tier PAs were primarily located along the border between
Russia and Kazakhstan. It is suggested that a new transboundary be
established in this region to better protect key ecological features and
maintain the integrity and connectivity of the transboundary ecological
networks for conservation. Kazakhstan and Russia should work together
to set common management goals and mechanisms to coordinate
ecological protection efforts in this area, enhancing the overall protec-
tion efciency.
Third-tier PAs were mainly distributed along the China–Mongolia
border, particularly in China’s Liangheyuan National Nature Reserve
and Mongolia’s Chigertein Golin Ai Sav National Park. Some potential
PAs in this region have not yet been integrated into existing protection
networks. To further strengthen ecological protection, expanding the
boundaries of the Chigertein Golin Ai Sav National Park and
Liangheyuan National Nature Reserve and establishing a new coopera-
tive management framework are recommended. Strengthening cross-
border conservation cooperation in this region would not only
improve the connectivity of ecological corridors but also ensure the
sustainability of biodiversity and ecosystem services.
Through the proposed three-tier management cooperation frame-
work, the Altai Mountains transboundary ecological protection network
could achieve greater ecological connectivity and protection efciency.
Close cooperation among countries, unied management standards, and
coordinated conservation actions will provide long-term ecological
protection for this unique transboundary region. By lling the gaps in
protection, expanding PAs, and relying on scientic management
mechanisms, a region can effectively address its ecological challenges
and be a successful model for global ecological conservation. The rst
level of the core area is located at the border junction of China, Russia,
Mongolia, and Kazakhstan and encompasses several key PAs.
4.2. Management and restoration recommendations for ecological
corridors
Countries should strengthen cooperation to formulate ecological
corridor management plans, clearly dene conservation goals, and
detail the management measures needed to maintain, restore, and
Fig. 5. Recommendations for the sequence of cooperation in the Altai Mountains transboundary ecological networks for conservation.
Table 3
EPAs Involved in different levels of management cooperation framework.
Levels of management
cooperation framework
Involved EPAs
First-tier management
cooperation framework
Baihaba National Forest Park (CN_1), Hanasi
National Reserve (CN_2), Jiadengyu National
Forest Park (CN_3), Golden Mountains of Altai
World Heritage Site (RU16), Katon-Karagay State
National Nature Park (KZ_3), Altai tavan bogd
National Park (MG_3)
Second-tier management
cooperation framework
Tigirekskij State Natural Zapovednik (RU_3),
CHaryshskij State Natural Zakaznik (RU_4),
Bashhelakskij State Natural Zakaznik (RU_5), West-
Altay State Nature Reserve (KZ_1)
Third-tier management
cooperation framework
Altay Mountain Liangheyuan State Nature Reserve
(CN_11), Fuyun Shenzhongshan State Forest Park
(CN_12), Keketuohai National Geopark (CN_13),
Chigertein golin ai sav National Park (MG_7)
J. Han et al.
Ecological Indicators 169 (2024) 112869
7

enhance ecological connectivity. Through collaborative management,
countries can jointly address global challenges such as climate change,
species migration, and ecosystem fragmentation, thereby promoting
regional biodiversity conservation and ecosystem restoration. For the
effective management of different types of ecological corridors, we
propose the following management recommendations (Table 4).
4.3. Innovation, limitations and prospects
This study develops an innovative research framework for trans-
boundary ecological networks for conservation, providing a scientic
foundation for international collaborative ecological protection. By
integrating habitat data of rare and endangered species and key
ecosystem service areas from China, Russia, Kazakhstan, and Mongolia,
the study identies potential conservation areas in the Altai Mountains.
Through overlay analysis of these potential conservation areas with
existing PAs, it precisely delineates core habitats within the trans-
boundary ecological networks for conservation. Furthermore, the
research denes the spatial distribution of newly proposed PAs and
OECMs, supporting the expansion and management of PAs. Driven by
the needs of transboundary conservation, this study proposes a phased,
collaborative conservation strategy that prioritizes border regions and
gradually extends to wider areas, offering an innovative pathway and
exemplary model for implementing transboundary ecological
protection.
This study’s limitations include reliance on species occurrence data
primarily from GBIF, which, despite its authority, has coverage gaps,
particularly in transboundary ecosystems. Future research should inte-
grate localized data from each country to enhance data accuracy and
representativeness. Additionally, the analysis of species migration cor-
ridors, based on macro-level protection areas, requires renement, as
species have varying migration needs, particularly large mammals
(Diniz et al., 2023; Kamath et al., 2024; Zhuo et al., 2024). Border fences
may restrict natural migration paths, leading to population isolation and
reduced genetic diversity. More detailed, species-specic analyses
integrating remote sensing with eld monitoring are needed to clarify
migration routes, assess human-made barrier impacts, and propose
conservation measures. Multinational cooperation and technological
innovation will be essential to establishing adaptive transboundary
ecological networks for conservation to ensure long-term biodiversity
and ecosystem protection in the Altai Mountains.
In the future, we will integrate social factors into building and
assessing ecological networks for conservation, exploring the synergy
between social and natural networks to benet both environmental
protection and socioeconomic development. We will analyze the impact
of social activities on ecological networks and develop policies to ensure
that conservation efforts align with social needs and development goals.
Additionally, to ensure the long-term effectiveness of transboundary
ecological networks for conservation, we will strengthen long-term
monitoring and evaluation, using regular data collection and analysis
to dynamically assess the function and effectiveness of the conservation
networks. This approach enables the adjustment of conservation stra-
tegies to effectively respond to environmental changes and societal
development needs.
5. Conclusion
In this study, by integrating the habitats of rare and endangered
species with key ecosystem service areas, we identied potential con-
servation areas in the Altai Mountains and overlaid them with existing
PAs in China, Russia, Kazakhstan, and Mongolia to determine the core
habitats of transboundary ecological networks for conservation. The
core habitat area accounted for 50.63 % (168,729.00 km
2
) of the study
area, of which 24.86 % (82833.50 km
2
) comprised potential conserva-
tion areas requiring further protection. Additionally, 116 ecological
corridors were identied with an average length of 38.15 km and a total
length of 4,425.78 km. The longest ecological corridor is 215.01 km,
connecting the southern potential PAs of the Jintasi Mountain Steppe
Grassland State Nature Reserve with the Wulunguhe National Wetland
Park. Eight transboundary ecological corridors linking core PAs across
different countries were identied.
Given the extensive scope of the Altai Mountains, a tiered approach
to protection cooperation is recommended. Initially, we focused on
broader and deeper collaboration in the four-country border area and
then gradually extended to other regions. Globally, the establishment of
transboundary ecological networks for conservation in the Altai
Mountains could be a model for other transboundary conservation
projects. This network has provided valuable empirical support for
similar transboundary ecological protection efforts and offered guidance
and examples for the development and implementation of ecological
Table 4
Management recommendations for Various Types of Ecological Corridors.
Ecological
Corridor Type
Main Distribution
Area
Restoration and
Management
Suggestions
References
Ecological
corridors across
transport
infrastructure
Altai foothills in
China
Construct wildlife
underpasses; protect
vegetation near road
infrastructure;
ensure the safety of
water sources near
transportation
facilities
(Clevenger and
Waltho, 2005;
Ding et al.,
2024a; H.
Zhang et al.,
2024
Ecological
corridors near
urban areas
Areas near towns
in East
Kazakhstan and
the Altai region of
China
Limit uncontrolled
urban expansion
and industrial
development;
control the impact
of excessive
recreational
activities;
recommend
pollution
monitoring and
early warning
systems
(Li et al., 2023;
Yuan et al.,
2023)
Agricultural
ecological
corridors
Agricultural
concentration
areas in
northeastern
Kazakhstan
Propose alternative
green livelihood
solutions; limit the
use of herbicides
and pesticides;
consider rotational
grazing to prevent
overgrazing;
establish semi-
natural buffer zones
between farmland
and corridors
(Maes et al.,
2012)
Riverine
ecological
corridors
Along riverbanks Protect river
hydrological cycles
and their seasonal
pulses; avoid
chemical shing
methods; restore
natural riparian
vegetation
(Gregory et al.,
2021)
Pristine alpine
forest ecological
corridors
Central Russia Monitor the effects
of extreme weather
and geological
disasters on natural
vegetation; strictly
prohibit illegal
logging
(An et al.,
2023)
Transboundary
ecological
corridors
China-Kazakhstan
and Russia-
Mongolia borders
Seasonally open
fences; construct
wildlife migration
culverts; prevent the
invasion of alien
species
(Ding et al.,
2024b; Terry
et al., 2006; Q.
Zhang et al.,
2024; Zhuo
et al., 2024)
J. Han et al.
Ecological Indicators 169 (2024) 112869
8

protection and sustainable management strategies in other regions
worldwide.
CRediT authorship contribution statement
Jiali Han: Writing – original draft, Visualization, Validation, Meth-
odology, Conceptualization. Fang Han: Writing – review & editing,
Supervision, Funding acquisition. Alexander Dunets: Supervision.
Bayarkhuu Batbayar: Supervision.
Acknowledgement
This study is supported by the Third Xinjiang Scientic Expedition
Program (No. 2022xjkk0805), “Tianshan Talents” training program (No.
2023TSYCCX0088), Chinese Academy of Sciences President’s Interna-
tional Fellowship Initiative (No. 2024VCA0013) and “One Belt One
Road” Innovative Talent Exchange Foreign Expert Project (No.
DL2023046003L).
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ecolind.2024.112869.
Data availability
Data will be made available on request.
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