Indonesian Journal of Forestry Research Vol. 11 No. 2, October 2024, 229-242

ISSN: 2355-7079/E-ISSN: 2406-8195

PREDICTING THE POTENTIAL DISTRIBUTION OF
Pinus cernua L.K. PHAN EX AVER., K.S. NGUYEN AND T.H. NGUYEN,
A CRITICALLY ENDANGERED CONIFER SPECIES
Trang Thanh Pham, Tuyen Thi Phung*, Hoang Nu Thi Ta, Dung Van Phan,
and Hoa Hai Nguyen
Faculty of Forest Resources and Environmental Management, Vietnam National University of Forestry,
Xuan Mai, Chuong My, Hanoi, Vietnam.
Received: 4 August 2023, Revised: 19 March 2024, Accepted: 14 October 2024
PREDICTING THE POTENTIAL DISTRIBUTION OF Pinus cernua L.K. PHAN EX AVER., K.S.
NGUYEN AND T.H. NGUYEN, A CRITICALLY ENDANGERED CONIFER SPECIES. Pinus cernua
L.K. Phan ex Aver., K.S. Nguyen and T.H. Nguyen (Pinaceae) is a critically endangered species restricted
to the Laos - Vietnam border. The population of this species has been declining due to habitat loss and
forest fire. Predicting the potential distribution of the species is an important priority for conservation
plans and strategies. In this study, a MaxEnt model was used for predicting the potential distribution of P.
cernua using 21 occurrence data and 13 environmental variables. Precipitation of driest month (with 32.3%
of contribution), annual mean diurnal range (23.4%), elevation (18.4%), and aspect (12.3%) are crucial
factors for predicting the species’ potential distribution in the MaxEnt model, while remaining factors are
less important factors. The suitable distribution was predicted in the north-western region of Vietnam and
the adjacent regions of Son La and Thanh Hoa provinces (Vietnam) and Houaphan province (Laos) with
1,544 km2 in total. The high, medium, and low suitability areas are 159 km2 (10%), 475 km2 (31%), and 910
km2 (59%), respectively. The current protected areas do not contain many of the possible habitats for this
species. Priority should be given to conservation efforts for species in these potentially suitable locations
both in- and outside of current protected areas, particularly in the surrounding area of Vietnam and Laos.
Keywords: Conservation, conifer, critically endangered species, ecological niche model, geographic
distribution
MEMPREDIKSI DISTRIBUSI POTENSIAL Pinus cernua L.K. PHAN EX AVER., K.S. NGUYEN DAN
T.H. NGUYEN, SPESIES KONIFER YANG TERANCAM PUNAH SECARA KRITIS. Pinus cernua
L.K. Phan ex Aver., K.S. Nguyen dan T.H. Nguyen (Pinaceae) adalah spesies yang terancam punah secara kritis dan
terbatas di perbatasan Laos - Vietnam. Populasi spesies ini telah menurun akibat hilangnya habitat dan kebakaran hutan.
Memprediksi distribusi potensial spesies ini adalah prioritas penting untuk rencana dan strategi konservasi. Dalam penelitian
ini, model MaxEnt digunakan untuk memprediksi distribusi potensial P. cernua menggunakan 21 data kemunculan dan
13 variabel lingkungan. Curah hujan pada bulan terkering (dengan kontribusi 32,3%), rata-rata tahunan kisaran diurnal
(23,4%), elevasi (18,4%), dan aspek (12,3%) adalah faktor penting untuk memprediksi distribusi potensial spesies dalam
model MaxEnt, sedangkan faktor lainnya kurang penting. Distribusi yang cocok diprediksi di wilayah barat laut Vietnam
dan daerah sekitar provinsi Son La dan Thanh Hoa (Vietnam) dan provinsi Houaphan (Laos) dengan total 1.544 km2.
Daerah dengan kesesuaian tinggi, sedang, dan rendah masing-masing adalah 159 km2 (10%), 475 km2 (31%), dan 910
km2 (59%). Area yang dilindungi saat ini tidak mencakup banyak habitat yang mungkin untuk spesies ini. Prioritas harus
diberikan kepada upaya konservasi untuk spesies di lokasi yang berpotensi sesuai ini baik di dalam maupun di luar area
yang dilindungi saat ini, khususnya di daerah sekitar Vietnam dan Laos.
Kata kunci: Konservasi, konifer, spesies yang terancam punah secara kritis, model relung ekologi, distribusi geografis

*

Corresponding author: tuyenpt@vnuf.edu.vn

©2024 IJFR. Open access under CC BY-NC-SA license. doi:10.59465/ijfr.2024.11.2.229-242

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Indonesian Journal of Forestry Research Vol. 11 No. 2, October 2024, 229-242

I. INTRODUCTION
Globally, a large number of species
(approximately 20 percent) are on the verge
of extinction (Brummitt & Bachman, 2010;
Brummitt et al., 2012) due to habitat degradation
and fragmentation, alien species invasion,
overexploitation, increasing human population,
and climate change (Barnosky et al., 2011;
Haddad et al., 2015; Nogué et al., 2009). The
rare, endemic species distributed in fragmented
and narrow (and single) geographical areas
(Işik, 2011; Jump & Penuelas, 2005; Primack,
2006) are prone to environmental changes.
Gymnosperms (i.e., conifers, cycads) are the
most threatened group (Brummitt & Bachman,
2010; Brummitt et al., 2012), with about
40 percent of the conifer species at risk of
extinction, especially in the Asia region (Forest
et al., 2018; IUCN, 2023a; Magri et al., 2020;
Rosenblad et al., 2019). Therefore, endemic
species (i.e., conifers) are important targets for
conservation and management (Myers et al.,
2000; Rosenblad et al., 2019).
To conserve endemic and threatened
species, detailed knowledge on the range
of their potential distribution is necessary.
However, incomplete information on the
distribution of endemic and threatened species
has caused many difficulties in conservation and
management activities (Gogol-Prokurat, 2011;
Mousikos et al., 2021). Species distribution
modeling (or ecological niche modeling) has
been an effective approach to determine the
current potential distribution areas and identify
the environmental requirements of species
(Guisan et al., 2002; Giriraj et al., 2008; GogolProkurat, 2011), project the species distribution
in the past, and assess the influence of climate
change on species distribution (Dai et al., 2022;
Qin et al., 2017), which may help managers and
conservationists save time and budget to inform
conservation planning and decisions. Among
species distribution models, Maximum Entropy
(MaxEnt) is considered to be a popular model
for estimating the potential distribution areas

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of species, especially endemic and threatened
species, because it has some advantages: 1) It
requires only species occurrence (even small
size) data and environmental information as
input data; 2) Both continuous and categorical
data are simultaneously handled; and 3) It
tests model robustness using cross-validation,
bootstrapping, and repeated subsampling
in replicated runs. (Phillips, Anderson, &
Schapire, 2006; Phillips & Dudík, 2008; Elith et
al., 2011; Garcia et al., 2013; Wang et al., 2015).
Pinus cernua L.K. Phan ex Aver., K.S. Nguyen
and T.H. Nguyen has been listed as Critically
Endangered (CR) in the International Union
for Conservation of Nature Red List (IUCN,
2023b), distributed on very steep slopes and
cliffs at approximately 900 – 1,800 m a.s.l.,on
the transboundary range between Son La
(Vietnam) and Houaphan (Laos) provinces
(Averyanov et al., 2014; Averyanov et al., 2015;
Averyanov et al., 2017; Loc et al., 2017). The
population of the species is small, with more
than 250 mature individuals, but the population
has declined significantly due to deforestation
and loss of habitat related to wildfire and human
activities (i.e. illegal logging) (Averyanov et al.,
2015; Averyanov et al., 2017; IUCN, 2023b).
In recent years, studies have focused on seed
germination, seedling cultivation, and biological
characteristics of P. cernua (Averyanov et al.,
2015; Hoa et al., 2016; Loc et al., 2017). However,
the information may not be adequate to build
a scientific conservation plan for the species.
Thus, a comprehensive study on the potential
distribution and environmental requirements
of P. cernua is essential for conservation aspects.
In this study, we predicted the possible
habitat range of P. cernua using data of its
presence in northern Vietnam and Laos,
and utilized Maxent modeling to identify the
important environmental factors affecting
P. cernua distribution. The results of this
investigation could help with planning for the
species’ conservation.

Predicting the Potential Distribution of Pinus Cernua ...........(Trang Thanh Pham et al.)

II. MATERIALS AND METHODS
A. Species occurrence data
Twenty-one location points of P. cernua were
obtained from previous published literatures,
including Averyanov et al. (2014), Averyanov
et al. (2015), Hoa et al. (2016), and Loc et al.
(2017), used in this study. (Figure 1).
B. Environmental variables
Twenty-four variables were selected and used
as predictors in models (Table 1). In particular,
19 bioclimatic variables from 1970 to 2000 were

obtained from the WorldClim website http://
www.worldclim.org/ (Fick & Hijmans, 2017) at
1 km resolution. The elevation variable with the
resolution of approx. 90m was extracted from
the Global Digital Elevation Map (GDEM)
generated from the CGIAR Consortium for
Spatial Information (https://srtm.csi.cgiar.
org) (Jarvis, Reuter, Nelson, & Guevara, 2008).
Slope and aspect layers were extracted from the
elevation layer using the Surface Analysis tool
in ArcGIS version 10.1 (ESRI, 2015).

Figure 1. The research site

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Table 1. Environmental variables used in this study
Variable

Unit

Code

Annual mean temperature
Annual mean diurnal range (Mean of monthly =max temp - min temp)
Isothermality (BIO2/BIO7) (* 100)
Temperature seasonality (standard deviation *100)
Max temperature of warmest month
Min temperature of coldest month
Annual temperature range (BIO5-BIO6)
Mean temperature of wettest quarter
Mean temperature of driest quarter
Mean temperature of warmest Quarter
Mean temperature of coldest quarter
Annual precipitation
Precipitation of Wettest month
Precipitation of driest month
Precipitation seasonality (Coefficient of variation)
Precipitation of wettest quarter
Precipitation of driest quarter
Precipitation of warmest quarter
Precipitation of coldest quarter
Land cover
Soil organic carbon (0-30cm in depth)
Elevation
Slope
Aspect

ºC
ºC
C of V
ºC
ºC
ºC
ºC
ºC
ºC
ºC
mm
mm
mm
mm
mm
mm
mm
mm
type
t/ha
m
degree
degree

bio_01
bio_02*
bio_03
bio_04*
bio_05
bio_06*
bio_07*
bio_08
bio_09
bio_10
bio_11
bio_12*
bio_13
bio_14*
bio_15*
bio_16
bio_17
bio_18*
bio_19
lulc*
soc*
elevation*
slope*
aspect*

Note: * indicated that these variables were used in modeling.

Soil organic carbon (SOC) with a spatial
resolution of 1 km was obtained from the
Global Soil Organic Carbon Map (GSOCmap,
version 1.5). The index is available and provided
by FAO and ITPS (2018). Land cover layers at
10 m resolution in 2020 were collected from
the website https://env1.arcgis.com/arcgis/
rest/services/Sentinel2_10m_LandCover/
ImageServer (Karra, 2023).
All the spatial layers of variables used in
MaxEnt models were resampled to 90 m using
the Resample tool in ArcGIS 10.1 (ESRI,
2015). In order to avoid the influence of
multicollinearity, we used ENMTools (version
1.4.4, Warren et al. (2010)) to determine the
correlation among variables. We did not include
variables with a high correlation (>|0.8|) in
the same model (Table A1). As a result, 13
variables were eventually chosen to model the
distribution of P. cernua (Table 1).
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C. Species distribution modeling
The Maximum Entropy Model (MaxEnt)
(version 3.4.4; Phillips et al., 2023) was used
for predicting the potential distribution of
P. cernua. The ENMval package in R (version
3.6.3, Muscarella et al. (2014)) was used to
choose the best model to predict species’
potential distribution based on the AICc value.
The minimum AICc value indicates the best
model (Muscarella et al., 2014). Ultimately, the
model with the feature class H (hinge) and a
regularization value of 3 was selected; thus, the
setting was chosen for the final models. The
species occurrence datasets in Section 2.1 were
repeated for 21 replicates (Pearson et al., 2007).
Maximum iterations were 5,000.
The area under the curve (AUC) was used to
determine the accuracy of the model. The AUC
value ranges from 0 (low accuracy) to 1 (high
accuracy) (Fielding & Bell, 1997; Phillips et al.,

Predicting the Potential Distribution of Pinus Cernua ...........(Trang Thanh Pham et al.)

2006). The Jacknife method was used to assess
the importance of variables for predicting the
potential distribution of P. cernua in the MaxEnt
model.
The response curves from MaxEnt’s result
show the relationship between species’ potential
distribution and environmental factors.
Finally, a map layer was generated by the
MaxEnt model, representing suitability levels
(from 0 (not suitable) to 1 (highly suitable).
The “10th percentile training presence logistic
threshold” was used as a threshold to identify
the suitable for the species, according to the
suggestion of Liu et al. (2005). Four levels
were categorized, including: unsuitable habitat
(<0.736), low suitable habitat (0.736-0.816),
medium suitable habitat (0.816-0.896), and high
suitable habitat (>0.896).
III. RESULTS AND DISCUSSION
A. Result
The MaxEnt model (AUC = 0.99) has
better performance than the random model
(AUC = 0.5) (Figure 2). Our model indicated
that climate group was identified as the main
factor contributing to the potential distribution
of this species with 61.7% of cumulative
contribution, especially precipitation of the

driest month ( 32.3%) and mean diurnal range
(23.4%), followed by topography (e.g., elevation
(18.4%), aspect (12.3%) (30.7%). Soil organic
carbon (soc) and land cover (lulc) were less
contributing with 6.8% and 0.8%, respectively
(Table 2).
The response curves of four main variables
and others to P. cernua habitat suitability are
shown in Figure 3. According to the obtained
response curves of the species, P. cernua prefers
habitats at 1,000 – 1,500 m elevation and in the
North and North-east areas with precipitation
of driest month less than 8 mm and an annual
mean diurnal range around 7.5º. Additionally,
the response curves of nine remaining
environmental variables, shown in Figure A1,
indicated that the potential distribution of this
species occurred in the areas where temperature
seasonality ranged 3.5 – 4ºC, the minimum
temperature of the coldest month was around
8ºC, and the annual temperature range was less
than 18ºC. The suitable annual precipitation,
precipitation seasonality, and precipitation of
the warmest quarter were approximately 1,400
mm, 91 mm, and 700 mm, respectively. In terms
of land cover, this species preferred clustering
of tall dense vegetation with a closed canopy.
Suitable soil organic carbon less than 50 tones/
ha.

Figure 2. The AUC value and ROC curve of the Maxent model for P. cernua

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Table 2. The percentage contribution of environmental variables used in the MaxEnt model for predicting
the potential distribution of P. cernua
Variable

Code

Percent
contribution

Precipitation of driest month

bio_14

32.3

Annual mean diurnal range (Mean of monthly =max temp - min temp) bio_02

23.4

Elevation

elevation

18.4

Aspect

aspect

12.3

Soil organic carbon

soc

6.8

Temperature seasonality (standard deviation *100)

bio_04

5.0

Land cover

lulc

0.8

Annual temperature range (BIO5-BIO6)

bio_07

0.8

Annual precipitation

bio_12

0.2

Min temperature of coldest month

bio_06

0

Precipitation of warmest quarter

bio_18

0

Slope

slope

0

Precipitation seasonality (Coefficient of Variation)

bio_15

0

Figure 3. P. cernua response curves in relation to the four most important environmental variables

Jackknife test results showed that climatic
variables demonstrated the highest gain in
AUC, indicating their crucial contribution to the
distribution of P. cernua, followed by topography
(i.e., elevation and aspect) and soil organic
carbon. Whereas land cover and slope had the
234

lowest gain in AUC, showing a minimal effect
on prediction of species distribution (Figure 4).
The results of the Maxent indicated that the
effect of climatic factors on the distribution of
P. cernua is greater than that of other factors.

Predicting the Potential Distribution of Pinus Cernua ...........(Trang Thanh Pham et al.)

Figure 4. The relative importance of environmental variables based on the jackknife test of area
under the curve (AUC). The abbreviations of variables were detailed in Table 1

Figure 5. Current potential suitable habitat of P. cernua in Northern, Vietnam
Notes:(a: the whole studied area; b: the north of the studied area; c: the south of the studied area).
The protected areas were downloaded from http://www.protectedplanet.vn/en.

Figures 5 and 6 show the potential
distribution map of P. cernua. The total
predicted suitable habitat area of the species
was approximately 1,544 km2, ranges c. 19ºN ‒
23ºN and 103ºE ‒ 106º30’E, mainly distributed
in north-western and north-central Vietnam
(Son La, Hoa Binh, Lao Cai, Lai Chau, Phu
Tho, Yen Bai, Thanh Hoa, and Nghe An

provinces) and Houaphan province (Laos). The
high, medium, and low suitable habitat areas
are 159 km2 (10%), 475 km2 (31%), and 910
km2 (59%), respectively. In the high suitable
areas, Thanh Hoa, Lao Cai, Son La, and Yen
Bai provinces (Vietnam) occupy the largest
areas at 46 km2, 44 km2, 24 km2, and 22 km2,
respectively, followed by smaller areas in Nghe
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Figure 6. Areas for the low, medium, and high potential distribution of P. cernua in Houaphan (Laos), Yen
Bai, Thanh Hoa, Nghe An, Hoa Binh, Lai Chau, Lao Cai, Phu Tho, and Son La, (Vietnam) provinces

Figure 7. Areas for the low, medium, and high potential distribution of P. cernua in total
and in protected areas

An, Hoa Binh, Lai Chau, Phu Tho (Vietnam),
and Huoaphan (Laos) provinces. Total of
predicted suitable habitat of this species within
protected areas is 479 km2 (~ 31% of the total
predicting potential suitable areas from model)
(Figure 7).

236

B. Discussion
Spatial distribution of species is linked to
environmental conditions due to long term
species – environment interactions (Jiang et al.,
2014; Kaeslin et al., 2012; Wisz et al., 2013).
Temperature and precipitation are important

Predicting the Potential Distribution of Pinus Cernua ...........(Trang Thanh Pham et al.)

factors influencing the growth and spread of
plant species (Cornett et al., 2000; García et
al., 2000; Ruan et al., 2012; Wiens et al., 2010;
Xie et al., 2022). The MaxEnt model shows
that climatic factors are the most important
for predicting the potential distribution of P.
cernua in the studied area. From the results of
the MaxEnt model, the suitable annual mean
diurnal range was around 7.5°C, precipitation
of the driest month was less than 8 mm, and
annual precipitation ranged 1,000 – 1,500 mm
(Figure 3 and A1), which is in accordance with
these climatic characteristics in the study area
obtained from the local meteorological station
(Quynh, 2024), including the annual mean
diurnal range (7.8°C), precipitation of the driest
month (10 mm) and annual precipitation (1,500
mm). Also, the results agree with Averyanov et
al. (2017) who reported that P. cernua occupies
areas with an average annual precipitation above
1,000 mm. Therefore, the suitable habitats of P.
cernua may prefer humid areas at high elevation
with low temperatures that strongly affect
buds for mountainous gymnosperms (Pan et
al., 2022). The effect of climatic factors on
P. cernua distribution range is consistent with
factors influencing the suitable habitats of
some high mountain coniferous species such as
Cephalotaxus oliveri (Xie et al., 2022) and Thuja
sutchuenenis (Qin et al., 2017), indicating that
climatic factors were the key drivers in these
species’ distribution. In Vietnam, Tuyet and Tra
(2021) showed that climatic factors had crucial
effects on the spatial distribution of Cupressus
torulosa. The importance of climatic factors in
the Maxent model for predicting the species’
potential distribution may indicate that climate
change will affect its distribution in the future.
Moreover, topography in mountain areas in
northern Vietnam is complex (Averyanov
et al., 2003), which is considered a major
factor influencing local climate (Meehl, 1992;
Ogwang et al., 2014) and results in strong
microclimate heterogeneity (Opedal et al.,
2015). Thus, in further in-depth studies on P.
cernua conservation, attention should be given
to microclimate at a fine scale and the effect of

climate change on this species distribution.
Topography (i.e., elevation, aspect) is also
an important variable for species distribution.
Topography is considered an influencing factor
of climate and soil condition, which contribute
to species distribution (Barry, 2008; Bunyan et
al., 2015; Grzyl et al., 2014; Jiao et al., 2009;
Youcefi et al., 2020). The results of the MaxEnt
model indicate that the suitable distribution areas
(especially high suitability) are concentrated on
the north, north-west, and north-east aspects
at high elevations (1,000 – 1,500 m) in tropical
forests in the north-western region of Vietnam
and the eastern areas of Houaphan provinces
(Laos), which have many peaks higher than
1000 m, such as Hoang Lien Son (3,143 m),
Pa Phanh (1,400 m), Pha Luong (1,500 m),
and Ta Sua (2,400 m), indicating this species
might respond sensitively to temperature and
precipitation as noted above. The result is in
accordance with the known distribution range
recorded by Averyanov et al. (2014), Averyanov
et al. (2017), and Loc et al. (2017), who reported
that this species occurred on the north, northwest, and north-east at 900 ‒ 1,800 m elevation,
which receive lower solar radiation, heavy
rainfall, and have higher tree cover compared
to other aspects.
Unlike the above factors, land cover is
less important for the model. The result
of the model shows that P. cernua tends to
distribute in areas characterized by tall, dense
vegetation (Figure A1). The result agrees with
Averyanov et al. (2017), who reported that P.
cernua mainly occurred in primary evergreen
coniferous tropical submontane forest and
primary evergreen mixed tropical submontane
forest, which are main land cover types at
elevations above 1,000 m a.s.l. in the studied
area. However, a part of these land cover types
is replaced by secondary forest, pastures, and
agricultural fields.
The areas of potential distribution for the
P. cernua from MaxEnt modeling are consistent
with the known distribution reported in many
previous studies (Averyanov et al., 2015;
Averyanov et al., 2017; Hoa et al., 2016; Loc et

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al., 2017; Thomas, 2022), and suggest that the
distribution areas of this species represent its
optimum climate in wet, cool, and/or cold sites
at high elevation. Additionally, the potential
suitable habitats for the P. cernua species
occupy not only in the forest areas with a high
elevation and characterized by the montane
climate in Son La (Vietnam) and Houaphan
(Laos) provinces (reported by Averyanov et
al. (2017)), but also in other forest places with
similar elevation and climate conditions in
the north-western region of Vietnam (e.g. Lai
Chau, Lao Cai, Phu Tho, Yen Bai, Thanh Hoa,
and Hoa Binh provinces) and the adjacent areas
of Son La and Thanh Hoa provinces (Vietnam)
and Houaphan province (Laos) (mainly in Pha
Luong and Pa Phanh mountain ranges), where
the species has not been recorded so far. These
results agree with previous studies (Kumar &
Stohlgren, 2009; Pearson, 2007; Yang et al.,
2013) that found that the Maxent model using
only presence data often overestimates the
potential distribution areas compared to the
observed niche of species.
From the results of models, most suitable
areas lie outside of existing protected areas,
which may cause adverse influences on the
species population in the future by human
activities (i.e., illegal logging, fire). It is
important that new protected areas should be
established for conservation of this species and
its habitat, but the establishment of protected
areas must depend on the overall and long-term
national planning. Thus, conservation outside
the protected areas is crucial for this species,
as noted below. The potential distribution areas
of this species lie in the adjacent areas between
Son La, Thanh Hoa (Vietnam), and Houaphan
(Laos) provinces. Thus, effective cross-border
conservation partnerships should be a key
component to successful management of this
transboundary critically endangered species.
For instance, managers should disseminate
local people about the species conservation
practices. In addition, inventories should
be conducted to explore the new locations,

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especially high-suitable areas where P. cernua
may already exist, as well as protect and
maintain its habitat and mature individuals.
In terms of ex-situ conservation, seeding
and cutting propagation should be used to
provide seedlings for conservation planting and
reintroduction programs in (highly) suitable
areas because natural regeneration of P. cernua
is poor (Averyanov et al., 2017). Indeed, seeding
and cutting propagation and an experimental
model for the species ex-situ conservation were
conducted by managers and scientists and have
been successful in Son La province (Averyanov
et al., 2015; Hoa et al., 2016). The successful
model may be applied in other suitable areas to
conserve this species and avoid extinction in
the future.
V. CONCLUSION
The MaxEnt model results indicate that the
suitable areas for P. cernua are approximately
1,544 km2 in the north-western of Vietnam and
on the Laos-Vietnamese border between Son La,
Thanh Hoa (Vietnam), and Houaphan (Laos)
provinces. The contribution of precipitation of
the driest month in the MaxEnt model to predict
the species’ potential distribution is highest,
followed by annual mean diurnal range and
elevation. Most suitable habitats of P. cernua are
not found within existing protected areas. This
study may provide a better understanding of
environmental factors influencing the species’
potential distribution in northern Vietnam and
provide recommendations for evidence-based
transboundary conservation management in
the distribution ranges of the endemic and
threatened tree species.
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