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B I O D I V E R S IT A S
ISSN: 1412-033X
Volume 23, Number 4, April 2022 E-ISSN: 2085-4722
Pages: 1726-1733 DOI: 10.13057/biodiv/d230402
Identifying the potential geographic distribution for Castanopsis
argentea and C. tungurrut (Fagaceae) in the Sumatra Conservation Area
Network, Indonesia
TRY SURYA HARAPAN1,2, , NURAINAS1,3, SYAMSUARDI1,3,, AHMAD TAUFIQ1,4
1Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Andalas. Jl. Universitas Andalas, Limau Manis, Padang 25163,
West Sumatra, Indonesia. Tel./fax. +62-751-71671, email: trysuryaharapan@gmail.com, email: syamsuardi@sci.unand.ac.id
2Southeast Asia Biodiversity Research Institute, Chinese Academy of Sciences & Center for Integrative Conservation, Xishuangbanna Tropical
Botanical Garden, Chinese Academy of Sciences. Mengla, Yunnan 666303, China
3Herbarium of Universitas Andalas. Jl. Universitas Andalas, Limau Manis, Padang 25163, West Sumatra, Indonesia
4Department of Biological Sciences, Graduate School of Science, Tokyo Metropolitan University. Minami-Osawa, Hachioji-shi, Tokyo 192-0397, Japan
Manuscript received: 6 January 2022. Revision accepted: 9 March 2022.
Abstract. Harapan TS, Nurainas, Syamsuardi, Taufiq A. 2022. Identifying the potential geographic distribution for Castanopsis
argentea and Castanopsis tungurrut (Family: Fagaceae) in the Sumatra Conservation Area Network, Indonesia. Biodiversitas 23: 1726-
1733. Recently, Castanopsis argentea (Blume) A.DC. and Castanopsis tungurrut (Blume) A.DC. have been listed as endangered
species by the International Union for the Conservation of Nature (IUCN). For conservation planning, it is important to know the full
distribution of species. This study aimed to predict the potential distribution of C. argentea and C. tungurrut using MaxEnt, and
understand key factors responsible for the distribution of these species. A total of 53 occurrences and six environmental variables were
used to model their distribution. The AUC values of C. argentea and C. tungurrut were 0.86 and 0.91, respectively, and the models
suggest the distribution of both species is mainly influenced by elevation, and temperature seasonality for C. tungurrut. The predicted
distributions of the species are in the mountains of the western part of Sumatra, and their range includes 12 conservation areas that have
highly suitable habitats for both species. After generating the MaxEnt prediction map, we conducted field validation to validate the
model predictions. Field surveys in two predicted areas showed that the predicted distribution maps accurately estimated the distribution
of C. argentea and C. tungurrut at those localities.
Keywords: Conservation, distribution, endangered plants, phytogeography, spatial modeling
INTRODUCTION
Indonesia is home to hundreds of threatened tree
species, including members of the family Fagaceae. The
Fagaceae is a large angiosperm family comprising eight
genera with more than 700 species, of which 112 species
have been recorded in Indonesia (Purwaningsih and
Pulosakan 2016). This paper investigates two Indonesian
species in the family which are listed as Endangered under
IUCN criteria, Castanopsis argentea (Blume) A.DC. and
C. tungurrut (Blume) A.DC. (Barstow and Kartawinata
2018a,b). According to Indonesian Forum for Threatened
Trees (FPLI), the trees are considered vulnerable to
extinction and are protected nationally by Indonesian Law
P.106/MENLHK/SETJEN/KUM.1/12/2018. Besides edible
fruits, the durable wood of many species of Castanopsis are
used for constructing houses, making wood charcoal and
their bark is used for dyeing rattan work black (Soepadmo
and van Steenis 1972). Castanopsis spp. are also
considered to be indicators of superior arable lands
(Soepadmo and van Steenis 1972). Hence these species are
of high utility, and overuse of timber from these species
will contribute to decreasing their populations and increase
the threat of extinction. In Sumatra, the two species under
study are recorded mainly from the Barisan Mountains in
the west of the island (Figure 1) (Laumonier 1997).
Castanopsis argentea is also found in Java, Indonesia (Mt.
Ungaran, and on Mt. Wilis at Ngebel) and C. tungurrut is
also found in the Malay Peninsula, Simalur and Banka
Island, and West Java (Soepadmo and van Steenis 1972).
Numerous factors such as agricultural clearing, forest
fires, illegal logging, illegal mining, and transport
infrastructure close to the forest are ascribed to biodiversity
loss. The Indonesian government is drafting a plan to build
a massive Trans-Sumatra Highway for connecting
Sumatra's entire island in 2024. Any infrastructure
development plan has its share of negative consequences
on its surrounding ecosystems (Sloan et al. 2019). For
conservation planning, it is important to know which
species is distributed where, so that appropriate
infrastructural development could be guided.
Understanding species distribution with a full ground
survey is costly and time-consuming. A number of
distribution modelling methods have been developed to
help predict the distribution of species, including those
employing principle of Maximum Entropy (Phillips et al.
2006). These models incorporate environmental data to
define the environmental niche of the species (McShea
2014).
HARAPAN et al. – Castanopsis argentea and Castanopsis tungurrut in Sumatra, Indonesia
1727
Maximum entropy (MaxEnt) is a widely used modeling
method for predicting species distribution in poorly-
surveyed areas. The algorithm typically outperforms other
methods based on predictive accuracy (Merow et al. 2013).
Compared to other SDM tools, a maximum entropy
algorithm can develop a good model with small number of
occurrences (Harapan et al. 2020). Because of this reason,
many studies on threatened plants, which typically have
small amounts of occurrence data, use MaxEnt to model
species distributions (Adhikari et al. 2012; Yang et al.
2013; Padalia et al. 2014; Pradhan 2015; Remya et al.
2015; Yuan et al. 2015; Yi et al. 2016; Pranata et al. 2019;
Ito et al. 2020; Anand et al. 2021; Du et al. 2021; Felix et
al. 2021; Liu et al. 2021; Mahatara et al. 2021; Nguyen et
al. 2021; Purohit and Rawat 2021; Su et al. 2021; Yang et
al. 2021; Ye et al. 2021). With effective conservation
planning focused on ensuring redundancy and resiliency
for sustainable future populations (Redford et al. 2011),
SDMs are a valuable tool for the conservation community
(Mcshea 2014). This study aims to predict the potential
distribution of C. argentea and C. tungurrut in the Sumatra
Conservation Area Network, Indonesia and to understand
key factors responsible for the distribution of these species.
MATERIALS AND METHODS
Study area
Castanopsis argentea occurrences were identified based
on field surveys between December 2017 to January 2019
in West Sumatra (Nyarai and Universitas Andalas
Biological Forest), North Sumatra (Sarula) and at the
border between West Sumatra and Jambi Province (Kerinci
Seblat National Park), Indonesia. Occurrences of C.
tungurrut were derived from herbarium specimen records
and GBIF records. A total of 52 occurrences (Figure 1)
were collected from our field surveys, Herbarium of
Andalas (Voucher Code: ANDA 0001-0005, ANDA 33381
for C. argentea and ANDA 00124-00141 for C. tungurrut)
and the Global Biodiversity Information Facility (GBIF
2020a,b).
Species description
The diagnostic characteristic of Fagaceae is the cupule,
a woody bract that partially covers the fruit. The C.
argentea and C. tungurrut have similar-looking sharply
spiny cupules. The cupule of C. argentea lacks branched
spines, and there are 3 fruits in a cupule, whereas C.
tungurrut has only a single fruit per cupule, which possesses
branched slender spines. The leaves of C. argentea are
glossy above and distinctly silvery below. Castanopsis
tungurrut leaves are glossy above, widest in the middle and
slightly acute at the leaf blade base (Figure 2).
Figure 1. Map of Sumatra, Indonesia and the occurrence data of the targeted Castanopsis argentea and C. tungurrut
B I O D I V E R S I T A S
23 (4): 1726-1733, April 2022
1728
Figure 2. The dry specimen of Castanopsis argentea and C.
tungurrut
Species distribution modeling
MaxEnt ver. 3.4.1 was used to identify the potential
distribution of C. argentea and C. tungurut in Sumatra. All
coordinates from species occurrences were converted to
decimal degrees. We included the following environmental
data in the models. Altitude derived from a digital elevation
model (DEM) was obtained from Jarvis et al. (2008),
climatic variables were downloaded from WorldClim
(Hijmans 2020), and soil quality was obtained from Fischer
et al. (2008). All remote sensing raster data were resampled
to 1 km spatial resolution using the R Raster package
(Hijmans 2020). All rasters in geotiff format were
converted to ASC format. Species distribution modelling
requires variable selection to enhance the analytical power
and avoid the model overfitting (Fourcade et al. 2014; Yi et
al. 2016; Pradhan and Setyawan 2021), hence we used
PCA to inform the exclusion of highly correlated
environmental variables. If two environmental variables
were significantly correlated (R>0.8), only one was
selected as a predictor (Harapan et al. 2020). Of the
original 21 variables, six variables were chosen, including
elevation, soil quality, temperature seasonality,
precipitation of warmest quarter, temperature annual range
and precipitation of wettest quarter (Table 1). On the
MaxEnt configuration, auto functions of the predictor
variables were selected for inclusion in the model. We
followed recommended default values that were used for
the convergence threshold (10-5) and a maximum number
of 500 iterations (Harapan et al. 2020). Ten replicated
model and background samples functions were used for
determining a good species location to reflect the
environmental conditions that one is affected in contrasting
on species presences based on the spatial scale (Saupe et al.
2012; Merow et al. 2013).
Distribution value in conservation areas
Raster output from MaxEnt bearing habitat suitability
values from 0-1 for each species was loaded in R and the
values were reclassified with Raster package (Hijmans
2020) to produce a potential distribution map with ≥ 0.8
thresholds (Figure 3). We used a Sumatra conservation area
shapefile derived from http://www.globalforestwatch.org to
check the species coverage inside and outside conservation
areas according to their predicted distribution. The shapefile
was read into a spatial polygons data frame using the
readOGR function in the rgdal package (Bivand et al.
2020).
RESULTS AND DISCUSSION
Castanopsis distribution model
The predicted distributions showed the Castanopsis
species have a considerable range overlap (Figure 3). The
highest potential distribution zones in Indonesia for C.
argentea were located along with the Barisan mountain
range, with C. tungurrut having a wider potential
distribution in the north of the island in the Lake Toba
environs. These species also showed distribution zones in
the southern of the island (Bengkulu - Lampung Province).
The success of the model in predicting the distributions
for both Castanopsis species was checked using mean area
under curve (AUC); the model performances were
satisfactory based on AUC values (Table 2). Analyses of
environmental variable contributions to each of the models
are different between species. Elevation, soil quality, and
temperature seasonality were the most important variables
for C. argentea while the most important variables for C.
tungurrut were elevation, temperature seasonality, and
precipitation of warmest quarter (Table 1).
Elevation was by far the highest contributing variable
influencing the predicted distribution for both species.
Based on a habitat suitability threshold of 0.8, suitable
habitat for C. argentea was above 700 m while for C.
tungurrut it was above 1700 m altitude (Figure 4). Both
Castanopsis species occur in slight-moderate soil limitation
that restricts their land use (Class 1 soils category) and is
found in the environment with low seasonality. Suitable
habitat for C. tungurrut receives about 800-1200 mm
precipitation in warmest quarter.
Table 1. Contributing environmental variables for Castanopsis
argentea (Ca) and C. tungurrut (Ct)
Code
Variable
Contribution (%)
Ca
Ct
DEM
Elevation
48.6
28.9
S
Soil Quality
28.2
14.7
bio4
Temperature Seasonality
16.3
25.4
bio18
Precipitation of Warmest Quarter
3
19.8
bio7
Temperature Annual Range
2
6.5
bio16
Precipitation of Wettest Quarter
1.9
4.7
Table 2. Model performance and total covered area of
Castanopsis argentea and C. tungurrut
Species
Presence
record
AUC
Model
Performance
(Swets 1988)
Total suitable
area covered
(>0.8)
C. argentea
15
0.91
Excellent
33,736 km2
C. tungurrut
37
0.86
Good
54,960 km2
HARAPAN et al. – Castanopsis argentea and Castanopsis tungurrut in Sumatra, Indonesia
1729
Figure 3. The predicted distribution of: A. Castanopsis argentea, B. C. tungurrut in Sumatra, Indonesia
Figure 4. Response of Castanopsis argentea and C. tungurrut to
elevation
In this study, we showed the conservation area that are
suitable for both the species. West Sumatra, North Sumatra
and Jambi conservation area networks were the most
suitable areas. The modeled distributions of both species
suggest that they are well covered by the conservation
areas of Sumatra, Indonesia (Figure 5).
Field validation
After identifying areas with the highest probability of
occurrence for both species in Sumatra, Indonesia, we
chose Marapi Mountain, West Sumatra and Mount Tujuh,
Jambi to do field surveys for both species. A total of 5
individuals of C. argentea and 11 individuals of C.
tungurrut were recorded across the sites (Figure 6).
Figure 5. The distribution probability value (%) in Conservation Areas for Castanopsis argentea and C. tungurrut. Red circles are C.
argentea; green circles are C. tungurrut. *Field validation site
B I O D I V E R S I T A S
23 (4): 1726-1733, April 2022
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Figure 6. Map of ground validation for Castanopsis argentea and C. tungurrut in Marapi Mountain and Mount Tujuh, Indonesia
In addition, we also recorded C. argentea occurrences
in Baruah Gunuang, West Sumatra, Indonesia (0.02329,
100.40715) and along the Padang - Solok road, West
Sumatra (-1.03290, 100.68198). These areas were also
predicted as highly suitable habitats outside protected
areas. Voucher specimens were deposited in Herbarium
ANDA (ANDA38871- ANDA38879).
Discussion
Prior to this study, little was known about the potential
distribution of the endangered tree species C. argentea and
C. tungurrut in Sumatra, Indonesia, and how much of this
was represented in the protected area network. Using a
modelling approach, we used herbarium and field records
to predict the distribution of both species and overlaid this
with a map of protected areas. We also gained insight into
the environmental variables which are most important in
determining the distribution of the species. This approach
allowed us to understand better future threats to the species
from land use change.
Our model suggested C. argentea and C. tungurrut are
strongly influenced by elevation (Table 1). Previous studies
(Pielou 1979; Adhikari et al. 2012; Chunco et al. 2013; Yi
et al. 2016; Kamyo and Asanok 2020) have reported a
significant relationship between elevation and plant
distribution. Environmental characteristics are very
important for determining potential species distributions,
and analyzing the various environmental factors related to a
habitat. It is also essential to discover the potential presence
of a species in their habitat and to recognize basic
ecological knowledge of the species (Koo et al. 2019). The
limitation of this study is MaxEnt model only predicts
species distribution by analyzing the relationship between
species and selected environmental variables using
presence data. The model suggests the presence of species
in areas with suitable environmental conditions (Li et al.
2020). Besides the environmental variables, the
distributions of the species are also affected by biotic
factors, speciation mechanisms, and dispersal ability (Kaky
et al. 2020). However, despite model limitations, MaxEnt
can determine habitat use and species distribution across
many different taxa and localities generated from
incomplete data.
Our field surveys revealed C. argentea and C. tungurrut
are closely distributed with C. rhamnifolia (Figure 6).
Based on recorded occurrences, C. argentea population is
relatively small in size compared to C. tungurrut.
Whitmore (1972) and Laumonier (1997) reported these
taxa as important components from lowland to high
montane forest. Our model confirmed that the altitudinal
characteristics of the plant is consistent with Fujii et al.
(2006) who conducted topographic census of Fagaceae in
West Sumatra. The study reported distribution of C.
argentea at altitudinal range of 1200-1800 m and C.
tungurrut at 1400-1800 m but also in the lowlands at about
400 m (Fujii et al. 2006). Our field surveys in two areas
with a high probability of occurrence indicated that both of
these species could be available in 2000 m asl at Gunung
Tujuh, Indonesia (Kerinci Seblat National Park). Similar to
HARAPAN et al. – Castanopsis argentea and Castanopsis tungurrut in Sumatra, Indonesia
1731
the predictions of the model, Laumonier (1997) recorded C.
argentea in upper montane forest about 2300 m in West
Sumatra, Indonesia. It is indicated these endangered
species occur at high altitudes in montane forest, however,
according to IUCN (Barstow and Kartawinata 2018a,b), C.
tungurrut has distributional range up to 1920 m and 150-
1400 m for C. argentea, while those reports also suggest
that both species are almost extinct in lowland areas due to
conversion of their native habitat to palm oil plantation.
Although in this study we could still find these species in
montane area.
The highest deforestation activity occurred in lowland
area. For example, Riau contributed 46% of total Sumatran
forest degradation between 1990 to 2010, and remaining
primary forest is located in upland mostly in Aceh (40%)
followed by West Sumatra (15%) and Bengkulu (12%)
(Margono et al. 2012). A study by Dwiyahreni et al. (2021)
showed in 2012 and 2017 Tesso Nilo National Park lost
47% of total forest cover while Kerinci Seblat National
Park only lost 1.96%. However, the upland in Sumatra is
not completely safe from forest cover loss. The road
constructions also play important role in driving
deforestation. The Trans-Sumatra road development would
pass several important ecosystems like northern boundary
of Kerinci Seblat National Park and northeast flank of
Gunung Leuser National Park. These protected areas are
expected to be negatively impacted by road development
(Sloan et al. 2019).
The first step for the conservation is to understand the
relationship between the geographical distribution of taxa
and the environmental conditions. Then, we need to assess
the predicted distribution areas for collecting the current
population data (Mir et al. 2020; Kaky et al. 2020). The
predicted areas from MaxEnt can be applied easily to help
identify important suitability areas specifically in Sumatra
where conservation efforts need to be executed at broad
scale. Our field survey into two predicted areas
successfully confirmed the model is fairly accurate. The
promising areas with a presence probability greater than
80% would be a base for a quantifiable assessment (Figure
4). This assessment can help the protection and restoration
efforts for the endangered plants to be more scientific and
cost-effective (Gillenwater et al. 2006). The spatial
distribution model has directed us to understand better the
potential habitat of both the Castanopsis species studied.
The suitable area must be protected for reforestation and a
future reintroduction to reserve the associated habitat. The
predicted geographical map can analyze tree distribution
data, potential habitat, and disturbance risks (Kamyo and
Asanok 2020).
We propose combining the ex-situ conservation with
reintroduction to multiply the individuals before their
release to the natural habitat. The botanical garden would
be a proper place for ex-situ conservation (Widyatmoko
2019). North Sumatra and West Sumatra have the highest
value for suitable habitat for establishing an ex-situ
conservation strategy. There are four botanical gardens in
Sumatra, Solok Botanical Garden, Samosir Botanical
Garden, Sriwijaya Botanical Garden and Bukit Sari
Botanical Garden. Solok Botanical Garden was found to be
located in suitable areas for these endangered plants, giving
us the benefit of focusing on the growing population in the
natural region. The botanical garden with suitable
environmental conditions can use its financial resources
and limited land more efficiently (Volis 2017). Solok
botanical garden can focus on conservation of living
collection Castanopsis. Eco-regional climatic conditions
are important for living collection of plant species.
However, it’s ineffective in creating a living collection if
these conditions are expected to become unsuitable. After
identifying current suitable habitat, future work can be
conducted with MaxEnt to identify the areas where the
habitat remains suitable over time, e.g., year 2080 (Volis
2017). Therefore, all stakeholder groups need to develop
protocols to equally and fairly share species and habitat
management costs. Practically, the majority of actions will
be governed by national policies. Hence, all management
actions should be developed and implemented in
association with appropriate monitoring programs where
possible, which may be strategically the best way to
increase their number of occurrences and reverse trend of
their declining populations.
ACKNOWLEDGEMENTS
This work was partly supported by Institute for
Research and Community Service (LPPM), Universitas
Andalas. We thank IdeaWild grant id (HARAINDO0320)
for computer support in this study. We thank reviewers for
their efforts towards improving our manuscript. We are
grateful to Erizal Mukhtar, Wilson Novarino, Tesri
Maideliza, Heru Handika, Kyle W Tomlinson and Mark
Hughes for valuable comments on the manuscript draft. We
thank Kuswata Kartawinata for the detail of IUCN data
support of these endangered plant species. We also thank
Rezi Rahmi Amolia and Ardea Musfar for helping with
plant specimens and occurrences collection.
REFERENCES
Adhikari D, Barik SK, Upadhaya K. 2012. Habitat distribution modelling
for reintroduction of Ilex khasiana Purk., a critically endangered tree
species of Northeastern India. Ecol Eng 40: 37-43. DOI:
10.1016/j.ecoleng.2011.12.004.
Anand V, Oinam B, Singh IH. 2021. Predicting the current and future
potential spatial distribution of endangered Rucervus eldii eldii
(sangai) using MaxEnt model. Environ Monit Assess 193: 147. DOI:
10.1007/s10661-021-08950-1.
Barstow M, Kartawinata K. 2018a. Castanopsis argentea. The IUCN Red
List of Threatened Species 2018: e.T62004506A62004510. DOI:
10.2305/IUCN.UK.2018-1.RLTS.T62004506A62004510.en.
Barstow M, Kartawinata K. 2018b. Castanopsis tungurrut. The IUCN Red
List of Threatened Species 2018: e.T62004621A62004623. DOI:
10.2305/IUCN.UK.2018-1.RLTS.T62004621A62004623.en.
Bivand R, Keitt T, Rowlingson B. 2020. rgdal: Bindings for the
‘Geospatial’ Data Abstraction Library. R package version 1.5-16.
https://cran. r-project. org/package=rgdal
Chunco AJ, Phimmachak S, Sivongxay N, Stuart BL. 2013. Predicting
environmental suitability for a rare and threatened species (Lao newt,
Laotriton laoensis) using validated species distribution models. PLoS
One 8 (3): e59853. DOI: 10.1371/journal.pone.0059853.
Du Z, He Y, Wang H, Wang C, Duan Y. 2021. Potential geographical
distribution and habitat shift of the genus Ammopiptanthus in China
B I O D I V E R S I T A S
23 (4): 1726-1733, April 2022
1732
under current and future climate change based on the MaxEnt model.
J Arid Environ 184: 104238. DOI: 10.1016/j.jaridenv.2020.104328.
Dwiyahreni A, Fuad HAH, Sunaryo S, Soesilo TEB, Margules C,
Supriatna J. 2021. Forest cover changes in Indonesia’s terrestrial
national parks between 2012 and 2017. Biodiversitas 22 (3): 1235-
1242. DOI: 10.13057/biodiv/d220320.
Felix Ribeiro KA, de Medeiros CM, Sanchez Agudo JÁ. 2021. How
effective are the protected areas to preserve endangered plant species
in a climate change scenario? The case of three Iberian endemics.
Plant Biosyst 1-14. DOI: 10.1080/11263504.2021.1918777.
Fischer G, Nachtergaele F, Prieler S, van Velthuizen HT, Verelst L,
Wiberg D. 2008. Global Agro-ecological Zones Assessment for
Agriculture (GAEZ 2008). IIASA, Laxenburg, Austria & FAO Rome,
Italy.
Fourcade Y, Engler JO, Rödder D, Secondi J. 2014. Mapping species
distributions with MaxEnt using a geographically biased sample of
presence data: A performance assessment of methods for correcting
sampling bias. PloS One 9 (5): e97122. DOI:
10.1371/journal.pone.0097122.
Fujii S, Nishimura S, Yoneda T. 2006. Altitudinal distribution of
Fagaceae in West Sumatra. Tropics 15: 153-163. DOI:
10.3759/tropics.15.153.
GBIF.org .2020a. GBIF Occurrence Download DOI: 10.15468/dl.5axy60.
GBIF.org. 2020b. GBIF Occurrence Download DOI: 10.15468/dl.0rod3c.
Gillenwater D, Granata T, Zika U. 2006. GIS-based modeling of spawning
habitat suitability for walleye in the Sandusky River, Ohio, and
implications for dam removal and river restoration. Ecol Eng 28: 311-
323. DOI: 10.1016/j.ecoleng.2006.08.003.
Harapan TS, Agung AP, Handika H, Novarino W, Tjong DH, Tomlinson
KW. 2020. New records and potential geographic distribution of
elongated caecilian, Ichthyophis elongatus Taylor, 1965 (Amphibia,
Gymnophiona, Ichthyophiidae), endemic to West Sumatra, Indonesia.
Check List 16: 1695-1701. DOI: 10.15560/16.6.1695.
Hijmans RJ. 2020. raster: Geographic Data Analysis and Modeling. R
package version 3.4-5. https://CRAN.R-project.org/package=raster
Ito H, Hayakawa K, Ooba M, Fujii T. 2020. Analysis of habitat area for
endangered species using maxEnt by urbanization in Chiba, Japan.
Intl J Geomate 18: 94-100. DOI: 10.21660/2020.68.5721.
Jarvis A, Guevara E, Reuter HI, Nelson AD. 2008. Hole-Filled SRTM for
the Globe: Version 4. CGIAR-CSI SRTM 90m Database. Retrieved
from http://srtm.csi.cgiar.org/, accessed on 06.02.2022
Kaky E, Nolan V, Alatawi A, Gilbert F. 2020. A comparison between
Ensemble and MaxEnt species distribution modelling approaches for
conservation: A case study with Egyptian medicinal plants. Ecol
Inform 60: 101150. DOI: 10.1016/j.ecoinf.2020.101150.
Kamyo T, Asanok L. 2020. Modeling habitat suitability of Dipterocarpus
alatus (Dipterocarpaceae) using MaxEnt along the Chao Phraya River
in Central Thailand. For Sci Technol 16: 1-7. DOI:
10.1080/21580103.2019.1687108.
Koo KS, Park D, Oh HS. 2019. Analyzing habitat characteristics and
predicting present and future suitable habitats of Sibynophis chinensis
based on a climate change scenario. J Asia-Pacific Biodivers 12: 1-6.
DOI: 10.1016/j.japb.2018.11.001.
Laumonier Y. 1997. The Vegetation and Physiography of Sumatra.
Kluwer Academic Publisher, Netherlands. DOI: 10.1007/978-94-009-
0031-8.
Li Y, Li M, Li C, Liu Z. 2020. Optimized MaxEnt model predictions of
climate change impacts on the suitable distribution of Cunninghamia
lanceolata in China. Forests 11: 302. DOI: 10.3390/f11030302.
Liu L, Guan L, Zhao H, Huang Y, Mou Q, Liu K, Chen T et al. 2021.
Modeling habitat suitability of Houttuynia cordata Thunb (Ceercao)
using MaxEnt under climate change in China. Ecol Inform
63:101324. DOI: 10.1016/j.ecoinf.2021.101324.
Mahatara D, Acharya AK, Dhakal BP, Sharma DK, Ulak S, Paudel P.
2021. MaxEnt modelling for habitat suitability of vulnerable tree
Dalbergia latifolia in Nepal. Silva Fenn 55: 10441. DOI:
10.14214/sf.10441.
Margono BA, Turubanova S, Zhuravleva I, Potapov P, Tyukavina A,
Baccini A, et al. 2012. Mapping and monitoring deforestation and
forest degradation in Sumatra (Indonesia) using Landsat time series
data sets from 1990 to 2010. Environ Res Lett 7: 034010. DOI:
10.1088/1748-9326/7/3/034010.
McShea WJ. 2014. What are the roles of species distribution models in
conservation planning? Environ Conserv 41: 93-96. DOI:
10.1017/S0376892913000581.
Merow C, Smith MJ, Silander JA. 2013. A practical guide to MaxEnt for
modeling species’ distributions: What it does, and why inputs and
settings matter. Ecography 36: 1058-1069. DOI: 10.1111/j.1600-
0587.2013.07872.x.
Mir AH, Tyub S, Kamili AN. 2020. Ecology, distribution mapping and
conservation implications of four critically endangered endemic
plants of Kashmir Himalaya. Saudi J Biol Sci 27: 2380-2389. DOI:
10.1016/j.sjbs.2020.05.006.
Nguyen TT, Gliottone I, Pham MP. 2021. Current and future predicting
habitat suitability map of Cunninghamia konishii Hayata using
MaxEnt model under climate change in Northern Vietnam. Eur J Ecol
7: 1-17. DOI: 10.17161/eurojecol.v7i2.15079.
Padalia H, Srivastava V, Kushwaha SPS. 2014. Modeling potential
invasion range of alien invasive species, Hyptis suaveolens (L.) Poit.
in India: Comparison of MaxEnt and GARP. Ecol Inform 22: 36-43.
DOI: 10.1016/j.ecoinf.2014.04.002.
Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy
modeling of species geographic distributions. Ecol Model 190: 231-
259. DOI: 10.1016/j.ecolmodel.2005.03.026.
Pielou EC. 1979. Biogeography. Wiley, New York.
Pradhan P, Setyawan AD. 2021. Filtering multi-collinear predictor
variables from multi-resolution rasters of WorldClim 2.1 for
Ecological Niche Modeling in Indonesian context. Asian J For 5 (2):
111-122. DOI: 10.13057/asianjfor/r050207.
Pradhan P. 2015. Potential distribution of Monotropa uniflora L. as a
surrogate for range of Monotropoideae (Ericaceae) in South Asia.
Biodiversitas 16 (2): 109-115. DOI: 10.13057/biodiv/d160201.
Pranata S, Sulistijorini, Chikmawati T. 2019. Ecology of Rafflesia
arnoldii (Rafflesiaceae) in Pandam Gadang West Sumatra. J Trop
Life Sci 9: 243-251. DOI: 10.11594/jtls.09.03.05.
Purohit S, Rawat N. 2021. MaxEnt modeling to predict the current and
future distribution of Clerodendrum infortunatum L. under climate
change scenarios in Dehradun district, India. Model Earth Syst
Environ 1:1-13. DOI: 10.1007/s40808-021-01205-5.
Purwaningsih P, Polosakan R. 2016. Keanekaragaman jenis dan sebaran
fagaceae di Indonesia. Ethos: J Penelitian dan Pengabdian kepada
Masyarakat 1: 85-92. DOI: 10.29313/ethos.v0i0.1687. [Indonesian]
Redford KH, Amato G, Baillie J, Beldomenico P, Bennett EL, Clum N et
al. 2011. What does it mean to successfully conserve a (vertebrate)
species? BioSci 61: 39-48. DOI: 10.1525/bio.2011.61.1.9.
Remya K, Ramachandran A, Jayakumar S. 2015. Predicting the current
and future suitable habitat distribu.tion of Myristica dactyloides
Gaertn. using MaxEnt model in the Eastern Ghats, India. Ecol Eng
82: 184-188. DOI: 10.1016/j.ecoleng.2015.04.053.
Saupe EE, Barve V, Myers CE, Soberón J, Barve N, Hensz CM et al.
2012. Variation in niche and distribution model performance: The
need for a priori assessment of key causal factors. Ecol Model 237-
238: 11-22. DOI: 10.1016/j.ecolmodel.2012.04.001.
Sloan S, Alamgir M, Campbell MJ, Setyawati T, Laurance WF. 2019.
Development corridors and remnant-forest conservation in Sumatra,
Indonesia. Trop Conserv Sci 12: 1-9. DOI:
10.1177/1940082919889509.
Soepadmo E, van Steenis CGGJ. 1972. Fagaceae. Flora Malesiana-Series
1. Spermatophyta 7: 265-403.
Su H, Bista M, Li M. 2021. Mapping habitat suitability for Asiatic black
bear and red panda in Makalu Barun National Park of Nepal from
MaxEnt and GARP models. Sci Rep 11: 14135. DOI:
10.1038/s41598-021-93540-x.
Swets JA. 1988. Measuring the accuracy of diagnostic systems. Science
240: 1285-1293. DOI: 10.1126/science.3287615.
Volis S. 2017. Conservation utility of botanic garden living collections:
Setting a strategy and appropriate methodology. Plant Divers 39: 365-
372. DOI: 10.1016/j.pld.2017.11.006
Whitmore TC. 1972. Tree Flora of Malaya, A Manual for Foresters.
Longmans, London.
Widyatmoko D. 2019. Strategi Dan Inovasi Konservasi Tumbuhan
Indonesia Untuk Pemanfaatan Secara Berkelanjutan. Seminar Nasional
Pendidikan Biologi Dan Saintek (SNPBS) Ke-IV. [Indonesian]
Yang XQ, Kushwaha SPS, Saran S, Xu J, Roy PS. 2013. Maxent
modeling for predicting the potential distribution of medicinal plant,
Justicia adhatoda L. in Lesser Himalayan foothills. Ecol Eng 51: 83-
87. DOI: 10.1016/j.ecoleng.2012.12.004.
Yang Z, Bai Y, Alatalo JM, Huang Z, Yang F, Pu X et al. 2021. Spatio-
temporal variation in potential habitats for rare and endangered plants
and habitat conservation based on the maximum entropy model. Sci
Total Environ 784: 147080. DOI: 10.1016/j.scitotenv.2021.147080.
HARAPAN et al. – Castanopsis argentea and Castanopsis tungurrut in Sumatra, Indonesia
1733
Ye P, Zhang G, Zhao X, Chen H, Si Q, Wu J. 2021. Potential
geographical distribution and environmental explanations of rare and
endangered plant species through combined modeling: A case study
of Northwest Yunnan, China. Ecol Evol 11: 13052-13067. DOI:
10.1002/ece3.7999.
Yi YJ, Cheng X, Yang ZF, Zhang SH. 2016. MaxEnt modeling for
predicting the potential distribution of endangered medicinal plant (H.
riparia Lour) in Yunnan, China. Ecol Eng 92: 260-269. DOI:
10.1016/j.ecoleng.2016.04.010.
Yuan HS, Wei YL, Wang XG. 2015. MaxEnt modeling for predicting the
potential distribution of Sanghuang, an important group of medicinal
fungi in China. Fungal Ecol 17: 140-145. DOI:
10.1016/j.funeco.2015.06.001.