Maxent Modeling for Predicting the Potential Distribution Of

Ecological Engineering 92 (2016) 260–269

Contents lists available at ScienceDirect

Ecological Engineering

jo urnal homepage: www.elsevier.com/locate/ecoleng

Maxent modeling for predicting the potential distribution of

endangered medicinal plant (H. riparia Lour) in Yunnan, China

a,b,∗ a,d a,b c

Yu-jun Yi , Xi Cheng , Zhi-Feng Yang , Shang-Hong Zhang

Ministry of Education Key Laboratory of water and sediment Science, School of Environment, Beijing Normal University, Beijing 100875, China

State Key Laboratory of Water Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China

Renewable Energy School, North China Electric Power University, Beijing 102206, China

Environmental Protection Bureau of Yaohai District, Anhui, Hefei, 230012, China

a r t i c l e i n f o a b s t r a c t

Article history: Climate change inﬂuences ecosystem by altering the habitat of species in it. We report the quantitative

Received 27 July 2015

predictions of climate change on riparian species. Homonoia riparia (H. riparia) Lour, a species native to

Received in revised form 21 April 2016

Yunnan Province, China, is a medicinal plant with high ecological and economic value. Its population has

Accepted 22 April 2016

declined signiﬁcantly, and the species has become locally endangered in recent decades. Understanding

the habitat requirements of this species, evaluating habitat quality, and predicting its potential habitat

Keywords:

are signiﬁcant for protecting H. riparia Lour. One positional variable, three topographic variables and

Climate change

eight bioclimatic variables were used to model its distribution and potential habitat. The eight main

Habitat suitability simulation

Maxent bioclimatic variables inﬂuencing species distribution were selected from 19 bioclimatic variables based

on correlation analysis and principal component analysis. An MAXENT model, because of the advantages

Species distribution models (SDMS)

Plant-climate interactions of using presence-only data and performing well with incomplete data, small sample sizes and gaps,

was employed to simulate the habitat suitability distribution. The results show that seven variables,

namely, annual mean temperature, altitude, precipitation seasonality, precipitation of coldest quarter,

the distance to the nearest river, temperature seasonality, and precipitation during the driest month, are

signiﬁcant factors determining H. riparia Lour’s suitable habitat. Habitat suitability for three historical

periods and two future climate warming scenarios were calculated. The habitat suitability of H. riparia

Lour in Yunnan Province is predicted to improve with global warming.

1. Introduction Lindenmayer 2007). Several reasons, such as climate change and

land use change, may shrink, degrade or destroy the habitats of

An organism’s habitat is the combination of the space it inhabits wild animals and plants (Grimm et al., 2008; Yang et al., 2015). Ilex

and all eco-factors in that space, including the abiotic environ- khasiana Purk, a tree species of northeastern India, was critically

ment and other organisms that are necessary for the existence of endangered by habitat loss; only approximately 3000 individu-

individuals or groups. Habitat quantity and quality have a signiﬁ- als of Ilex khasiana Purk currently survives (Adhikari et al., 2012).

cant impact on a species’ distribution and species richness within The demands of an ever-increasing human population – the most

environments. Habitat loss affects the spatial pattern of residual important being of land for agriculture, industry and urbanization –

habitat and induces microclimatic change and habitat fragmen- has strong impacts on the habitat of Malabar nut (Justiciaadhatoda

tation (Purves and Dushoff 2005). Thus, habitat loss has negative L.), a medicinal plant. The population of Malabar nut (Justiciaadha-

effects on species richness that may be of long duration and high toda L.) is shrinking in India’s Dun Valley due to habitat loss (Yang

intensity (Kruess and Tscharntke 1994; Anadón et al., 2014). Habi- et al., 2013). By 2010, approximately one-ﬁfth of all of the world’s

tat loss is the main reason for species endangerment, species plants species were at risk of extinction (Brummitt and Bachman

extinction and biodiversity loss (Tilman et al., 2001; Fischer and 2010).

H. riparia Lour is a rheophyte native to Yunnan Province. It is a

medicinal plant with high ecological and economic values. Its abun-

∗ dance has decreased sharply in recent decades. A ﬁeld investigation

Corresponding author at: Ministry of Education Key Laboratory of water and sed-

iment Science, School of Environment, Beijing Normal University, Beijing 100875, in 1984 (before the construction of Manwan reservoir) showed that

China . H. riparia Lour was present in at least four habitats in the Manwan

E-mail address: [email protected] (Y.-j. Yi).

http://dx.doi.org/10.1016/j.ecoleng.2016.04.010

Y.-j. Yi et al. / Ecological Engineering 92 (2016) 260–269 261

Fig. 1. (a) Map of China and the location of Yunnan Province; (b) and (c) H. riparia Lour; (d) distribution of the main rivers and the H. riparia Lour’s presence points in Yunnan

Province.

Fig. 2. The results of the AUC curves in developing H. riparia Lour’s habitat suitability model. (The red (training) line shows the “ﬁt” of the model to the training data. The

blue (testing) line indicates the ﬁt of the model to the testing data and is the real test of the model’s predictive power.). (For interpretation of the references to colour in this

ﬁgure legend, the reader is referred to the web version of this article.)

reservoir area, and its abundance was signiﬁcantly more than 400; ing suitable survival conditions for H. riparia Lour are crucial to its

only one habitat among these four remained in 1997 (after Manwan conservation.

reservoir’s construction). Two habitats in the Manwan reservoir The ﬁrst task was to understand how the environment struc-

lake and one habitat below Manwan dam were ﬂooded. The only tures the distribution of H. riparia Lour. To do so, we built a species

remaining H. riparia Lour are scattered throughout the ﬂoodplain distribution model (SDM) as a function of climate, topography and

between the upstream stretches and the estuary of Luozha river, location. Species distribution models (SDMS) mainly use distribu-

but their condition in 1997 was worse than that in 1984 (Wang tion data of species (presence or absence) and environmental data

et al., 2000). However, few studies of the habitat quality of H. riparia to algorithmically estimate species’ niches, and then project those

Lour have been undertaken. Consequently, researching the habi- niches onto the landscape, reﬂecting a species’ habitat preferences

tat preferences of H. riparia Lour, developing a habitat suitability in the form of a probability (Guisan and Thuiller 2005; Elith and

model to calculate the spatial distribution of this species, and seek- Leathwick, 2009). The results can be explained as the probabil-

ity of species presence, species richness, habitat suitability, and

262 Y.-j. Yi et al. / Ecological Engineering 92 (2016) 260–269

Fig. 3. The results of the jackknife test of variables’ contribution in modelling H. riparia Lour’s habitat distribution. (The regularized training gain describes how much better

the Maxent distribution ﬁts the presence data compared to a uniform distribution. The dark blue bars indicate that the gain from using each variable in isolation, the light

blue bars indicate the gain lost by removing the single variable from the full model, and the red bar indicates the gain using all of the variables). (For interpretation of the

references to colour in this ﬁgure legend, the reader is referred to the web version of this article.)

so on. SDMS has been used to predict the ranges of plant dis- ables and bioclimatic variables); (3) the habitat suitability of H.

eases and insects, model the distributions of species, communities riparia Lour in three historical periods (1950–1959, 1975–1985 and

or ecosystems, assess the impact of climate, land use and other 2000–2009) were simulated using the developed model; and 4)

environmental changes on species distributions (Thomas et al., the potential habitats of H. riparia Lour under two climate warm-

2004; Yi et al., 2014a), evaluate the risk of species invasion and ing scenarios (RCP2.6 and RCP8.5, given by the IPCC) were pre-

proliferation (Peterson 2003; Beerling et al., 2009), identify unsur- dicted.

veyed areas with high suitability for precious endangered species

(Raxworthy et al., 2003), contribute to the site selection of natu-

ral preserves (Ferrier 2002), and identify target areas for species

2. Study area and species

reserves and reintroductions (Adhikari et al., 2012). Typical SDMS

include MAXENT (Phillips et al., 2004), BIOCLIM (Busby 1991),

2.1. Study area

DOMAIN (Carpenter et al., 1993), GAM (Yee and Mitchell 1991),

GLM (Lehmann et al., 2002), BIOMAPPER (Hirzel and Guisan, 2002),

Yunnan Province is located in southwestern China,

and so on. ◦ ◦ ◦ ◦

(21 8 N–29 15 N, 97 31 E–106 11 E). With a total area of approx-

SDMS is based on presence and absence data, which may be

imately 390,000 square kilometers (Fig. 1a and d). The north side

obtained from ﬁeld investigation, specimen records, and litera-

is higher than the south side in Yunnan Province, and signiﬁcant

tures. In practice, it is very difﬁcult to obtain absence data. Even

temperature differences exist between the north and south. The

when absence data can be obtained, it is unreliable. Presence data

climate of Yunnan varies regionally and with altitude. The seasonal

for rare and endangered species is also limited. Elith et al. (2006)

temperature difference is small, and the diurnal temperature

used 16 methods to model the distributions of 226 species from six

difference is large. Rainfall is plentiful, with clearly delineated

regions around the globe. The results indicated that the predictive

wet and dry seasons, but precipitation is not uniform throughout

ability of Maxent was always stable and reliable, and it outper-

the province. The annual precipitation in most parts of Yunnan is

formed several SDMS (such as DOMAIN, BIOCLIM, GAM, and GLM)

approximately 1100 mm, but precipitation in the southern part

for presence-only data. As a result, among SDMS, MAXENT was

may reach 1600 mm. Thus, the seasonal distribution and regional

selected because of its various advantages: (1) The input species

distribution of precipitation are uneven; rainfall in the winter is

data can be presence-only data; (2) both continuous and categori-

sparse, and rainfall in the summer is abundant.

cal data can be used as input variables; (3) its prediction accuracy is

The special geographical location and complex natural environ-

always stable and reliable, even with incomplete data, small sam-

ment of Yunnan Province have is the richest in wildlife species

ple sizes and gaps; (4) a spatially explicit habitat suitability map

and ecosystem types. There are many endemic genera, endemic

can be directly produced; and (5) the importance of individual

species, and rare and endangered species. Over half of all of China’s

environmental variables can be evaluated using a built-in jackknife

species can be found in Yunnan Province, and 67.5% of all of the rare

test.

species in the country are found in Yunnan, ranking ﬁrst in China

This study used occurrence records of H. riparia Lour to

(Jia and Zhang 2006). Due to natural and geographical restrictions

model its habitat suitability distribution to better conserve its

and other factors, the diversity of biological resources in Yunnan is

habitat using the following approach: (1) key environmental vari-

vulnerable. Yunnan is one of the 17 key regions of biodiversity in

ables highly correlated with H. Riparia Lour’s distribution were

China and one of the 34 global biodiversity hotspots. Its biodiversity

selected; (2) a Maxent model was developed to quantify the

ranks ﬁrst in China and attracts attention both from within China

relationship between H. riparia Lour’s presence and the selected

and from abroad (Yunnan Environmental Protection Department

environmental variables (including position, topographical vari-

(YEPD, 2013).

Y.-j. Yi et al. / Ecological Engineering 92 (2016) 260–269 263

◦

Fig. 4. Response curves of 12 environmental variables in H. riparia Lour’s habitat distribution model. (Bio1: Annual Mean Temperature ( C); Bio2: Mean Diurnal Temperature

◦ ◦ ◦

Range (Mean of monthly (max temp-min temp)) ( C); Bio4:Temperature Seasonality ( C); Bio7: Annual Range of Temperature ( C); Bio12: Annual Precipitation (mm); Bio14:

Precipitation of Driest Month (mm); Bio15: Precipitation Seasonality; Bio19: Precipitation of Coldest Quarter (mm)).

2.2. Species dikes (Wei, 1992). In recent decades, its population in Yunnan

Province has declined signiﬁcantly (Zhao et al., 2014).

H. riparia Lour (Euphorbiaceae) (Fig. 1b and c)is an evergreen

shrub 1–3 m high. H. riparia has many aliases in China, such as

3. Materials and methods

Shuima, Xiagongchashu, Shuizhuimu, and Xiyangliu. It is an endan-

gered species native to Yunnan Province, China. In China, it is

3.1. Data sources

mainly found in Yunnan Province, but it is also distributed in

Guangxi and Guangdong Provinces (Institute of Botany the Chinese

Fifty-one occurrence records of H. riparia Lour in Yunnan

Academy of Sciences (IBCAS, 1972). It is a medicinal plant with

Province were collected from databases, including ﬁeld survey data

high ecological and economic value. Its root can be used to treat

in June and December 2010, the Global Biodiversity Information

hepatitis, joint pain, stomachache, or empyrosis, and it has detoxi-

Facility (http://www.gbif.org), the National Specimen Informa-

fying and diuretic effects (Xishuangbanna National Drug Research

tion Infrastructure (http://www.nsii.org.cn/), the Chinese Virtual

Ofﬁce(XNDRO, 1980; Che et al., 2009).

Herbarium (http://www.cvh.org.cn/) and in the literature (Wang

H. riparia Lour is a rheophyte commonly found along the banks

et al., 2000).

of drains and on rocky ﬂood plain, but it also grows in shrubl and

Bioclimatic variables are very biologically meaningful for deﬁn-

on riverine (Kumar, 2013) and in regularly ﬂooded areas. H. riparia

ing the environmental niche of a species. Data for 19 bioclimatic

Lour is a deep-rooted tree; it has good resistance to drowning and

variables were downloaded from http://www.worldclim.org. A

scouring, effectively prevents erosion, ﬁxes sands and reinforces

geographical base map of China was obtained from National Funda-

mental Geographic Information System (http://nfgis.nsdi.gov.cn).

264 Y.-j. Yi et al. / Ecological Engineering 92 (2016) 260–269

to every extracted principal component and the correlation analy-

sis between the 19 bioclimatic variables, eight bioclimate variables

(bio1, bio2, bio4, bio7, bio12, bio14, bio15, bio19) were extracted

(Table 1).

3.3. Maximum entropy (MaxEnt) model

In 1957, Jaynes proposed maximum entropy theory (Maximum

Entropy, MAXENT), the essence of which is that on the basis of par-

tial knowledge, the most reasonable inference about the unknown

probability distribution is the most uncertain or the most random

inference which matches the known knowledge. This is the only

unbiased choice we can make; any other choice would introduce

other constraints and assumptions that cannot be derived from the

information we have (Jaynes,1957).

Given a random variable ␰ that has n different potential results

X1, X2, . . . Xn, for which the occurrence probabilities are p1, p2,

. . .pn, respectively, the entropy of ␰ is given by the formula:

n n

= 1

= − H() pi log pi log pi pi

i=1 i=1

The application of maximum entropy theory in species habi-

tat suitability prediction can be expressed as follows: if we know

Fig. 5. Habitat suitability distribution of H. riparia Lour according to occurrence

nothing about a species’ life habit or local ecological conditions,

records.

the most reasonable prediction is that the probabilities of the area

being suitable for the species and of the area not being suitable are

Using the longitude and latitude coordinates and the DEM, vari- both 0.5. Any data that a species is present within a set of local

ables including the distance to the nearest river, altitude, aspect ecological conditions is information that reduces the uncertainty

and slope, were calculated using ArcGIS 9.3. The DEM data were of a Maxent model. The more information there is, the more uncer-

obtained from http://www.gscloud.cn/. All environmental data tainty is reduced. The Maxent approach is to establish a model

used in this model were at 30 arc-second spatial resolution (often with a maximum entropy in accordance with known knowledge

referred to as 1-km spatial resolution). (Phillips et al., 2006; Phillips and Dudík, 2008). Based on maximum

The climate data for three historical periods (1950–1959, entropy theory, the Java-based software package Maxent, which

1975–1985 and 2000–2009) were obtained from China Meteoro- can be used for habitat suitability simulation, was developed by

logical Data Sharing Service System (http://data.cma.cn). In the ﬁfth Phillips et al. (2006). The Maxent (version 3.3.3) we used in this

IPCC report, using the total radiative forcing (RF) in 2100 as an study was obtained from http://www.cs.princeton.edu/ ∼schapire/

index, four representative concentration pathways (RCPs) were set, MaxEnt/ and can be downloaded freely for scientiﬁc research. The

representing scenarios in which the total RF in 2100 had reached training data were 75% of the sample data selected randomly, and

2 2 2 2

2.6 W/m , 4.5 W/m , 6.0 W/m and 8.5 W/m over the value in 1750 the test data were the remaining 25% of the sample data. The habitat

(The ﬁfth IPCC report). Here, two scenarios, RCP2.6 and RCP8.5, suitability curves of each variable were calculated, and the contri-

were selected. Of the four RCPs, RCP2.6 is the only scenario in butions of each variable to the habitat model of H. riparia Lour were

◦

which global warming in 2100 does not exceed 2 C compared to calculated using the software’s built-in jackknife test.

1850–1900. H. riparia Lour’s habitat suitability distributions in each There are four possible prediction results of the model: (1) the

of these two scenarios were modeled. The climate data are climate species exists where predicted to exist (true positive, TP); (2) the

projections for the years 2061 through 2080 from global climate species does not exist where predicted to exist (false positive, FP);

models (GCMs) for RCP2.6 and RCP8.5. These are available at http:// (3) the species exists where not predicted to exist (false negative,

www.worldclim.org. FN); (4) the species does not exist where not predicted to exist (true

negative, TN). Generally, both FN and FP errors always occur in the

3.2. Variables selection prediction results of SDMS. These two types of errors both relate

to the threshold that is used to determine presence or absence.

To select variables that can contribute more predictive power Indexes frequently used for the evaluation of SDM performance are

to the model, eliminate multiple linearity between variables, and calculated based on true positive, false positive, true negative and

␬

establish a model that has better performance with fewer variables, false negative rates, including Cohen’s (Cohen, 1960), TSS (true

cross-correlations (Pearson correlation coefﬁcient, r) and princi- skill statistic) (Allouche et al., 2006), AUC (area under ROC (receiver

pal component analysis (PCA) of 19 bioclimate variables from 51 operating characteristic curve)) (Hanley and McNeil, 1982) and oth-

species’ occurrence records were tested. These 19 bioclimate vari- ers.

ables were extracted from the corresponding layers using Arcgis A number of different thresholds are set to calculate a series

9.3. Only one variable from each set of highly cross-correlated vari- of sensitivities (positive rate in the positive results, TP+FP ) and

ables (r > 0.8) was kept for further analysis. The decision of which speciﬁcities (negative rate in the negative results, TN+FN ). Plotting

variable was to be kept was based on both the correlation analysis sensitivity on the ordinate against 1-speciﬁcity on the abscissa

and the PCA. For instance, the variables bio3 and bio2 were corre- gives the ROC. The larger the AUC is, the better the model per-

lated (r = 0.804), and so were bio3 and bio4 (r = −0.913); considering formance is. The AUC is not affected by choice of threshold, so

the PCA result, bio3 was dropped, and both bio2 and bio4 were it is an excellent index to evaluate model performance (Vanagas

reserved. According to the contributions of 19 bioclimatic variables 2004). The software package Maxent employs the AUC to evaluate

Y.-j. Yi et al. / Ecological Engineering 92 (2016) 260–269 265

Table 1

Environmental variables used for modeling the habitat suitability distribution of H. riparia Lour in this study.

Data source Category Variables Abbreviation Units

National Fundamental Geographic Information System Positional The distance to the nearest river Dist. m

Geospatial Data Cloud Topographic Altitude Alt. m

Aspect Asp. Degree

Slope Slop. Degree

WorldClim Bioclimatic Annual Mean Temperature Bio1 ◦C

◦

Mean Diurnal Range (Mean of monthly (max temp-min temp)) Bio2 C

◦

Temperature Seasonality (standard deviation *100) Bio4 C

◦

Temperature Annual Range Bio7 C

Annual Precipitation Bio12 mm

Precipitation of Driest Month Bio14 mm

Precipitation Seasonality (Coefﬁcient of Variation) Bio15 Dimensionless

Precipitation of Coldest Quarter Bio19 mm

Table 2

4.2. Variables’ response to suitability

Model performance categorization (Swets 1988).

AUC value Model performance Response curves show the quantitative relationship between

environmental variables and the logistic probability of presence

<0.5 Fails to describe reality

0.5 Pure chance (also known as habitat suitability), and they deepen the under-

0.5–0.6 Fail standing of the ecological niche of the species. The responses of

0.6–0.7 Poor

12 variables to H. riparia Lour’s suitability are illustrated in Fig. 4.

0.7–0.8 Fair

According to the response curves, the suitable elevation range is

0.8–0.9 Good

0 m–1200 m, which is consistent with the descriptions in Xishuang-

>0.9 Excellent

banna Dai Medicine Zhi (Xishuangbanna National Drug Research

Ofﬁce(XNDRO, 1980), which records that H. riparia Lour mainly

Table 3

grow at altitude between 50 and 1400 m on riversides with sand or

H. riparia Lour’s habitat condition and growth status before and after the construc-

gravel or scrub on a hillside. Altitude usually is a key eco-factor for

tion of Manwan Reservoir ().

local plants distribution (Adhikari et al., 2012). The slopes of all sam-

Habitat number 1 2 3 4 ◦

ple points were lower than 12 , and 92.2% of sample points were

◦

before after before after before after before after lower than 9 . Furthermore, the model predicts increasing suitabil-

ity with decreasing distance to the nearest river. This indicates that

Altitude (m) 922 920 930 985

◦

Aspect ( ) 0 SE40 NW81 NE15 H. riparia Lour normally lives on ﬂat ﬂoodplains (Xishuangbanna

◦

Slope ( )05 4 5 National Drug Research Ofﬁce(XNDRO, 1980), and agrees with pre-

Habitat area (m ) >1500 1200 >200 0 >200 0 >200 0

vious understanding of H. riparia Lour as an aquatic plant, and the

presence/absence + + + − + − + −

living environment consisting of river sands and streams matches

Species height(m) 1.6 1.0 1.7 0 1.2 0 2 0

the life habits of a rocky place (Tang et al., 1996).

Species coverage (%) 25 2 30 0 30 0 60 0

◦

species abundance >200 14 >50 0 >50 0 >100 0 The suitable annual mean temperature is higher than 20 C, indi-

a cating that H. riparia Lour prefers warm sites. Flora of China records

+, presence; −, absence.

that H. riparia Lour lives in the south of Yunnan, in the downstream

reaches of the Yalong river and Jinsha river in Sichuan province,

model performance. In general, AUC is between 0.5 and 1. AUC < 0.5

the Nanpan river region in Guizhou province, the south and west

describes models that perform worse than chance and occurs rarely

of Guangxi province, Hainan Island, and some other countries in

in reality. An AUC of 0.5 represents pure guessing. Model per-

southeast Asia (Tang et al., 1996). Climate is often thought to be the

formance is categorized as failing (0.5–0.6), poor (0.6–0.7), fair

predominant range-determining mechanism at large spatial scales

(0.7–0.8), good (0.8–0.9), or excellent (0.9–1) (Table 2) (Swets

(Blach-Overgaard et al., 2010; Cao and Tang 2014). In this study,

1988). The closer the AUC is to 1, the better the model performance

climatic variables were also found to contribute a lot when model-

is.

ing the distribution of H. riparia Lour. It is normally found in areas

◦ ◦

where Bio2 is higher than 6.2 C; Bio4 and Bio7 are lower than 4.3 C

◦

4. Results and discussion and 25 C, respectively; Bio12, Bio14, and Bio19 are higher than

1100 mm, 12.5 mm, and 50 mm, respectively; and Bio15 is lower

4.1. Model performance and variables’ contribution than 80. Lower variation of seasonal rainfall and higher precipita-

tion in coldest season are conducive to the presence of H. riparia

The calculated ROC showed that the AUC values of training data Lour.

set and test data sets were 0.899 and 0.840, respectively. Accord-

ing to Table 2, the model is classiﬁed as satisfactory with the given

set of training and test data (Fig. 2). The results of the jackknife

4.3. Model application

test of variables’ contribution are shown in Fig. 3. Bio1 and altitude

provided very high gains (>1.0) when used independently, indicat-

4.3.1. The H. riparia lour distribution

ing that Bio1 and altitude contained more useful information by

The distribution of H. riparia Lour in Yunnan Province is shown

themselves than the other variables did. Bio1 and altitude also are

in Fig. 5. The annual mean temperature is the most important vari-

the important facts inﬂuence other plants (Adhikari et al., 2012).

able structuring H. riparia Lour distribution. As the annual average

Bio15, Bio19, distance to nearest river, Bio4, Bio14 had moder-

temperature increases, the suitability of habitat increases. There-

ate gain when used independently. Other variables including Bio2,

fore, the suitability of the southern region is higher than that of the

Bio7, Bio12, aspect and slope, had low gains when used in isolation,

northern region, and 84.8% of habitat with suitability greater than

they did not contain much information by themselves.

0.6 is distributed in southern Yunnan province.

266 Y.-j. Yi et al. / Ecological Engineering 92 (2016) 260–269

The elevation and the distance to nearest river are the next riparia Lour in other areas is less than 0.3. This corresponds well

two important variables. The suitable habitats are distributed along with the record that H. riparia Lour prefers running water (Tang

the rivers, with suitability increasing as the distance to the river et al., 1996).

decreases. Areas with habitat suitability larger than 0.6 are along

rivers, such as the middle reaches of the Nujiang river, the upper

4.3.2. Habitat suitability simulation of three historical periods

and lower reaches of Lancang river, the middle and lower reaches

Habitat suitability simulation results for three historical peri-

of the Lixian and Yuan rivers, and some tributaries of the lower

ods (1950–1959, 1975–1985, 2000–2009) are illustrated in Fig. 6.

Lancang river. Areas with habitat suitability 0.3-0.6 are located at

From a spatial perspective, the suitability of southern Yunnan was

the middle reaches of Lancang river, the lower reaches of Nujiang

higher than northern Yunnan, and the suitability was higher when

river, and some other small tributaries. The habitat suitability of H.

the distance to the nearest river was shorter in all three histor-

Y.-j. Yi et al. / Ecological Engineering 92 (2016) 260–269 267

Fig. 7. (a) Daily fluctuation of the water level during the investigation period, approximately 6 m in front of dam; (b) Daily water level fluctuation at the confluence of Luodi

tributary. Authors’ investigation boat entered the Luodi tributary one day before, but a sand bar resurfaced and a boat run aground on the next day.

ical periods. Compared with 1950–1959, H. riparia Lour’s habitat 2014b). As a consequence, some land was submerged, including the

suitability in 2000–2009 increased gradually, with the area of previously existing H. Riparia Lour’s habitats. At the same time, the

high suitability (>0.6) gradually becoming larger. The total area water level in the reservoir ﬂuctuated greatly to meet the require-

with suitability larger than 0.6 were 3069 km in 1950–1959 and ments of power-generation. Huge daily water level ﬂuctuations in

3701 km in 1975–1985. Compared with 1950–1959, H. riparia the reservoir have occurred. The Manwan reservoir is a pondage

Lour’s habitat suitability in 1975–1985 improved, and the total station. Its normal pool level is 994 m, and the level of dead water

area with suitability greater than 0.6 increased by 632 km ; the is 988 m. Fig. 7 shows that daily water levels in the reservoir varied

area with suitability greater than 0.6 in the lower reaches of the by approximately 6 m during the ﬁeld survey period. This water

Lancang river and near some tributaries in those lower reaches regime would seriously inﬂuence the habitat of riverine plants. At

improved signiﬁcantly. This result agrees with historical investi- the same time, water was impounded in the reservoir, reducing the

gations, which documented that H. riparia Lour’s abundance in ﬂow downstream, narrowing the river, and changing the distance

the Lancang river watershed were greater in 1975–1985 than in of plants originally living on the river bank. H. riparia Lour’s liv-

1950–1959 (Wang et al., 2000). In 2000–2009, H. riparia Lour’s ing environments changed, and the original conditions suitable for

habitat suitability improved continually, the area with suitability its survival were not maintained. H. riparia Lour’s habitat area and

2 2

larger than 0.6 was 7020 km , increased by 3319 km compared distribution area thus decreased, and its abundance were reduced.

with 1975–1985 (a 90% increase), increased by 3951 km compared In conclusion, dam construction induced changes in surface and

with 1950–1959 (a 130% increase). The area with suitability larger ground water levels that resulted in the loss of H. riparia Lour’s

than 0.6 in 2000–2009 accounted for 1.80% of Yunnan Province’s original habitats.

total area. However, according to survey data, H. riparia Lour’s abun-

dance in the Lancang river watershed in 2000–2009 was lower than

that in 1975–1985, as a number of the previously existing habitats 4.3.3. Suitable habitat distributions under global warming

had been lost (Zhao et al., 2014; Wang et al., 2000). scenarios

Table 3 shows the comparison of investigation results of habitat The ﬁfth IPCC report described four future climate-warming sce-

condition and growth status of H. riparia Lour in Manwan Reser- nario the basis of total Radiative Forcing (RF) in 2100. RCP2.6 and

voir area in 1984 and 2010. Before the construction of Manwan RCP8.5 were two of these scenarios. The computed results of H.

Reservoir in 1984, there were at least four H. riparia Lour habitats riparia Lour’s habitat suitability in RCP2.6 and RCP8.5 showed that

covering the total area was larger than 2300 m , and its abundance H. riparia Lour’s habitat suitability increased with climate warming,

in Manwan Reservoir was larger than 400. However, only one habi- and the warmer the climate is, the higher the habitat suitability is.

tat with an area of 1200 m remained in 2010, and the other three In RCP8.5, the area suitable for H. riparia Lour’s survival and the

habitats already disappeared. Because of habitat degradation and overall suitability of the landscape were both larger than in RCP2.6

loss, H. riparia Lour’s abundance decreased by more than 96%, and (Fig. 8). In RCP2.6, the habitat suitability of the middle reaches

its average height had decreased by 0.6 m. H. riparia Lour now is at of the Nujiang river, the upper and lower reaches of the Lancang

high ecological risk (Zhao et al., 2014). river and some tributaries in the lower reaches of the Lancang,

In 1986, construction began on Manwan Reservoir at the middle Yuan river and Lixian river were greater than 0.6; the habitat suit-

reach of main stream of Lancang river. Subsequently, Dachaoshan ability of a few regions in the lower reaches of the Nujiang river,

hydroelectric station and Xiaowan hydroelectric station were built most regions in the middle reaches of the Lancang river and some

in succession along the main stream of Lancang river (construc- places far from tributaries in the lower reaches of the Lancang river

tion started in 1996 and 2002, respectively). The dams impounded was between 0.3 and 0.6; the habitat suitability of other places

water, and the water level upstream of the dams was raised (Yi et al., far from the river system was less than 0.3. In RCP2.6, approxi-

5 2

mately 3.4 × 10 km of area with habitat suitability less than 0.3

268 Y.-j. Yi et al. / Ecological Engineering 92 (2016) 260–269

less than 0.3 accounted for 79.66% of Yunnan Province’s total area,

5 2

and totaled approximately 3.1 × 10 km ; the area with habitat

suitability between 0.3 and 0.6 accounted for 14.26%, approxi-

4 2

mately 5.6 × 10 km ; the area with habitat suitability larger than

4 2

0.6 accounted for 6.08%, approximately 2.4 × 10 km . Compared

with the result in 2000–2009, in RCP8.5, the area with habitat suit-

ability larger than 0.6 approximately increases by 2.5 times, and

the area with habitat suitability between 0.3 and 0.6 approximately

increases by 50%. Compared with RCP2.6, in RCP8.5, the area with

habitat suitability larger than 0.6 nearly doubles, and the area with

habitat suitability between 0.3 and 0.6 approximately increases by

50%.

Numerous previous founding ﬁndings proved that the global

warming would have negative effect on ecosystem and species

(Monteith et al., 2015; Thomas et al., 2004). Contrary to these

founding that the simulation results of this study showed that cli-

mate warming will have positive effects on the habitat suitability

and suitable area of H. Riparia Lour’s. However, the natural dis-

tribution of H. riparia Lour is under threat, and its population has

decreased signiﬁcantly. This might be because of the disturbance

of riverine eco-system caused by construction and operation of

hydraulic projects.

5. Conclusion

A habitat suitability model based on maximum entropy theory

was developed to evaluate and predict the existence and potential

habitat quality of H. riparia Lour. The model is reasonable and cor-

rect according to the evaluation results of AUC index. Among the 12

variables selected for model construction, H. riparia Lour’s habitat

was mainly inﬂuenced by seven variables: annual mean tempera-

ture, altitude, precipitation seasonality, precipitation of the coldest

quarter, the distance to the nearest river, temperature seasonality,

and precipitation of the driest month. Regions with habitat suitabil-

ity greater than 0.6 were almost distributed along rivers. The closer

to the riverbank, the higher the suitability is. At the same time,

the suitability of southern Yunnan was higher than northern Yun-

nan, because H. riparia Lour prefers warm sites. From 1950–1959

to 2000–2009, habitat suitability increased and the area with high-

est suitability (>0.6) of H. riparia Lour became larger. The simulated

result of habitat development trend from 1950–1959 to 1975–1985

is in accordance with historical investigation (Wang et al., 2000);

however, the situation of 2000–2009 was contrasted with the ﬁeld

survey result. The discrepancy is due to the construction of hydro-

electric stations on Lancang river after 1985. The construction and

operation of water conservancy projects induced changes in sur-

face and ground water levels in the reservoir area and below the

dam. The environmental conditions of the original habitats of H.

riparia Lour were changed greatly.

The simulation results of H. riparia Lour’s habitat suitability in

RCP2.6 and RCP8.5 showed that H. riparia Lour’s habitat suitabil-

ity increased with climate warming, and the warmer the climate

Fig. 8. Suitable habitat distribution of H. riparia Lour at two globe warming situa-

tions. (a) RCP2.6; (b) RCP8.5. is, the higher the habitat suitability is. In RCP8.5, both the suitabil-

ity and the suitable area of H. riparia Lour were larger than those

in RCP2.6. As a result, only minimization of the disturbances from

accounted for 87.23% of Yunnan Province’s total area; the area with

human activities is needed to protect H. riparia Lour. If stable and

habitat suitability between 0.3 and 0.6 accounted for 9.49%, approx-

4 2 nature habitat are available, and with the climate becoming more

imately 3.7 × 10 km ; the area with habitat suitability larger than

4 2 and more suitable, the abundance and population of this species

0.6 accounted for 3.28%, approximately 1.3 × 10 km . Compared

will soon recover.

with the results in 2000–2009, in RCP2.6, the area with habitat

suitability larger than 0.6 will have nearly doubled. In RCP8.5,

Acknowledgements

the habitat suitability of places near river systems (including the

Nujiang river, Lancang river, Yuan river, and Lixian river) was

The study is supported by the National Natural Science Founda-

greater than 0.6, the habitat suitability of places far from river sys-

tion of China (No. 51439001 and No. 51279220), the 12th Five-Year

tem was between 0.3 and 0.6, and the habitat suitability of other

National Key Technology R&D Program (No. 2012BAB05B05).

places was less than 0.3. In RCP8.5, the area with habitat suitability

Y.-j. Yi et al. / Ecological Engineering 92 (2016) 260–269 269

References Lehmann, A., Overton, J.M., Leathwick, J.R., 2002. GRASP: generalized regression

analysis and spatial prediction. Ecol. Modell. 157, 189–207.

Monteith, K.L., Klaver, R.W., Hersey, K.R., Holland, A.A., Thomas, T.P., Kauffman,

Adhikari, D., Barik, S.K., Upadhaya, K., 2012. Habitat distribution modelling for

M.J., 2015. Effects of climate and plant phenology on recruitment of moose at

reintroduction of Ilex khasiana Purk., a critically endangered tree species of

the southern extent of their range. Oecologia 178, 1137–1148, http://dx.doi.

northeastern India. Ecol. Eng. 40, 37–43.

org/10.1007/s00442-015-3296-4.

Allouche, O., Tsoar, A., Kadmon, R., 2006. Assessing the accuracy of species

Peterson, A.T., 2003. Predicting the geography of species’ invasions via ecological

distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl.

niche modeling. Q. Rev. Biol. 78, 419–433.

Ecol. 43, 1223–1232.

Phillips, S.J., Dudík, M., 2008. Modeling of species distributions with Maxent: new

Anadón, J.D., Sala, O.E., Maestre, F.T., 2014. Climate change will increase savannas

extensions and a comprehensive evaluation. Ecography 31, 161–175.

at the expense of forests and treeless vegetation in tropical and subtropical

Phillips, S.J., Dudík, M., Schapire, R.E., 2004. A maximum entropy approach to

Americas. J. Ecol. 102, 1363–1373.

species distribution modeling. In: Brodley, C., Cavazos, J., Chan, P., Jacob, N.,

Beerling, D.J., Huntley, B., Bailey, J.P., 2009. Climate and the distribution of Fallopia

Kak, A., Lippman, R., Moss, E., Pavlopoulou, C., Precup, D., Scheeff, D.,

japonica: use of an introduced species to test the predictive capacity of

Stefanovic, D., Utgoff, P., Yurcik, B. (Eds.), Proceedings of 21st International

response surface. J. Veg. Sci. 6, 269–282.

Conference on Machine Learning. ACM Press, New York, pp. 655–662.

Blach-Overgaard, A., Svenning, J.C., Dransﬁeld, J., Greve, M., Balslev, H., 2010.

Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of

Determinants of palm species distributions across Africa: the relative roles of

species geographic distributions. Ecol. Modell. 190, 231–259.

climate, non-climatic environmental factors, and spatial constraints.

Purves, D.W., Dushoff, J., 2005. Directed seed dispersal and meta population

Ecography 33, 380–391.

response to habitat loss and disturbance: application to Eichhorniapaniculata.

Brummitt, N., Bachman, S., 2010. Plants Under Pressure-a Global Assessment. The

J. Ecol. 93, 658–669.

First Report of the IUCN Sampled Red List Index for Plants. Royal Botanic

Raxworthy, C.J., Enrique, M.M., Ned, H., Nussbaum, R.A., Schneider, G.E., 2003.

Gardens, Kew, UK.

Predicting distributions of known and unknown reptile species in Madagascar.

Busby, J.R., 1991. BIOCLIM-a bioclimate analysis and prediction system. In:

Nature 426, 837–841.

Margules, C.R., Austin, M.P. (Eds.), Nature Conservation: Cost Effective

Swets, J.A., 1988. Measuring the accuracy of diagnostic systems. Science 240,

Biological Surveys and Data Analysis. CSIRO, Melbourne, pp. 64–68.

1285–1293.

Cao, D.M., Tang, W.C., 2014. Satellite-derived vegetation indices contribute

Tang, C.L., Zhou, L.H., Wu, Z.Y., 1996. Flora of China. Science Press, Beijing (in

signiﬁcantly to the prediction of epiphyllous liverworts. Ecol. Indic. 38, 72–80.

Chinese).

Carpenter, G., Gillison, A.N., Winter, J., 1993. DOMAIN: a ﬂexible modelling

Thomas, C.D., Cameron, A., Green, R.E., Michel, B., Beaumont, L.J., 2004. Extinction

procedure for mapping potential distributions of plants and animals. Biodivers.

risk from climate change. Nature 427, 145–147.

Conserv. 2, 667–680.

Tilman, D., Fargione, J., Wolff, B., D’Antonio, C., Dobson, A., Howarth, R., 2001.

Che, R.Y., Zhao, J., Wang, S.Y., 2009. Determination of content of riparsapon in H.

Forecasting agriculturally driven global environmental change. Science 292,

riparia Lour extract by high performance liquid chromatography. J. Third Mil.

281–284.

Med. Univ. 31, 2275–2277 (in Chinese with English abstract).

Vanagas, G., 2004. Receiver operating characteristic curves and comparison of

Cohen, J., 1960. A coefﬁcient of agreement for nominal scales. Educ. Psychol. Meas.

cardiac surgery risk stratiﬁcation systems. Interact. Cardiovasc. Thorac. Surg. 3, 20, 37–46.

319–322.

Elith, J., Leathwick, J.R., 2009. Species distribution models: ecological explanation

Wang, Z.Z., Zhang, X.M., Jin, Z.Z., 2000. The Ecological Environment and Biological

and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 667–697.

Resources of Manwang Hydropower Station Reservoir Along Lancang River in

Elith, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R.J.,

Yunnan, China. Yunnan Science and Technology Press, China (in Chinese).

2006. Novel methods improve prediction of species’ distributions from

Wei, F.N., 1992. Guangxi some genus of the Euphorbiaceae of brief records on

occurrence data. Ecography 29, 129–151.

classiﬁcation. Guangxi Plant 12, 193–208 (in Chinese).

Ferrier, S., 2002. Mapping spatial pattern in biodiversity for regional conservation

XishuangbannaNational Drug Research Ofﬁce(XNDRO), 1980. Xishuangbanna Dai

planning: where to from here? Syst. Biol. 51, 331–363.

Medicine Zhi. State Health Bereau Press, China (in Chinese).

Fischer, J., Lindenmayer, D.B., 2007. Landscape modiﬁcation and habitat

Yang, X.Q., Kushwaha, S.P.S., Saran, S., Xu, J., Roy, P.S., 2013. Maxent modeling for

fragmentation: a synthesis. Global Ecol. Biogeogr. 16, 265–280.

predicting the potential distribution of medicinal plant, Justiciaadhatoda L: in

Grimm, N.B., Faeth, S.H., Golubiewski, N.E., Redman, C.L., Wu, J.G., Bai, X.M., Briggs,

Lesser Himalayan foothills. Ecol. Eng. 51, 83–87.

J.M., 2008. Global change and the ecology of cities. Science 319, 756–760.

Yang, Z.F., Cai, Y.P., Mitsch, W.J., 2015. Ecological and hydrological responses to

Guisan, A., Thuiller, W., 2005. Predicting species distribution: offering more than

changing environmental conditions in China’s river basins. Ecol. Eng. 76, 1–6.

simple habitat models. Ecol. Lett. 8, 993–1009.

Yee, T.W., Mitchell, N.D., 1991. Generalized additive models in plant ecology. J.

Hanley, J.A., McNeil, B.J., 1982. The meaning and use of the area under a receiver

Veg. Sci. 2, 587–602.

operating characteristic (ROC) curve. Radiology 143, 29–36.

Yi, Y.J., Cheng, X., Wieprecht, S., Tang, C., 2014a. Comparison of habitat suitability

Hirzel, A., Guisan, A., 2002. Which is the optimal sampling strategy for habitat

models using different habitat suitability evaluation methods. Ecol. Eng. 71,

suitability Modeling. Ecol. Modell. 157, 331–341.

335–345.

Institute of Botany the Chinese Academy of Sciences (IBCAS), 1972. Pictorial

Yi, Y.J., Tang, C.H., Yang, Z.F., Cheng, X., 2014b. Inﬂuence of Manwan Reservoir on

Handbook of Chinese Advanced Botany. Science Press, Beijing (in Chinese).

ﬁsh habitat in the middle reach of the Lancang River Original Research Article.

Jaynes, E.T., 1957. Information theory and statistical mechanics. Phys. Rev. 106,

620. Ecol. Eng. 69, 106–117.

Yunnan Environmental Protection Department (YEPD), (2013). Joint meeting of

Jia, J., Zhang, S.X., 2006. The characteristics of the current situation and protection

biodiversity conservation in Yunnan province, Yunnan biodiversity

of biodiversity in Yunnan. Green China 7, 50–54 (in Chinese with English

abstract). conservation strategy and action plan. (in Chinese).

Zhao, Q.H., Liu, S.L., Deng, L., Dong, S., Wang, C., 2014. Soil degradation associated

Kruess, A., Tscharntke, T., 1994. Habitat fragmentation, species loss, and biological

with water-level ﬂuctuations in the Manwan Reservoir, Lancang River Basin.

control. Science 264, 1581–1584.

Catena 113, 226–235.

Kumar, B., (2013) Homonoiariparia. The IUCN Red List of Threatened Species.

Version 2014.3. www.iucnredlist.org. Downloaded on 29 November 2014.