Journal of Threatened
Taxa | www.threatenedtaxa.org | 26 December 2022 | 14(12): 22221–22231
ISSN 0974-7907 (Online) | ISSN 0974-7893
(Print)
https://doi.org/10.11609/jott.7953.14.12.22221-22231
#7953 | Received 05 April 2022 | Final
received 14 November 2022 | Finally accepted 06 December 2022
Species distribution modeling of
a cucurbit Herpetospermum darjeelingense in Darjeeling Himalaya, India
Debasruti Boral 1 & Saurav Moktan
2
1,2 Department of Botany, University
of Calcutta, 35, B.C. Road, Kolkata, West Bengal 700019, India.
1 debasruti.boral@gmail.com, 2
smbot@caluniv.ac.in (corresponding author)
Editor: Afroz Alam, Banasthali
Vidyapith, Rajasthan, India. Date
of publication: 26 December 2022 (online & print)
Citation: Boral, D. & S. Moktan (2022). Species
distribution modeling of a cucurbit Herpetospermum darjeelingense
in Darjeeling Himalaya, India. Journal of Threatened Taxa 14(12): 22221–22231. https://doi.org/10.11609/jott.7953.14.12.22221-22231
Copyright: © Boral & Moktan 2022. Creative
Commons Attribution 4.0 International License.
JoTT allows unrestricted use, reproduction,
and distribution of this article in any medium by providing adequate credit to
the author(s) and the source of publication.
Funding: DB is thankful
to University of Calcutta for
the University Research Fellowship.
Competing interests: The authors
declare no competing interests.
Author details: D. Boral is a research scholar under the
guidance of SM. Her research work focuses on the taxonomy and distribution modeling of rare and threatened plants from Darjeeling
Himalaya. Dr. S. Moktan is
a faculty in the Centre of Advanced Study, Department of Botany, University of
Calcutta. Areas of interest in research include taxonomy, ecology, biodiversity
and conservation.
Author contributions: DB—field data collection, data
analysis, writing original draft; SM—conceptualization, reviewing, editing and
supervision.
Acknowledgements: The first author is thankful to
University of Calcutta for financial assistance. The authors are also thankful
to the Department of Forests, Government of West Bengal, India for all the
necessary permissions.
Abstract: Herpetospermum darjeelingense
(C.B.Clarke) H. Schaef. & S.S.
Renner is a rare cucurbit found in Darjeeling, Himalaya. It is
known for its use as food and medicine with possible pharmaceutical
applications. Here we assess the current and future habitat suitability
of H. darjeelingense in the study
area using MaxEnt modeling. In order to obtain
accurate results for future models, the ensemble method was used. The current
suitable habitat covers only 13% of the study area, while the future models for
2050 and 2070 show zero habitat suitability for the species. This strongly
indicates a possible local extinction of the species indicating a need for
rapid and decisive conservation efforts.
Keywords: BioClim, climate change, ecology,
elevation, ensemble, habitat suitability, MaxEnt,
population, taxonomy, vulnerable.
Abbreviations: AUC—Area Under the Curve |
CCSM4—Community Climate System Model 4 | CMIP5—Coupled Model Intercomparison Project 5 | GCM—General Circulation Model |
GFDL-CM3—Geophysical Fluid Dynamics Laboratory- Climate Model 3 |
IPCC—International Panel on Climate Change | LPT—Least Presence Threshold |
MIROC5—Model for Interdisciplinary Research on Climate 5 | RCP—Representative
Concentration Pathways | ROC—Receiver Operating Curve | SDM—Species
Distribution Modeling | SEM—Scanning Electron Microscopy | TSS—True Skill
Statistic.
INTRODUCTION
The Himalaya biodiversity hotspot
is one of the 36 currently recognised by CEPF (2021).
The eastern region of the hotspot stands out in its global significance as it
contains several centres of plant diversity (CEPF
2005). The complex landscape of the region has contributed to its floristic
diversity, which includes several threatened plants (Kandel et al. 2019). In
particular, the political boundary of India harbours
an estimated 5,800 species of plants from the eastern Himalaya (Pande & Arora 2014).
The Darjeeling Himalaya is a part
of the extension with its characteristic vegetation & landscape (CEPF
2005). Some of the major threats to this region include rapid urbanisation and climate change (Pandit et al. 2014). The
impact of climate change on plants results in changes in phenology (Hart et al.
2014) and geographic ranges (Gómez-Ruiz & Lacher
Jr. 2019). A distinctive pattern of upward altitudinal shift is also observed
in mountainous regions (Dullinger et al. 2012).
Another impact of climate change includes invasion by alien species which are
hardier and more competitive (Pandit et al. 2014).
As the effects of climate change
become more drastic, there is an urgent need to study consequences for
significant species such as H. darjeelingense,
which have vulnerable status. SDM functions on the principle of comparing the
environmental conditions of the known location of the species to novel climatic
conditions (Pearson 2007). Several different algorithms have been developed to
model species distribution, such as MaxEnt (Elith et al. 2011), BIOCLIM (Beaumont et al. 2005), and
GARP (Peterson et al. 2007). The accuracy of each modeling system is dependent
on the sampling size and ecology of the species. Ultimately, species
distribution models are an effective tool that can provide focus to possible
practical applications (Hernandez et al. 2006). Among these tools, MaxEnt has been used widely for many different species such
as Picrorhiza kurroa
Royle ex Benth. (Chandra et
al. 2021), Podophyllum hexandrum (Royle) T.S. Ying (Banerjee et al. 2017), Rhododendron
niveum Hook.f. (Chhetri
& Badola 2017) and including vulnerable species
such as Ornduffia calthifolia
(F.Muell.) Tippery
& Les, O. marchantii (Ornduff)
Tippery & Les (Ball et al. 2020), and Lavatera
acerifolia Cav. (Villa-Machío
et al. 2020). MaxEnt uses presence-only data to
create a probability map predicting the distribution of a species across a
spatial dimension (Elith et al. 2011). Thus, the
objectives of the present study were to: i.
characterize the taxonomy and habitat ecology of the taxa in Darjeeling
Himalaya and, ii. identify current and future potential habitat and
environmental variables determining distribution.
MATERIALS AND METHODS
Study Area
The study encompassed the
Darjeeling Himalayan region that extends between 27°13’10”–26°27’05’’ N &
88°53’–87° 30’’ E covering an altitudinal range between 130–3,636 m in the lap
of the eastern Himalaya hotspot. The region is bordered by Bangladesh to the south-east,
Nepal to the west, and Bhutan to the east. The region is also flanked by the
state of Sikkim (Figure 1).
As an extended part of the
Himalayan hotspot, the region boasts several types of vegetation ranging from
tropical to sub-alpine (Das 1995). A combination of topography & climate
along with its location makes the region floristically diverse. The region harbours vegetation of Indo-Chinese, Indo-Malaysian, and
western Himalayan origin including rare species such as Gastrochilus
corymbosus A.P. Das & Chanda, Liparis tigerhillensis A.P.
Das & Chanda, Globba teesta S. Nirola & A.P.
Das to mention a few (Nirola & Das 2017).
The Species
The present study uses MaxEnt to explore the distribution of Herpetospermum
darjeelingense (C.B.Clarke)
H. Schaef. & S.S. Renner, a member of the family Cucurbitaceae in Darjeeling Himalaya (Image 1). The
genus Herpetospermum comprises of four known
species found restricted in the Himalaya and southeastern Asia (POWO 2021), of
which three are found in the Darjeeling Himalayan region (Renner & Pandey
2013). H. darjeelingense (syn. Edgaria darjeelingensis C.B.
Clarke) is one of the species found in the eastern Himalaya (Renner &
Pandey 2013). The presence of this species has been recorded in Bhutan
(Grierson & Long 1991), southern China, and Nepal (Renner & Pandey
2013). In India, the species is distributed sparsely in the states of Sikkim
and Arunachal Pradesh. Threat search classified the species as Vulnerable in
2017 (BGCI 2021).
Species Occurrence Data
The occurrence points were
gathered through a field study conducted during 2019–2020 within the Darjeeling
Himalaya. The coordinate points in the locations were recorded using Garmin eTrex H. The collected coordinates were first converted to
decimal degrees and then thinned using spThin package
in R in order to remove duplicates and to remove any coordinates with a
distance of less than 1 km between them. The resulting 21 coordinates were used
for modeling suitable habitat. The taxonomy of the species was studied through
the collection of voucher specimens. Pollen grains were collected from the
partially opened bud, and the process of acetolysis
was followed (Erdtman 1960) and thereby, SEM
observations were made. The population of the species was assessed along with
its habitat ecology and the associated species.
Environmental variables
Elevation data were sourced at
30-arc second (~1 km2) resolution from WorldClim
2.1 (Fick & Hijmans 2017). From this, slope and
aspect data were generated using QGIS 3.4 Madeira software in ASCII format. The
elevation, slope, and aspect constituted the three topographic predictors used
in this paper. The current bioclimatic variables were obtained from WorldClim 2.1 at 30-arc second (~1 km2)
resolution (Fick & Hijmans 2017). The future
bioclimatic variables were based on CMIP5, obtained from WorldClim
1.4 (Hijmans et al. 2005). The selected dataset were
the GCMs (General Circulation Models) GFDL-CM3 (Griffies
et al. 2011; Chaturvedi et al. 2012), CCSM4 (Meehl et
al. 2012; Purohit & Rawat 2021) and MIROC5 (Watanabe et al. 2010) for years
2050 & 2070 for three different Representative Concentration Pathways
(RCPs), RCP 2.6, RCP 4.5, and RCP 8.5. The RCP 2.6, RCP 4.5, and RCP 8.5
represent three different carbon emission levels (IPCC, 2014). All data were
trimmed to the appropriate size and converted to ASCII format using QGIS 3.4
Madeira.
Modeling Procedure
First, highly correlated
variables (variables with Pearson’s coefficient r value > 0.9) were
identified and removed using ENM Tools 1.3 (Warren et al. 2010) (Figure 2). The
remaining list of environmental variables is given in Table 1. Overall, seven
bioclimatic variables and three topographic variables, i.e., elevation, slope,
and aspect, were used for modeling. Models were run on MaxEnt
ver.3.4.1 (Phillips et al. 2006). As there were merely 21 occurrence points,
only linear and quadratic features were applied. Five replicated models were
run using the random test percentage of 25% (Srivastava et al. 2018; Qin et al.
2020). For predictions based on future climate, current occurrence data was
projected onto future climactic variables. These were from the datasets
GFDL-CM3, CCSM4, and MIROC5 for years 2050 & 2070; for RCP 8.5, 4.5 &
2.6. This resulted in 18 different future models to consider. An ensemble
approach was applied wherein; the three different models from each GCM for each
RCP of a particular year were combined (Araújo & New 2007; Khanum et al.
2013).
Model Validation
The area under the curve (AUC)
values were used to assess individual models. Along with AUC, models were also
appraised by true skill statistic (TSS) values (Allouche
et al. 2006). TSS values were calculated for each model iteration with the
lowest presence threshold (LPT). The value of LPT is equal to the lowest
probability at a species occurrence point. LPT thus excluedes
all areas that are at least not as suitable as locations where the species
occurred (Pearson et al. 2007).
RESULTS
Taxonomy and Ecology of H. darjeelingense
H. darjeelingense
is described
as being an annual with a climbing habit, bifid tendrils, deeply cordate-ovate,
and unlobed leaves. The leaves were pubescent with
undulate and denticulate margin. The plant is dioecious with male flowers being
paired. Bracts are absent or inconspicuous. Both male and female flowers have
elongated calyx tube, teeth subulate; corolla is rotate, bright yellow, with
deep lobes. Male flowers carry three stamens, anthers connate, single-celled.
Female flowers are solitary, with ellipsoid ovary, three stigmas. Fruits are
broadly fusiform, carrying about three-six seeds. SEM analysis of the pollen
grains revealed that they are spherical, triporate,
with distinctly spinous exine (Image 1).
Ecologically, the species is
found to grow on roadsides, hilly slopes, stream banks, jhoras,
and scrubs within an elevation range of around 1,400–2,600 m. The associated
species in the niche includes major trees like Magnolia cathcartii
(Hook.f. & Thom.) Noot.,
Symplocos glomerata
King ex Clarke, Alnus nepalensis
D. Don, and Cryptomeria japonica (Thunb.
ex L.f.) D. Don. The associated undershrubs
are Tetrastigma serrulatum
(Roxb.) Planch., Aconogonon
molle (D. Don) Hara, Boehmeria
macrophylla Hornem., Yushania
maling (Gamble) Majumdar & Karth., Ageratina adenophora (Spreng.) King
& Rob, Girardinia diversifolia
(Link) Friis, while the ground covers include Galium elegans
Wall. ex Roxb., Strobilanthes
divaricata (Nees) T.
Anders., Persicaria chinensis
(L.) H. Gross, Drymaria cordata
(L.) Willd. ex Schult.,
Pouzolzia hirta
Blume ex Hassk., Lecanthus
peduncularis (Wall. ex Royle)
Wedd., and species of Pilea. It is difficult
to tally number of individuals of H. darjeelingense
as it has climbing/creeping habit and thus in some cases forms dense sprawling
clumps. The site characteristics revealed 48% of the population was distributed
towards south-east, followed by south-west with 28% and north-east with 24%
aspect location. Majority of the populations was distributed on the hilly slope
with around 15°─30° inclination followed by roadside while only few populations
were distributed at steep habitat.
Reportedly, H. darjeelingense is used both as food (Mueller-Boeker 1993) and as medicine to treat cattle (Shrestha
& Khadgi 2019), traditionally among different
communities from the Himalayan belt. A recent study also reports the presence
of 13 antioxidants from leaf material, indicating the pharmaceutical potential
of the species (Chakraborty et al. 2021). The species is classified as
Vulnerable (BGCI 2021) regionally in China. However, information regarding its
current status in the study area is scant.
Habitat Suitability for Present
Day
The different variables used for
predicting suitable habitat for H. darjeelingense
included temperature,precipitation data, altitude,
slope, and aspect. The present-day model with the predicted suitable habitat is
shown in Figure 3 along with the ROC curve and the jackknife in Figure 4. The
current model performed very robustly with the AUC value at 0.986 and the TSS
value 0.948. The potential distribution of H. darjeelingense
was stretched over an area of 416.25 km2 (13.21%) after
application of LPT. The percentage of contribution is highest for the
bioclimatic variable mean temperature in the coldest quarter (BIO11) at 61.2 %,
followed by precipitation of seasonality (BIO15) at 24.5%, mean diurnal range
(BIO02) at 4.4% and precipitation of warmest quarter (BIO18) at 4.4%. The
jackknife also reveals that BIO11 is the most important environmental variable
while the other influential variable according to the jackkife
is precipitation of seasonality (BIO15) (Figure 4b).
Response to Variables
The species response curve of H.
darjeelingense to each variable is depicted in
Figure 5. The probability of the
presence of the species increases with ALT sharply peaking at 2,000 m (Figure
5a) with the range 1,500─3,000 m. The altitude of almost all sample points fell
within this range. For aspect, the response increases with an increase in
degree (Figure 5b). For BIO02, BIO03, BIO18, response decreases with increase
in variable while, the response increases as BIO15 increases. For BIO11,
suitable habitat requires a mean temperature ranging from 5°C─12°C in the
coldest quarter. For BIO19, suitable habitat required mean precipitation
between 40─90 mm for the coldest quarter.
Habitat Suitability for Future
Models
The six future ensemble models
have an AUC value ranging from 0.99─0.985. The TSS value ranges from
0.903─0.944. The highest percentage of contribution is mean temperature in the
coldest quarter (BIO11) for all six ensemble models. Similarly the altitude
(ALT) has the highest permutation of importance for both the current and future
models. The jackknife shows some difference in the results for the future
models where ALT has the highest training gain when used in isolation in some
models while mean temperature in the coldest quarter (BIO11) has the highest
training gain when used in isolation in other models. The prediction accuracy
details of the individual models, along with the ensemble models, are given in
Table 3. After the LPT value (0.49) was applied for all future models, probable
spatial distribution was 0 km2 for all.
DISCUSSION
The present study explores the
ecological status and assesses the habitat distribution of H. darjeelingense in current and future climate
scenarios. Previous studies on other species have been conducted using MaxEnt, such as Angelica glauca Kitam. (Singh et al. 2020), Rosa arabica (Crép. ex Boiss.) Déségl. (Abdelaal et al.
2019), Ixora sp. (Banag et al.
2015), Berkheya cuneata (Thunb.) Willd. (Potts et al.
2013), Acer cappadocicum subsp.
lobelia (Ten.) A.E. Murray (Sumarga
2011), Pterocarpus santalinus L.f. (Babar et al. 2012), Aglaia bourdillonii Gamble (Irfan-Ullah et al. 2006). MaxEnt has also been used to explore the distribution of
endangered species such as Dioscorea sp.
(Hills et al. 2019). MaxEnt is one of several
modeling algorithms available for species distribution modeling. MaxEnt predicts probable distribution using presence-only
data and a set of climatic grids generating output where each grid cell has a
value ranging from 0 (least suitable) to 1 (most suitable) (Phillips et al.
2017). MaxEnt is also effective even with small
sample sizes making it suitable for studying endangered species (Pearson et al.
2007). Concerning the performance of MaxEnt models,
both AUC and TSS values were used. Swets (1988)
classified model performance into failing (0.5–0.6), bad (0.6–0.7), reasonable
(0.7–0.8), good (0.8–0.9), or great (0.9–1) based on AUC value. Like AUC, TSS
also ranges from 0─1, with a higher value indicating a better-performing model
(Allouche et al. 2006). The LPT was also used to
prevent an over-fitted model. In the current study, only about 13.21% of the
total study area was determined to be suitable habitat for H. darjeelingense. The current model was well-performing,
with high AUC (0.986) & TSS (0.948) values.
The IPCC 5th
assessment report (IPCC 2014) presents the projected climate in the future
driven by anthropogenic carbon emissions. The report highlights the projected
scenarios based on the mitigation strategy applied. The RCPs 2.6, 4.5, and 8.5
represents scenarios where either stringent, intermediate or poor
implementation of climate strategy occurred. As each GCM is published by
separate research groups, it can make modeling future climate change tricky.
Hence, the ensemble method as per Khanum et al. (2013) was applied which
reduces the ambiguity of using a single GCM. Overall, all future models created
using the ensemble method, which combines three different GCMs, show the
probable complete disappearance of H. darjeelingense. Hence,
no matter the climate change mitigation strategy, it is quite possible that the
species under study might disappear from the study area by 2050. In the case of
endangered species, a complete disappearance from the local environment can
indicate further downstream effects on other plants. It should be noted that
the results species distribution models, are based on extrapolation from
available data and methods (Elith & Leathwick 2009). However, these models can provide valuable
awareness of urgent future steps to be taken for the preservation of the
species under study.
CONCLUSION
The present study highlights the
probable suitable habitat of the cucurbit Herpetospermum
darjeelingense in the future as well as the
present day. The taxon that is often found along roadsides and hilly slopes
make its current population vulnerable to habitat destruction due to
anthropogenic pressure as well as natural catastrophes. This along with climate
change can result in the complete disappearance of the species. MaxEnt modeling of the present-day scenario exhibits a
narrow habitat range. Furthermore, future models show that regardless of the
climate mitigation strategy, the species faces local extinction. Keeping in
mind the availability of limited data on distribution coordinates and
population status of the taxa including the rarity of the species in the
present study, the taxa should be immediately assigned to Endangered in the
IUCN Red List. Furthermore, an urgent requirement to investigate active in situ
and ex situ conservation strategies through botanical gardens and local
nurseries is of the utmost priority at this juncture because the taxon has both
traditional and pharmaceutical potential. One possible method can include the
collection of seeds for storage and germination.
Table 1. Site characteristics of Herpetospermum darjeelingense
in different habitats.
|
Latitude (N) |
Longitude (E) |
Altitude (m) |
Aspect |
Slope (◦) |
Habitat |
Population |
|
26.99395 |
88.28557 |
2449 |
SW |
0–15 |
Hilly slope |
5 |
|
27.00408 |
88.22867 |
2176 |
SE |
15–30 |
Roadside |
1 |
|
27.05187 |
88.27033 |
1830 |
NE |
15–30 |
Roadside |
5 |
|
26.98553 |
88.1428 |
2246 |
SE |
15–30 |
Roadside |
1 |
|
26.99013 |
88.1141 |
2170 |
SW |
15–30 |
Hilly slope |
3 |
|
26.9908 |
88.15693 |
2197 |
SE |
0–15 |
Hilly slope |
3 |
|
27.00958 |
88.17978 |
2151 |
SE |
15–30 |
Hilly slope |
8 |
|
27.01318 |
88.19185 |
2188 |
SE |
15–30 |
Hilly Sslope |
23 |
|
26.87283 |
88.28515 |
1457 |
SW |
15–30 |
Jhora |
1 |
|
27.01334 |
88.29806 |
2134 |
SE |
15–30 |
Roadside |
2 |
|
27.02173 |
88.31473 |
1951 |
SE |
15–30 |
Roadside |
4 |
|
27.03048 |
88.3302 |
1799 |
SE |
30–45 |
Stream bank, Hilly slope |
8 |
|
27.0599 |
88.3569 |
1628 |
NE |
15–30 |
Hilly slope |
7 |
|
27.0764 |
88.62195 |
1847 |
NE |
30–45 |
Hilly slope |
2 |
|
27.08777 |
88.64798 |
2079 |
SE |
15–30 |
Hilly slope |
3 |
|
27.08153 |
88.67965 |
2219 |
SE |
0–15 |
Hilly slope |
5 |
|
27.09168 |
88.69067 |
1910 |
SW |
15–30 |
Hilly slope |
1 |
|
27.09618 |
88.6527 |
2150 |
NE |
15–30 |
Hilly slope |
4 |
|
27.10963 |
88.65315 |
1938 |
NE |
15–30 |
Scrub |
1 |
|
27.07688 |
88.66888 |
2090 |
SW |
15–30 |
Scrub |
1 |
|
27.06005 |
88.67223 |
1772 |
SW |
15–30 |
Hilly slope |
4 |
Table 2. Variables used for
species distribution modeling in MaxEnt.
|
Variable
abbreviation |
Variable
Name |
Units |
|
BIO02 |
Mean
diurnal range |
°C |
|
BIO03 |
Isothermality |
% |
|
BIO07 |
Temperature
annual range |
°C |
|
BIO11 |
Mean
temperature of coldest quarter |
°C |
|
BIO15 |
Precipitation
seasonality |
mm |
|
BIO18 |
Precipitation
of warmest quarter |
mm |
|
BIO19 |
Precipitation
of coldest quarter |
mm |
|
ALT |
Altitude |
m |
|
ASPECT |
Aspect |
NA |
|
SLOPE |
Slope |
(°) |
Table 3. Prediction accuracy with
important variables of Herpetospermum darjeelingense models.
|
|
AUC |
TSS |
Percentage contribution |
Permutation importance |
Jackknife training gain |
|||||
|
Variable |
Value |
Variable |
Value |
In isolation |
In absence |
|||||
|
Current |
0.986 |
0.949 |
BIO11 |
61.2 |
ALT |
59.5 |
BIO11 |
BIO15 |
||
|
2050 |
RCP 2.6 |
CCSM4 |
0.988 |
0.946 |
BIO11 |
59.9 |
ALT |
66.2 |
ALT |
BIO15 |
|
GFDL-CM3 |
0.987 |
0.942 |
BIO11 |
63 |
ALT |
46.2 |
BIO11 |
BIO15 |
||
|
MIROC5 |
0.987 |
0.933 |
BIO11 |
60.3 |
ALT |
61.2 |
BIO11 |
BIO15 |
||
|
Ensemble |
0.987 |
0.94 |
BIO11 |
61.1 |
ALT |
57.9 |
- |
- |
||
|
RCP 4.5 |
CCSM4 |
0.985 |
0.957 |
BIO11 |
63.6 |
ALT |
46.7 |
BIO11 |
BIO15 |
|
|
GFDL-CM3 |
0.986 |
0.93 |
BIO11 |
63.6 |
ALT |
56.8 |
BIO11 |
BIO15 |
||
|
MIROC5 |
0.99 |
0.93 |
BIO11 |
57.9 |
ALT |
43.7 |
BIO11 |
BIO15 |
||
|
Ensemble |
0.987 |
0.939 |
BIO11 |
61.7 |
ALT |
49.1 |
- |
- |
||
|
RCP 8.5 |
CCSM4 |
0.989 |
0.966 |
BIO11 |
60.3 |
ALT |
57.8 |
BIO11 |
BIO15 |
|
|
GFDL-CM3 |
0.989 |
0.946 |
BIO11 |
58.7 |
ALT |
63.1 |
BIO11 |
BIO15 |
||
|
MIROC5 |
0.985 |
0.921 |
BIO11 |
60.5 |
ALT |
45.5 |
BIO11 |
BIO15 |
||
|
Ensemble |
0.987 |
0.944 |
BIO11 |
59.8 |
ALT |
55.5 |
- |
- |
||
|
2070 |
RCP 2.6 |
CCSM4 |
0.988 |
0.954 |
BIO11 |
61.7 |
ALT |
48.4 |
BIO11 |
BIO15 |
|
GFDL-CM3 |
0.989 |
0.94 |
BIO11 |
56.3 |
ALT |
60.7 |
BIO11 |
BIO15 |
||
|
MIROC5 |
0.988 |
0.906 |
BIO11 |
61.5 |
ALT |
52.7 |
ALT |
BIO15 |
||
|
Ensemble |
0.988 |
0.933 |
BIO11 |
59.8 |
ALT |
53.9 |
- |
- |
||
|
RCP 4.5 |
CCSM4 |
0.988 |
0.926 |
BIO11 |
60.9 |
ALT |
32 |
BIO11 |
BIO15 |
|
|
GFDL-CM3 |
0.989 |
0.891 |
BIO11 |
62.5 |
ALT |
48.9 |
BIO11 |
BIO15 |
||
|
MIROC5 |
0.986 |
0.892 |
BIO11 |
61 |
ALT |
54.8 |
BIO11 |
BIO15 |
||
|
Ensemble |
0.987 |
0.903 |
BIO11 |
61.4 |
ALT |
45.2 |
- |
- |
||
|
RCP 8.5 |
CCSM4 |
0.985 |
0.949 |
BIO11 |
59.4 |
ALT |
61.6 |
BIO11 |
BIO15 |
|
|
GFDL-CM3 |
0.988 |
0.955 |
BIO11 |
60.9 |
ALT |
47.4 |
BIO11 |
BIO15 |
||
|
MIROC5 |
0.988 |
0.918 |
BIO11 |
60.4 |
ALT |
58.6 |
BIO11 |
BIO15 |
||
|
Ensemble |
0.987 |
0.941 |
BIO11 |
60.2 |
ALT |
55.9 |
- |
- |
||
For figures &
images - - click here for full PDF
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