Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 October 2020 | 12(14): 16962–16970
ISSN 0974-7907 (Online) | ISSN 0974-7893
(Print)
doi: https://doi.org/10.11609/jott.6106.12.14.16962-16970
#6106 | Received 06 May 2020 | Final received
19 August 2020 | Finally accepted 10 October 2020
Evaluating performance of four
species distribution models using Blue-tailed Green Darner Anax
guttatus (Insecta:
Odonata) as model organism from the Gangetic riparian zone
Kritish De 1, S. Zeeshan Ali
2, Niladri Dasgupta 3, Virendra
Prasad Uniyal 4, Jeyaraj
Antony Johnson 5 & Syed Ainul Hussain 6
1–6 Wildlife
Institute of India, Chandrabani, Dehradun,
Uttarakhand 248001, India.
1 kritish.de@gmail.com
(corresponding author), 2 zeeshanearth@gmail.com, 3 niladri4all@gmail.com,
4 uniyalvp@wii.gov.in, 5 jaj@wii.gov.in, 6 hussain@wii.gov.in
Abstract: In this paper we evaluated the
performance of four species distribution models: generalized linear (GLM),
maximum entropy (MAXENT), random forest (RF) and support vector machines (SVM) model,
using the distribution of the dragonfly Blue-tailed Green Darner Anax guttatus in
the Gangetic riparian zone between Bijnor and Kanpur
barrage, Uttar Pradesh, India. We used
forest cover type, land use, land cover and five bioclimatic variable layers:
annual mean temperature, isothermality, temperature
seasonality, mean temperature of driest quarter, and precipitation seasonality
to build the models. We found that the
GLM generated the highest values for AUC, Kappa statistic, TSS, specificity and
sensitivity, and the lowest values for omission error and commission
error, while the MAXENT model
generated the lowest variance in variable importance. We suggest that
researchers should not rely on any single algorithm, instead, they should test
performance of all available models for their species and area of interest, and
choose the best one to build a species distribution model.
Keywords: Generalized linear model, Kappa
statistic, maximum entropy model, omission and commission error, random forest
model, receiver operating characteristic curve, sensitivity, specificity,
support vector machines model, true skill statistic
Editor: Neelesh Dahanukar, Indian Institute
of Science Education and Research, Pune, India. Date of
publication: 26 October 2020 (online & print)
Citation: De,
K., S.Z. Ali, N. Dasgpta, V.P. Uniyal,
J.A. Johnson & S.A. Hussain (2020). Evaluating performance of four species distribution
models using Blue-tailed Green Darner Anax guttatus (Insecta: Odonata)
as model organism from the Gangetic riparian zone. Journal of Threatened Taxa 12(14): 16962–16970. https://doi.org/10.11609/jott.6106.12.14.16962-16970
Copyright: © De et al. 2020. 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: This work is funded by the National Mission for Clean Ganga, Ministry of Jal Shakti, Department of Water
Resources, River development and Ganga
Rejuvenation, Government of
India (Grant No. B-02/2015-16/1259/NMCG-WIIPROPOSAL).
Competing interests: The authors declare no competing interests.
Author details: Kritsh De is working as project fellow at Wildlife Institute of
India. His research interests are biodiversity and ecology. Sk. Zeeshan Ali is working as spatial
analyst at Wildlife Institute of India. His research interests are geospatial
modelling and spatial ecology. Niladri
Dasgupta is working as project coordinator at Wildlife Institute of
India. His research interests are river ecology and aquatic wildlife
conservation. Virendra Prasad Uniyal is working as Scientist G at Wildlife
Institute of India. His research interests are ecology and systematics of
insects, bioindicators, biodiversity surveys and ecological monitoring. Jeyaraj Antony Johnson is working as Scientist E at Wildlife Institute of India. His research interests
are ecology and monitoring of aquatic ecosystem. Syed Ainul Hussain worked as
Scientist G at Wildlife Institute of India. His research interests are aquatic
ecology and conservation biology.
Author contribution: KD-—conceptualization, field work, formal analysis,
writing original draft; SZA—field work, formal analysis, writing original
draft; ND—editing the draft; VPU—supervision, review and editing the draft;
JAJ—supervision, review and editing the draft; SAH—supervision, review and
editing the draft, funding acquisition.
Acknowledgements: Authors are thankful to the National Mission for
Clean Ganga, Ministry of Jal Shakti, Department of Water Resources, River
development and Ganga Rejuvenation, Government of India for sponsoring the work
under the project “Biodiversity conservation and Ganga Rejuvenation”. Authors
express gratitude to the Director and Dean, Wildlife Institute of India for
their administrative support for the study. Authors acknowledge the
Environment, Forest and Climate Change Department, Government of Uttar Pradesh
for necessary support during fieldwork.
INTRODUCTION
Species distribution models (SDMs) are tools that
integrate information about species occurrence or abundance with environmental
estimates of a landscape, used to predict distribution of a species across
landscapes (Elith & Leathwick
2009). When applied in a geographic
information system (GIS), SDMs can produce spatial predictions of occurrence
likelihood at locations where information on species distribution was
previously unavailable (Václavík & Meentemeyer 2009).
Though various types of algorithms are used to build different SDMs (Elith et al. 2006), they share common and general
approaches (Hirzel et al. 2002) such as: (i) at a specified resolution, the study area is divided
into grid cells; (ii) species presence localities (and sometimes absence
localities) data are used as the dependent variable; (iii) several
environmental variables (e.g., temperature, precipitation, soil type, aspect,
land cover type) are collected for each grid cell as predictor variables; and
(iv) the suitability of each cell for the species distributions defined as a
function of the environmental variables (Stanton et al. 2012). The species distribution prediction is
central to applications in ecology, evolution and conservation science (Elith et al. 2006) across terrestrial, freshwater, and
marine realms (Elith & Leathwick
2009). But it remains a question for
researchers which model should be selected for particular organisms and
habitats of interest, particularly when few samples are present for large
under-sampled areas (Mi et al. 2017).
Riparian zones are broadly defined as terrestrial
landscapes with characteristic vegetation associated with temporary or
permanent aquatic ecosystems (Meragiaw et al.
2018). These areas are highly complex
biophysical systems, and their ecological functions are maintained by strong spatio-temporal connectivity with adjacent riverine and
upland systems (Décamps et al. 2009). It has been observed that species
distribution models are used more often for terrestrial environments than for
aquatic or riparian ecosystems.
Globally, odonates are used as model organisms
to study climate change, data simulation, environmental assessment and
management, effects of urbanization, landscape planning, habitat monitoring and
evaluation, and conservation of rare species (Bried
& Samways 2015). To date, no work
has been done on the comparative use of species distribution models in India
using insects as model organisms in riparian or freshwater ecosystems. With this background, in the present work we
evaluated the effectiveness of four species distribution models using odonates from the Gangetic riparian zone as model
organisms.
MATERIALS AND METHODS
Study area and field data collection
For the study, we selected Anax
guttatus (Burmeister, 1839) commonly
called Blue-tailed Green Darner (Image 1) as the model insect species. It is a dragonfly (suborder Anisoptera Selys, 1854) under the
family Aeshnidae Leach, 1815 and superfamily Aeshnoidea Leach, 1815 (Dijkstra et al. 2013). The species can be identified in the field
due to its large size, highly active behaviour, green colour of the thorax
& first, second, & third abdominal segments, and presence of turquoise
blue colour on the dorsal part of the second abdominal segment (Subramanian
2005).
We conducted the study during May 2019 from Bijnor, Uttar Pradesh to Kanpur, Uttar Pradesh (Fig.
1). The river flows through alluvial
plain and covers a length of about 450km in this stretch. For the study we selected four sites, and the
distance between each two successive sites was about 150km. In each site we chose a 10km river stretch
and observed the presence of Blue-tailed Green Darner. We collected a total of 10 sighting
locations.
Data processing and analysis
We derived the thematic layer of LULC (N.R.S.C. 2016)
from multi-temporal advanced wide field sensor (AWiFS)
images with 56m spatial resolution using digital and rule-based image
classification methods, and forest cover type (F.S.I. 2009) from IRS P6 (Linear
Imaging Self Scanning Sensor) LISS III with 23.5m spatial resolution using a
combined method of digital and on-screen visual image classification and
bioclimatic layers from worldclim gridded climatic
data (Fick & Hijmans 2017) with 1km spatial
resolution. For analysis, we took 2km
buffer zones from the river bank and resampled all the layers to 1km spatial
resolution.
We used ‘stack’ function of package ‘raster’ (Hijmans 2019) to stack all the 19 available bioclimatic
variable, forest cover and land use land cover (LULC) layers. After that we used ‘pairs’ function of the
package ‘raster’ (Hijmans 2019) to find the
correlation coefficient between stacked layers.
Then we selected the variables which had a correlation coefficient less
than 0.60 (Pozzobom et al. 2020), and again stacked
the selected layers with ‘stack’ function of package ‘raster’ (Hijmans 2019). These
selected layers were LULC, forest cover and five bioclimatic layers: annual
mean temperature (Bio 1), isothermality (Bio 3), temperature
seasonality (Bio 4), mean temperature of driest quarter (Bio 9), and
precipitation seasonality (Bio 15).
We built four species distribution models: generalized
linear model (GLM), maximum entropy (MAXENT) model, random forest (RF) model,
and support vector machines (SVM).
GLM is an extension of classic linear regression modeling, where the iterative weighted linear regression
technique is used to estimate maximum-likelihood of the parameters, with
observations distributed in terms of an exponential family and systematic
effects made linear by the suitable transformation that allow for analysis of
non-linear effects among variables and non-normal distributions of the
independent variables (McCullagh & Nelder 1989; Chefaoui & Lobo 2008; Shabani
et al. 2016).
RF modeling is a machine
learning technique which is a bootstrap-based classification and regression
trees method (Cutler et al. 2007). It is
used to model species distributions from both the abundance and the
presence-absence data (Howard et al. 2014).
It is insensitive to data distribution (Hill et al. 2017) and also takes
a large number of potentially collinear variables; it is robust to over-fitting
which makes it very useful for prediction (Prasad et al. 2006; Segal 2004).
MAXENT modeling is a
general-purpose machine learning method to estimate a target probability
distribution by finding the probability distribution of maximum entropy and it
has several aspects that make it well-suited for species distribution modelling
(Phillips et al. 2006). It is relatively
less sensitive to the spatial errors associated with location data and needs
few locations to build useful models (Baldwin 2009) and it is one of the most
accurate and trusted modelling methods for presence-only distribution data
(Huerta & Peterson 2008; Srinivasulu & Srinivasulu 2016).
SVM modeling is developed
from the theory of statistical learning, in which the error involved with
sample size is minimized and the upper limit of the error involved in model
generalization is narrowed, which solve the problems of nonlinearity,
over-learning and the curse of dimensionality during modelling (Fielding &
Bell 1997; Howley & Madden 2005; Huang & Wang 2006). It can be used on small data sets as it is
independent of any distributional assumptions or asymptotic arguments (Wilson
2008).
We used ‘load_var’ function
to normalize and load environmental variables, then used ‘load_occ’
function to load species occurrence data and then used ‘modelling’ function to
build the models with 100 iterations by the package ‘SSDM’ (Schmitt et al.
2017) to plot the models.
We evaluated and compared four models by comparing
values of area under the receiver operating characteristic curve (AUC), Kohen’s
Kappa, true skill statistic (TSS), model sensitivity, model specificity, and
omission error.
The area under the receiver operating characteristic
curve or AUC measures the ability of a model to discriminate between the sites
where a species is present and the sites where a species is absent (Fielding
& Bell 1997; Elith et al. 2006) and it provides a
single measure of overall accuracy that is independent of a particular
threshold (Fielding & Bell 1997).
The evaluation criteria for the AUC statistic are as follows: excellent
(0.90–1.00), very good (0.8–0.9), good (0.7–0.8), fair (0.6–0.7), and poor
(0.5–0.6) (Swets 1988; Duan
et al. 2014).
The Kappa statistic is based on the optimal threshold,
measure the performance of the model by using the best of the information in
the mixed matrix (Duan et al. 2014) ranges from −1 to
+1, where +1 indicates perfect agreement and values of zero or less than zero
indicate a performance no better than random (Allouche
et al. 2006; Cohen 1960) and the
evaluation criteria for the Kappa statistic are as follows: excellent
(0.85–1.0), very good (0.7–0.85), good (0.55–0.7), fair (0.4–0.55), and fail
(<0.4) (Duan et al. 2014; Monserud
& Leemans 1992).
The true skill statistic (TSS) is expressed as
Sensitivity + Specificity – 1 (Allouche et al. 2006)
and ranges from −1 to +1, where +1 indicates a perfectly performing model with
no error, 0 indicates the model with totally random error and -1 indicates the
model with total error (Marcot 2012; Ruete & Leynaud 2015).
The model sensitivity denotes the proportion of
correctly predicted presences, thus quantifying omission errors (Ward 2007; Shabani et al. 2016) and model specificity denotes the
proportion of correctly predicted presences, thus quantifying commission errors
(Shabani et al. 2016).
Omission error (1- sensitivity) is the
under-prediction or false-negative result in areas being classified as
unsuitable when they are not and commission error (1- specificity) is the
over-prediction or false-positive result in areas being classified as suitable
when they are not (Ward 2007) and for a good SDM, both of the omission error
and commission error should be low.
For evaluation of model performance and variable
importance we used ‘knitr::kable(Modelname@evaluation)’ function and ‘knitr::kable(Modelname@variable.importance)’
function of the package ‘SSDM’ (Schmitt et al. 2017), respectively.
We chose five probability classes (0 to <0.20, 0.20
to <0.40, 0.40 to <0.60, 0.60 to <0.80 and 0.80 to 1.00) to know what
percentage of the area is being declared the best and worst by each of the
models by ‘ratify’ function of package ‘raster’ (Hijmans
2019)
We performed all the analysis in the ArcMap 10.3.1,
QGIS 2.14.7 and in R language and environment for statistical computing (R Core
Team 2019).
RESULT
The plot for each of the four models is given in Fig.
2. We found that the AUC value was
highest for GLM (0.983), followed by RF (0.833), MAXENT (0.829) and SVM (0.667); the value
of Kappa statistic was highest for RF (0.667), followed
by GLM (0.356), SVM (0.333) and MAXENT (0.049); the value
of TSS was highest for GLM (0.965), followed by RF (0.666), MAXENT (0.658) and SVM (0.334); the value
of model sensitivity was 1 for GLM, 0.833 for both MAXENT and RF and 0.667 for SVM; the value of
model specificity was maximum for GLM (0.965), followed
by RF (0.833), MAXENT (0.825) and SVM) (0.667); the omission error was lowest for GLM (0.00), for
both MAXENT and RF models it was 0.167 and for
SVM it was
0.333; the commission error was lowest
for GLM (0.035), followed by RF model (0.167),
MAXENT (0.175) and SVM (0.333)
(Table 1, Fig. 3)
For GLM, RF, and SVM models the forest had the highest
importance but for MAXENT model the Precipitation seasonality (Bio 15) had the
highest importance (Table 2, Fig. 4).
For GLM and SVM models the Precipitation seasonality (Bio 15) had lowest
importance, for MAXENT forest had lowest importance, while for RF model Isothermality (Bio 3) had lowest importance (Table 2, Fig.
4). Overall, the variation in the
variable importance was lowest in MAXENT model (SD = 3.367), followed by GLM
(SD = 24.344), RF (SD = 30.868) and SVM (SD = 37.071) (Fig. 5).
By comparative analysis, we found that GLM showed
1.62% of total area as the best (occurrence probability, 0.80 to 1) and 65.50%
of total area as the worst (occurrence probability, 0 to 0.20) for suitable
habitat. MAXENT model showed 10.08% of
total area as the best and 77.70% of total area as the worst for suitable
habitat. RF model showed 5.39% of total
area as the best and 23.79% of total area as the worst for suitable
habitat. SVM model showed 4.53% of total
area as the best and 27.68% of total area as the worst for suitable habitat
(Table 3, Fig. 6).
DISCUSSION
Freshwater ecosystems, which include rivers, lakes,
peat lands, swamps, fens, and springs, are highly dynamic and host a great
diversity of life forms, particularly freshwater endemic species (He et al.
2019; Tickner et al. 2020). They are
among the most threatened ecosystems (He et al. 2019), as globally wetlands are
vanishing more rapidly than forests and freshwater species are declining faster
than terrestrial or marine populations (Tickner et al. 2020). Therefore, for proper conservation
management, we should understand the distribution of plants and animals
inhabiting aquatic ecosystems. Species
distribution models can play an important role on such efforts, because they
can produce credible, defensible and repeatable information and provide tools
for mapping habitats to inform decisions (Sofaer et
al. 2019). Species distribution models
can forecast the potential impacts of future environmental changes (Howard et
al. 2014) and predict how species will respond (Buckley et al. 2010). Yet debate remains over the most robust
species distribution modelling approaches for making projections (Howard et al.
2014), because these models have sensitivity to data inputs and methodological
choices. This makes it important to
assess the reliability and utility of the model predictions (Sofaer et al. 2019).
In the present study we compared the GLM, MAXENT, RF,
and SVM approaches. We found that GLM
generated the highest values for AUC, TSS, specificity and sensitivity, and the
lowest values for omission error and commission error. The value of Kappa statistic was highest for
RF modelling. The MAXENT model used
roughly all variables equally, which is not true of the other models which put
more emphasis on forest cover.
The success of a model depends on many factors, such
as sample size, spatial extent of the study area, and number of ecological and
statistical significant variables which affect the distribution of species of
interest. We acknowledge that there were some limitations to the current work,
such as that our sample size was small (only 10 presence locations), we used
only seven variables, we tested only four species distribution models, and we
selected a species whose distribution depends on other factors, such as the
physiochemical parameters of water and availability of resources. We did not
include such variables as this study was preliminary.
Collins & McIntyre (2015) reviewed 30 studies on
species distribution modelling of odonates across the
world, and found that 43% used GLM, 33% MAXENT and 20% RF models. Other models used were BIOMOD, general
additive model (GAM), generalized boosted model (GBM), artificial neural
networks (ANN), multivariate adaptive regression splines (MARS), classified
tree analysis (CTA), flexible discriminant analysis (FDA), boosted regression
trees (BRT), surface range envelopes (SRE), and mixture discriminant analysis
(MDA). Different species distribution
models produce different results (Shabani et al.
2016), and the same model can give different results for different species and
areas. We urge researchers not to rely
on just one model, rather they should compare different available species
distribution models and select the best one.
Our study was in India where an insect was used for comparative
evaluation of species distribution models in a riverine riparian zone. We recommend that further studies on
different species distribution models using different animals and ecological
variables should be carried out in the riparian zones of Indian river systems
for proper design and implementation of ecological habitat management plans.
Table 1. Values of AUC, Kappa statistic, TSS,
sensitivity, specificity, omission error, and commission error generated by
generalized linear model (GLM), maximum entropy (MAXENT) model, random forest
(RF) model, and support vector machines (SVM) model.
|
GLM |
MAXENT |
RF |
SVM |
AUC |
0.983 |
0.829 |
0.833 |
0.667 |
Kappa statistic |
0.356 |
0.049 |
0.667 |
0.333 |
True skill statistic |
0.965 |
0.658 |
0.666 |
0.334 |
Sensitivity |
1 |
0.833 |
0.833 |
0.667 |
Specificity |
0.965 |
0.825 |
0.833 |
0.667 |
Omission error |
0 |
0.167 |
0.167 |
0.333 |
Commission error |
0.035 |
0.175 |
0.167 |
0.333 |
Table 2. Comparative importance (%) of seven variables
from generalized linear model (GLM), maximum entropy (MAXENT) model, random
forest (RF) model, and support vector machines (SVM) model.
|
GLM |
MAXENT |
RF |
SVM |
Annual mean temperature (Bio 1) |
11.831 |
16.352 |
2.254 |
0.198 |
Isothermality (Bio 3) |
8.062 |
14.789 |
0.513 |
0.337 |
Temperature seasonality) (Bio 4) |
5.709 |
15.405 |
4.076 |
0.239 |
Mean temperature of driest quarter (Bio 9) |
3.241 |
13.638 |
0.907 |
0.069 |
Precipitation seasonality (Bio 15) |
1.103 |
16.417 |
2.817 |
0.019 |
Forest |
68.799 |
7.014 |
84.186 |
98.353 |
Land use land cover |
1.252 |
16.384 |
5.247 |
0.785 |
Table 3. Comparison of percentage of total area
obtained from each model for five occurrence probability classes,
Occurrence probability class |
Models |
|||
|
GLM |
MAXENT |
RF |
SVM |
0 to <0.20 |
65.50 |
77.70 |
23.79 |
27.68 |
0.20 to <0.40 |
7.94 |
3.93 |
35.61 |
42.55 |
0.40 to <0.60 |
19.58 |
4.04 |
17.97 |
18.04 |
0.60 to <0.80 |
5.35 |
4.24 |
17.24 |
7.19 |
0.80 to 1.00 |
1.62 |
10.08 |
5.39 |
4.53 |
For
image & figures - - click here
REFERENCES
Allouche, O., A. Tsoar
& R. Kadmon (2006). Assessing the accuracy of
species distribution models: prevalence, kappa and the true skill statistic
(TSS). Journal of Applied Ecology 43(6): 1223–1232. https://doi.org/10.1111/j.1365-2664.2006.01214.x
Baldwin R.
(2009). Use of
Maximum Entropy Modeling in Wildlife Research. Entropy
11(4): 854–866. https://doi.org/10.3390/e11040854
Bried, J.T. & M.J. Samways
(2015). A review of odonatology in freshwater applied ecology and conservation
science. Freshwater Science, 34(3): 1023–1031. https://doi.org/10.1086/682174
Buckley,
L.B., M.C. Urban, M.J. Angilletta, L.G. Crozier, L.J.
Rissler & M.W. Sears (2010). Can mechanism inform species’
distribution models? Ecology Letters 13(8): 1041–1054. https://doi.org/10.1111/j.1461-0248.2010.01479.x
Chefaoui, R.M. & J.M. Lobo (2008). Assessing the effects of
pseudo-absences on predictive distribution model performance.Ecological
Modelling 210(4): 478–486. https://doi.org/10.1016/j.ecolmodel.2007.08.010
Cohen, J.
(1960). A
coefficient of agreement for nominal scales. Educational and Psychological
Measurement 20(1): 37–46. https://doi.org/10.1177/001316446002000104
Collins,
S.D. & N.E. McIntyre (2015). Modeling the distribution
of odonates: a review. Freshwater Science
34(3): 1144–1158. https://doi.org/10.1086/682688
Cutler,
D.R., T.C. Edwards Jr, K.H. Beard, A. Cutler, K.T. Hess, J. Gibson & J.J.
Lawler (2007). Random
forests for classification in ecology. Ecology 88(11): 2783–2792. https://doi.org/10.1890/07-0539.1
Décamps, H., R.J. Naiman
& M.E. McClain (2009). Riparian Zones. In: Encyclopedia of
Inland Waters (pp. 396–403). https://doi.org/10.1016/b978-012370626-3.00053-3
Dijkstra,
K.D.B., G. Bechly, S.M. Bybee, R.A. Dow, H.J. Dumont,
G. Fleck, R.W. Garrison, M. Hämäläinen, V.J. Kalkman, H. Karube, M.L. May,
A.G. Orr, D.R. Paulson, A.C. Rehn, G. Theischinger,
J.W.H. Trueman, J.V. Tol,
N.V. Ellenrieder & J. Ware (2013). The classification and diversity
of dragonflies and damselflies (Odonata). Zootaxa
3703(1): 036–045. https://doi.org/10.11646/zootaxa.3703.1.9
Duan, R.Y., X.Q. Kong, M.Y. Huang,
W.Y. Fan & Z.G. Wang (2014). The predictive performance and stability of six
species distribution models. PLoS ONE
9(11): e112764. https://doi.org/10.1371/journal.pone.0112764
Elith, J. & J.R. Leathwick (2009). Species distribution models:
ecological explanation and prediction across space and time. Annual review
of Ecology, Evolution and Systematics 40: 677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159
Elith, J., C.H. Graham, R.P.
Anderson, M. Dudík, S. Ferrier, A. Guisan, R.J. Hijmans, F. Huettmann, J.R. Leathwick, A.
Lehmann, J. Li, L.G. Lohmann, B.A. Loiselle, G. Manion,
C. Moritz, M. Nakamura, Y. Nakazawa,
J.McC.M. Overton, A.T. Peterson, Steven J. Phillips, K. Richardson, R. Scachetti-Pereira, R.E. Schapire, J. Soberón, S. Williams,
M.S. Wisz & N.E. Zimmermann (2006). Novel methods improve
prediction of species’ distributions from occurrence data. Ecography
29(2): 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
F.S.I.
(2009). India State
of Forest Report – 2009. Forest Survey of India (Ministry of Environment
Forests and Climate Change, Government of India), Dehradun.
Fick, S.E.
& R.J. Hijmans (2017). WorldClim 2: new 1-km spatial resolution
climate surfaces for global land areas. International Journal of Climatology
37(12): 4302–4315. https://doi.org/10.1002/joc.5086
Fielding,
A.H. & J.F. Bell (1997). A review of methods for the assessment of prediction errors in
conservation presence/absence models. Environmental Conservation 24(1):
38–49. https://doi.org/10.1017/S0376892997000088
He, F., C. Zarfl, V. Bremerich, J.N.W.
David, Z. Hogan, G. Kalinkat, K. Tockner &
S.C. Jähnig (2019). The global decline of freshwater
megafauna.Global Change Biology 25(11):
3883–3892. https://doi.org/10.1111/gcb.14753
Hijmans, R.J. (2019). raster: Geographic Data Analysis
and Modeling. R package version 3.0-7.
https://CRAN.R-project.org/package=raster
Hill, L., A.
Hector, G. Hemery, S. Smart, M. Tanadini & N.
Brown (2017). Abundance
distributions for tree species in Great Britain: A two-stage approach to modeling abundance using species distribution modeling and random forest. Ecology and Evolution
7(4): 1043–1056. https://doi.org/10.1002/ece3.2661
Hirzel, A.H., J. Hausser,
D. Chessel & N. Perrin (2002). Ecological-niche factor
analysis: how to compute habitat-suitability maps without absence data?. Ecology
83(7): 2027–2036. https://doi.org/10.1890/0012-9658(2002)083[2027:ENFAHT]2.0.CO;2
Howard, C.,
P.A. Stephens, J.W. Pearce-Higgins, R.D. Gregory & S.G. Willis (2014). Improving species distribution
models: the value of data on abundance. Methods in Ecology and Evolution
5(6): 506–513. https://doi.org/10.1111/2041-210X.12184
Howley, T.
& M.G. Madden (2005). The genetic kernel support vector machine: Description and evaluation. Artificial
Intelligence Review 24(3–4): 379–395. https://doi.org/10.1007/s10462-005-9009-3
Huang, C.L.
& C.J. Wang (2006). A GA-based feature selection and parameters optimizationfor
support vector machines. Expert Systems with Applications 31(2):
231–240. https://doi.org/10.1016/j.eswa.2005.09.024
Huerta,
M.A.O. & A.T. Peterson (2008). Modeling ecological niches and
predicting geographic distributions: a test of six presence-only methods. Revista Mexicana de Biodiversidad
1(1): 205–216.
Marcot, B.G. (2012). Metrics for evaluating
performance and uncertainty of Bayesian network models. Ecological Modelling
230: 50–62. https://doi.org/10.1016/j.ecolmodel.2012.01.013
McCullagh,
P. & J.A. Nelder (1989). Generalized Linear Models.
Chapman and Hall, London, 511pp
Meragiaw, M., Z. Woldu,
V. Martinsen & B.R. Singh (2018). Woody species composition and
diversity of riparian vegetation along the Walga
River, Southwestern Ethiopia. PLoS ONE
13(10): e0204733. https://doi.org/10.1371/journal.pone.0204733
Mi, C., F. Huettmann, Y. Guo, X. Han & L. Wen (2017). Why choose Random Forest to
predict rare species distribution with few samples in large undersampled
areas? Three Asian crane species models provide supporting evidence. PeerJ 5: p.e2849. https://doi.org/10.7717/peerj.2849
Monserud, R.A. & R. Leemans (1992). Comparing global vegetation maps
with the Kappa statistic. Ecological Modelling 62(4): 275–293. https://doi.org/10.1016/0304-3800(92)90003-W
N.R.S.C.
(2016). National
Land Use and Land Cover (LULC) mapping using multi-temporal AWiFS
Data (2015–16). National Remote Sensing Centre, Hyderabad, India.
Phillips,
S.J., R.P. Anderson & R.E. Schapire (2006). Maximum entropy modeling of species geographic distributions. Ecological
Modelling 190(3–4): 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Pozzobom, U.M., J. Heino,
M.T. da S. Brito & V.L. Landeiro (2020). Untangling the determinants of
macrophyte beta diversity in tropical floodplain lakes: insights from
ecological uniqueness and species contributions. Aquatic Sciences 82(3):
56. https://doi.org/10.1007/s00027-020-00730-2
Prasad,
A.M., L.R. Iverson & A. Liaw (2006). Newer classification and
regression tree techniques: bagging and random forests for ecological
prediction. Ecosystems 9(2): 181–199. https://doi.org/10.1007/s10021-005-0054-1
R Core Team
(2019). R: A
language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. https://www.R-project.org/
Ruete, A. & G.C. Leynaud (2015). Goal-oriented evaluation of
species distribution models’ accuracy and precision: True Skill Statistic
profile and uncertainty maps. PeerJ PrePrints 3:
e1208v1. https://doi.org/10.7287/peerj.preprints.1208v1
Schmitt, S.,
R. Pouteau, D. Justeau, F.
de Boissieu & P. Birnbaum (2017). ssdm: An r package to predict
distribution of species richness and composition based on stacked species
distribution models. Methods in Ecology and Evolution 8(12): 1795–1803. https://doi.org/10.1111/2041-210X.12841
Segal, M.R.
(2004). Machine
Learning Benchmarks and Random Forest Regression. UCSF: Center
for Bioinformatics and Molecular Biostatistics. Retrieved from
https://escholarship.org/uc/item/35x3v9t4
Shabani, F., L. Kumar & M. Ahmadi
(2016). A
comparison of absolute performance of different correlative and mechanistic
species distribution models in an independent area. Ecology and Evolution
6(16): 5973–5986. https://doi.org/10.1002/ece3.2332
Sofaer, H.R., C.S. Jarnevich,
I.S. Pearse, R.L. Smyth, S. Auer, G.L. Cook, T.C.
Edwards Jr, G.F. Guala, T.G. Howard, J.T. Morisette & H. Hamilton (2019). Development and delivery of
species distribution models to inform decision-making. BioScience
69(7): 544–557. https://doi.org/10.1093/biosci/biz045
Srinivasulu, A. & C. Srinivasulu (2016). All that glitters is not gold:
A projected distribution of the endemic Indian Golden Gecko Calodactylodes
aureus (Reptilia: Squamata: Gekkonidae)
indicates a major range shrinkage due to future climate change. Journal of
Threatened Taxa 8(6): 8883–8892. https://doi.org/10.11609/jott.2723.8.6.8883-8892
Stanton,
J.C., R.G. Pearson, N. Horning, P. Ersts & H. ReşitAkçakaya (2012). Combining static and dynamic
variables in species distribution models under climate change. Methods in
Ecology and Evolution 3(2): 349–357. https://doi.org/10.1111/j.2041-210X.2011.00157.x
Subramanian,
K.A. (2005). Dragonflies
and Damselflies of Peninsular India-A Field Guide. E-Book of Project Lifescape. Centre for Ecological Sciences, Indian Institute
of Science and Indian Academy of Sciences, Bangalore, 118pp.
Swets, J.A. (1988). Measuring the accuracy of
diagnostic systems. Science 240(4857): 1285–1293. https://doi.org/10.1126/science.3287615
Tickner, D.,
J.J. Opperman, R. Abell, M. Acreman, A.H. Arthington,
S.E. Bunn, S.J. Cooke, J. Dalton, W. Darwall, G.
Edwards, I. Harrison, K. Hughes, T. Jones, D. Leclère,
A.J. Lynch, P. Leonard, M.E. McClain, D. Muruven,
J.D. Olden, S.J. Ormerod, J. Robinson, R.E. Tharme,
M. Thieme, K. Tockner, M.
Wright & L. Young (2020). Bending the curve of global freshwater biodiversity
loss: an emergency recovery plan. BioScience
70(4): 330–342. https://doi.org/10.1093/biosci/biaa002
Václavík, T. & R.K. Meentemeyer (2009). Invasive species distribution modeling (iSDM): are absence data
and dispersal constraints needed to predict actual distributions?. Ecological
Modelling 220(23): 3248–3258. https://doi.org/10.1016/j.ecolmodel.2009.08.013
Ward, D.F.
(2007). Modelling
the potential geographic distribution of invasive ant species in New Zealand. Biological
Invasions 9(6): 723–735. https://doi.org/10.1007/s10530-006-9072-y
Wilson, M.D. (2008). Support Vector Machines. pp. 3431–3437. In: Jørgensen,
S.E. & B.D. Fath (eds.). Encyclopedia
of Ecology. Elsevier, 3120pp. https://doi.org/10.1016/B978-008045405-4.00168-3