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Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP
Ecological Informatics 22 (2014) 36–43
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Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP Hitendra Padalia ⁎, Vivek Srivastava, S.P.S. Kushwaha Forestry and Ecology Department, Indian Institute of Remote Sensing, ISRO, Dehradun 248001, India
a r t i c l e
i n f o
Article history: Received 3 August 2013 Received in revised form 21 April 2014 Accepted 28 April 2014 Available online 5 May 2014 Keywords: Alien invasive Bushmint Niche modeling MaxEnt GARP
a b s t r a c t Bushmint (Hyptis suaveolens (L.) Poit.) is one among the world's most noxious weeds. Bushmint is rapidly invading tropical ecosystems across the world, including India, and is major threat to native biodiversity, ecosystems and livelihoods. Knowledge about the likely areas under bushmint invasion has immense importance for taking rapid response and mitigation measures. In the present study, we model the potential invasion range of bushmint in India and investigate prediction capabilities of two popular species distribution models (SDM) viz., MaxEnt (Maximum Entropy) and GARP (Genetic Algorithm for Rule-Set Production). We compiled spatial layers on 22 climatic and non-climatic (soil type and land use land cover) environmental variables at India level and selected least correlated 14 predictor variables. 530 locations of bushmint along with 14 predictor variables were used to predict bushmint distribution using MaxEnt and GARP. We demonstrate the relative contribution of predictor variables and species-environmental linkages in modeling bushmint distribution. A receiver operating characteristic (ROC) curve was used to assess each model's performance and robustness. GARP had a relatively lower area under curve (AUC) score (AUC: 0.75), suggesting its lower ability in discriminating the suitable/unsuitable sites. Relative to GARP, MaxEnt performed better with an AUC value of 0.86. Overall the outputs of MaxEnt and GARP matched in terms of geographic regions predicted as suitable/unsuitable for bushmint in India, however, predictions were closer in the spatial extent in Central India and Western Himalayan foothills compared to North-East India, Chottanagpur and Vidhayans and Deccan Plateau in India. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Biological invasion by non-native or alien species is recognized to pose significant losses in the economic value, biodiversity and health of invaded systems (Hulme, 2007; Wittenberg and Cock, 2001). India is one among the ten mega-biodiversity countries in the world (MoEF, 2009), housing four global biodiversity hotspots viz., the Himalaya, the Indo-Burma, the Western Ghats and Andaman and Nicobar islands (Conservation International, 2012). With only 2.4% of the land area of the world, India has 11% of world's floral diversity with over 45,500 plant species and nearly 147 endemic genera (MoEF, 2009). Indian flora also has a significant percentage (173 species) of alien invasive plant species (Reddy, 2008) notably Lantana camara, Chromolaena odorata, Parthenium hysterophorus, Hyptis suaveolens and Ageratum conyzoides which have spread widely and caused perceptible negative impacts on the native biodiversity.
⁎ Corresponding author. Tel.: +91 135 2524176, +91 9411193962 (Mobile). E-mail addresses: [email protected] (H. Padalia), [email protected] (V. Srivastava), [email protected] (S.P.S. Kushwaha).
http://dx.doi.org/10.1016/j.ecoinf.2014.04.002 1574-9541/© 2014 Elsevier B.V. All rights reserved.
Bushmint or pignut (H. suaveolens (L.) Poit.), one among the world's most noxious weeds, which are invading natural ecosystems across tropical and sub-tropical regions of the world (Afolayan, 1993; Sarmiento, 1984; Wulff and Medina, 1971). It is a native of tropical America. Because of its widespread occurrence in the tropics, it is now regarded as a pan-tropical weed. In India, bushmint occurrence is reported from North-East India, Vindhyas, Deccan Peninsula, and Andaman and Nicobar islands (Wealth of India, 1959; Yoganarasimhan, 2000). Bushmint is a soft broad-leaved herb of family Lamiaceae. Due to its ruderal nature, it causes heavy infestation outcompeting the native flora. It forms large thickets and is believed to produce allelochemicals, which impedes the seed germination of native species. It is unpalatable to livestock and wild animals. It is threatening the course of natural succession, existence of vulnerable taxa, and wild animals in the invaded areas (Padalia et al., 2013). Its vegetative parts possesses certain medicinal constituents having anti-cancerous (Mudgal et al., 1997) and tumorigenic (Peerzada, 1997) properties. Knowledge about the invasion range of alien species is crucial for understanding the ecology of invasive species and for conservation and management planning. Researchers have in many cases relied on predictive models for assessing patterns of species distribution (Guisan and Thuiller, 2005; Xue-Qing et al., 2013). Species distribution
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models (SDMs) have emerged from efforts to determine relationships between species and their environments (Guisan and Thuiller, 2005; Robertson et al., 2004) and provides one of the best ways to overcome the sparseness of the species distributional data. SDMs are used to predict climate change impacts, study biogeography, assist in reserve selection, improve species management and answer many conservation biology questions (Guisan and Zimmermann, 2000). SDMs attempt to define the potential ecological niche of any species. An ecological niche is comprised of the fundamental niche and the realized niche (Silvertown, 2004). The fundamental niche is an n-dimensional hyper-volume defined by the environmental conditions, within which populations of a species are able to maintain a longterm average net reproduction rate in the absence of inter-specific competition and natural enemies. The realized niche is the reduced n-dimensional hyper-volume that results from limiting ecological processes such as inter-specific competition, herbivory, and dispersal (Austin et al., 1990; Silvertown, 2004). The potential distribution of species is thus considered analogous to the fundamental niche. Several species distribution models differing in concepts, underlying assumptions, advantages and limitations are discussed in literature: Generalized Linear Model (GLM) and Generalized Additive Model (GAM) (Elith et al., 2006; Guisan et al., 2006), General Rule Set Production (GARP) (Elith et al., 2006; Stockwell and Peters, 1999,) Maximum entropy (MaxEnt) (Phillips et al., 2006), Bioclimatic envelope (BioClim) and DOMAIN (Elith et al., 2006). These models use presence/absence or presence/pseudo-absence or presence only data for making prediction about the species distribution. Among others models MaxEnt and GARP have often shown accurate prediction capabilities in simulations and evaluations with presence only data, outperforming classical modeling approaches, such as domain, bioclim, and logistic regression (Hijmans and Graham, 2006; Phillips and Dudik, 2008). The MaxEnt approach is used to estimate probability of distribution of target species by analyzing the probability distribution of maximum entropy (Phillips et al., 2006). MaxEnt ver.3.1 software is freely downloadable from http://www.cs.princeton.edu.
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GARP is a different flexible and commonly used species distribution modeling tool. GARP generates a random set of mathematical rules following an iterative process of rule selection, through testing, incorporation and rejection (Peterson et al., 2007). These sets of rules are combined in a random way to generate the potential niche of the species limited by the environmental conditions. GARP has been applied to studies that seek to forecast the risks on sites prone to be infested by alien invasive species, based upon the degree of environmental matching between the species' native and non-native ranges (Ganeshaiah et al., 2003; Underwood et al., 2004). The objectives of the present study are the: (i) prediction of the potential invasion range of bushmint across India using MaxEnt, (ii) comparison of the performance of MaxEnt-based predictions with the traditionally used GARP predictions, and (iii) identification of the environmental correlates of bushmint defining its invasion in India. The present study is the first-ever attempt to generate wall to wall spatial information on the potential distribution of any fast-growing alien invasive species in India. 2. Materials and methods 2.1. Study area and species occurrence records The present study was carried for the entire India which has ca. 3.29 million km2 of land area (Fig. 1). India has a considerably large area in which tropical and sub-tropical climatic conditions prevail. This makes it prone to the invasion of many noxious alien invasive species tropical in origin. We used 530 presence records of bushmint available for India. In the study Biodiversity Characterization at Landscape Level (BCLL) (Roy et al., 2012), bushmint was recorded in 463 vegetation plots out of total 16,518 plots laid out between year 2002 and 2007. 67 records of bushmint were compiled from the independent surveys carried out from 2007 to 2011 (Fig. 1). Occurrence records included the latitude/longitude of plots in which bushmint was recorded. In the BCLL study, the vegetation types in India were
Fig. 1. Bushmint in India (black dots depict presence locations, field photographs: (a) Bushmint in flowering during September and (b) monothickets of bushmint (in light violet color) along the river course during December in Dun valley).
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sampled following systematic random sampling with probability proportional to size. Bushmint was primarily recorded from tropical dry deciduous, moist deciduous and thorn forests which cover 41.87%, 19.73%, 2.25% of forest area in the country (FSI, 2011). Since India is a vast country, and therefore species data was collected in different phases over a period. It is possible to assume that bushmint cover would have increased during the period. However, considering the observed rate of its spread, it is unlikely that an increase in bushmint cover would significantly affect the modeling of its potential distribution at a grid resolution of 1 km2. 2.2. Environmental layers The environmental layers of climatic, topographic and non-climatic (soil and land use/land cover) nature were used to predict bushmint's ecological environment. Nineteen climatic variables were obtained from WorldClim dataset (Hijmans et al., 2005, http://worldclim.org/ bioclim.htm). These climatic variables have been found useful in defining the eco-physiological tolerances of species (Hijmans et al., 2005). Elevation information was derived from the GeoTOPO digital elevation model (http:/gsi.go.jp). Soil type information up to soil orders prepared by the National Bureau of Soil Survey and Land Use Planning (NBSS & LUP), Nagpur, India at 1 million scale was used. Soil classes were namely Alfisol, Entisols, Inceptisols, Mollisols, Ultisols, Aridisols and Vertisols. Land use/land cover data at 1:250,000 scale of year 2009 was obtained from National Remote Sensing Centre (NRSC), Hyderabad, India. ArcGIS ver. 9.2. spatial analyst tool was used to resample the data layers to a 1 km2 grid resolution, and to match their geographic extent. The high correlation among predictor variables makes interpretation of variables difficult. Multi-collinearity among predictor variables was reduced following the methods suggested by Kumar et al. (2006). Among two highly cross-correlated variables (Pearson Correlation Coefficient r N 0.75, alpha = 0.05), one was selected which is biologically relevant to the species, and offers ease in interpretation of model (Kumar and Stohlgren, 2009). For instance, if variables annual precipitation and precipitation of wettest month were found highly correlated, we retained precipitation of wettest month since it captures seasonal variability in precipitation. Eventually, 14 predictor variables were retained to run MaxEnt and GARP models. 2.3. Model design setting The setting of the model design for the comparison of MaxEnt and GARP models is not straightforward. The two models despite their similarities to make prediction based on presence only data, and their ability to support continuous as well as categorical environmental variables, differ in terms of model conceptualization, sample size requirement and output generation (Peterson et al., 2007; Phillips et al., 2006). MaxEnt has been found effective despite small sample sizes (Benito et al., 2009; Hernandez et al., 2006). There is considerable random variability in MaxEnt and GARP predictions. We used k-fold cross validation function in MaxEnt to randomly partition the occurrence data into ten random subsets. The random partitions rather than a single one was used to assess the average behavior of the algorithms following Phillips et al. (2006). However, in order to minimize the model variation in case of GARP, we implemented the “best subset” selection procedure (Anderson et al., 2003; Phillips et al., 2006; Stockwell and Peters, 1999). The Openmodeller software package (http://openmodeller.sourceforge. net.) provides functionality to run the best-fit GARP model. In the case of the k-fold cross validation procedure of MaxEnt ten different output predictions were generated based on default parameters values (0.01 convergence limit, and 1000 maximum iterations). An auto feature limiting function was used to fit the speciesenvironmental curves, and to train MaxEnt model. Initially, multiplier value was set very small (e.g. 0.02) with default set of parameter to
have a highly over-fit model. Subsequently, multiplier value was increased and modeled distribution and changes in AUC was analyzed. MaxEnt outputs are sensitive to the selection of background data. If the selection of background data points is from the area larger than the sampling extent it may result in a high AUC. We, therefore, restricted selection of background data points to sampling extent following Elith et al. (2010) and Flory et al. (2012). Moreover, direct comparison MaxEnt and GARP output is problematic. While GARP predicts the presence or absence of a species in a location, MaxEnt estimates the occurrence probability of the species in that location. Therefore, MaxEnt generated continuous outputs with 10 replicates were converted into binary maps (presence/absence) using the 10th percentile of the training presence method available in MaxEnt. The 10th percentile of training presence selects values above which 90% of the training locations are correctly classified. It gives conservative estimates of prediction compared to the minimum training presence threshold. Minimum training presence threshold correctly predicts every training location and may however lead to overprediction (Jarnevich and Reynolds, 2011). 2.4. Evaluation of models We assessed the consistency of the MaxEnt model and the level of uncertainty associated with certain variables by examining species– environmental relationships and the contribution of individual variables in the model. Species–environment relationships are one of the most important aspects of species distribution modeling as it serves both to inform us about the relationship between the species and the environment, but also it serves as a very important accuracy check to ensure that the relationships that the model results are based on are realistic. Species–environment curves were analyzed to explain the patterns observed in the MaxEnt output. We reported mean and range values for the variable contribution and species–relationship curves. We used a Jackknife test to examine the importance of individual variables for MaxEnt predictions. Jackknife test gives training, test and AUC gains for three scenarios (without variable, with only one variable and with all variables) for different environment variables used for prediction. We used receiver operating characteristic (ROC) area under curve (AUC) method to evaluate the performances of MaxEnt and GARP models. AUC provides a single measure of model performance independent of any particular choice of threshold. The AUC measures model performance that ranges from 0 to 1. AUC is a widely used procedure for comparing species distribution models performances (Babar et al., 2012; Phillips et al., 2006; Stohlgren et al., 2010). 3. Results 3.1. Predicted potential invasion range Fig. 2 depicts the probability of occurrence of bushmint modeled using MaxEnt. Higher probability (values closer to 1) represents areas suitable for bushmint. Zero probability or lower probability indicates areas less suitable for bushmint. A comparison between potential invasion ranges modeled through MaxEnt and GARP is shown in Fig. 3. We used a 10% minimum threshold to define the minimum probability of suitable habitats in MaxEnt output. As a result the values N0.32 were categorized as suitable. A significantly large area of India was predicted as suitable for bushmint. MaxEnt and GARP predicted 40.23% (1,322,341 km2) and 40.10% (1,317,896 km2) of the country suitable for bushmint growth respectively. Hence, MaxEnt and GARP predicted almost the same area suitable for bushmint. The common areas predicted as suitable MaxEnt and GARP are large areas in Central India, and part of Western Himalayan foothills. Whereas, extent of predicted area varied for North-East India, part of Deccan Plateau, Vindhyas
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Fig. 2. MaxEnt based probability distribution map of bushmint over India.
Fig. 3. Potential invasion range of bushmint: (a) MaxEnt and (b) GARP. The dark shaded area represents areas suitable for bushmint. MaxEnt predicted area is 10th percentile of training presence (N0.32 probability as suitable). GARP predicted N0.5 probability area as suitable.
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Table 1 Table 1. Variable contribution in MaxEnt models averaged over 10 replicates along with general statistics environmental profile of bushmint calculated based on 597 occurrence records in India. Environmental variables
Mean
Standard deviation
Min
Max
Mean
Temperature Seasonality (°C) Elevation (m) Precipitation of Wettest Month (mm) Mean Diurnal Range (°C) Mean Temperature of Wettest Quarter (°C) Isothermality Precipitation of Wettest Quarter (mm) Maximum Temperature of Warmest Month (°C) Mean Temperature of Coldest Quarter (°C) Precipitation of Driest Month (mm) Precipitation of Warmest Quarter (mm) Mean Temperature of Driest Quarter (°C) Soil Type Lulc Type
16.80 13.61 9.99 8.59 7.84 7.80 6.79 6.94 6.77 5.68 4.49 1.03 2.82 0.83
118.34 195.08 168.68 1.48 1.52 4.25 412.48 3.38 2.66 3.05 450.72 3.58 – –
147.00 23.00 76.00 8.00 21.00 38.00 187.00 28.00 12.00 0.00 47.00 15.00 – –
785.00 1183.00 1013.00 15.00 31.00 60.00 2542.00 42.00 24.00 21.00 2328.00 30.00 – –
452.85 374.70 346.10 11.45 26.54 43.73 868.30 38.40 19.18 3.40 328.18 22.90 – –
and Chotanagpur regions. Some of the regions predicted as suitable (e.g. foothills of Western Himalaya, tropical parts of North-East India) are at present not seriously infested by bushmint as indicated by field surveys. The areas predicted as suitable corroborated well with the survey records. 3.2. Evaluation of models Table 1 shows the mean values of relative contribution of predictor variables in the MaxEnt model. The values reported are averaged over 10 replicates. Temperature seasonality, elevation, precipitation of wettest month and mean diurnal range were the strongest predictors of bushmint distribution with 16.8, 13.61, 9.99 and 8.59% contributions, respectively. Fig. 4 shows the relative importance of different environmental variables based on results of jackknife tests in MaxEnt. Jackknife results also showed that the temperature of wettest quarter, temperature seasonality, elevation, precipitation of wettest month and warmest quarters are the most important predictors of bushmint distribution in India. Among all top predictors variables, the mean temperature of wettest quarter was a highly uncertain variable showing larger standard deviation in 10 replicates. Fig. 5b suggests that bushmint distribution with a N 0.5 probability of presence was limited between 100 m to 800 m amsl. Above 1000 m altitude, bushmint probability of presence was very low. Its probability of presence (N 0.5) increased with rise in mean temperature of the
wettest quarter from 25 °C to 28 °C and thereafter sharply decreased (Fig. 5c). Rainfall had a positive effect on bushmint distribution between 200 m to 1000 m rainfall (Fig. 5e & f). Very low rainfall (e.g. Rajasthan) and very high rainfall regions in the country (e.g. Western Ghats and eastern parts of North-East India), however, show very low or zero probability of presence for bushmint. The MaxEnt-based predicted area suitable for bushmint was found differing with those resulting from GARP (Fig. 3), but AUC produced by MaxEnt was significantly higher than that by GARP. The testing AUC of the MaxEnt model was 0.86 against the GARP model (AUC: 0.75). This concludes that MaxEnt has better prediction accuracy as compared to GARP. The higher AUC of the MaxEnt model in comparison to GARP demonstrates the stronger prediction capability of MaxEnt. 4. Discussion Areas under risk from plant invasions are difficult to predict. Researchers have relied upon broader environmental surrogates for establishing species-environment relationships to improve predictions on potential distribution and suitable habitats of invasive species (Rejmánek et al., 2005). However, predicting when species will become invasive is elusive as many plants exhibit a lag phase that cannot be determined a priori (Ewel et al., 1999; Rejmánek et al., 2005). The modeling of potential distribution of species helps in incorporating the dispersal lag period of invasive (e.g. meta-population processes) and
Fig. 4. Jackknife test for AUC of individual environmental variable importance (blue bars) relative to all environmental variables (red bar) for MaxEnt model. Values shown are averages over 10 replicate runs. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 5. Relationships between top environmental predictors and the probability of presence of bushmint in India: (a) temperature seasonality,(b) elevation (meter), (c) mean temperature of wettest quarter (°C),(d) mean diurnal range (°C), (f) precipitation of wettest month (mm), (g) precipitation of wettest quarter (mm). Red curves show the mean response and blue margins are ±1 SD calculated over 10 replicates. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
in redefining the control strategies based on the data collected, most often only from accessible localities. Globally, geographical distribution of bushmint is limited to regions falling within 31° N and 24° S latitudes (Padalia et al., unpublished). In India, MaxEnt and GARP both have predicted a significantly larger area as eco-climatically suitable for bushmint. Field surveys report widespread occurrence of bushmint in India. Our model predictions suggest that bushmint has wide ecological amplitude that is why bushmint has become widely invasive in India. Bushmint introduction in India has occurred long ago as Flora of British India (Hooker, 1885) reports its occurrence in Deccan peninsula, Cachar and Nicobar Islands. Further studies have also reported its invasion from different part of the country viz., Himalayan foothills (Padalia et al., 2013), Vidhayns
(Sharma et al., 2009), Andaman and Nicobar Islands (Yoganarasimhan, 2000). The predicted habitats are consistent with the wide-ranging habitat associations of bushmint in its well-established sites. Study has indentified hotspots of bushmint invasion in India and also indicated the possible dispersal pathways. Percent variable contribution and jackknife procedures in MaxEnt indicated relative importance of different bioclimatic predictors. Higher temperature seasonality of temperate and arid regions of country is negatively related to bushmint distribution. Cold stress and lack of thermal accumulation in higher altitude regions (above ca. 1500 m amsl) show zero or very low probability of bushmint occurrence. Bushmint occurrence is reported up to ~1560 m amsl altitude within its native range, in Aragua in Venezuela, (GBIF, 2013) while in India it has invaded the
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central highlands (altitude ~850 m amsl) in Maharastra state. Being an annual herb from tropics, bushmint prefers wet and warmer environments to grow and spread. Therefore, the mean temperature and precipitation of the wet season characterized areas suitable for bushmint. The very low relative importance of non-climatic variables such as soil type and land use/land cover in the MaxEnt model (as shown by variable importance and jackknife procedure) indicate that bushmint is highly plastic as far as its preference for different soil and land use land cover types is concerned. Further studies are needed to see the role of scale of study on incorporating the effect of forest density or fragmentation of species invasion with occurrence records of bushmint from high forest density landscape. At present, most of the occurrence records of bushmint were from regions having open and degraded forests areas in the country. Field based study has shown bushmint preference for rocky dry substrate in Vidhayans (Sharma et al., 2009). The occurrence records for invasive species over large areas are generally poorly documented. Even if large areas were considered, occurrence data was compiled from a variety of sources with varying degrees of sampling intensities. In this study, occurrence data on bushmint was collected randomly with uniform sampling intensity over the entire study area. Therefore, the study strength lies in comparing the model performances with good quality species occurrence data. The AUC which provides a global measure for model's performance has highlighted the higher discriminating power of MaxEnt in predicting the suitable/ unsuitable sites as compared to GARP. The finding of our study therefore coincides with other recent studies which also have suggested the superiority of MaxEnt among other models (Babar et al., 2012; Phillips et al., 2006; Stockwell and Peterson, 2002). The cross-correlation of predicted potential distribution ranges also provided a measure of agreement between MaxEnt and GARP. Spatial uncertainty in the potential invasion ranges of bushmint predicted by MaxEnt and GARP can be minimized through ensemble modeling (Stohlgren et al., 2010). At present, policy and decision makers seriously lack data on areas under the invasion risk from bushmint in the country. The potential invasion modeled in this study would help in targeting field surveys to figure out the actual areas under bushmint invasion in the country. The area under bushmint cover can be effectively detected and mapped using spectral un-mixing of multispectral data (Padalia et al., 2013). The potential distribution maps would help to take rapid response and early mitigation of the species. Further, it possible to get insights into climatic factors affecting the potential distribution of bushmint under projected climate change scenarios using a MaxEnt projection feature (Donnell et al., 2012; Evangelista et al., 2011). 5. Conclusions Species distribution models are powerful approaches to model the distribution of invasive species. With the fine-scale spatial information available on environmental variables and developments in species distribution models based on presence only data, ecologists have been able to define broad ecological conditions suitable or unsuitable for species over large geographic regions. The outcome of this study indicates that the potential distribution of bushmint in India facilitated by environmental conditions is vast, and far larger than its current geographical extent. The study identifies the environmental factors favorable to establishment of bushmint in India. The study also established that MaxEnt has stronger predictions as compared to GARP as reported by other studies as well. The potential invasion maps developed in this study can assist in developing and implementing mitigation measures to minimize the bushmint invasion in India. Acknowledgments We are grateful to Dr. Y. V. N. Krishna Murthy, Director, Indian Institute of Remote Sensing, Dehradun for his keen interest in the study, and
his support. Authors also acknowledge the valuable suggestions given by the anonymous reviewers.
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Ecological Informatics journal homepage: www.elsevier.com/locate/ecolinf
Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP Hitendra Padalia ⁎, Vivek Srivastava, S.P.S. Kushwaha Forestry and Ecology Department, Indian Institute of Remote Sensing, ISRO, Dehradun 248001, India
a r t i c l e
i n f o
Article history: Received 3 August 2013 Received in revised form 21 April 2014 Accepted 28 April 2014 Available online 5 May 2014 Keywords: Alien invasive Bushmint Niche modeling MaxEnt GARP
a b s t r a c t Bushmint (Hyptis suaveolens (L.) Poit.) is one among the world's most noxious weeds. Bushmint is rapidly invading tropical ecosystems across the world, including India, and is major threat to native biodiversity, ecosystems and livelihoods. Knowledge about the likely areas under bushmint invasion has immense importance for taking rapid response and mitigation measures. In the present study, we model the potential invasion range of bushmint in India and investigate prediction capabilities of two popular species distribution models (SDM) viz., MaxEnt (Maximum Entropy) and GARP (Genetic Algorithm for Rule-Set Production). We compiled spatial layers on 22 climatic and non-climatic (soil type and land use land cover) environmental variables at India level and selected least correlated 14 predictor variables. 530 locations of bushmint along with 14 predictor variables were used to predict bushmint distribution using MaxEnt and GARP. We demonstrate the relative contribution of predictor variables and species-environmental linkages in modeling bushmint distribution. A receiver operating characteristic (ROC) curve was used to assess each model's performance and robustness. GARP had a relatively lower area under curve (AUC) score (AUC: 0.75), suggesting its lower ability in discriminating the suitable/unsuitable sites. Relative to GARP, MaxEnt performed better with an AUC value of 0.86. Overall the outputs of MaxEnt and GARP matched in terms of geographic regions predicted as suitable/unsuitable for bushmint in India, however, predictions were closer in the spatial extent in Central India and Western Himalayan foothills compared to North-East India, Chottanagpur and Vidhayans and Deccan Plateau in India. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Biological invasion by non-native or alien species is recognized to pose significant losses in the economic value, biodiversity and health of invaded systems (Hulme, 2007; Wittenberg and Cock, 2001). India is one among the ten mega-biodiversity countries in the world (MoEF, 2009), housing four global biodiversity hotspots viz., the Himalaya, the Indo-Burma, the Western Ghats and Andaman and Nicobar islands (Conservation International, 2012). With only 2.4% of the land area of the world, India has 11% of world's floral diversity with over 45,500 plant species and nearly 147 endemic genera (MoEF, 2009). Indian flora also has a significant percentage (173 species) of alien invasive plant species (Reddy, 2008) notably Lantana camara, Chromolaena odorata, Parthenium hysterophorus, Hyptis suaveolens and Ageratum conyzoides which have spread widely and caused perceptible negative impacts on the native biodiversity.
⁎ Corresponding author. Tel.: +91 135 2524176, +91 9411193962 (Mobile). E-mail addresses: [email protected] (H. Padalia), [email protected] (V. Srivastava), [email protected] (S.P.S. Kushwaha).
http://dx.doi.org/10.1016/j.ecoinf.2014.04.002 1574-9541/© 2014 Elsevier B.V. All rights reserved.
Bushmint or pignut (H. suaveolens (L.) Poit.), one among the world's most noxious weeds, which are invading natural ecosystems across tropical and sub-tropical regions of the world (Afolayan, 1993; Sarmiento, 1984; Wulff and Medina, 1971). It is a native of tropical America. Because of its widespread occurrence in the tropics, it is now regarded as a pan-tropical weed. In India, bushmint occurrence is reported from North-East India, Vindhyas, Deccan Peninsula, and Andaman and Nicobar islands (Wealth of India, 1959; Yoganarasimhan, 2000). Bushmint is a soft broad-leaved herb of family Lamiaceae. Due to its ruderal nature, it causes heavy infestation outcompeting the native flora. It forms large thickets and is believed to produce allelochemicals, which impedes the seed germination of native species. It is unpalatable to livestock and wild animals. It is threatening the course of natural succession, existence of vulnerable taxa, and wild animals in the invaded areas (Padalia et al., 2013). Its vegetative parts possesses certain medicinal constituents having anti-cancerous (Mudgal et al., 1997) and tumorigenic (Peerzada, 1997) properties. Knowledge about the invasion range of alien species is crucial for understanding the ecology of invasive species and for conservation and management planning. Researchers have in many cases relied on predictive models for assessing patterns of species distribution (Guisan and Thuiller, 2005; Xue-Qing et al., 2013). Species distribution
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models (SDMs) have emerged from efforts to determine relationships between species and their environments (Guisan and Thuiller, 2005; Robertson et al., 2004) and provides one of the best ways to overcome the sparseness of the species distributional data. SDMs are used to predict climate change impacts, study biogeography, assist in reserve selection, improve species management and answer many conservation biology questions (Guisan and Zimmermann, 2000). SDMs attempt to define the potential ecological niche of any species. An ecological niche is comprised of the fundamental niche and the realized niche (Silvertown, 2004). The fundamental niche is an n-dimensional hyper-volume defined by the environmental conditions, within which populations of a species are able to maintain a longterm average net reproduction rate in the absence of inter-specific competition and natural enemies. The realized niche is the reduced n-dimensional hyper-volume that results from limiting ecological processes such as inter-specific competition, herbivory, and dispersal (Austin et al., 1990; Silvertown, 2004). The potential distribution of species is thus considered analogous to the fundamental niche. Several species distribution models differing in concepts, underlying assumptions, advantages and limitations are discussed in literature: Generalized Linear Model (GLM) and Generalized Additive Model (GAM) (Elith et al., 2006; Guisan et al., 2006), General Rule Set Production (GARP) (Elith et al., 2006; Stockwell and Peters, 1999,) Maximum entropy (MaxEnt) (Phillips et al., 2006), Bioclimatic envelope (BioClim) and DOMAIN (Elith et al., 2006). These models use presence/absence or presence/pseudo-absence or presence only data for making prediction about the species distribution. Among others models MaxEnt and GARP have often shown accurate prediction capabilities in simulations and evaluations with presence only data, outperforming classical modeling approaches, such as domain, bioclim, and logistic regression (Hijmans and Graham, 2006; Phillips and Dudik, 2008). The MaxEnt approach is used to estimate probability of distribution of target species by analyzing the probability distribution of maximum entropy (Phillips et al., 2006). MaxEnt ver.3.1 software is freely downloadable from http://www.cs.princeton.edu.
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GARP is a different flexible and commonly used species distribution modeling tool. GARP generates a random set of mathematical rules following an iterative process of rule selection, through testing, incorporation and rejection (Peterson et al., 2007). These sets of rules are combined in a random way to generate the potential niche of the species limited by the environmental conditions. GARP has been applied to studies that seek to forecast the risks on sites prone to be infested by alien invasive species, based upon the degree of environmental matching between the species' native and non-native ranges (Ganeshaiah et al., 2003; Underwood et al., 2004). The objectives of the present study are the: (i) prediction of the potential invasion range of bushmint across India using MaxEnt, (ii) comparison of the performance of MaxEnt-based predictions with the traditionally used GARP predictions, and (iii) identification of the environmental correlates of bushmint defining its invasion in India. The present study is the first-ever attempt to generate wall to wall spatial information on the potential distribution of any fast-growing alien invasive species in India. 2. Materials and methods 2.1. Study area and species occurrence records The present study was carried for the entire India which has ca. 3.29 million km2 of land area (Fig. 1). India has a considerably large area in which tropical and sub-tropical climatic conditions prevail. This makes it prone to the invasion of many noxious alien invasive species tropical in origin. We used 530 presence records of bushmint available for India. In the study Biodiversity Characterization at Landscape Level (BCLL) (Roy et al., 2012), bushmint was recorded in 463 vegetation plots out of total 16,518 plots laid out between year 2002 and 2007. 67 records of bushmint were compiled from the independent surveys carried out from 2007 to 2011 (Fig. 1). Occurrence records included the latitude/longitude of plots in which bushmint was recorded. In the BCLL study, the vegetation types in India were
Fig. 1. Bushmint in India (black dots depict presence locations, field photographs: (a) Bushmint in flowering during September and (b) monothickets of bushmint (in light violet color) along the river course during December in Dun valley).
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sampled following systematic random sampling with probability proportional to size. Bushmint was primarily recorded from tropical dry deciduous, moist deciduous and thorn forests which cover 41.87%, 19.73%, 2.25% of forest area in the country (FSI, 2011). Since India is a vast country, and therefore species data was collected in different phases over a period. It is possible to assume that bushmint cover would have increased during the period. However, considering the observed rate of its spread, it is unlikely that an increase in bushmint cover would significantly affect the modeling of its potential distribution at a grid resolution of 1 km2. 2.2. Environmental layers The environmental layers of climatic, topographic and non-climatic (soil and land use/land cover) nature were used to predict bushmint's ecological environment. Nineteen climatic variables were obtained from WorldClim dataset (Hijmans et al., 2005, http://worldclim.org/ bioclim.htm). These climatic variables have been found useful in defining the eco-physiological tolerances of species (Hijmans et al., 2005). Elevation information was derived from the GeoTOPO digital elevation model (http:/gsi.go.jp). Soil type information up to soil orders prepared by the National Bureau of Soil Survey and Land Use Planning (NBSS & LUP), Nagpur, India at 1 million scale was used. Soil classes were namely Alfisol, Entisols, Inceptisols, Mollisols, Ultisols, Aridisols and Vertisols. Land use/land cover data at 1:250,000 scale of year 2009 was obtained from National Remote Sensing Centre (NRSC), Hyderabad, India. ArcGIS ver. 9.2. spatial analyst tool was used to resample the data layers to a 1 km2 grid resolution, and to match their geographic extent. The high correlation among predictor variables makes interpretation of variables difficult. Multi-collinearity among predictor variables was reduced following the methods suggested by Kumar et al. (2006). Among two highly cross-correlated variables (Pearson Correlation Coefficient r N 0.75, alpha = 0.05), one was selected which is biologically relevant to the species, and offers ease in interpretation of model (Kumar and Stohlgren, 2009). For instance, if variables annual precipitation and precipitation of wettest month were found highly correlated, we retained precipitation of wettest month since it captures seasonal variability in precipitation. Eventually, 14 predictor variables were retained to run MaxEnt and GARP models. 2.3. Model design setting The setting of the model design for the comparison of MaxEnt and GARP models is not straightforward. The two models despite their similarities to make prediction based on presence only data, and their ability to support continuous as well as categorical environmental variables, differ in terms of model conceptualization, sample size requirement and output generation (Peterson et al., 2007; Phillips et al., 2006). MaxEnt has been found effective despite small sample sizes (Benito et al., 2009; Hernandez et al., 2006). There is considerable random variability in MaxEnt and GARP predictions. We used k-fold cross validation function in MaxEnt to randomly partition the occurrence data into ten random subsets. The random partitions rather than a single one was used to assess the average behavior of the algorithms following Phillips et al. (2006). However, in order to minimize the model variation in case of GARP, we implemented the “best subset” selection procedure (Anderson et al., 2003; Phillips et al., 2006; Stockwell and Peters, 1999). The Openmodeller software package (http://openmodeller.sourceforge. net.) provides functionality to run the best-fit GARP model. In the case of the k-fold cross validation procedure of MaxEnt ten different output predictions were generated based on default parameters values (0.01 convergence limit, and 1000 maximum iterations). An auto feature limiting function was used to fit the speciesenvironmental curves, and to train MaxEnt model. Initially, multiplier value was set very small (e.g. 0.02) with default set of parameter to
have a highly over-fit model. Subsequently, multiplier value was increased and modeled distribution and changes in AUC was analyzed. MaxEnt outputs are sensitive to the selection of background data. If the selection of background data points is from the area larger than the sampling extent it may result in a high AUC. We, therefore, restricted selection of background data points to sampling extent following Elith et al. (2010) and Flory et al. (2012). Moreover, direct comparison MaxEnt and GARP output is problematic. While GARP predicts the presence or absence of a species in a location, MaxEnt estimates the occurrence probability of the species in that location. Therefore, MaxEnt generated continuous outputs with 10 replicates were converted into binary maps (presence/absence) using the 10th percentile of the training presence method available in MaxEnt. The 10th percentile of training presence selects values above which 90% of the training locations are correctly classified. It gives conservative estimates of prediction compared to the minimum training presence threshold. Minimum training presence threshold correctly predicts every training location and may however lead to overprediction (Jarnevich and Reynolds, 2011). 2.4. Evaluation of models We assessed the consistency of the MaxEnt model and the level of uncertainty associated with certain variables by examining species– environmental relationships and the contribution of individual variables in the model. Species–environment relationships are one of the most important aspects of species distribution modeling as it serves both to inform us about the relationship between the species and the environment, but also it serves as a very important accuracy check to ensure that the relationships that the model results are based on are realistic. Species–environment curves were analyzed to explain the patterns observed in the MaxEnt output. We reported mean and range values for the variable contribution and species–relationship curves. We used a Jackknife test to examine the importance of individual variables for MaxEnt predictions. Jackknife test gives training, test and AUC gains for three scenarios (without variable, with only one variable and with all variables) for different environment variables used for prediction. We used receiver operating characteristic (ROC) area under curve (AUC) method to evaluate the performances of MaxEnt and GARP models. AUC provides a single measure of model performance independent of any particular choice of threshold. The AUC measures model performance that ranges from 0 to 1. AUC is a widely used procedure for comparing species distribution models performances (Babar et al., 2012; Phillips et al., 2006; Stohlgren et al., 2010). 3. Results 3.1. Predicted potential invasion range Fig. 2 depicts the probability of occurrence of bushmint modeled using MaxEnt. Higher probability (values closer to 1) represents areas suitable for bushmint. Zero probability or lower probability indicates areas less suitable for bushmint. A comparison between potential invasion ranges modeled through MaxEnt and GARP is shown in Fig. 3. We used a 10% minimum threshold to define the minimum probability of suitable habitats in MaxEnt output. As a result the values N0.32 were categorized as suitable. A significantly large area of India was predicted as suitable for bushmint. MaxEnt and GARP predicted 40.23% (1,322,341 km2) and 40.10% (1,317,896 km2) of the country suitable for bushmint growth respectively. Hence, MaxEnt and GARP predicted almost the same area suitable for bushmint. The common areas predicted as suitable MaxEnt and GARP are large areas in Central India, and part of Western Himalayan foothills. Whereas, extent of predicted area varied for North-East India, part of Deccan Plateau, Vindhyas
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Fig. 2. MaxEnt based probability distribution map of bushmint over India.
Fig. 3. Potential invasion range of bushmint: (a) MaxEnt and (b) GARP. The dark shaded area represents areas suitable for bushmint. MaxEnt predicted area is 10th percentile of training presence (N0.32 probability as suitable). GARP predicted N0.5 probability area as suitable.
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Table 1 Table 1. Variable contribution in MaxEnt models averaged over 10 replicates along with general statistics environmental profile of bushmint calculated based on 597 occurrence records in India. Environmental variables
Mean
Standard deviation
Min
Max
Mean
Temperature Seasonality (°C) Elevation (m) Precipitation of Wettest Month (mm) Mean Diurnal Range (°C) Mean Temperature of Wettest Quarter (°C) Isothermality Precipitation of Wettest Quarter (mm) Maximum Temperature of Warmest Month (°C) Mean Temperature of Coldest Quarter (°C) Precipitation of Driest Month (mm) Precipitation of Warmest Quarter (mm) Mean Temperature of Driest Quarter (°C) Soil Type Lulc Type
16.80 13.61 9.99 8.59 7.84 7.80 6.79 6.94 6.77 5.68 4.49 1.03 2.82 0.83
118.34 195.08 168.68 1.48 1.52 4.25 412.48 3.38 2.66 3.05 450.72 3.58 – –
147.00 23.00 76.00 8.00 21.00 38.00 187.00 28.00 12.00 0.00 47.00 15.00 – –
785.00 1183.00 1013.00 15.00 31.00 60.00 2542.00 42.00 24.00 21.00 2328.00 30.00 – –
452.85 374.70 346.10 11.45 26.54 43.73 868.30 38.40 19.18 3.40 328.18 22.90 – –
and Chotanagpur regions. Some of the regions predicted as suitable (e.g. foothills of Western Himalaya, tropical parts of North-East India) are at present not seriously infested by bushmint as indicated by field surveys. The areas predicted as suitable corroborated well with the survey records. 3.2. Evaluation of models Table 1 shows the mean values of relative contribution of predictor variables in the MaxEnt model. The values reported are averaged over 10 replicates. Temperature seasonality, elevation, precipitation of wettest month and mean diurnal range were the strongest predictors of bushmint distribution with 16.8, 13.61, 9.99 and 8.59% contributions, respectively. Fig. 4 shows the relative importance of different environmental variables based on results of jackknife tests in MaxEnt. Jackknife results also showed that the temperature of wettest quarter, temperature seasonality, elevation, precipitation of wettest month and warmest quarters are the most important predictors of bushmint distribution in India. Among all top predictors variables, the mean temperature of wettest quarter was a highly uncertain variable showing larger standard deviation in 10 replicates. Fig. 5b suggests that bushmint distribution with a N 0.5 probability of presence was limited between 100 m to 800 m amsl. Above 1000 m altitude, bushmint probability of presence was very low. Its probability of presence (N 0.5) increased with rise in mean temperature of the
wettest quarter from 25 °C to 28 °C and thereafter sharply decreased (Fig. 5c). Rainfall had a positive effect on bushmint distribution between 200 m to 1000 m rainfall (Fig. 5e & f). Very low rainfall (e.g. Rajasthan) and very high rainfall regions in the country (e.g. Western Ghats and eastern parts of North-East India), however, show very low or zero probability of presence for bushmint. The MaxEnt-based predicted area suitable for bushmint was found differing with those resulting from GARP (Fig. 3), but AUC produced by MaxEnt was significantly higher than that by GARP. The testing AUC of the MaxEnt model was 0.86 against the GARP model (AUC: 0.75). This concludes that MaxEnt has better prediction accuracy as compared to GARP. The higher AUC of the MaxEnt model in comparison to GARP demonstrates the stronger prediction capability of MaxEnt. 4. Discussion Areas under risk from plant invasions are difficult to predict. Researchers have relied upon broader environmental surrogates for establishing species-environment relationships to improve predictions on potential distribution and suitable habitats of invasive species (Rejmánek et al., 2005). However, predicting when species will become invasive is elusive as many plants exhibit a lag phase that cannot be determined a priori (Ewel et al., 1999; Rejmánek et al., 2005). The modeling of potential distribution of species helps in incorporating the dispersal lag period of invasive (e.g. meta-population processes) and
Fig. 4. Jackknife test for AUC of individual environmental variable importance (blue bars) relative to all environmental variables (red bar) for MaxEnt model. Values shown are averages over 10 replicate runs. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 5. Relationships between top environmental predictors and the probability of presence of bushmint in India: (a) temperature seasonality,(b) elevation (meter), (c) mean temperature of wettest quarter (°C),(d) mean diurnal range (°C), (f) precipitation of wettest month (mm), (g) precipitation of wettest quarter (mm). Red curves show the mean response and blue margins are ±1 SD calculated over 10 replicates. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
in redefining the control strategies based on the data collected, most often only from accessible localities. Globally, geographical distribution of bushmint is limited to regions falling within 31° N and 24° S latitudes (Padalia et al., unpublished). In India, MaxEnt and GARP both have predicted a significantly larger area as eco-climatically suitable for bushmint. Field surveys report widespread occurrence of bushmint in India. Our model predictions suggest that bushmint has wide ecological amplitude that is why bushmint has become widely invasive in India. Bushmint introduction in India has occurred long ago as Flora of British India (Hooker, 1885) reports its occurrence in Deccan peninsula, Cachar and Nicobar Islands. Further studies have also reported its invasion from different part of the country viz., Himalayan foothills (Padalia et al., 2013), Vidhayns
(Sharma et al., 2009), Andaman and Nicobar Islands (Yoganarasimhan, 2000). The predicted habitats are consistent with the wide-ranging habitat associations of bushmint in its well-established sites. Study has indentified hotspots of bushmint invasion in India and also indicated the possible dispersal pathways. Percent variable contribution and jackknife procedures in MaxEnt indicated relative importance of different bioclimatic predictors. Higher temperature seasonality of temperate and arid regions of country is negatively related to bushmint distribution. Cold stress and lack of thermal accumulation in higher altitude regions (above ca. 1500 m amsl) show zero or very low probability of bushmint occurrence. Bushmint occurrence is reported up to ~1560 m amsl altitude within its native range, in Aragua in Venezuela, (GBIF, 2013) while in India it has invaded the
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central highlands (altitude ~850 m amsl) in Maharastra state. Being an annual herb from tropics, bushmint prefers wet and warmer environments to grow and spread. Therefore, the mean temperature and precipitation of the wet season characterized areas suitable for bushmint. The very low relative importance of non-climatic variables such as soil type and land use/land cover in the MaxEnt model (as shown by variable importance and jackknife procedure) indicate that bushmint is highly plastic as far as its preference for different soil and land use land cover types is concerned. Further studies are needed to see the role of scale of study on incorporating the effect of forest density or fragmentation of species invasion with occurrence records of bushmint from high forest density landscape. At present, most of the occurrence records of bushmint were from regions having open and degraded forests areas in the country. Field based study has shown bushmint preference for rocky dry substrate in Vidhayans (Sharma et al., 2009). The occurrence records for invasive species over large areas are generally poorly documented. Even if large areas were considered, occurrence data was compiled from a variety of sources with varying degrees of sampling intensities. In this study, occurrence data on bushmint was collected randomly with uniform sampling intensity over the entire study area. Therefore, the study strength lies in comparing the model performances with good quality species occurrence data. The AUC which provides a global measure for model's performance has highlighted the higher discriminating power of MaxEnt in predicting the suitable/ unsuitable sites as compared to GARP. The finding of our study therefore coincides with other recent studies which also have suggested the superiority of MaxEnt among other models (Babar et al., 2012; Phillips et al., 2006; Stockwell and Peterson, 2002). The cross-correlation of predicted potential distribution ranges also provided a measure of agreement between MaxEnt and GARP. Spatial uncertainty in the potential invasion ranges of bushmint predicted by MaxEnt and GARP can be minimized through ensemble modeling (Stohlgren et al., 2010). At present, policy and decision makers seriously lack data on areas under the invasion risk from bushmint in the country. The potential invasion modeled in this study would help in targeting field surveys to figure out the actual areas under bushmint invasion in the country. The area under bushmint cover can be effectively detected and mapped using spectral un-mixing of multispectral data (Padalia et al., 2013). The potential distribution maps would help to take rapid response and early mitigation of the species. Further, it possible to get insights into climatic factors affecting the potential distribution of bushmint under projected climate change scenarios using a MaxEnt projection feature (Donnell et al., 2012; Evangelista et al., 2011). 5. Conclusions Species distribution models are powerful approaches to model the distribution of invasive species. With the fine-scale spatial information available on environmental variables and developments in species distribution models based on presence only data, ecologists have been able to define broad ecological conditions suitable or unsuitable for species over large geographic regions. The outcome of this study indicates that the potential distribution of bushmint in India facilitated by environmental conditions is vast, and far larger than its current geographical extent. The study identifies the environmental factors favorable to establishment of bushmint in India. The study also established that MaxEnt has stronger predictions as compared to GARP as reported by other studies as well. The potential invasion maps developed in this study can assist in developing and implementing mitigation measures to minimize the bushmint invasion in India. Acknowledgments We are grateful to Dr. Y. V. N. Krishna Murthy, Director, Indian Institute of Remote Sensing, Dehradun for his keen interest in the study, and
his support. Authors also acknowledge the valuable suggestions given by the anonymous reviewers.
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