Effects of Climate Change on Invasion Potential Distribution Of

Priyanka and Joshi, J Earth Sci Clim Change 2013, 4:6 Earth Science & Climatic Change http://dx.doi.org/10.4172/2157-7617.1000164

Research article Article OpenOpen Access Access Effects of Climate Change on Invasion Potential Distribution of Lantana camara Neena Priyanka1,2,* and Joshi PK1 1Department of Natural Resources, TERI University, New Delhi, India 2Pitney Bowes Software, Noida, India

Abstract Climate change appears to be affecting global patterns of invasive species distribution. Forecasts based on ecological niche modeling suggest that greater impacts can be expected in the future. However, such projections are contingent on assumptions regarding the future climate conditions and invasion potential of a species. This study explores the relationship between climate change and potential distribution of Lantana camara in the National Parks of Jim Corbett and Rajaji (Uttarakhand, India). Using three representative climate change models viz., CSIRO (Commonwealth Scientific and Industrial Research Organization), CCCMA (Canadian Centre for Climate Modeling and Analysis) and HadCM3 (Hadley Centre for Climate Prediction and Research’s General Circulation Model) across the time slices 2020 to 2080 under two regional climate change scenarios A2a and B2a, Lantana camara potential distribution models were derived. The model projections were in consensus that invasion range was likely to expand and infestation would be more severe under the A2a scenario indicating that the species may prefer warmer conditions. Taken together, the modeled results suggest that in the future, the two National Parks may be impacted largely by the gregarious presence of Lantana camara. Predictive models can provide resource managers with a tool for the early detection of invasive species and help circumvent negative ecological impacts resulting in substantial economic savings.

Keywords: Lantana camara; Niche modeling; Climate change; Climate scenarios are the alternative images of how the climate in Invasion; National parks the future may unfold. The Special Report Emissions Scenarios (SRES) describes four climate scenario “families”, each with a divergent future Introduction description. Since it is difficult to project far-off future emissions and Invasive plants have altered the biodiversity and ecological other human factors that influence climate, scientists use a range integrity of native habitats [1,2]. They have affected natural processes of scenarios making various assumptions about economic, social, [3], homogenized flora [4], caused the extinction of species [5,6], demographic, technological, and environmental conditions to project compromised agriculture production [7], and damaged ecosystem future global warming (Table 1). Scenarios range from Low emissions resources [8,9]. The problem arises because questions of when and why (B1, B2: less pronounced future warming than A1 and A2) to High emissions (A1 and A2) in three time slices: ‘2020s’ (2010–2039), ‘2050s’ some invasive species escape and cause nuisance remain unanswered (2040–2069) and ‘2080s’ (2070–2099). [10]. The problem has been further exacerbated by a considerable climate change in the natural ecosystems. Climate change may be To predict the potential distribution of invasive species under the described as a long-term shift in the regional trends of weather different climate change scenarios, various modeling algorithms have measured by changes in its rate, range and magnitude [11]. It has been been used by researchers [18-21]. Here, the choice of the modeling plaguing much of the world and is a serious concern at continental, algorithm plays a critical role in determining the accuracy of the regional, national and local scales [11]. predictions. Most algorithms require both presence and absence data sets. This is difficult to collate because while the presence data can The relationship between species invasion and climate change is be collected with confidence, the absence data has high inherent complex and linked in several ways. Climate change is likely to enhance uncertainties [22,18]. Hence, modeling methods that require presence the dimensions of invasive to occupy new areas, while simultaneously only dataset, and can predict effectively even with limited available data increasing the adaptability in natural communities by disturbing the have been used more commonly [23,21]. dynamic equilibrium maintaining them [12-14]. On the other hand, invasive species influence the magnitude, rate and impact of climate The aim of the present study is to assess through modeling the future change by altering ecosystem composition, structure and function [15- distribution of the robust and gregarious invasive species-Lantana 17]. The Intergovernmental Panel on Climate Change (IPCC) covers camara - relative to its current distribution potential in the Western the issue in considerable detail and reports that the recent worldwide Himalaya regions of India under different climate change scenarios. warming will result in pole ward and altitudinal shifts in plant ranges. The report also forecasts that many ecosystems will become vulnerable to biological invasions as climatic barriers are removed. The IPCC *Corresponding author: Neena Priyanka, Department of Natural Resources, concludes that an increase of greater than 1.5°C–2.5°C in the global TERI University, New Delhi, India, E-mail: [email protected] average temperature will cause dramatic changes in species distribution Received August 05, 2013; Accepted September 20, 2013; Published September and ecosystem function, resulting in overwhelmingly negative 28, 2013 consequences for ecosystem sustainability [11]. Citation: Priyanka N, Joshi PK (2013) Effects of Climate Change on Invasion Thus, in order to improve our understanding of likely impacts of Potential Distribution of Lantana camara. J Earth Sci Clim Change 4: 164. doi:10.4172/2157-7617.1000164 invasion of a given species in the wake of climate change, knowledge of the current presence or absence and likelihood of its establishment Copyright: © 2013 Priyanka N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits or spread under future climate change scenarios is of the utmost unrestricted use, distribution, and reproduction in any medium, provided the importance. original author and source are credited.

J Earth Sci Clim Change ISSN:2157-7617 JESCC, an open access journal Volume 4 • Issue 6 • 1000164 Citation: Priyanka N, Joshi PK (2013) Effects of Climate Change on Invasion Potential Distribution of Lantana camara. J Earth Sci Clim Change 4: 164. doi:10.4172/2157-7617.1000164

Page 2 of 9

Scenarios Hypothesis Very rapid economic growth Global population reaches 9 billion in 2050 then decreases thereafter Rapid introduction of new and more efficient technologies A1 A1F1 (massive use of fossil fuels); A1T (a strong call on non-fossil sources) A1B (balanced call on various energy sources without heavily relying on one in particular) World population reaches 15 billion people in 2100, and rising A2 Economic growth and spread of new efficient technology changes fragmented and slower •High emissions at regional level World population reaches 9 billion people in 2050 then decreases B1 Economy is dominated by services and informational technologies Addressing economic, social, and environmental problems World population reaches more than 10.4 billion people in 2100 and increasing at a slow rate Intermediate levels of economic development B2 Development and spread of new technologies uneven and slower than for B1 or A1 Low emissions at regional level (Source: SRES, IPCC, 2007) Table 1: IPCC climate change scenarios

Figure 1: Location of Study Area.

The Maxent modeling technique has been employed for assessing range of 300-1,345 m absl with a minimum temperature around 13.1°C the current distribution. In addition, three other models-CSIRO and maximum temperature around 38.9°C and average rainfall of 1200 (Commonwealth Scientific and Industrial Research Organization), mm. The spatial coverage of this study was deliberately made larger (a CCCMA (Canadian Centre for Climate Modeling and Analysis) section of the Western Himalayan region was considered instead of and HadCM3 (Hadley Centre for Climate Prediction and Research’s the geographical areas of two parks) than the actual range occupied by General Circulation Model)-have been employed to predict the future Lantana camara to ensure encompassing the full extent of the predicted distribution range of Lantana. range (Figure 1). Both the parks are engrossed in the Shivalik ecosystem Material and Methods and the beginning of the vast Indo-Gangetic plains, thus representing vegetation of several distinct zones and forest types and diverse flora Study Area and fauna. The study area comprises a part of the Western Himalayas including This region encircling two important national parks was selected two important National Park (NP) viz. the Jim Corbett and Rajaji. The for study because of its gregarious Lantana camara presence, high Corbett NP (29°50'–30°20' N and 77°50'-78°30' E) covering an area ecological and economic value, and its charisma of conservation of 1318.54 sq. km, lies in two districts– Nainital and Pauri Garhwal significance, which houses large populations of huge mammals like the of Uttarakhand, India. The park is situated at an altitudinal range of Indian Tiger, Asian Elephant and Swamp Deer among other important 385-1100 m absl with a temperature range of 4˚C-42˚C and rainfall of medicinal and endemic variety of plant and animal species. High 1400-2800 mm. The Rajaji NP (29°56'- 30°20' N and 77°55'-79°80' E) human density in and around forests has resulted in strong gradients covers an area of 820.42 sq. km and is located between Haridwar and of degradation and anthropogenic pressures from the forest edge to the Dehradun districts of Uttarakhand. The park is situated at an altitudinal interior, thus paving the way for invasion by Lantana camara.

Page 3 of 9

Input datasets indicating the degree of linear dependence between variables. In present study, Pearson value threshold of ±0.80 was considered as elimination A total of 137 occurrence records of Lantana camara were obtained criteria for selecting unique variables. Amongst 73 variables obtained, from native locations of Jim Corbett and Rajaji NP through field numbers of predictor variables were reduced to 23 based on Pearson survey. The establishments of these occurrence records were validated correlation test. from park officials and by electronic database searches such as Google, Scopus, Science Direct and published works. Current climate and Modeling scenarios of future projections (IPCC 3rd assessment data) datasets The analysis was conducted using Maxent (http://www.cs.princeton. at a spatial resolution of 1 km were downloaded from the WorldClim edu/~schapire/maxent/). The Maxent (Maximum Entropy) modeling database [24]. Three climate-change models CCCMA (Canadian algorithm developed by Phillips [21] is reliable even when data availability Centre for Climate Modeling and Analysis), CSIRO (Commonwealth or reliability is less. It can handle both continuous and categorical Scientific and Industrial Research Organization) and HadCM3 (Hadley variables, incorporating interactions, and modeling non-linearities Centre for Climate Prediction and Research’s General Circulation [31,23,32]. It has been used in developing ecological models for various Model), for the period of 2020–2080 including two scenarios A2a biodiversity applications [20,33,34]. Several studies have demonstrated and B2a were used for building invasion potential distribution model the reliability of Maxent [4,35,36,32]. Maxent is extensively being used of Lantana camara. Bioclimatic layers were not available for future by the Australian government agencies to predict the distribution of datasets, so these were computed from temperature and precipitation key weed species under future climatic conditions [13,10,37-39]. Thus, datasets and equations available at Worldclim resources (http://www. in the current research Maxent modeling technique was used. worldclim.org/). Climatic envelopes have information on temperature, precipitation, its seasonality and deviations and thus impact species Default values for the convergence threshold (10^-5) and maximum distribution greatly. Besides exerting direct physiological effects on number of iterations (1000) were selected for the model settings as species, climatic variables also reflect energy and water availability suggested by Phillips [21]. Regularization multiplier was set to 1 and the which in turn impinges ecosystem productivity. Topographical variables “fade by clamping” option was used to control extrapolation beyond the such as altitude, slope, aspect, flow direction, flow accumulation range of the training dataset. The linear, quadratic, product, hinge and and compound topographic index (CTI), have an indirect effect in threshold functions were used. Setting of regularization values, which influencing microclimates within species ranges [25], so these were address the problems of over-fitting, and the selection of ‘features’ were included in present study. CTI was computed using equation 1: performed automatically by Maxent as per the default rules depending

As upon the number of distribution records and features used in model CTI = ln  (1) Slope construction. One third of the distribution records were retained

Where, As = Flow accumulation obtained from elevation after at random for model evaluation using Area under Curve (AUC) of usual pre-processing to fill sinks and find flow directions. Slope=Slope the Receiver Operating Characteristic (ROC) plot of sensitivity vs. percent derived from altitude. 1-specificity [21]. Models found to have good predictive performance have test AUC value greater than 80%. Models were projected from Monthly potential evapo-transpiration (PET) was calculated using the present to the future climate scenarios. The Jackknife test for Thornthwaite equation [26,27] as illustrated in equation 2. a variable importance was carried out to determine predictive ability Ta PET( mm / day )= 16 ×× dl  10 × (2) of the variables used and their contribution to species expansion or I contraction [21]. where,

Ta = Mean monthly temperature (°C) Results −−52 73 a=0.492 +() 0.0179 II −×()() 7.71 10 +× 6.75 10 I2 Spatial invasion potential distribution of Lantana camara dl=Day length in hours/12z Using 137 occurrence localities of Lantana camara and 23 predictor variables, models were generated for depicting the potential distribution I =∑ i (Annual heat index) and i=Monthly heat index given by of Lantana camara under the current and the future climate change 1.5 T i = a projections. 5 These predictor variables were selected based on biological relevance Current climate projections: The Maxent model depicted high to species distribution and information obtained from other spatial infestation of Lantana camara in the Western Himalayan foothills modeling studies carried out on Lantana camara [6,28-30]. To enable enclosing Haridwar, Pauri Garhwal, Dehradun, parts of Tehri Garhwal projections of future species invasion potential ranges, environmental and Almora regions. The species was found to be predominant in predictors were required to be in pairs, one for current and other for regions with altitude less than 2000 m absl, mean annual precipitation future conditions. Thus, only those variables that existed for future ranging from 800 to 2000 mm, annual temperature regime of 10 climate scenarios and matched with variables of current climate to 30˚C, and soil ranging from sandy to gravelly and clayey loams. scenarios were selected. Since topographical variables are not subject Lantana camara populations were found to be more in the northern to change in future these were assumed to be constant for both time and western limits as compared to the southern and eastern regions. periods due to high uncertainties in projecting their future conditions. The region in and around Jim Corbett and Rajaji NP were highly The predictor variables were subjected to multi-collinearity diagnostics infested with the species which has already reached nuisance status. using Pearson product-moment correlation coefficient to reduce data Most regions of the Jim Corbett NP manifested very high potential dimensionality. The correlation coefficient values range between +1 distribution with infestations severe in the Jhirna, Dhikala and Bijrani and −1 inclusive where +1 in the case of a perfect positive (increasing) zones. Subsequently, species expansion varied from medium to high linear relationship, −1 in the case of a perfect decreasing (negative) in the south-western zone of the park. Other eastern and northern linear relationship, and some value between −1 and 1 in all other cases, regions such as Morghatti, Halduparao, Mundiyapani, Hathikund and

Page 4 of 9

Kalagarh displayed a more recent movement of the species. In the Rajaji likely to expand its invasion regime in the future as per the CCCMA NP, very high infestation was seen in the southern and western regions model. including Haridwar, Dholkhand, Motichur and the Chilla range. Other In the CSIRO model, potential distribution of Lantana camara regions such as Chillawali, Kansrao, and Mohand showed a more recent showed altitudinal expansion under varied time slices. Greater movement of the species in the northern and eastern areas (Figure 2). expansion was observed towards the upward and downward regions Future Climate projections: CCCMA, CSIRO and HadCM3 of the study area (Figure 4). Similar to the CCCMA model, very high climate models. Potential distribution of Lantana camara was extensive potential distribution was observed under both A2a and B2a emission scenarios in increasing order of magnitude from the current distribution towards the north-western, eastern, and south-western regions under to 2080. However, the changes in the predicted areas were more in the CCCMA model. A slight range shift from moderate to very high magnitude here as compared to the CCCMA model. Under future invasion potential class was projected in the north-eastern and south- climate projections ranging from the time slice 2020-2080, considerable western regions of the study area from the time slice 2020-2080 (Figure range shift from moderate to very high probability of invasion potential 3). A remarkable upward and downward expansion was also evident was projected in the northern and southern regions of the study area, in response to the increasing temperatures, resulting in further spread i.e., altitudinal shifts. This was unlike the CCCMA model where lateral of Lantana camara. The density of infestation by the species showed shift or change in the western and eastern regions was more evident. similar increasing trends in both scenarios. Thus, Lantana camara is Increase in high and moderate ranges in the central regions to very

Figure 2: Invasion potential distribution model of Lantana camara under current climate projections.

Figure 3: Invasion potential distribution of Lantana camara under CCCMA model projections.

Page 5 of 9

Figure 4: Invasion potential distribution of Lantana camara under CSIRO model projections.

Figure 5: Invasion potential distribution of Lantana camara under HadCM3 model projections. high and high suitability ranges, respectively, is indicative of a shift of invasion were concerned. The only difference lies in the directional in the invasive regime. The projected future distribution maps for spread in 2080, where only an upward movement is observed unlike B2a scenario for 2020, 2050 and 2080 were almost similar to the A2a A2a scenario is evident. scenario in directional projections of invasion; however, they differed in the magnitude of infestation. The increase in the potential distribution area was more under A2a than B2a (Table 2) across all the climate models, thus further ratifying The HadCM3 model showed a mixed response of the potential that the species will likely expand its invasion regime under warmer distribution of Lantana camara with expansion along all gradients viz. climate (A2a scenario). lateral, upwards and downwards (Figure 5). Unlike the other two climate models, the HadCM3 model predictions revealed that A2a and B2a Current and future climate projections: Rajaji and Jim Corbett emission scenarios were significantly different from each other in the National Park (NP). High Lantana camara infestation was observed magnitude of infestation, with the infestation being more prominent in in Jim Corbett (Figure 6) and Rajaji NP (Figure 7) in current as well the former. Considerable range shift from low to very high probability as future climate models. The species spread was more remarkable in of invasion potential was projected in the eastern, south-western and both these NP as compared to the overall study area. The density of north-eastern regions of the study area. A remarkable southern and infestation was observed to increase with time from 2020 to 2080 for northern expansion was also evident, especially in the north-eastern all climate models. Aligning to the common projections of greater region, in response to the increasing temperatures. The projected future infestation under A2a emission scenario than B2a, projections in the distribution maps for the B2a scenario for 2020, 2050 and 2080 were Jim Corbett and Rajaji NP regions were no exception across all the almost similar to the A2a scenario in so far as the directional projections climate models.

Page 6 of 9

CCCMA climate model projections Invasion 2020 2050 2080 Potential A2a B2a A2a B2a A2a B2a Very Low 29007.15 29007.15 28647.47 28560.09 28005.05 28683.89 Low 4763.78 4763.2 5352.24 5281.25 5812.53 5288.31 Moderate 1892.82 1774.01 2027.86 2015.42 2051.55 2072.73 High 1357.34 1368.36 1473.31 1381.99 1488.7 1473.35 Very High 554.54 549.02 589.49 578.97 622.96 615.47 CSIRO climate model projections Invasion 2020 2050 2080 Potential A2a B2a A2a B2a A2a B2a Very Low 28800.03 28252.17 27791.03 30209.35 29109.86 27559.4 Low 4819.95 4919.01 5780.4 4244.66 4802.8 5381.03 Moderate 1815.01 2035.31 2093.29 1640.74 1861.77 2049.11 High 1490.35 1561.04 1694.1 1300.14 1493.76 1771.6 Very High 605.68 623.95 689.88 549.02 578.97 615.43 HADCM3 climate model projections Potential 2020 2050 2080 Invasion A2a B2a A2a B2a A2a B2a Very Low 29000.69 29373.71 28501.23 28563.21 28251.12 28576.58 Low 5097.8 5083.11 5170.77 5231.49 5545.58 5253.55 Moderate 1930.16 1714.48 2107.75 1944.14 2021.42 2192 High 1264.27 1175.82 1479.69 1317.16 1421.73 1489.39 Very High 557.46 503.26 590.95 546.51 610.53 586.72 Table 2: Suitable area of invasion potential distribution of Lantana camara (in sq. km.) under future climate model viz. CCCMA, CSIRO and HADCM3 projections.

CCCMA climate model projections Predictor Description Contribution (%) Variable 2020 2050 2080 A2a B2a A2a B2a A2a B2a Bio 2 Mean diurnal range 31.2 30.4 33.9 30.7 36.3 31.2 Bio 12 Annual precipitation 17.6 21.1 18.9 22.5 19.4 24.9 Potential evapo-transpiration PET3 16.8 18.3 15.3 16.8 16.5 18.7 of March Bio1 Annual mean temperature 7.5 6.2 8.7 7.9 9.4 8.7 Alt Elevation 5.8 5.1 6.2 6.0 6.2 6.1 Minimum temperature of Bio 6 4.7 3.8 4.8 4.2 4.9 4.1 coldest period Bio 14 Precipitation of driest period 3.7 3.9 3.2 4.1 2.9 4.1 CSIRO climate model projections Bio 2 Mean diurnal range 31.5 30.3 32.5 31.6 35.8 33.4 Figure 6: Invasion potential distribution of Lantana camara in Jim Corbett Bio 12 Annual precipitation 16.4 18.2 18.0 20.9 18.4 21.7 national park Potential evapo-transpiration PET3 15.8 16.5 15.3 16.7 15.9 16.7 of March Looking at the direction of spread, it seems that Lantana camara Bio1 Annual mean temperature 6.9 5.6 7.7 7.1 8.4 7.8 will consolidate its hold on the latitudes in which it currently occurs, Alt Elevation 5.4 5.3 5.6 5.2 5.7 5.4 Minimum temperature of Bio 6 3.8 3.4 3.6 3.2 3.9 3.1 and show lateral, upward and downward expansion in the future. All coldest period models were consensus on range shifts across time slice 2020-2080 with Bio 14 Precipitation of driest period 2.9 3.2 3.1 3.5 3.4 3.9 more infestations under A2a than under B2a scenario, indicating the HADCM3 climate model projections preference of Lantana camara for warmer climatic conditions that are Bio 2 Mean diurnal range 30.9 29.8 31.2 30.6 32.5 31.4 likely to be available with future climate change. Also, the change was Bio 12 Annual precipitation 21.3 23.2 21.9 24.1 22.4 24.9 more pronounced in the CSIRO model than the other two. Potential evapo-transpiration PET3 15.7 16.5 16.4 17.1 16.5 17.6 of March Analysis of variable contribution to species models Bio1 Annual mean temperature 9.1 8.4 8.7 7.8 8.4 7.0 The environmental variables that were most influential in predicting Alt Elevation 4.5 4.3 4.2 3.9 4.2 4.1 Minimum temperature of the occurrence of Lantana camara for all the future climate models Bio 6 3.7 3.4 4.6 3.2 4.7 4.0 coldest period were diurnal temperature range, annual precipitation, and potential Bio 14 Precipitation of driest period 2.9 3.0 3.2 3.2 4.1 3.6 evapotranspiration of March, with variable contribution above 5% (Table 3). However, the relative contribution of these environmental Table 3: Contribution (%) of the environmental predictor variables to invasion potential distribution model of Lantana camara in CCCMA, CSIRO and HADCM3 variables varied among the three climate models and across time slices. models under A2a and B2a climate scenarios.

Page 7 of 9

Direction of spread w.r.t current invasion the analysis of model projections showed varying degree of range potential distribution of Lantana camara expansion, all models concurred that range expansion was greater in Key: Climate magnitude and direction under the A2a scenario as compared to B2a Time slices √ - Spread exist; - Spread Negligible; Model + Very Low invasion potential; ++ Moderate (Table 4). invasion potential; +++ Vey High invasion potential Lateral Upwards Downwards The CSIRO and HadCM3 showed the largest modeled area under 2020 √ (+) √ (+) √ (+) high potential distribution of Lantana camara across all time units 2050 √ (++) √ (++) - under the A2a scenario. These were also the areas with high clamping. CCCMA A2a Clamping was considerably lower under the A2 scenarios and climate 2080 √ (+++) √ (++) √ (+++) model CSIRO (<0.17). Nevertheless, clamping suggests that the results 2020 √ (+) √ (+) √ (+) remain robust for a majority of the study area. Also, the projected CCCMA B2a 2050 √ (++) - √ (+) potential distribution of Lantana camara is expected to expand across 2080 √ (++) - √ (+) time slices viz. 2020, 2050 and 2080. The results further revealed that 2020 √ (+++) √ (+) - model gains and AUC values for Lantana camara were higher in CSIRO A2a 2050 √ (++) √ (++) √ (++) HadCM3 models as compared with those of the other two models. The 2080 √ (+++) √ (+) √ (+++) expansionary trend revealed by Lantana camara was in agreement with 2020 √ (+) - √ (+) Dobhal and Kumar [30,32]. In agreement with this, the results showed CSIRO B2a 2050 √ (++) - - 2080 √ (+) √ (+) √ (+) 2020 √ (++) √ (+) √ (+) HADCM3 2050 √ (+++) - √ (++) A2a 2080 √ (+++) √ (++) √ (++) 2020 √ (+) √ (+) - HADCM3 2050 √ (++) - √ (+) B2a 2080 √ (+) √ (+) √ (++) Table 4: Magnitude and direction of Lantana camara spread under future relative to current climate projections.

The diurnal temperature range was the most influential predictor variable, with its contribution ranging between 30.9-31.5% across all models in the time slice 2020 under the A2a scenario. The contribution increased in the range of 31 -34% in 2050 and 32-36% in 2080. Annual precipitation and potential evapotranspiration followed a similar trend though the contribution percentage was more pronounced under the B2a scenario. Thus, a general trend of increasing contribution of temperature-related predictor variables in the A2a model and that of precipitation-related predictor variables in the B2a model was observed. In all the models, temperature and precipitation alone contributed more than 50% of the entire variable contribution. The contribution of variables like elevation, minimum temperature of coldest period and precipitation of driest period was low, but significant nonetheless. Thus, Maxent model’s internal Jackknife test of variable importance indicated that diurnal temperature, annual precipitation and potential evapotranspiration of March were the most important predictors of potential distribution of Lantana camara under future climate change. Discussion Projecting species distributional shifts across time The potential distribution under future projections were strikingly similar to the current but were much larger in extent under all climate models and scenarios. Future predictions seem to be robust in most of the regions of the study area for all the climate models. Models suggest that most of the southern and western regions of the Western Himalaya may become infested with Lantana by 2080, because of the temperature increase. Pole ward shift of invasive species due to higher temperature and elevated precipitation has been reported in several other studies [40-42]. Given the prolific nature of Lantana camara, its ability to invade fragmented habitats, greater amplitude of ecological tolerance, and high dispersal ability and reproduction rate, the species may be able Figure 7: Invasion potential distribution of Lantana camara in Rajaji national park to benefit from the widening of climatically suitable areas. Although

Page 8 of 9 that the species will have the largest expansion in its range (44%) by the economy, more emphasis on environmental protection, lower emissions year 2080. and therefore a less pronounced future warming. Predictor variables The area of predicted future suitability is strikingly similar to current situation and much larger in extent under all predicted future scenarios. Results revealed that climatic variables were better fit to training Future predictions seem to be robust in most of the regions of study data and accuracy of predictions. This suggests that at the meso-scale, area for all climate models. Models suggest that most of the southern distribution limits of Lantana camara in the Western Himalayan and western regions of western Himalaya may become suitable by regions are mainly mediated by climate predictor variables viz., 2080 because of the increase in temperature. Higher temperatures and temperature and precipitation. Almost all physiological processes of elevated precipitation regime triggered pattern is reminiscent of studies Lantana are influenced by temperature, which in itself depends on on invasive species responding to climate change by shifting their range the surrounding physical environment [43-45]. The physiological toward the poles. However, given the prolific nature of Lantana camara, processes that are dependent on temperature include reproduction and its ability to invade fragmented habitat, greater amplitude of ecological allelopathic reactions [46], rates of dispersal [47] and ability to survive tolerance, high dispersal ability and reproduction rate, the species under adverse conditions [48]. Hence, distribution limits of Lantana are may be able to benefit from the widening of areas climatically suitable. highly dependent on optimal temperatures as illustrated by the species The analysis of model projections showed varying degree of range potential distribution niche models. expansion; however, all models were consensus on that under A2a range expansion was greater in magnitude and direction with respect Sources of errors and uncertainties in climate modeling of B2a scenarios. The results support the view that species ecological There are several sources of errors and uncertainties originating traits such as habitat will influence their response to changing climate from location data and climate scenarios when predicting current and causing vulnerability to native population extinction. The study such as future ranges. To reduce these uncertainties associated with location these will henceforth enable decisions makers to develop management data, records capturing the most well-known, long-standing and easily strategies for invasive species keeping the future in the purview. verifiable locations of species were used in this study. The climate change References variables are based on projected changes in population, technology, 1. Shigesada N, Kawasaki K (1997) Biological invasions: theory and practice. socio-economic and policies [11]. These are generated through weather Oxford University Press, Oxford, UK. station data that are prone to interpolation errors and biases associated 2. Lockwood JL, Hoopes MF, Marchetti MP (2011) Non-natives: plusses of with uneven spatial and temporal distribution of the weather stations invasion ecology. Nature 475: 36. [24]. In addition, climate surfaces generated by the interpolation of 3. Dogra KS, Kohli RK, Sood SK (2009) An assessment and impact of three weather station data may have spatial auto-correlation as closer stations invasive species in the Shivalik hills of Himachal Pradesh, India. Int J Bio are more likely to have the same climatic conditions. The uncertainty Conserv 1: 4-10. stemming from the future projections is overcome by using several 4. Bradley BA (2009) Regional analysis of the impacts of climate change on cheat models in the present study. This approach presumes that the different grass invasion shows potential risk and opportunity. Glob Change Biol 15: models are internally sound and represent at least a part of the elusive 196–08. truth by combining a range of information [49]. 5. Bhatt YD, Rawat YS, Singh SP (1994) Changes in ecosystem functioning after replacement of forest by Lantana shrubland in Kumaon Himalaya. J Veg Sci Conclusion 5: 67–70. Climate change affects all components of the earth system. Yet 6. Sharma GP, Singh JS, Raghubanshi AS (2005) Plant invasions: Emerging trends and future implications. Curr Sci 88: 726-734. individual components of the system react with a broad range of response times to increasing atmospheric GHG concentrations. The 7. DeGasperis BG, Motzkin G (2007) Windows of opportunity: historical and combined consideration of spatial and non-linear climate envelope ecological controls on Berberis thunbergii invasions. Ecology 88: 3115-3125. and environmental variables applied within the context of species 8. Raghubanshi AS, Tripathi A (2009) Effect of disturbance, habitat fragmentation interaction dynamics may be useful in assessing the potential and alien invasive plants on floral diversity in dry tropical forests of Vindhyan highland: a review. Trop Ecol 50: 57-69. consequences of climate change for plant invasion. However, lack of empirical data, complexity of current and future projection models, 9. Roberston C, Nelson TA, Jelinski DE, Wulder MA, Boots B (2009) Spatial- temporal analysis of species range expansion: the case of the mountain pine and uncertainty are inherent and remain challenges to be overcome in beetle, Dendroctonus ponderosae. J Biogeogr 36: 1446–1458. the future. This research was undertaken to showcase the importance 10. Beaumont LJ, Gallagher RV, Thuiller W, Downey PO, Leishman MR, et al, of modeling tools in assessing invasion potential under climate change (2009) Developing climatic envelopes among invasive populations may lead trajectories, with special emphasis on Lantana camara in the Western to underestimations of current and future biological invasions. Divers Distrib Himalayan regions. 15: 409–420. 11. Intergovernmental Panel on Climate Change (2007) The Physical Science Three climate-change models derived from HadCM3 (Hadley Basis. Contribution of Working Group I to the Fourth Assessment Report of Centre for Climate Prediction and Research’s General Circulation the Intergovernmental Panel on Climate Change. Cambridge University Press, Model), CSIRO (Commonwealth Scientific and Industrial Research Cambridge, UK. Organization) and CCCMA (Canadian Centre for Climate Modeling 12. Walther GR (2007) Ecology. Tackling ecological complexity in climate impact and Analysis) for the period of 2020-2080 including two scenarios research. Science 315: 606-607. A2a and B2a were used to obtain likely estimates of invasion potential 13. Beaumont LJ, Hughes L, Pitman AJ (2008) Why is the choice of future climate distribution of Lantana camara in future. A2a scenario envisions scenarios for species distribution modelling important? Ecol Lett 11: 1135-1146. population growth to 15 billion by the year 2100 and rather slow 14. Holt AC, Salkeld DJ, Fritz CL, Tucker JR, Gong P (2009) Spatial analysis of economic and technological development, and B2a, envisions slower plague in California: niche modeling predictions of the current distribution and population growth (10.4 billion by 2100) with a more rapidly evolving potential response to climate change. Int J Health Geogr 8: 38.

Page 9 of 9

15. Pyšek P, Richardson DM (2010) Invasive species, environmental change and 34. Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. management, and health. Ann Rev Env Resour 35: 25-55. Meth Ecol Evol 1: 330–342.

16. Evangelista PH, Kumar S, Stohlgren TJ, Young NE (2011) Assessing forest 35. Pearson RG, Raxworthy CJ, Nakamura M, Peterson AT (2007) Predicting vulnerability and the potential distribution of pine beetles under current and species distributions from small numbers of occurrence records: A test case future climate scenarios in the Interior West of the US. Forest Ecol Manag 262: using cryptic geckos in Madagascar. J Biogeogr 34: 102–117. 307–316. 36. Wisz MS, Hijmans RJ, Peterson AT, Graham CH, Guisan A (2008) Effects of 17. Smith AL, Hewitt N, Klenk N, Bazley DR, Norman Y, et al. (2012) Effects of sample size on the performance of species distribution models. Divers Distrib climate change on the distribution of invasive alien species in Canada: a 14: 763-773. knowledge synthesis of range change projections in a warming world. Env Reviews 20: 1-16. 37. Gallagher RV, Beaumont L, Downey PO, Hughes L, Leishman MR (2008) Weeds in a warmer climate: a tool for assessing tolerance to changing temperatures. 18. Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP, et al. (2011) Proceedings of the 16th Australian Weeds Conference, Queensland Weeds The crucial role of the accessible area in ecological niche modeling and species Society, Brisbane, Australia. distribution modeling. Ecol Model 222: 1810–1819. 38. Downey PO, Gallagher R, Beaumont L, Leishman MR, Hughes L (2007) Weeds 19. Stohlgren TJ, Kumar S, Barnett DT, Evangelista PH (2011) Using Maximum and Climate Change: what do we know and where to from here? Proceedings Entropy Modeling for Optimal Selection of Sampling Sites for Monitoring of NSW Weeds Conference, Wollongong, Australia. Networks. Divers 3: 252-261. 39. Gallagher RV, Beaumont L, Downey PO, Hughes L, Leishman MR (2006) 20. Hadley SW, Erickson DJ, Hernandez JL, Broniak CT, Blasing TJ (2006) Assessing the potential impacts of climate change on weeds in New South Responses of energy use to climate change: A climate modeling study. Geopys Wales: establishing priorities. The Proceedings of the 15th Australian Weeds Res Lett 33. Conference, Weed Management Society of South Australia, Adelaide, Australia.

21. Phillips SJ, Anderson RP, Schaphire RE (2006) Maximum entropy modeling of 40. Parmesan C (2006) Ecological and evolutionary responses to recent climate species geographic distributions. Ecol Model 190: 231–259. change. Ann Rev Ecol Evol Sys 37: 637-669.

22. Anderson RP, Lew D, Peterson AT (2003) Evaluating predictive models of 41. Rödder D, Weinsheimer F (2009) Will future anthropogenic climate change species’ distributions: criteria for selecting optimal models. Ecol Model 162: increase the potential distribution of the alien invasive Cuban treefrog (Anura: 211–232. Hylidae)? J Nat Hist 43: 1207–1217.

23. Ortega-Huerta MA, Peterson AT (2008) Modeling ecological niches and 42. Ficetola GF, Maiorano L, Falcucci A, Dendoncker N, Boitani L, et al. (2010) predicting geographic distributions: a test of six presenceonly methods. Rev Knowing the past to predict the future: landuse change and the distribution of Mex Biod 79: 205–216. invasive Bullfrogs. Global Change Biol 16: 528–537.

24. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high 43. Hiremath AJ, Sundaram B (2005) The Fire-Lantana Cycle Hypothesis in Indian resolution interpolated climate surfaces for global land areas. Int J Climatol Forests. Conserv Soc 3: 26–42. 25: 1965–1978. 44. Sharma GP (2007) Habitat Fragmentation Induced Species Invasion in 25. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than Vindhyan Highlands. Ph.D. Thesis, Banaras Hindu University, India. simple habitat models. Ecol Lett 8: 993–1009. 45. Kimothi MM, Dasari A (2010) Methodology to map the spread of an invasive 26. Roy PS, Joshi PK, Singh S, Agarwal S, Yadav D, et al. (2006) Biome mapping plant (Lantana camara L.) in forest ecosystems using Indian remote sensing in India using vegetation type map derived using temporal satellite data and satellite data. Int J Remote Sens 31: 3273–3289. environmental parameters. Ecol Model 197: 148–158. 46. Day MD, Wiley CJ, Playford J, Zaluvki MP (2003) Lantana: Current management 27. Thornthwaite CW (1948) An approach toward a rational classification of climate. status and future prospects. ACIAR Monograph, Canberra, Australia. Geogr Rev 38: 55-94. 47. Sharma GP, Raghubanshi AS (2006) Tree population structure, regeneration 28. Kumar S, Stohlgren TJ, Chong GW (2006) Spatial heterogeneity influences and expected future composition at different levels of Lantana camara L. native and nonnative plant species richness. Ecology 87: 3186-3199. invasion in the Vindhyan tropical dry deciduous forest of India. Lyonia 11: 27–39. 29. Murienne J, Guilbert E, Grandcolas P (2009) Species’ diversity in the New Caledonian endemic genera Cephalidiosus and Nobarnus (Insecta: 48. Bradley BA, Mustard JF (2006) Characterizing the landscape dynamics of an Heteroptera: Tingidae), an approach using phylogeny and species’ distribution invasive plant and risk of invasion using remote sensing. Ecol Appl 16: 1132- modelling. Bot J Linn Soc 97: 177–184. 1147.

30. Dobhal PK, Kohli RK, Batish DR (2011) Impact of Lantana camara L. invasion 49. Thuiller W (2004) Patterns and uncertainties of species’ range shifts under on riparian vegetation of Nayar region in Garhwal Himalayas (Uttarakhand, climate change. Global Change Biol 10: 2020–2027. India). J Ecol Nat Environ 3:11–22.

31. Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecogr 29: 129–151. 32. Kumar S, Stohlgren TJ (2009) Maxent modeling for predicting suitable Submit your next manuscript and get advantages of OMICS habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. J Ecol Nat Environ 1: 94–98. Group submissions Unique features: 33. Soberón J, Nakamura M (2009) Niches and distributional areas: concepts, methods, and assumptions. Proc Natl Acad Sci U S A 106 Suppl 2: 19644- • User friendly/feasible website-translation of your paper to 50 world’s leading languages 19650. • Audio Version of published paper • Digital articles to share and explore Special features:

• 250 Open Access Journals • 20,000 editorial team • 21 days rapid review process • Quality and quick editorial, review and publication processing • Indexing at PubMed (partial), Scopus, EBSCO, Index Copernicus and Google Scholar etc • Sharing Option: Social Networking Enabled Citation: Priyanka N, Joshi PK (2013) Effects of Climate Change on Invasion • Authors, Reviewers and Editors rewarded with online Scientific Credits Potential Distribution of Lantana camara. J Earth Sci Clim Change 4: 164. • Better discount for your subsequent articles doi:10.4172/2157-7617.1000164 Submit your manuscript at: www.omicsonline.org/submission

J Earth Sci Clim Change ISSN:2157-7617 JESCC, an open access journal Volume 4 • Issue 6 • 1000164