基于生态位模型预测野生油茶的潜在分布

油茶(Camellia oleifera)是我国第一大木本油料作物, 野生油茶是油茶育种的宝贵遗传资源。本研究从中国数字植物标本馆(CVH, http://www.cvh.org.cn/)获得可靠的野生油茶分布点数据, 结合气象和土壤数据, 分别应用最大熵(MaxEnt)模型和规则集遗传算法(GARP)模型构建了野生油茶的生态位模型, 预测了野生油茶的潜在分布区, 并分析了影响野生油茶分布的主要环境变量。根据生态位模型预测的分布概率值, 对野生油茶的潜在分布区划分适生等级, 并与主要油茶产地的实际分布数据进行比较, 以验证适生等级划分的可靠性。结果表明, 两种模型的预测结果均能较好地反映油茶的分布情况。GARP模型预测的潜在分布区更广, 而MaxEnt模型的预测结果更精确。两种模型的预测结果均显示, 野生油茶的潜在分布区大部分位于中国, 但在中南半岛也有部分分布。MaxEnt模型预测的野生油茶在中国的潜在分布区与我国亚热带常绿阔叶林的分布区基本吻合, 高适生区主要可以分为3大区域: (1)东北-西南走向的武夷山脉及附近的群山区域; (2)东西走向的南岭山脉及附近的群山区域; (3)东北-西南走向的武陵山脉及附近的群山区域。MaxEnt模型分析显示, 影响野生油茶分布的主要环境变量是昼夜温差月均值、最干季降水量与最暖季降水量。油茶生长面积较大的地区绝大部分都位于MaxEnt模型预测的中、高适生区, 说明适生等级的划分较可靠。实地考察显示, 生态位模型的预测结果对于寻找野生油茶资源具有较高的参考价值。此外, 本研究也充分显示, 利用中国数字植物标本馆的植物分布数据, 结合相应的环境数据构建生态位模型, 有助于了解作物野生近缘种的地理分布。     

ROC曲线分析在评价入侵物种分布模型中的应用

生态位模型（ecological niche models,ENMs）已广泛应用于物种潜在分布区预测,ENMs的应用也为外来入侵物种的风险分析提供了重要的定量化分析工具,但如何评价不同模型之间的预测效果成了当今研究的热点问题。本文介绍了受试者工作特征（ROC）曲线分析在评价不同生态位模型预测效果中的应用原理和分析方法,并以一种植物病原线虫-相似穿孔线虫（Radopholus similis）为例,应用ROC曲线分析法对其5种模型（BIOCLIM,CLIMEX,DOMAIN,GARP,MAXENT）的预测结果进行了比较分析。5种模型的ROC曲线下面积AUC（Area Under Curve）值分别为0.810,0.758,0.921,0.903和0.950,以MAXENT模型的AUC值最大,表明其预测效果最好;方差分析结果表明,除GARP与DOMAIN模型之间AUC值差异不显著外,其余各模型之间差异显著。     

蒙古黄芪潜在分布区预测的多模型比较

根据蒙古黄芪（Astragalus membranaceus（Fisch.）Bge.var.mongholicus（Bge.）Hsiao）123个样本点数据和19个环境数据,采用4种生态位模型对蒙古黄芪在中国的潜在适生区进行综合分析,并采用受试者工作特征曲线ROC和Kappa统计量,比较不同模型的预测效果。结果显示：4个模型预测精度良好,一致性显著。AUC值均达到0.8以上,Kappa值均达到0.6以上;其中DOMAIN模型的AUC值和Kappa值均最大,说明该模型的预测精度最佳,预测结果最稳定。潜在适生区的预测结果发现,GARP模型预测的最适宜区范围最广;MAXENT和BIOCLIM模型预测结果较为相似;DOMAIN模型预测结果比较分散。4个模型预测结果均表明西北一带可以作为蒙古黄芪栽培引种的主要产区。蒙古黄芪潜在适生区主要分布于中国北纬33°以北地区;最适宜区主要分布于甘肃、宁夏、陕西、山西、河北和内蒙古等地区。     

基于生态位模型预测天麻全球潜在适生区

目前对药用植物天麻(Gastrodia elata)的全球潜在适生区研究较少,基于多个生态位模型预测天麻在全球范围内的潜在适生区,对天麻人工引种栽培及其产业发展具有重要意义。该文收集220个天麻全球分布点和19个生态因子数据,最终筛选出8个环境变量参与模型训练,基于3个生态位模型(BIOCLIM、DOMAIN和MAXENT)预测天麻全球潜在适生区,并采用受试者工作特征曲线ROC和Kappa统计量分析比较不同模型的预测效果。结果表明:3个模型的预测结果基本一致,天麻全球潜在适生区主要分布在20°–50°N的亚洲地区,其中中国、日本和韩国是集中分布地,此外,印度、尼泊尔以及欧洲地中海附近有少量适生区。其中最适宜区主要分布在:中国四川盆地附近的省区以及中东部;韩国中东部的忠清北道、庆尚北道和庆尚南道;日本本州岛、九州岛以及四国岛,因此中国、日本和韩国是天麻的主要产区。3个模型的受试者工作特征曲线下面积(AUC值)平均值均达到0.9以上,Kappa平均值均达到0.65以上,能较好地预测天麻的潜在适生区,其中MAXENT模型的精度较高,其次是DOMAIN和BIOCLIM模型。     

蒙古黄芪潜在分布区预测的多模型比较

根据蒙古黄芪(Astragalus membranaceus (Fisch.) Bge. var. mongholicus (Bge.) Hsiao) 123个样本点数据和19个环境数据,采用4种生态位模型对蒙古黄芪在中国的潜在适生区进行综合分析,并采用受试者工作特征曲线ROC和Kappa统计量,比较不同模型的预测效果。结果显示:4个模型预测精度良好,一致性显著。AUC值均达到0.8以上,Kappa值均达到0.6以上;其中DOMAIN模型的AUC值和Kappa值均最大,说明该模型的预测精度最佳,预测结果最稳定。潜在适生区的预测结果发现,GARP模型预测的最适宜区范围最广; MAXENT和BIOCLIM模型预测结果较为相似; DOMAIN模型预测结果比较分散。4个模型预测结果均表明西北一带可以作为蒙古黄芪栽培引种的主要产区。蒙古黄芪潜在适生区主要分布于中国北纬33°以北地区;最适宜区主要分布于甘肃、宁夏、陕西、山西、河北和内蒙古等地区。     

基于Maxent生态位模型预测球果角胫象在云南的潜在地理分布

掌握球果角胫象Shirahoshizo conifera Chao在云南潜在分布区,对了解其危害和防控具有重要的意义。本文利用球果角胫象的分布点数据和环境因子数据,通过Maxent生态位模型预测其在云南的潜在分布区。结果表明：球果角胫象的潜在分布区主要集中滇中地区;在ArcGIS中进行显示与风险等级划分,按栅格数值的大小将球果角胫象的适生范围分为4级,分级标准为：高适生区、中适生区、低适生区、非适生区,经ROC曲线分析法验证,Maxent生态位模型的AUC值为0998,表明预测获得了较好的效果。     

外来入侵种——苹果绵蚜在中国的适生区预测

苹果绵蚜Eriosoma lanigerum(Hausmann)是我国重要的检疫性害虫,主要为害苹果、海棠等苹果属(Malus Mill.)植物。目前,该种害虫已在我国一些苹果主产区迅速扩散,并给我国的苹果产业造成了较为严重的经济损失。为了对其进行有效监控,控制其蔓延,制定合理的防治策略,本研究利用GARP和MAXENT两种生态位模型,结合其寄主地理分布,预测苹果绵蚜在我国的潜在地理分布区。研究结果表明:GARP和MAXENT预测结果相似,但前者预测面积比后者广泛。苹果绵蚜在我国的最适适生区主要分布在东北(辽宁南部)、华北(河北东、南部、北京、天津和山西南部)、华东(山东大部)、华中(河南北部)和西北(陕西中部)。另外,河北南部、山东和河南南部、甘肃东部、四川中南部、陕西大部、云南与西藏的零星地区是苹果绵蚜的中度适生区;黑龙江、吉林、新疆等20个省份(市、自治区)的全境是苹果绵蚜低度适生区或不适生区。此外,刀切法(Jackknifetest)检验结果表明,1月份平均最高温是影响苹果绵蚜分布最重要的环境变量。最后,提出几点管理苹果绵蚜的方法和防治策略,避免该种害虫传播或入侵到其它苹果产区。     

应用多个生态位模型预测福寿螺在中国的潜在适生区

高危性外来入侵种福寿螺严重危害我国的农业生产、生态系统完整性和人体健康.为制定有效的防控策略提供科学依据,本研究通过选取最适的生态位模型以预测福寿螺在我国的潜在适生区.结合福寿螺在我国的337条分布记录和年均温、年降水量等19个生物气候变量数据,本文采用MaxEnt、GARP、BIOCLIM和DOMAIN等4种生态位模型分别模拟预测了福寿螺在我国的潜在适生区,并利用受试者工作特征曲线(ROC)和Kappa统计量分析比较不同模型的预测效果.结果表明: 4种模型均能较好地模拟福寿螺在我国的分布,其中MaxEnt模型的模拟准确度最高(受试者工作特征曲线下的面积AUC=0.955±0.004,Kappa=0.845±0.017),其次是GARP和DOMAIN,准确度相对较小的是BIOCLIM,但其平均AUC也达0.898±0.017,平均Kappa值为0.771±0.025.MaxEnt模型的预测结果显示,福寿螺的潜在适生区主要分布在30° N以南地区,但其中也有部分地区地处30°N以北.适生区面积占国土面积的13.2%,广东、广西、湖南、重庆、浙江和福建沿海地区具有高度潜在入侵风险.本研究可以为福寿螺的科学防控提供参考,并且对大尺度上外来水生生物的适生区预测具有一定的借鉴意义.     

新疆贝母潜在分布区域及生态适宜性预测

基于新疆贝母的62个自然分布点和15个环境因子,利用Arc GIS软件和最大熵模型(MAXENT),预测、分析了该植物在基准气候1961—1990及2050 (2041—2060,基于RCP2.6和RCP6.0情景)条件下的潜在适生区、驱动因子及其生态位参数。结果表明:(1)基准气候下,新疆贝母的适生区主要集中在阿勒泰地区、准噶尔盆地西部、南部、阿拉山口西南部、伊犁河谷南部及吐鲁番盆地西部地区。其中,最适宜的分布主要集中在准噶尔盆地西南部、塔城地区和伊犁河谷中部和南部;(2)新疆贝母在2050时段气候情境下的潜在分布范围与基准气候相比,将分别增加0.94%和0.23%,新增的潜在生境主要分布在准噶尔盆地西部。但最适生的分布区将在伊犁河谷中部、南部及塔城地区略有减少(0.42%和0.39%);(3)年平均降水量、最干月降水量、最干季平均气温和海拔主要限制了新疆贝母的潜在分布,累积贡献率之和达88.58%;基准气候下该植物最适宜分布区的生态位参数为:年平均降水量248—469 mm,最干月降水量3—19 mm,最干季平均气温-22.7—-2.0℃,海拔1350—2100 m。     

基于MaxEnt模型的中国沙棘潜在适宜分布区分析

中国沙棘主要分布于我国华北、西北、西南等地森林—草原过渡地带，是我国北方地区退耕还林、生态修复等工程的重要造林树种，对维持干旱、半干旱地区的生态环境稳定具有重要意义。探讨限制中国沙棘分布的主导气候因子，模拟其潜在适宜分布区，以期为中国沙棘在林业生态工程和生态经济林建设中的合理种植和推广提供理论依据。基于中国沙棘自然分布的328个地理样点，利用最大熵(MaxEnt)模型对中国沙棘的潜在分布区的主导气候因子进行分析，并预测中国沙棘的潜在分布范围。结果表明，基于气候变量的MaxEnt模型训练集和测试集受试者工作特征曲线下面积(AUC值)分别为0.962±0.001和0.949±0.001,均大于0.9,表明MaxEnt模型对中国沙棘潜在分布区的预测具有极高的准确度，可信度好。基于环境变量贡献率和刀切法的结果表明年降雨情况、生长季的水热状况、最干季降雨和最冷月最低温等是限制中国沙棘分布的主要气候因素，其中年降雨是限制中国沙棘分布的主导气候因子。通过模拟得到现代中国沙棘潜在地理分布的总适生区面积为165.1万km~2;其中高适生区和中适生区面积共93.3万km~2,主要集中分布于河北西部、北部，...     

Species Distribution Models of Tropical Deep-Sea Snappers

Deep-sea fisheries provide an important source of protein to Pacific Island countries and territories that are highly dependent on fish for food security. However, spatial management of these deep-sea habitats is hindered by insufficient data. We developed species distribution models using spatially limited presence data for the main harvested species in the Western Central Pacific Ocean. We used bathymetric and water temperature data to develop presence-only species distribution models for the commercially exploited deep-sea snappers Etelis Cuvier 1828, Pristipomoides Valenciennes 1830, and Aphareus Cuvier 1830. We evaluated the performance of four different algorithms (CTA, GLM, MARS, and MAXENT) within the BIOMOD framework to obtain an ensemble of predicted distributions. We projected these predictions across the Western Central Pacific Ocean to produce maps of potential deep-sea snapper distributions in 32 countries and territories. Depth was consistently the best predictor of presence for all species groups across all models. Bathymetric slope was consistently the poorest predictor. Temperature at depth was a good predictor of presence for GLM only. Model precision was highest for MAXENT and CTA. There were strong regional patterns in predicted distribution of suitable habitat, with the largest areas of suitable habitat (> 35% of the Exclusive Economic Zone) predicted in seven South Pacific countries and territories (Fiji, Matthew & Hunter, Nauru, New Caledonia, Tonga, Vanuatu and Wallis & Futuna). Predicted habitat also varied among species, with the proportion of predicted habitat highest for Aphareus and lowest for Etelis. Despite data paucity, the relationship between deep-sea snapper presence and their environments was sufficiently strong to predict their distribution across a large area of the Pacific Ocean. Our results therefore provide a strong baseline for designing monitoring programs that balance resource exploitation and conservation planning, and for predicting future distributions of deep-sea snappers.     

Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder

Aim Species distribution models (SDMs) or, more specifically, ecological niche models (ENMs) are a useful and rapidly proliferating tool in ecology and global change biology. ENMs attempt to capture associations between a species and its environment and are often used to draw biological inferences, to predict potential occurrences in unoccupied regions and to forecast future distributions under environmental change. The accuracy of ENMs, however, hinges critically on the quality of occurrence data. ENMs often use haphazardly collected data rather than data collected across the full spectrum of existing environmental conditions. Moreover, it remains unclear how processes affecting ENM predictions operate at different spatial scales. The scale (i.e. grain size) of analysis may be dictated more by the sampling regime than by biologically meaningful processes. The aim of our study is to jointly quantify how issues relating to region and scale affect ENM predictions using an economically important and ecologically damaging invasive species, the Argentine ant (Linepithema humile). Location California, USA. Methods We analysed the relationship between sampling sufficiency, regional differences in environmental parameter space and cell size of analysis and resampling environmental layers using two independently collected sets of presence/absence data. Differences in variable importance were determined using model averaging and logistic regression. Model accuracy was measured with area under the curve (AUC) and Cohen's kappa. Results We first demonstrate that insufficient sampling of environmental parameter space can cause large errors in predicted distributions and biological interpretation. Models performed best when they were parametrized with data that sufficiently sampled environmental parameter space. Second, we show that altering the spatial grain of analysis changes the relative importance of different environmental variables. These changes apparently result from how environmental constraints and the sampling distributions of environmental variables change with spatial grain. Conclusions These findings have clear relevance for biological inference. Taken together, our results illustrate potentially general limitations for ENMs, especially when such models are used to predict species occurrences in novel environments. We offer basic methodological and conceptual guidelines for appropriate sampling and scale matching.     

基于生态位模型的艾比湖鹅喉羚生境评价

明确物种生境空间分布格局及其与环境因素的关系,对了解该物种的生境需求和适宜生境空间分布至关重要。生境评价和预测是对物种进行有效保护的基础。以鹅喉羚(Gazella subgutturosa)为研究对象,以其重要栖息地新疆博州艾比湖国家级湿地自然保护区为研究区域,选取115个鹅喉羚分布点数据和23个环境变量因子,应用MAXENT模型分析其生境空间分布及主要影响因子,划分了鹅喉羚在研究区域的适宜生境,并对它的栖息地特征进行了分析。探讨了鹅猴羚生境选择与环境因子的关系。结果表明:气温日较差是影响鹅喉羚生境分布的主要环境因子。植被类型,坡度和最干月降水量对艾比湖鹅喉羚的生境选择影响不大。除了温度和降水在内的19项生物气候变量是鹅猴羚选择生境的重要因素之外,海拔和坡向等地形特征也影响鹅猴羚的生境选择性。鹅喉羚的高度适宜生境区主要分布在研究区域的北部和东部,中度及低度适宜生境区则分布于高度适宜生境区的边缘,而非适宜生境区主要集中在西部地区。研究不仅提供了鹅喉羚在艾比湖的实际分布状况及其栖息地特征,也为鹅喉羚在栖息地方面的研究,即鹅猴羚的栖息地选择和环境因子的关系方面提供了一个重要的依据。     

Ecological niches and potential geographical distributions of Mediterranean fruit fly (Ceratitis capitata) and Natal fruit fly (Ceratitis rosa)

Aim To predict and compare potential geographical distributions of the Mediterranean fruit fly (Ceratitis capitata) and Natal fruit fly (Ceratitis rosa). Location Africa, southern Europe, and worldwide. Methods Two correlative ecological niche modelling techniques, genetic algorithm for rule‐set prediction (GARP) and a technique based on principal components analysis (PCA), were used to predict distributions of the two fly species using distribution records and a set of environmental predictor variables. Results The two species appear to have broadly similar potential ranges in Africa and southern Europe, with much of sub‐Saharan Africa and Madagascar predicted as highly suitable. The drier regions of Africa (central and western regions of southern Africa and Sahelian zone) were identified as being less suitable for C. rosa than for C. capitata. Overall, the proportion of the region predicted to be highly suitable is larger for C. capitata than for C. rosa under both techniques, suggesting that C. capitata may be tolerant of a wider range of climatic conditions than C. rosa. Worldwide, tropical and subtropical regions are highlighted as highly suitable for both species. Differences in overlap of predictions from the two models for these species were observed. An evaluation using independent records from the adventive range for C. capitata and comparison with other predictions suggest that GARP models offer more accurate predictions than PCA models. Main conclusions This study suggests that these species have broadly similar potential distributions worldwide (based on climate), although the potential distribution appears to be broader for C. capitata than for C. rosa. Ceratitis capitata has become invasive throughout the world, whereas C. rosa has not, despite both species having broadly similar potential distributions. Further research into the biology of these species and their ability to overcome barriers is necessary to explain this difference, and to better understand invasion risk.     

Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents

The conservation of poorly known species is difficult because of incomplete knowledge on their biology and distribution. We studied the contribution of two ecological niche modelling tools, the Genetic Algorithm for Rule-set Prediction (GARP) and maximum entropy (Maxent), in assessing potential ranges and distributional connectivity among 12 of the least known African and Asian viverrids. The level of agreement between GARP and Maxent predictions was low when < 15 occurrences were available, probably indicating a minimum number below that necessary to obtain models with good predictive power. Unexpectedly, our results suggested that Maxent extrapolated more than GARP in the context of small sample sizes. Predictions were overlapped with current land use and location of protected areas to estimate the conservation status of each species. Our analyses yielded range predictions generally contradicting with extents of occurrence established by the IUCN. We evidenced a high level of disturbance within predicted distributions in West and East Africa, Sumatra, and South-East Asia, and identified within West African degraded lowlands four relatively preserved areas that might be of prime importance for the conservation of rainforest taxa. Knowing whether these species of viverrids may survive in degraded or alternative habitats is of crucial importance for further conservation planning. The level of coverage of species suitable ranges by existing and proposed IUCN reserves was low, and we recommend that the total surface of protected areas be substantially increased on both continents.     

Modeling potential invasion range of alien invasive species,Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP

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.     

Predicted and verified distributions of <Emphasis Type="Italic">Ateles geoffroyi</Emphasis> and <Emphasis Type="Italic">Alouatta palliata</Emphasis> in Oaxaca,Mexico

Primate conservation requires a better knowledge of the distributions and statuses of populations in both large areas of habitat and in areas for which we currently have no information. We focused on spider monkeys (Ateles geoffroyi) and howler monkeys (Alouatta palliata) in the state of Oaxaca, Mexico. This Mexican state has protected large tracts of forest, and has historical records for both primates, although very little is known about them. To update our knowledge of the distributions of these primates and identify potential areas in which they are present, we modeled their geographic distributions by characterizing their ecological niches using the genetic algorithm for rule-set production (GARP), performed interviews and carried out field surveys. The predicted distributions, surveys and interviews indicate that the distributions of these primates are restricted to northeastern Oaxaca. The results suggest that spider monkeys occupy a wider area and elevational range than howler monkeys. Throughout that range there is a wide variety of suitable habitats for these primates. Most of the sites where monkeys were recorded in the field are not officially protected and there was evidence of hunting and habitat destruction. It is important to improve protection, economic alternatives and environmental education as we move towards an integral solution for the conservation of these species. Validation of the GARP model was done for A. geoffroyi, since we had obtained enough field data for this species; this validation indicated that the predicted distribution of the species was statistically better than expected by chance. Hence, ecological niche modeling is a useful approach when performing an initial assessment to identify distribution patterns, detecting suitable areas for future exploration, and for conservation planning. Our findings provide an improved basis for primate conservation and productive fieldwork in Oaxaca.     

Invasive and flexible: niche shift in the drosophilid <Emphasis Type="Italic">Zaprionus indianus</Emphasis> (Insecta,Diptera)

Although predictions of potential distributions of invasive species often assume niche conservatism, recent analyses suggest that niche shifts can also occur. Thus, further studies are necessary to provide a better understanding of niche dynamics and to predict geographic distribution in invaded areas. The present study investigated the niche shift hypothesis at a broad biogeographical scale, using the comprehensive distribution of the invasive species Zaprionus indianus in its native (Africa) and invaded (America and India) ranges. Z. indianus is a very successful invasive species that presents high adaptive flexibility and extreme physiological tolerance. To investigate whether Z. indianus changed its climatic niche from Africa to America and India, multivariate analyses, as well as ecological niche modeling procedures (GARP, MAXENT and Mahalanobis distances), were used. Multivariate analyses showed that the niche spaces of Z. indianus in Africa, India and the Americas were significantly different (Wilks’ λ from a Multivariate Analysis of Variance, MANOVA = 0.115; P < 0.0001). Out of 108 occurrences in America, only 11 (ca 10%) were classified, by Canonical Variate Analysis scores, as belonging to its original range in Africa, whereas only 5% of the 39 occurrences in India were classified as belonging to Z. indianus’ original range. Consensus results from MAXENT, GARP and Mahalanobis distances correctly predicted only 27% of the occurrences in India and 85% of occurrences in America. Thus, all analyses showed that Zaprionus indianus quickly expanded ranges into different environments in the invaded areas, suggesting climatic niche shifts, primarily in India.