Using MaxEnt Model to Predict Suitable Habitat Changes for Key Protected Species in Koshi Basin, Central Himalayas

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1 Jan., 2017 Journal of Resources and Ecology Vol. 8 No.1 J. Resour. Ecol (1) DOI: /j.issn x Using MaxEnt Model to Predict Suitable Habitat Changes for Key Protected Species in Koshi Basin, Central Himalayas LIU Linshan 1, ZHAO Zhilong 1,2, ZHANG Yili 1,2,*, WU Xue 1,2 1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing , China; 2. University of the Chinese Academy of Sciences, Beijing , China Abstract: Because of its landscape heterogeneity, Koshi Basin (KB) is home to one of the world s most abundant, diverse group of species. Habitat change evaluations for key protected species are very important for biodiversity protection in this region. Based on current and future world climate and land cover data, MaxEnt model was used to simulate potential habitat changes for key protected species. The results shows that the overall accuracy of the model is high (AUC > 0.9), suggesting that the MaxEnt-derived distributions are a close approximation of real-world distribution probabilities. The valley around Chentang Town and Dram Town in China, and Lamabagar and the northern part of Landtang National Park in Nepal are the most important regions for the protection of the habitat in KB. The habitat area of Grus nigricollis, Panax pseudoginseng, and Presbytis entellus is expected to decrease in future climate and land cover scenarios. More focus should be placed on protecting forests and wetlands since these are the main habitats for these species. Key words: MaxEnt model; Land use; Habitat loss; Koshi Basin; Himalayas 1 Introduction The Koshi Basin (KB) is the maximum elevation drop basin in the central Himalayas; the vertical difference of the ecosystem is notable and habitat diversity is high (Zhang et al., 2013). The area contains rich biodiversity and is a source of valuable ecosystem services that sustain the lives and livelihoods of millions of people in China, India, and Nepal (ICIMOD, 2014). Because of its landscape heterogeneity, KB is home to one of the world s most abundant, diverse group of species. The whole of Sagamartha National Park, and parts of Langtang National Park and Qomolangma National Nature Reserve have been established in KB to protect the ecosystem and biodiversity of the region. However, a clear understanding of the distribution of key protected species is lacking. Habitat protection is a key component of the drive to protect biodiversity. Habitat is a place for wildlife to complete their life cycle; habitats can promote the survival of wild animal and plant populations (Wu et al., 2016). Habitat loss and fragmentation are regarded as the most important factors in the loss of biodiversity (Jiao et al., 2016). Climate change and land use intensification have been identified as major causes of habitat loss (Khanum et al., 2013; Kumar, 2012; Newbold et al., 2015). Land use activities can lead to changes in habitat area, structure and the intensity of stress (such as habitat fragmentation). Studies based on historical documents, and historical and recent spatial data on land use and land cover changes, have clearly shown an increase in cropland areas, along with a decrease in forest and snow/ glacier coverage, and significant urban land expansion over the past 30 years (Paudel et al., 2016). Studying the current and potential distribution of species on a large scale and examining the key environmental fac- Received: Accepted: Foundation: National Natural Science Foundation of China ( ); Tibet Key Science and Technology Program (2015XZ01G72); The Australian Government-funded Koshi Basin Programme at the International Centre for Integrated Mountain Development (ICIMOD). *Corresponding author: ZHANG Yili, zhangly@igsnrr.ac.cn. Citation: LIU Linshan, ZHAO Zhilong, ZHANG Yili, et al Using MaxEnt Model to Predict Suitable Habitat Changes for Key Protected Species in Koshi Basin, Central Himalayas. Journal of Resources and Ecology. 8(1):

2 78 Journal of Resources and Ecology Vol. 8 No. 1, 2017 tors that affect their natural distribution can help us understand the overall distribution patterns of key protected species. This information can then be used to determine suitable areas to protect the habitat through the planning of nature reserves. With the rise of ecological statistical modeling and GIS technology, species distribution models have become an important tool for basic ecology and biogeography research and have been widely used in ecological research, for example, to determine the relationship between species distribution and climate change (Yu et al., 2014; Hu et al., 2015), to understand ecosystem management and protection at different scales (Svenning et al., 2005), and to predict the potential distribution of key protected species for protected area planning and ecosystem restoration (Xu et al., 2012). Today, there are many species distribution models, such as BIOCLIM (bioclimatic modeling), Domain (domain environmental envelope), CLIMEX (climate change experiment), GARP (genetic algorithm for rule-set production) and MaxEnt (maximum entropy). Researches have shown that MaxEnt is more accurate than other models, especially when distribution data for the species being studied are minimal (Elith et al., 2006; Xu et al., 2015; Zhu et al., 2013). Its operation is simple and fast and it is suitable for prediction of the potential distribution of reserve scale habitat for species (Xu, 2015; Phillips et al., 2008). MaxEnt model has already been used to predict the habitat suitability of medicinally, as well as ecologically, important species like Justicia adhatoda L. in the Lesser Himalayan foothills (Yang et al., 2013), Tibetan spruce (Picea smithiana) in Qomolangma National Nature Preserve (Zhang et al., 2011), and Himalayan hemlock (Tsuga dumosa) on the Tibetan Plateau (Yu et al., 2015). This study aims to model the current distribution of suitable habitat for key protected species in KB and model future changes to suitable habitat distribution, in order to give advice concerning the protection of biodiversity in this region. 2 Materials and methods 2.1 Study area The Koshi River is an important tributary of the Ganges, and KB is a transboundary watershed located in the central Himalayas that straddles parts of China, Nepal and India (Fig. 1) and has a total area of km 2 (Zhang, et al., 2013; Zhang et al., 2016). KB is one of the world s biodiversity hotspots, with rich flora and fauna, including 6244 species of vascular plants (Deng et al., 2014). Mt. Qomolangma National Nature Reserve has 2176 species of higher plants and more than 263 kinds of vertebrates (Zhang et al., 2009). Among these species, more than 10 species are national key protected plant species, including Alcimandra cathcartii, and Trillium govanianum, etc., and there are Fig.1 Location of study area

3 LIU Linshan, et al.: Using MaxEnt Model to Predict Suitable Habitat Changes for Key Protected Species in Koshi Basin, Central Himalayas 79 more than 30 key protected animals species. Among the animal species are first class national key protected animals, such as Grus nigricollis, Panthera pardus, Presbytis entellus, etc., and second class national key protected animals, such as Pseudois nayaur and Tetraogallus tibetanus, etc. 2.2 Modeling procedure Maximum entropy (MaxEnt) was used as the basis for predictions concerning the habitat. This model requires data on the presence of the species being modelled and related environmental and climatic variables for the habitat. The freely downloadable MaxEnt Model version 3.3.3k ( was used for the habitat suitability modeling of key protected species. 2.3 Datasets Key protected species Key protected species were chosen to make the distribution map, which was selected based on the literature: Key protected plants: Alcimandra cathcartii, Panax pseudoginseng, Trillium govanianum. Key protected animals: Grus nigricollis, Panthera pardus, Presbytis entellus, Pseudois nayaur, Tetraogallus tibetanus. The distribution data of key protected species were obtained from the Tibetan Plateau Biological Specimen Museum in the Northwest Institute of Plateau Biology, Chinese Academy of Sciences. The database contains information on the location and associated environmental and morphological features of samples collected from the 1970s to the present. Due to the limited number of sample points, some points were generated randomly based on habitat distribution in the KB. Finally, 50 sample points were selected for each species as MaxEnt model input samples. 80% of the samples were used for the training of the model, 20% of the samples were used for the model test Bioclimatic variables The environmental layers Bioclimatic Variables were used in the predicting procedures (Hijmans et al., 2005). These consist of 19 bioclimatic factors on a global scale; they were downloaded from (Table 2). The current data layers were generated through interpolation of average monthly climate data for the years 1960 to 1990 from weather stations on a 30 arc-second resolution grid and from these annual mean values were calculated. To develop a climate dataset for the future, the website provides IPPC5 climate projections from global climate models (GCMs) for four representative concentration pathways (RCPs). The downscaled CMIP5 data at 30 seconds resolution based on Representative Concentration Pathway (RCP) 4.5 and the Community Climate System Model version 4 (CCSM4) in 2050 were used in this study. The environmental layers were down-scaled to 30m resolution to match with the elevation and vegetation data, as finer resolution data was not available Elevation data Digital elevation model (DEM) with a resolution of 30m was obtained from the Earth Remote Sensing Data Analysis Centre ( Land cover data Land cover data is a very important environment layer for the habitat estimation in the MaxEnt model. Land cover data for 1992 and land cover data for 2050 were used were used to present current and future land cover statuses. Land cover data for 1992 were generated by ICIMOD and the land change science and regional adaptation research group, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. The land cover data in 2050 were generated through CA-Markov model (Zhao et al., 2017). The land cover types include forest, shrubland, grassland, wetland, alpine sparse vegetation, bare land, cropland, water body, and built-up area. 3 Results and discussions 3.1 Current distribution of key protected species based on MaxEnt predictions Two indicators were used to examine the performance of the model: the fraction of predicted area and extrinsic omission rate as the threshold-dependent test, and the area under the ROC curve (AUC) as the threshold-independent test (Table 2) The indicators were obtained using 20% of the training data. Table 1 Bioclimatic variables description Variables Description Variables Description bio_1 Annual Mean Temperature bio_11 Mean Temperature of Coldest Quarter bio_2 Mean Diurnal Range bio_12 Annual Precipitation bio_3 Isothermality bio_13 Precipitation of Wettest Month bio_4 Temperature Seasonality bio_14 Precipitation of Driest Month bio_5 Max Temperature of Warmest Month bio_15 Precipitation Seasonality bio_6 Min Temperature of Coldest Month bio_16 Precipitation of Wettest Quarter bio_7 Temperature Annual Range bio_17 Precipitation of Driest Quarter bio_8 Mean Temperature of Wettest Quarter bio_18 Precipitation of Warmest Quarter bio_9 Mean Temperature of Driest Quarter bio_19 Precipitation of Coldest Quarter bio_10 Mean Temperature of Warmest Quarter

4 80 Journal of Resources and Ecology Vol. 8 No. 1, 2017 Table 2 Results of threshold-dependent omission test and threshold-independent ROC test Species Threshold-dependent test Fractional Test omission predicted area rate Thresholdindependent test Test AUC Alcimandra cathcartii Grus nigricollis Panax pseudoginseng Panthera pardus Presbytis entellus Pseudois nayaur Tetraogallus tibetanus Trillium govanianum as test localities for evaluating the performance statistics. The AUC value is higher than 0.99 for most of the key protected species, a result similar to that in other studies of the Himalaya region (Remya et al., 2015; Sarania et al., 2016). This result was obtained for both the training and the test data, with the small difference in AUC values suggesting a robust performance of the MaxEnt algorithm to capture the changes in environmental variables over point localities. The omission test was calculated at a 10% threshold value. At this threshold, the fractional predicted area shows the fraction of all the pixels that were predicted suitable for the species. The omission rate was quite small, suggesting that only a small fraction of the test locations fell into pixels not predicted as suitable. The overall accuracy of the model was high, implying that the MaxEnt derived distributions were a close approximation of real world distribution probabilities. The MaxEnt model predictions of current distributions of key protected species in KB are shown in Fig. 2. The distribution of Alcimandra cathcartii is most likely to be in the forest along the Pengqu valley near Chentang Town and Boqu valley near Dram Town (Khasa in Nepali). Grus nigricollis (black-necked crane) is likely to be found in the wetlands, lakes and river beach zones in the high altitude areas in the northern part of KB, especially in the west of Dinggyê County, around the conjunction of the Pengqu, Yeruzangbo and Jinlongqu Rivers. Panax pseudoginseng has similar distribution with Alcimandra cathcartii, mostly in the forest near Chentang Town and Dram Town, but the distribution area is larger and the elevation is higher than that of Alcimandra cathcartii. Panthera pardus can be find in the forest and shrub area from 2200 m to 4800 m in the mountain areas near Chentang Town, Rongxia Village and Dram Town. There are also some possible distribution areas in Langtang National Park in Nepal. Presbytis entellus is most likely to be found in forested valleys along the border between China and Nepal. The distribution area is similar to Panax pseudoginseng. The habitat of Pseudois nayaur is extensive; the species is a good climber and most likely to be found in grasslands and rocky areas in the northern high elevation parts of KB. Tetraogallus tibetanus has a very large habitat area similar to but bigger than that of Pseudois nayaur; the species most likely inhabits alpine barren rock zones and rocky alpine meadow in the northern part of KB. Trillium govanianum is only distributed in the wooded valley near Chentang Town. 3.2 Analysis of the contributions of environmental variables Table 3 gives estimates of the relative contributions of environmental variables to the MaxEnt model. In general, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of wettest quarter, mean temperature of coldest quarter, max temperature of warmest month, precipitation seasonality (coefficient of variation), precipitation of wettest quarter, aspect, temperature annual range (bio_5-bio_6) contributed very little to the model. This means that these variable have very little influence on the habitat of the key protected species in KB. Among the environmental variables, precipitation of coldest quarter (bio_19), land cover data, min temperature of coldest month (bio_6), elevation, isothermality (bio_3), temperature seasonality (bio_4), and precipitation of wettest month (bio_13) are most important for the modeling of potential distribution: they contribute 78% in the model result on average. For Alcimandra cathcartii, bio_19, bio_3, elevation and lc1992 contribute more than other variables and are the most important environment factors. However, the situation is quite different for the Grus nigricolli; bio_13 and bio_4 contribute more than half the performance of the model, indicating that precipitation of driest month and temperature seasonality have considerable influence over the habitat of Grus nigricollis. This is because wetlands and farmlands are the most important food sources for the species (Dong et al., 2016; Song et al., 2014). Precipitation in the driest month has an impact on the quality of wetlands. Bio_19, elevation, bio_3 and lc1992 contributed more for Panax pseudoginse. Panthera pardus depends more on land cover, elevation, and bio_6 and bio_12. As for Presbytis entellus, precipitation of coldest quarter and land cover are the most important environmental variables; more precipitation in the coldest quarter is beneficial to this species. Bio_6 is the most important factor for the distribution of Panthera pardus and Tetraogallus tibetanus; it contributes 47.51% and 31.01%, respectively. For Trillium govanianum, more precipitation in the coldest quarter and in the driest month is beneficial. 3.3 Response curves of environmental variables Trillium govanianum is used as an example to analyze the response curves of environmental variables. The six response curves for environmental variables that contributed the most to the MaxEnt model predictions for distribution of

5 LIU Linshan, et al.: Using MaxEnt Model to Predict Suitable Habitat Changes for Key Protected Species in Koshi Basin, Central Himalayas 81 Fig.2 Predicted distributions of key protected species in current Trillium govanianum (Table 3) were isothermality (bio_3), temperature seasonality (bio_4), min temperature of coldest month (bio_6), temperature annual range (bio_7), precipitation of driest month (bio_14), and precipitation of coldest quarter (bio_19). These variables were chosen to analyze the response of Trillium govanianum distribution to each typical environmental variable (Fig. 3). Based on these response curves, Trillium govanianum prefers higher isothermality (bio_3), the higher the better. Temperature seasonality (bio_4) is also important. The response curve rises very steeply from 4000 to 4500, and probability of presence is very sensitive when temperature seasonality is between those values. The effects continue to increase after 4500, but the curve very smooth, showing much less sensitivity. The min temperature of coldest month (bio_6) is best around zero degrees. It seems that Trillium govanianum likes temperature annual range (bio_7) to be higher. Precipitation of driest month (bio_14) is also

6 82 Journal of Resources and Ecology Vol. 8 No. 1, 2017 Table 3 Contributions of environmental variables (expressed as %) to MaxEnt model performance Variables Alcimandra cathcartii Grus nigricollis Panax pseudoginseng Panthera pardus Presbytis entellus Pseudois nayaur Tetraogallus tibetanus Trillium govanianum bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ bio_ Elevation LC Average very important to Trillium govanianum. More precipitation in the driest month is suitable for this species. Precipitation of coldest quarter (bio_19) is the most important key factor for the Trillium govanianum (Table 3); more precipitation in the coldest quarter is good for this species (Fig. 3). 3.4 Habitat changes of key protected plant and animal species A comparison of the distribution data of the key protected species in 1992 and 2050 shows that the habitats of the key protected species selected for this research work have undergone and are continuing to undergo considerable change. The habitat of Grus nigricollis, Panax pseudoginseng and Presbytis entellus will decrease in the future, while the habitat of other key protected species will increase. The habitat of Alcimandra cathcartii will increase by almost 40 percent. Alcimandra cathcartii is a rare and endangered plant with a predicted distribution area in KB that is very small. It is a first-class nationally protected species in China (Yuan et al., 2012). The habitat change rate is the highest among the selected key protected species in KB (Fig. 4). The increases and decreases in area are distributed near the boundary of the whole distribution area (Fig. 5), especially on the Nepal side of the border. The decrease in habitat occurs mainly between elevations of 2600m and 2800m while the increase occurs mainly between 2500m and 2700m (Fig. 6). Declines in the populations of Grus nigricollis have been reported (Namgay et al., 2016). Wetland is the most important habitat for the Grus nigricollis and degradation of wetlands caused by climate change and human activity has influenced these habitats and biological behaviors of inhabitants (Song, et al., 2014). The total habitat area of Grus nigricollis will decrease 20% based on estimates of the impact of future climate and land cover changes. The changes are occurring around an elevation of 4200m near the conjunction of the Pengqu, the Yeruzangbo and the Jinlongqu Rivers, the area where most wetlands are distributed (Fig.s 4, 5). Scientific habitat management is a key issue for effective conservation of Grus nigricollis; the retention and restoration of wetlands is essential to conserve habitat for the species (Dong, et al., 2016; Kong et al., 2011). The habitat of Panax pseudoginseng, which is used as a Chinese traditional medicine, is expected to decrease 16 percent in the future. The habitat loss happens mainly in the m elevation range while increases to habitat area will happen mainly at lower elevations between 2700m and 3000m on the Nepal side of the border. The habitat area of Panthera pardus is expected to be 19 percent larger in 2050, with changes occurring mainly near the boundaries of the current habitat area. Change in forest land coverage is the primary contributor to habitat change for Panthera pardus (Fig. 5), with both increases and decreases of area occurring mainly in the m elevation range. The habitat area of Presbytis entellus is expected to decrease by 16 percent, with changes occurring mainly in the southern part of the habitat area; change to forest land

7 LIU Linshan, et al.: Using MaxEnt Model to Predict Suitable Habitat Changes for Key Protected Species in Koshi Basin, Central Himalayas 83 Fig.3 Response curves of environmental variables for Trillium govanianum Fig.4 Habitat change of the selected key protected species in KB from current to 2050 coverage in Nepal is the primary contributor to habitat change (Fig. 5). The increase of area occurs mainly around 2800 m while the decrease in area occurs mainly between 2400 m and 3100 m (Fig. 6). Suitable habitat for Pseudois nayaur and Tetraogallus tibetanus will increase 17% and 18%, respectively (Fig. 4, Fig. 5). The habitat loss for Pseudois nayaur occurs mainly in the northwest part of KB, in northern Nyalam county and western Tingri county between elevations of 4500 m to 5100 m. The increase occur mainly in the m elevation range. The habitat change for Tetraogallus tibetanus is at higher elevation; it occurs mainly in Sagamartha National Park and Dinggye county (Fig. 5). The habitat area of Trillium govanianum will increase

8 84 Journal of Resources and Ecology Vol. 8 No. 1, 2017 Fig.5 Habitat changes for key protected species in KB from current to 2050

9 LIU Linshan, et al.: Using MaxEnt Model to Predict Suitable Habitat Changes for Key Protected Species in Koshi Basin, Central Himalayas 85 Fig.6 Elevation gradients of habitat change of key protected species in KB

10 86 Journal of Resources and Ecology Vol. 8 No. 1, %, with increases occurring mainly at lower elevations from 2800 to 3500 m. Decreases in area are occurring from 2600 to 3200 m. More research is needed to understand the impact of climate change on key protected species, since some of them, such as Alcimandra cathcartii, are quite sensitive to changes in climate conditions. Unfortunately, high spatial resolution climate data is not available for KB. The complex, mountainous terrain makes it difficult to generate credible climate surfaces in this region, and errors in the climate data naturally affect the results of MaxEnt model. Selection of environmental variables is very important for the MaxEnt model. The habitat distributions of the key protected species are not sensitive to some of the environment variables. It should be noted that the impact of human activity on habitats was not considered in this study. Distances to roads and settlements should be considered in subsequent studies. Land cover data is also very limited. If a more detailed land cover classification system were available, the MaxEnt model would have more information to work with and the results would be much better. In short, more detailed data for the KB area would allow better modeling and more understanding of conditions. However, it is worth noting that the modeling result still show significant habitat change caused by climate and land cover changes. Another problem that must be considered is the fact that most of the key protected species studied in this paper are distributed across national boundaries. Transboundary cooperation is need for to promote research and protect the key protected species. 4 Conclusions MaxEnt model is suitable for habitat distribution modeling of key protected species in the Koshi Basin. The overall accuracy of the model is high. The results showing the extent and locations of key protected species distribution could give valuable insights on how to protect mountain biodiversity in KB. The modelled distributions and changes to distributions give conservation planners the precise locations of areas that are more sensitive and need to be given more attention in the development of protection plans. The response curves generated by environmental variables testing give planners a better understanding of the habitat selections of the animals and plants. When evaluating the distribution data of the keys species in 1992 and 2050, we suggest that more attention be paid to the habitats of Grus nigricollis, Panax pseudoginseng, and Presbytis entellus, because these species are expected to experience considerable decreases in habitat area in the future. The valley around Chentang Town and Dram Town in China, and Lamabagar and the northern part of Landtang National Park in Nepal are the most important regions for the protection of the habitats of the key protected species in KB. To protect biodiversity and endangered animals and plants, more attention should be paid to these areas, especially to the forests and wetlands, which are the main habitats for these key protected species, especially for the species with their habitat area expected to decreases in the future. Climate change in this region also matters, the climate variables precipitation of coldest quarter and min temperature of coldest month are especially important. Extreme climate change may have a great impact on the animals and plants in the region. Due to limits on the availability of data, the accuracy of the current model simulation is limited. In future studies, the impact of human activities on animal and plant habitats should be considered in order to reduce data deviations, and make simulated distributions closer to real distributions. This will provide more scientific and theoretical references to inform habitat conservation practices. Acknowledgement We are extremely grateful to Mr. Basanta Shrestha, Dr. Arun Bhakta Shrestha, Dr. SM Wahid and MSR Murthy of ICIMOD, Nepal, for their support in this work. We are grateful to the anonymous reviewers for their constructive comments and suggestions regarding this paper. References Deng W., Zhang Y Resources, Environment and Development of Koshi River Basin. Chengdu: Sichuan Science and Technology Press. Dong H., Lu G., Zhong X. et al Winter diet and food selection of the Black-necked Crane Grus nigricollis in Dashanbao, Yunnan, China. PEERJ, 4(e1968). Elith J., H. Graham C., P. Anderson R. et al Novel methods improve prediction of species distributions from occurrence data. 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