Separation of the bioclimatic spaces of Himalayan tree rhododendron species predicted by ensemble suitability models

Ranjitkar, Sailesh; Kindt, Roeland; Sujakhu, Nani Maiya; Hart, Robbie; Wen, Guo Chun; Yang, Xuefei; Shrestha, Krishna K.; Xu, Jianchu; Luedeling, Eike

doi:10.1016/j.gecco.2014.07.001

Cited by 54 publications

(29 citation statements)

References 39 publications

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“…This result is consistent with Ranjitkar, Kindt, et al. (), who noted separate bioclimatic space in habitat suitability modeling of rhododendron trees in the Himalayas (Ranjitkar, Kindt et al., ), a major component of vegetation in red panda habitats. Temperature‐associated bioclimatic variables have great importance in predicting habitat suitability for A. f. styani , whereas precipitation‐associated bioclimatic variables were the most important habitat predictor for A. f. fulgens .…”

Section: Discussionsupporting

confidence: 90%

“…However, actual habitat is likely smaller than predicted habitat because climatic variables are not the only determinants of red panda habitat suitability. Other factors such as edaphic and biogeographic factors limit the species distribution, even in areas that are climatically suitable (Ranjitkar, Kindt, et al., ). MaxEnt modeling approach has been implemented successfully to build current and future habitats under climate change scenarios for the sympatric macrohabitat dwellers red and giant panda (Li, Xu, Wong, Qiu, Li, et al., ; Li, Xu, Wong, Qiu, Sheng, et al., ; Liu et al., ; Songer, Delion, Biggs, & Huang, ; Sun, ).…”

Section: Discussionmentioning

confidence: 99%

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Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling

Thapa

et al. 2018

Ecology and Evolution

111

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An upsurge in anthropogenic impacts has hastened the decline of the red panda (Ailurus fulgens). The red panda is a global conservation icon, but holistic conservation management has been hampered by research being restricted to certain locations and population clusters. Building a comprehensive potential habitat map for the red panda is imperative to advance the conservation effort and ensure coordinated management across international boundaries. Here, we use occurrence records of both subspecies of red pandas from across their entire range to build a habitat model using the maximum entropy algorithm (MaxEnt 3.3.3k) and the least correlated bioclimatic variables. We found that the subspecies have separate climatic spaces dominated by temperature‐associated variables in the eastern geographic distribution limit and precipitation‐associated variables in the western distribution limit. Annual precipitation (BIO12) and maximum temperature in the warmest months (BIO5) were major predictors of habitat suitability for A. f. fulgens and A. f. styani, respectively. Our model predicted 134,975 km2 of red panda habitat based on 10 percentile thresholds in China (62% of total predicted habitat), Nepal (15%), Myanmar (9%), Bhutan (9%), and India (5%). Existing protected areas (PAs) encompass 28% of red panda habitat, meaning the PA network is currently insufficient and alternative conservation mechanisms are needed to protect the habitat. Bhutan's PAs provide good coverage for the red panda habitat. Furthermore, large areas of habitat were predicted in cross‐broader areas, and transboundary conservation will be necessary.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling

Thapa

et al. 2018

Ecology and Evolution

111

View full text Add to dashboard Cite

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“…This study opens up possibilities for future ecological research in the Himalaya, based on the invaluable capability of UAS as a tool for providing VHR data that can be useful for ecological monitoring in remote and inaccessible areas. Our results improve upon previous studies that utilized ecological niche models based on the probability of the occurrence approach to map the potential distribution of selected Himalayan species [56,57] instead of the direct classification approach.…”

Section: Discussionsupporting

confidence: 75%

Species-Level Vegetation Mapping in a Himalayan Treeline Ecotone Using Unmanned Aerial System (UAS) Imagery

Mishra

Mainali

Shrestha

et al. 2018

IJGI

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Understanding ecological patterns and response to climate change requires unbiased data on species distribution. This can be challenging, especially in biodiverse but extreme environments like the Himalaya. This study presents the results of the first ever application of Unmanned Aerial Systems (UAS) imagery for species-level mapping of vegetation in the Himalaya following a hierarchical Geographic Object Based Image Analysis (GEOBIA) method. The first level of classification separated green vegetated objects from the rest with overall accuracy of 95%. At the second level, seven cover types were identified (including four woody vegetation species). For this, the suitability of various spectral, shape and textural features were tested for classifying them using an ensemble decision tree algorithm. Spectral features alone yielded ~70% accuracy (kappa 0.66) whereas adding textural and shape features marginally improved the accuracy (73%) but at the cost of a substantial increase in processing time. Contrast in plant morphological traits was the key to distinguishing nearby stands as different species. Hence, broad-leaved versus fine needle leaved vegetation were mapped more accurately than structurally similar classes such as Rhododendron anthopogon versus non-photosynthetic vegetation. Results highlight the potential and limitations of the suggested UAS-GEOBIA approach for detailed mapping of plant communities and suggests future research directions.

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“…Modelling ecological niches and species distributions in remote high mountain regions like the Himalayas are challenging tasks. Current studies in the field of plant species distribution modelling in the Himalayan mountains mainly use climatic variables to predict species' distribution or to forecast species range shifts under climate change scenarios (e.g., [7,32,41,[70][71][72][73][74][75][76][77]). With regard to Betula utilis, reasonable results were obtained using solely climate for predicting the potential distribution [9,32].…”

Section: Discussionmentioning

confidence: 99%

Application of Thermal and Phenological Land Surface Parameters for Improving Ecological Niche Models of Betula utilis in the Himalayan Region

et al. 2018

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Modelling ecological niches across vast distribution ranges in remote, high mountain regions like the Himalayas faces several data limitations, in particular nonavailability of species occurrence data and fine-scale environmental information of sufficiently high quality. Remotely sensed data provide key advantages such as frequent, complete, and long-term observations of land surface parameters with full spatial coverage. The objective of this study is to evaluate modelled climate data as well as remotely sensed data for modelling the ecological niche of Betula utilis in the subalpine and alpine belts of the Himalayan region covering the entire Himalayan arc. Using generalized linear models (GLM), we aim at testing factors controlling the species distribution under current climate conditions. We evaluate the additional predictive capacity of remotely sensed variables, namely remotely sensed topography and vegetation phenology data (phenological traits), as well as the capability to substitute bioclimatic variables from downscaled numerical models by remotely sensed annual land surface temperature parameters. The best performing model utilized bioclimatic variables, topography, and phenological traits, and explained over 69% of variance, while models exclusively based on remotely sensed data reached 65% of explained variance. In summary, models based on bioclimatic variables and topography combined with phenological traits led to a refined prediction of the current niche of B. utilis, whereas models using solely climate data consistently resulted in overpredictions. Our results suggest that remotely sensed phenological traits can be applied beneficially as supplements to improve model accuracy and to refine the prediction of the species niche. We conclude that the combination of remotely sensed land surface temperature parameters is promising, in particular in regions where sufficient fine-scale climate data are not available.

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Separation of the bioclimatic spaces of Himalayan tree rhododendron species predicted by ensemble suitability models

Cited by 54 publications

References 39 publications

Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling

Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling

Species-Level Vegetation Mapping in a Himalayan Treeline Ecotone Using Unmanned Aerial System (UAS) Imagery

Application of Thermal and Phenological Land Surface Parameters for Improving Ecological Niche Models of Betula utilis in the Himalayan Region

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