Seeing from space makes sense: Novel earth observation variables accurately map species distributions over Himalaya. (1st January 2023)

• Record Type: Journal Article
• Authors: Satish, K.V.; Dugesar, Vikas; Pandey, Manish K.; Srivastava, Prashant K.; Pharswan, Dalbeer S.; Wani, Zishan Ahmad
• Is Part Of: Journal of environmental management. Volume 325:Part A(2023)
• Publication Date: 2023-01-01
• Subjects: Alpine species distributions -- Niche overlap -- Machine learning algorithms -- Remote sensing -- MODIS -- Conservation and management; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2022.116428
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Topical advances in earth observation have enabled spatially explicit mapping of species' fundamental niche limits that can be used for nature conservation and management applications. This study investigates the possibility of applying functional variables of ecosystem retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard sensor data to map the species distribution of two alpine treeline species, namely Betula utilis D.Don and Rhododendron campanulatum D.Don over the Himalayan biodiversity hotspot. In this study, we have developed forty-nine Novel Earth Observation Variables (NEOVs) from MODIS products, an asset to the present investigation. To determine the effectiveness and ecological significance of NEOVs combinations, we built and compared four different models, namely, a bioclimatic model (BCM) with bioclimatic predictor variables, a phenology model (PhenoM) with earth observation derived phenological predictor variables, a biophysical model (BiophyM) with earth observation derived biophysical predictor variables, and a hybrid model (HM) with a combination of selected predictor variables from BCM, PhenoM, and BiophyM. All models utilized topographical variables by default. Models that include NEOVs were competitive for focal species, and models without NEOVs had considerably poor model performance and explanatory strength. To ascertain the accurate predictions, we assessed the congruence of predictions by pairwise comparisons of their performance. Among the three machine learning algorithms tested (artificial neural networks, generalised boosting model, and maximum entropy), maximum entropy produced the most promising predictions for BCM, PhenoM, BiophyM, and HM. Area under curve (AUC) and true skill statistic (TSS) scores for the BCM, PhenoM, BiophyM, and HM models derived from maximum entropy were AUC ≥0.9 and TSS ≥0.6 for the focal species. The overall investigation revealed the competency of NEOVs in the accurate prediction of species' fundamental niches, but conventional bioclimatic variables were unable to achieve such a level of precision. A principal component analysis of environmental spaces disclosed that niches of focal species substantially overlapped each other. We demonstrate that the use of satellite onboard sensors' biotic and abiotic variables with species occurrence data can provide precision and resolution for species distribution mapping at a scale that is relevant ecologically and at the operational scale of most conservation and management actions. Highlights: This study mapped niches of B. utilis and R. campanulatum over Himalaya. Investigated niche overlapping of study species over Himalaya. Forty-nine NEOVs were developed to enhance SDMs in Himalaya. NEOVs based models yielded realistic distributions compared to bioclimatic models. Outputs of the study play a crucial role in conservation and management plans.