Novelty predictors for shrub (and climbers) ecological niche modeling,based on their successional stage |
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| Institution: | 1. VNU University of Science, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam;2. Hanoi National University of Education, 136 Xuan Thuy, Cau Giay, Hanoi, Viet Nam;3. School of Biological and Marine Science, University of Plymouth, Plymouth, Devon PL4 8AA, United Kingdom;1. Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India;2. ICAR-National Institute of Abiotic Stress Management, Baramati, 413115 Pune, Maharashtra, India;3. Division of Plant Pathology, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India;4. ICAR-Mahatma Gandhi Integrated Farming Research Institute, Piprakothi, Motihari Bihar 845429, India;1. School of Technology, Beijing Forestry University, 100083 Beijing, China;2. BFU Research Center for Biodiversity Intelligent Monitoring, 100083 Beijing, China;3. Key Laboratory of Forestry Equipment and Automation National Forestry and Grassland Administration, 100083 Beijing, China;1. Department of Mining Engineering, National Institute of Technology, Rourkela 769008, Odisha, India;2. Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi 835222, India;3. School of Geography and Environmental Science, University of Southampton, Highfield, Southampton, SO17 1BJ, United Kingdom |
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| Abstract: | The identification of species' environmental predictors constitute a key challenge for decision making, especially when using ecological niche modeling based on these drivers and when presence points are limited. More specifically, shrub species are affected by ecosystem dynamics, and appear in degraded formations, in dense mid-stage vegetation formations, or under late climax-forest canopy. In this study, we tested novelty predictors to understand the drivers that affect the selected species distribution in the Mediterranean biome, targeting different vegetation successional stages, and further improve ecological models' performance, when presence points are limited. Land surface temperature (LST) in association with temperature related predictors, allowed differentiating between species thriving in the understory of the forest canopy, from those that are co-dominant with dense vegetation cover and a third group/species, thriving in degraded vegetation. In addition, the Normalized Difference Vegetation Level Index (NDVI) played a key role in the models for species growing in highly degraded ecological niches such as Spartium junceum, Calicotome villosa, but also forest-fringe vegetation like the climber Hedera helix. Our study highlights the importance of integrating remote sensed predictors, combined with appropriate climate drivers, when using ecological niche modeling. |
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