2024-05-01
Vulnerability of lowland and upland orchids in their spatially response to climate change and land cover change
Publication
Publication
Ecological Informatics , Volume 80 - Issue 102534
Climate change and land cover change often interactively affect plant species distributions. This study addresses the vulnerability of lowland and upland orchids to climate change and land cover change. Endemic orchids of New Guinea were grouped into four classes (lowland epiphyte, lowland terrestrial, upland epiphyte, upland terrestrial) based on their life form and elevation range. Forty occurrence records of endemic orchids were selected for each class, totaling 160 occurrence records. Ensemble modelling combining two machine learning algorithms was used to generate predictive current and future suitable areas for orchid classes. Model performance was evaluated using the AUC and TSS metrics. Suitable areas for both lowland and upland orchids (epiphyte and terrestrial) were predicted decrease in the future due to climate change and land cover change. The loss of suitable areas for upland terrestrial orchids was predicted to be most significant in the worst-case climate change scenario (SSP 5–8.5). Both lowland and upland orchids (epiphyte and terrestrial) tend to shift to higher elevation ranges from the present distributions. The predictive models have AUC values >0.90 and TSS value >0.80, indicating the models have excellent potential for predicting the impact of climate change and land cover change on orchid distributions.
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| doi.org/10.1016/j.ecoinf.2024.102534 | |
| Ecological Informatics | |
| Released under the CC BY-NC-ND 4.0 (“Attribution-NonCommercial-NoDerivs 4.0 International”) Licence | |
| Organisation | Staff publications |
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Yudaputra, Angga, Munawaroh, Esti, Usmadi, Didi, Purnomo, Danang Wahyu, Astuti, Inggit Puji, Puspitaningtyas, Dwi Murti, … Cropper, Wendell P. (2024). Vulnerability of lowland and upland orchids in their spatially response to climate change and land cover change. Ecological Informatics, 80(102534). doi:10.1016/j.ecoinf.2024.102534 |
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