Version 1
: Received: 13 May 2023 / Approved: 15 May 2023 / Online: 15 May 2023 (07:19:47 CEST)
How to cite:
Sengupta, S.; Arandhara, S.; Saikia, B. Certainty Over Uncertainty: A Search for an Endemic Amphibia in Indo-Burma Hotspot Guided by a Predictive Distribution Modeling Approach. Preprints2023, 2023050987. https://doi.org/10.20944/preprints202305.0987.v1
Sengupta, S.; Arandhara, S.; Saikia, B. Certainty Over Uncertainty: A Search for an Endemic Amphibia in Indo-Burma Hotspot Guided by a Predictive Distribution Modeling Approach. Preprints 2023, 2023050987. https://doi.org/10.20944/preprints202305.0987.v1
Sengupta, S.; Arandhara, S.; Saikia, B. Certainty Over Uncertainty: A Search for an Endemic Amphibia in Indo-Burma Hotspot Guided by a Predictive Distribution Modeling Approach. Preprints2023, 2023050987. https://doi.org/10.20944/preprints202305.0987.v1
APA Style
Sengupta, S., Arandhara, S., & Saikia, B. (2023). Certainty Over Uncertainty: A Search for an Endemic Amphibia in Indo-Burma Hotspot Guided by a Predictive Distribution Modeling Approach. Preprints. https://doi.org/10.20944/preprints202305.0987.v1
Chicago/Turabian Style
Sengupta, S., Subhasish Arandhara and Bhaskar Saikia. 2023 "Certainty Over Uncertainty: A Search for an Endemic Amphibia in Indo-Burma Hotspot Guided by a Predictive Distribution Modeling Approach" Preprints. https://doi.org/10.20944/preprints202305.0987.v1
Abstract
Conservation of tropical endemic amphibians largely suffers from Wallacean shortfall, a gap to which predictive species distribution models have contributed significantly to bridge by delineating probable distribution and the underlying suitable habitat within their distributional range. However, rarely is a prediction model ground-truthed to evaluate their predictive performance. Here we present a species distribution modeling approach using maximum entropy algorithm corrected for smaller sample size, in guiding explorative surveys aimed at optimizing survey effort and discovering unrecorded populations of Zhangixalus suffry, a rhacophorid tree-frog endemic to the northeastern part of the Indian subcontinent along with the factors limiting their distribution. With only 16 established historical locality data to model for (after spatial thinning to reduce autocorrelation) and a set of environmental predictors (climatic, topographic, and landscape composition), our model prediction enabled the successful discovery of seven new population records from unreported landscapes, extending its southernmost distributional limit over a considerable distance. The final composite distribution model combining all the locality records (n=23) predicted similar core areas of suitable habitat consistent with the known geographic distribution of the species but showed poor representation under existing coverage of Protected Area (PA) network in the Region with only 7% representation of suitable habitat under protection. Habitat suitability of a site was significantly governed by factors related to precipitation (precipitation seasonality and precipitation of the warmest quarter) and topographic factors that can influence it (elevation and aspect). This corroborates with the known ecology of Rhacophorid frogs, especially concerning their seasonal explosive reproductive strategy and foam nest-building behavior.Through this study, we propose explorative surveys guided by species distribution models to expedite unknown population discovery of rare, tropical endemic amphibians and using such taxa as surrogates in identifying conservation priority zones that can be directly applied to reserve design and conservation and management planning.
Keywords
Maximum Entropy; species distribution model; ground validation; endemic; tree-frog; Protected Area; Biodiversity Hotspot
Subject
Biology and Life Sciences, Life Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
