bioRxiv Science⌕ Search

bioRxiv · 10.1101/2023.10.05.561022

Predicting Suitable Habitats Of Critically Endangered Chinese Pangolin In Assam, India

Abstract

Chinese pangolin is a least known species in terms of its ecology and population status in India. The present study of Chinese pangolin in Kaziranga-Karbi-Anglong landscape aimed to gather information on species distribution and population status and also an account of threats of the species was investigated. A total of 24 occurrence points were collected from all over Assam and used to get a predictive potential distribution map for the species via Maxent and Random Forest models. To understand the knowledge level of the local people, a total of 13 villages in the north east boundary of North Karbi Anglong Wildlife Sactuary have been surveyed and 160 respondents were interviewed. The AUC values for the two models namely Maxent (0.736) and Random Forest (0.87) were different. Out of the six environmental parameters chosen, the altitude clarified the maximum variation in the model (62 %) followed by the seasonal temperature (bio4; 19.6 %) in the Maxent model. At the same time, these two variables were also found to be of greater significance in the Random Forest model. The species has been least seen by most of the respondents in the last 10 years (43.7%). Most of the respondent of age class 18-24 years never saw a pangolin (82.5%) in the area which depict the population decline of the spcies. Scales were the most used body parts for traditional medicine (58.8%) especially for exceesive saliva secretion in children. The resultant potential habitat in the predictive distribution map in Assam needs to be confirmed with ground verification and accordingly inclusion in the priority conservation zone for Chinese pangolin in Assam should be done.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sarma, K., C, M. K., Boro, B. K., Saikia, M. K., Sarania, B., Basumatary, H., Saikia, B. P., Saikia, P. K.. 2023-10-07. Predicting Suitable Habitats Of Critically Endangered Chinese Pangolin In Assam, India. https://doi.org/10.1101/2023.10.05.561022

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗

A training protocol for human classification of Asian elephant images from trail cameras

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

ecology↗

Forest belowground productivity and carbon allocation predominantly driven by soil properties rather than climate

Forests are threatened by a multitude of stressors, including anthropogenic disturbances and climate change. Assessing how forests will respond to these stressors requires a comprehensive understanding of net primary productivity (Npp), environmental constraints on growth, and adaptive capacity. A parameter of significant uncertainty is belowground Npp (bNpp), which can account for up to 80% of total Npp but is poorly estimated and rarely measured directly. We used a cross-biome dataset of direct, field-based measurements of aboveground and belowground primary productivity and 21 climatic and soil variables to identify potential constraints on bNpp and belowground carbon allocation in boreal and cold temperate forests. Soil variables, rather than climate variables, were the main drivers of bNpp and belowground allocation across biomes. The importance of soil variables suggests that soil nutrient dynamics, especially soil nutrient pool and flux variables, must be explicitly modeled to more accurately predict feedbacks between climate, productivity, and within-tree carbon allocation. Within biomes, environmental drivers of belowground allocation varied between low versus high allocation forests, indicating that environmental drivers are site-specific and the development of within-biome, site-scale classifications for forest ecosystems could be useful. Changes in soil variables, such as increasing soil nitrogen pools, caused abrupt and large decreases in bNpp for boreal, but not cold temperate forests. Threshold-like shifts indicate that boreal forests might have lower adaptive capacity and higher sensitivity to disturbances than cold temperate forests. With 70% of boreal forests characterized by low bNpp, disturbances such as anthropogenic nitrogen deposition could cause large-scale decreases in bNpp that could push these forests beyond their adaptive capacity.

ecology↗