Habitat association in the critically endangered Mangshan pit viper (Protobothrops mangshanensis), a species endemic to China

Abstract

<jats:p>Habitat directly affects the population size and geographical distribution of wildlife species, including the Mangshan pit viper (<jats:italic>Protobothrops mangshanensis</jats:italic>), a critically endangered snake species endemic to China. We searched for Mangshan pit viper using randomly arranged transects in their area of distribution and assessed their habitat association using plots, with the goals of gaining a better understanding of the habitat features associated with <jats:italic>P. mangshanensis</jats:italic> detection and determining if the association with these features varies across season. We conducted transect surveys, found 48 individual snakes, and measured 11 habitat variables seasonally in used and random plots in Hunan Mangshan National Nature Reserve over a period of 5 years (2012–2016). The important habitat variables for predicting Mangshan pit viper detection were fallen log density, shrub density, leaf litter cover, herb cover and distance to water. In spring, summer and autumn, Mangshan pit viper detection was always positively associated with fallen log density. In summer, Mangshan pit viper detection was related to such habitats with high canopy cover, high shrub density and high herb cover. In autumn, snakes generally occurred in habitats near water in areas with high fallen log density and tall shrubs height. Our study is the first to demonstrate the relationship between Mangshan pit viper detection and specific habitat components. Mangshan pit viper detection was associated with habitat features such as with a relatively high fallen log density and shrub density, moderately high leaf litter cover, sites near stream, and with lower herb cover. The pattern of the relationship between snakes and habitats was not consistent across the seasons. Identifying the habitat features associated with Mangshan pit viper detection can better inform the forestry department on managing natural reserves to meet the habitat requirements for this critically endangered snake species.</jats:p>

Spatial ecology and microhabitat selection of the nocturnal pitviper Viridovipera stejnegeri (Squamata: Viperidae) in relation to prey

“…To predict their distribution and habitat selection, autocorrelation analysis of the continuous variables of V. stejnegeri was conducted using Pearson correlation analysis, with highly autocorrelated variables excluded (Gardiner et al., 2015). Random forest analysis was then constructed using continuous variables in the habitat factors of V. stejnegeri (R Development Core Team, 2018; Zhang et al., 2020), and the importance of predictive variables in the random forest analysis was plotted according to the mean decrease Gini index. The top five most important habitat variables were selected, and a total of 31 linear logarithmic models incorporating multiple independent habitat variables were built by combining each variable using a generalized linear model (GLM):$$$$ \mathrm{Logit}\frac{P\left(Y=1\right)}{1-P\left(Y=1\right)}={\beta}_0+{\beta}_1{x}_1+{\beta}_2{x}_2+{\beta}_3{x}_3+\dots +{\beta}_{\mathrm{k}}{x}_{\mathrm{k}}, $$$$where x represents different habitat variables and β represents selection coefficients.…”

“…Recent advancements in technology, such as radio telemetry, have led to a rapid increase in research on snake habitat selection (Northrup et al., 2022 ), and the scope of habitat selection research has broadened to encompass various fields, including spatial ecology and distribution prediction (Barnes et al., 2017 , 2023 ; Bartoszek et al., 2021 ; Natusch et al., 2022 ; Silva et al., 2020 ; Strine et al., 2018 ). These studies are instrumental for unveiling species life histories and animal conservation (Robson & Blouin‐Demers, 2021 ; Zhang et al., 2020 ).…”

Space use and microhabitat selection of wild giant pandas in Meigu Dafengding National Nature Reserve, China

“…Microhabitat research depends on data within specific patches rather than data across the whole habitat. Data in specific patches are more accurate, intuitive and authentic, and can often better reflect problems than whole-habitat data (Zhang, 2021).…”