A density estimation model of plateau pika (Ochotona curzoniae) supporting camera‐monitoring programs. Issue 15 (13th July 2021)

• Record Type: Journal Article
• Authors: Jia, Ying‐Hui; Qiu, Jun; Ma, Cang; Wang, Jin‐Zhao; Wang, Guang‐Qian; Li, Fang‐Fang
• Is Part Of: Ecology and evolution. Volume 11:Issue 15(2021)
• Page Start: 10566
• Page End: 10581
• Publication Date: 2021-07-13
• Subjects: activity intensity -- burrow system -- camera trapping -- density estimation model -- Monte Carlo method -- plateau pika -- population density; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.7865
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract: As an important species in the Qinghai‐Tibet Plateau, the roles played by plateau pikas in grassland degradation and protection are controversial. The behavior characteristics and population density of this species are important in answering this question, but these traits have not been fully elucidated to date. Camera‐capture methods have been used widely in recent years to characterize or calculate population density with the advantage of simple operation and nonintrusive investigation. However, establishing the relationship between actual population density and monitoring data with the condition that individual identification is not possible is a major challenge for this method. In this study, a model composed of a behavioral module and a burrow system module is proposed and applied to simulate the moving path of each individual pika. Based on Monte Carlo method, the model is used to develop the relationship between population density and recorded capture number, which is compared with the results derived from the random encounter model (REM) based on field observations. The simulated results mixed with the calculated density based on observation data could reach R 2 = 0.98 using linear fitting, with proper parameter settings. A novel index named activity intensity of pikas per population density is also proposed, providing information on both the ecological physical characteristics and monitoring space. The influence of different parameters on this index, mainly the pika number per burrow system, pika activity time outside the burrow, and activity intensity, is discussed. The proposed methodology can be applied to different scenarios in further studies when behavioral characteristics of pikas change for such reasons as climate change and vegetation degradation. Abstract : A digital twin model of pikas' activity composed of a behavior model and a cave system model is proposed. The model is used to estimate the population density of pikas, compared with the calculated density derived from random encounter model (REM) and field observation. A novel index named activity intensity of pikas per population density is also proposed, indicating the information of both the ecological physical parameters and the monitoring space.