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Swarm behavior simulation of plateau pika

Citation

Yinghui, Jia et al. (2021), Swarm behavior simulation of plateau pika, Dryad, Dataset, https://doi.org/10.5061/dryad.m905qfv0f

Abstract

  As an important species on the Qinghai-Tibet Plateau, the pika has great controversy in grassland protection and ecological function service. The population change of pika is related to the fragile and sensitive ecological chain of Qinghai-Tibet Plateau. The traditional methods of population density survey include sampling method, marker recapture method, removal sampling method, etc., which are cumbersome to operate and consume a lot of manpower and material resources. In recent years, more and more scholars use camera capture method to characterize or calculate population density. This method is simple to operate and widely applicable, but how to establish the relationship between actual population density and monitoring data under the condition that individual identification cannot be carried out is a big challenge faced by this method. In order to solve this problem, the density of pika was estimated by two methods. First, random encounter model (SEM) was used to estimate the density of pika based on actual field observation data. Secondly, a Monte Carlo model is established to describe the behavior of pika and the practical problems are simulated by using probability statistics. The results obtained by the two methods are compared and mutually verified, and it is found that the two model results are in good agreement, and the probabilistic model can effectively establish the relationship between the ecological physical parameters of the population and the monitoring space. 

Methods

The field observation is implemented in Dari County, which is located in the southeast of the Qinghai-Tibet Plateau with the geographic coordinates of 98°15' ~100°33' east longitude and 32°36' ~ 34°15' north latitude. The average altitude of Dari County is over 4200 m, and it has an alpine and semi-humid climate, belonging to “National Nature Reserve of Three Rivers Source” in China.

The cameras are installed at 6 different habitat location, which are the benchland of Jiqu River, 100 m and 300m from the river bank of Jiqu River, beside the road, gentle sunny slope, and steep sunny slope. A 7-day monitoring was conducted from Aug 12th to Aug 18th, 2019. The foresafe H885 field infrared cameras were applied under the photo & video mode, and the recording time is set to be 30s. The camera works 24 hours a day. If an animal enters the monitoring range of the camera sensor, the camera is triggered and takes a picture recording the shooting date and time, and then record a video of 30 seconds. After that, it returns to the standby mode.

The simulation model contains a cave system model and a pika behaviour model. First, n control points are randomly generated in the simulation area to construct a Delaunay triangulation. Thiessen polygons are divided according to the given boundary conditions, each of which represents the area occupied by a cave system. With the given maximum and minimum number of caves in a cave system   and  , as well as the minimum distance between any two caves in the cave system , caves in each cave system are randomly generated. Then, a certain number of pikas are randomly generated for each cave system given the maximum and minimum number of pikas for each cave system   and  . The pika’s daily ground activity time is , and the fixed time length of each step when the pika goes out is  Each pika has two alternative states at each time step, i.e., outside and inside the cave. The behavior of pikas when going out is classified into two categories: high-intensity activities, such as running; and low-intensity activities, such as foraging and playing. The moving direction of a pika in each step is random with constrained maximize distance. 

 

Usage Notes

The full code for the swarm behaviour simulation model is provided. The program is coded using matlab. Before running the code, one should download a tool kit named mpt and add it to the path of  matlab. Any suggestions and advice to the code is welcomed.

The original video data is too large, so we only give the raw images instantly taken after the cameras were triggered. Due to unexpected natural conditions and cameras overturned, some of the pictures may have people, birds shown up, and some may be blurry, resulting in varied normal operating days for each camera. By checking the videos taken  after, we determined whether this shoot was effective or not. If the time interval between two images are less than two minutes, we assumed that the camera tracked the same pika. Notably, we are very sorry that we lost the images and videos taken from the benchland of Jiqu River  and some at the steep sunny slope due to computer crush. 

Funding

National Natural Science Foundation of China, Award: 91847302

National Natural Science Foundation of China, Award: 51879137

National Natural Science Foundation of China, Award: 51979276