K. Pike, M. Golchin, J. Perry, E. Vanderduys, A. Hoskins
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Advances in Global Positioning System (GPS) tracking technology allows managers to safely collect high frequency, remotely sensed data on animal locations in space and time that overcome some of the issues of working in logistically challenging locations. The next challenge then becomes extracting ecological metrics from these data with appropriate modelling techniques that accounts for the inherent spatio-temporal autocorrelation and restrictions of high frequency data (Calabrese et al., 2016). Here we present a success story of harnessing spatially explicit movement models to understand buffalo movement and social behaviour to provide data-rich decision support to wildlife managers. We used continuous time movement models to produce autocorrelated kernel density estimates of buffalo home ranges and social encounter area from 126,567 locations from 17 buffalo GPS tracked over a 16-month period. We compared the movement, space use, and social behaviour of buffalo between the wet and dry season of the Djelk area, when resource availability is vastly different in the wetlands of the Northern Territory. We found in the dry season, buffalo space use was restricted, and the size of their home range was significantly smaller than in the wet season","PeriodicalId":390064,"journal":{"name":"MODSIM2023, 25th International Congress on Modelling and Simulation.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using spatially explicit models to determine seasonal differences in space use and behaviour of feral buffalo in the Northern Territory\",\"authors\":\"K. Pike, M. Golchin, J. Perry, E. Vanderduys, A. 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Advances in Global Positioning System (GPS) tracking technology allows managers to safely collect high frequency, remotely sensed data on animal locations in space and time that overcome some of the issues of working in logistically challenging locations. The next challenge then becomes extracting ecological metrics from these data with appropriate modelling techniques that accounts for the inherent spatio-temporal autocorrelation and restrictions of high frequency data (Calabrese et al., 2016). Here we present a success story of harnessing spatially explicit movement models to understand buffalo movement and social behaviour to provide data-rich decision support to wildlife managers. We used continuous time movement models to produce autocorrelated kernel density estimates of buffalo home ranges and social encounter area from 126,567 locations from 17 buffalo GPS tracked over a 16-month period. We compared the movement, space use, and social behaviour of buffalo between the wet and dry season of the Djelk area, when resource availability is vastly different in the wetlands of the Northern Territory. 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引用次数: 0
摘要
当前位置管理澳大利亚北部的野牛已成为一项艰巨的挑战。在北领地,有超过20万只亚洲水牛(Bubalus bubalis),它们庞大而密集的人口造成了众多的经济、生物安全、文化和环境问题(Collier et al., 2011;Robinson and Whitehead, 2003)。因此,传统的所有者、环境管理者和土地所有者需要知道水牛在景观中的位置,以及他们正在做什么来充分管理和缓解这些问题。然而,由于水牛聚居区地处偏远,基础设施和通道有限,而且水牛体型庞大,具有攻击性,因此很难在野外观察和管理它们。全球定位系统(GPS)跟踪技术的进步使管理人员能够安全地在空间和时间上收集动物位置的高频遥感数据,从而克服了在物流具有挑战性的地点工作的一些问题。接下来的挑战是使用适当的建模技术从这些数据中提取生态指标,这些建模技术考虑了固有的时空自相关性和高频数据的限制(Calabrese et al., 2016)。本文介绍了一个利用空间显性运动模型来理解水牛运动和社会行为的成功案例,为野生动物管理者提供数据丰富的决策支持。我们使用连续时间运动模型对17头水牛在16个月的时间里追踪的126,567个地点进行了自相关核密度估计,得出了水牛的家庭范围和社会接触面积。我们比较了Djelk地区湿季和旱季水牛的活动、空间使用和社会行为,当时北领地湿地的资源可用性差异很大。我们发现,在旱季,水牛的空间利用受到限制,它们的活动范围明显小于雨季
Using spatially explicit models to determine seasonal differences in space use and behaviour of feral buffalo in the Northern Territory
: Managing feral buffalo in northern Australia has become a formidable challenge. In the Northern Territory, there are over 200,000 Asian water buffalo ( Bubalus bubalis ), and their large and dense population causes a multitude of economic, biosecurity, cultural, and environmental problems (Collier et al., 2011; Robinson and Whitehead, 2003). Traditional Owners, environmental managers, and landowners, thus need to know where buffalo are in the landscape and what they are doing to adequately manage and mitigate these issues. However, due to the remoteness of buffalo inhabited areas there is limited infrastructure and access available, and the buffalo’s large size and aggressive nature make them very difficult to observe and manage in the wild. Advances in Global Positioning System (GPS) tracking technology allows managers to safely collect high frequency, remotely sensed data on animal locations in space and time that overcome some of the issues of working in logistically challenging locations. The next challenge then becomes extracting ecological metrics from these data with appropriate modelling techniques that accounts for the inherent spatio-temporal autocorrelation and restrictions of high frequency data (Calabrese et al., 2016). Here we present a success story of harnessing spatially explicit movement models to understand buffalo movement and social behaviour to provide data-rich decision support to wildlife managers. We used continuous time movement models to produce autocorrelated kernel density estimates of buffalo home ranges and social encounter area from 126,567 locations from 17 buffalo GPS tracked over a 16-month period. We compared the movement, space use, and social behaviour of buffalo between the wet and dry season of the Djelk area, when resource availability is vastly different in the wetlands of the Northern Territory. We found in the dry season, buffalo space use was restricted, and the size of their home range was significantly smaller than in the wet season