Linkie Matthew, Guillera-Arroita Gurutzeta, Smith Joseph, Rayan D Mark
Fauna & Flora International, Cambridge, UKNational Centre for Statistical Ecology, School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, UKPanthera, New York, NY, USAWWF Malaysia, Selangor, MalaysiaDurrell Institute of Conservation and Ecology, University of Kent, Canterbury, UK.
Integr Zool. 2010 Dec;5(4):342-350. doi: 10.1111/j.1749-4877.2010.00215.x.
With only 5% of the world's wild tigers (Panthera tigris Linnaeus, 1758) remaining since the last century, conservationists urgently need to know whether or not the management strategies currently being employed are effectively protecting these tigers. This knowledge is contingent on the ability to reliably monitor tiger populations, or subsets, over space and time. In the this paper, we focus on the 2 seminal methodologies (camera trap and occupancy surveys) that have enabled the monitoring of tiger populations with greater confidence. Specifically, we: (i) describe their statistical theory and application in the field; (ii) discuss issues associated with their survey designs and state variable modeling; and, (iii) discuss their future directions. These methods have had an unprecedented influence on increasing statistical rigor within tiger surveys and, also, surveys of other carnivore species. Nevertheless, only 2 published camera trap studies have gone beyond single baseline assessments and actually monitored population trends. For low density tiger populations (e.g. <1 adult tiger/100 km(2)) obtaining sufficient precision for state variable estimates from camera trapping remains a challenge because of insufficient detection probabilities and/or sample sizes. Occupancy surveys have overcome this problem by redefining the sampling unit (e.g. grid cells and not individual tigers). Current research is focusing on developing spatially explicit capture-mark-recapture models and estimating abundance indices from landscape-scale occupancy surveys, as well as the use of genetic information for identifying and monitoring tigers. The widespread application of these monitoring methods in the field now enables complementary studies on the impact of the different threats to tiger populations and their response to varying management intervention.
自上个世纪以来,全球野生老虎( Panthera tigris Linnaeus,1758)仅余5%,自然资源保护主义者迫切需要了解当前所采用的管理策略是否能有效保护这些老虎。这一认知取决于能否在空间和时间上可靠地监测老虎种群或其亚种群。在本文中,我们聚焦于两种具有开创性的方法(相机陷阱法和占有率调查法),它们使人们能够更有信心地监测老虎种群。具体而言,我们:(i)描述其统计理论及在实地的应用;(ii)讨论与调查设计和状态变量建模相关的问题;以及(iii)探讨它们的未来发展方向。这些方法对提高老虎调查以及其他食肉动物物种调查的统计严谨性产生了前所未有的影响。然而,仅有两项已发表的相机陷阱研究超越了单一基线评估,实际监测了种群趋势。对于低密度老虎种群(例如<1只成年老虎/100平方公里),由于检测概率不足和/或样本量不够,从相机陷阱获取足够精确的状态变量估计值仍是一项挑战。占有率调查通过重新定义抽样单位(例如网格单元而非个体老虎)克服了这一问题。当前的研究重点是开发空间明确的捕获-标记-重捕模型,从景观尺度的占有率调查中估计丰度指数,以及利用遗传信息识别和监测老虎。这些监测方法在实地的广泛应用,现在使得能够对不同威胁对老虎种群的影响及其对不同管理干预的反应进行补充研究。