Popescu Viorel D, Iosif Ruben, Pop Mihai I, Chiriac Silviu, Bouroș George, Furnas Brett J
Department of Biological Sciences Ohio University Athens OH USA.
Centre for Environmental Research (CCMESI) University of Bucharest Bucharest Romania.
Ecol Evol. 2017 Aug 1;7(18):7134-7144. doi: 10.1002/ece3.3177. eCollection 2017 Sep.
Accurate population size estimates are important information for sustainable wildlife management. The Romanian Carpathians harbor the largest brown bear () population in Europe, yet current management relies on estimates of density that lack statistical oversight and ignore uncertainty deriving from track surveys. In this study, we investigate an alternative approach to estimate brown bear density using sign surveys along transects within a novel integration of occupancy models and home range methods. We performed repeated surveys along 2-km segments of forest roads during three distinct seasons: spring 2011, fall-winter 2011, and spring 2012, within three game management units and a Natura 2000 site. We estimated bears abundances along transects using the number of unique tracks observed per survey occasion via N-mixture hierarchical models, which account for imperfect detection. To obtain brown bear densities, we combined these abundances with the effective sampling area of the transects, that is, estimated as a function of the median (± bootstrapped SE) of the core home range (5.58 ± 1.08 km) based on telemetry data from 17 bears tracked for 1-month periods overlapping our surveys windows. Our analyses yielded average brown bear densities (and 95% confidence intervals) for the three seasons of: 11.5 (7.8-15.3), 11.3 (7.4-15.2), and 12.4 (8.6-16.3) individuals/100 km. Across game management units, mean densities ranged between 7.5 and 14.8 individuals/100 km. Our method incorporates multiple sources of uncertainty (e.g., effective sampling area, imperfect detection) to estimate brown bear density, but the inference fundamentally relies on unmarked individuals only. While useful as a temporary approach to monitor brown bears, we urge implementing DNA capture-recapture methods regionally to inform brown bear management and recommend increasing resources for GPS collars to improve estimates of effective sampling area.
准确的种群数量估计是野生动物可持续管理的重要信息。罗马尼亚喀尔巴阡山脉拥有欧洲最大的棕熊种群，但目前的管理依赖于缺乏统计监督且忽略了来自足迹调查不确定性的密度估计。在本研究中，我们在占用模型和家域方法的新型整合中，使用沿样带的迹象调查来研究一种估计棕熊密度的替代方法。我们在三个狩猎管理单元和一个2000自然网络站点内，于2011年春季、2011年秋冬和2012年春季这三个不同季节，沿着2公里长的林道段进行了重复调查。我们通过N - 混合分层模型，利用每次调查中观察到的独特足迹数量来估计样带上的熊数量，该模型考虑了不完全检测的情况。为了获得棕熊密度，我们将这些数量与样带的有效采样面积相结合，有效采样面积根据17只熊的遥测数据估计，这些熊在与我们调查窗口重叠的1个月期间被追踪，核心家域的中位数（±自抽样标准误）为函数，即5.58 ± 1.08公里。我们的分析得出三个季节的平均棕熊密度（及95%置信区间）为：11.5（7.8 - 15.3）、11.3（7.4 - 15.2）和12.4（8.6 - 16.3）只/100平方公里。在各个狩猎管理单元中，平均密度在7.5至14.8只/100平方公里之间。我们的方法纳入了多种不确定性来源（如有效采样面积、不完全检测）来估计棕熊密度，但推断基本上仅依赖于未标记个体。虽然作为监测棕熊的临时方法有用，但我们敦促在区域内实施DNA捕获 - 重捕方法以指导棕熊管理，并建议增加用于GPS项圈的资源以改进有效采样面积的估计。
