One-stage spatial mark–resight analysis reveals an increasing grizzly bear population with declining density near roads

IF 2.7 3区 环境科学与生态学 Q2 ECOLOGY
Ecosphere Pub Date : 2025-04-14 DOI:10.1002/ecs2.70246
Jesse Whittington, Mark Hebblewhite, Connor Meyer, Barb Johnston, Anne Forshner, Bryan J. Macbeth, Anthony L. Einfeldt, Seth G. Cherry
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引用次数: 0

Abstract

Wildlife ecologists throughout the world strive to monitor trends in population abundance to help manage wildlife populations and conserve species at risk. Spatial capture–recapture studies are the gold standard for monitoring density, yet they can be difficult to apply because researchers must be able to distinguish all detected individuals. Spatial mark–resight (SMR) models only require a subset of the population to be marked and identifiable. Recent advances in SMR models with radio-collared animals required a two-staged analysis. We developed a one-stage generalized SMR (gSMR) model that used detection histories of marked and unmarked animals in a single analysis. We used simulations to assess the performance of one- and two-stage gSMR models. We then applied the one-stage gSMR with telemetry and remote camera data to estimate grizzly bear (Ursus arctos) abundance from 2012 to 2023 within the Canadian Rocky Mountains. We estimated abundance trends for the population and reproductive females (females with cubs of the year). Simulations suggest that one- and two-stage models performed equally well. One-stage models are more dependable as they use exact likelihoods, whereas two-stage models have shorter computation times for large data sets. Both methods had >95% credible interval coverage and minimal bias. Increasing the number of marked animals increased the accuracy and precision of abundance estimates, and ≥10 marked animals were required to obtain coefficients of variation <20% in most scenarios. The grizzly bear population increased slightly (growth rate λmean = 1.02) to a 2023 density of 10.4 grizzly bears/1000 km2. Reproductive female abundance had high interannual variability and increased to 1.0 bears/1000 km2. Population density was highest within protected areas, within high-quality habitat and far from paved roads. The density of activity centers declined near paved roads over time. Mechanisms of decline may have included direct mortality and shifting activity centers to avoid human activity. Our study demonstrates the influence of human activity on localized density and the importance of protected areas for carnivore conservation. Finally, our study highlights the widespread utility of remote camera and telemetry-based SMR models for monitoring spatiotemporal trends in abundance.

Abstract Image

单阶段空间标记-视觉分析表明,灰熊的数量在增加,而道路附近的密度在下降
世界各地的野生动物生态学家都在努力监测种群数量的变化趋势,以帮助管理野生动物种群和保护濒危物种。空间捕获-重捕研究是监测密度的黄金标准,但由于研究人员必须能够分辨出所有检测到的个体,因此很难应用。空间标记-重捕(SMR)模型只要求对种群中的一部分进行标记和识别。最近在使用无线电领标动物的 SMR 模型方面取得的进展要求进行两阶段分析。我们开发了一种单阶段广义 SMR(gSMR)模型,在一次分析中使用有标记和无标记动物的探测历史。我们通过模拟来评估一阶段和二阶段 gSMR 模型的性能。然后,我们将一阶段 gSMR 与遥测和远程照相机数据结合起来,估算了加拿大落基山脉灰熊(Ursus arctos)从 2012 年到 2023 年的丰度。我们估计了种群和繁殖期雌熊(当年产崽的雌熊)的丰度趋势。模拟结果表明,单阶段模型和双阶段模型表现同样出色。单阶段模型使用精确似然,因此更可靠,而两阶段模型对于大型数据集的计算时间更短。两种方法的可信区间覆盖率均为 95%,偏差最小。增加标记动物的数量可以提高丰度估计的准确性和精确度,在大多数情况下,需要≥10只标记动物才能获得<20%的变异系数。灰熊数量略有增加(增长率λmean = 1.02),2023 年的密度为 10.4 头灰熊/1000 平方公里。繁殖期雌熊数量的年际变化很大,增至 1.0 头/1000 平方公里。保护区内、优质栖息地内以及远离铺设道路的地方,灰熊的种群密度最高。随着时间的推移,铺设道路附近的活动中心密度有所下降。下降的机制可能包括直接死亡和转移活动中心以避开人类活动。我们的研究证明了人类活动对局部密度的影响,以及保护区对食肉动物保护的重要性。最后,我们的研究强调了基于遥控相机和遥测技术的 SMR 模型在监测丰度时空趋势方面的广泛实用性。
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来源期刊
Ecosphere
Ecosphere ECOLOGY-
CiteScore
4.70
自引率
3.70%
发文量
378
审稿时长
15 weeks
期刊介绍: The scope of Ecosphere is as broad as the science of ecology itself. The journal welcomes submissions from all sub-disciplines of ecological science, as well as interdisciplinary studies relating to ecology. The journal''s goal is to provide a rapid-publication, online-only, open-access alternative to ESA''s other journals, while maintaining the rigorous standards of peer review for which ESA publications are renowned.
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