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首页> 外文期刊>Conservation Biology >Spatial Patterns of Breeding Success of Grizzly Bears Derived from Hierarchical Multistate Models
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Spatial Patterns of Breeding Success of Grizzly Bears Derived from Hierarchical Multistate Models

机译：分层多状态模型衍生的灰熊繁殖成功的空间格局

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摘要

Conservation programs often manage populations indirectly through the landscapes in which they live. Empirically, linking reproductive success with landscape structure and anthropogenic change is a first step in understanding and managing the spatial mechanisms that affect reproduction, but this link is not sufficiently informed by data. Hierarchical multistate occupancy models can forge these links by estimating spatial patterns of reproductive success across landscapes. To illustrate, we surveyed the occurrence of grizzly bears (Ursus arctos) in the Canadian Rocky Mountains Alberta, Canada. We deployed camera traps for 6 weeks at 54 surveys sites in different types of land cover. We used hierarchical multistate occupancy models to estimate probability of detection, grizzly bear occupancy, and probability of reproductive success at each site. Grizzly bear occupancy varied among cover types and was greater in herbaceous alpine ecotones than in lowelevation wetlands or mid-elevation conifer forests. The conditional probability of reproductive success given grizzly bear occupancy was 30% (SE = 0.14). Grizzly bears with cubs had a higher probability of detection than grizzly bears without cubs, but sites were correctly classified as being occupied by breeding females 49% of the time based on raw data and thus would have been underestimated by half. Repeated surveys and multistate modeling reduced the probability of misclassifying sites occupied by breeders as unoccupied to <2%. The probability of breeding grizzly bear occupancy varied across the landscape. Those patches with highest probabilities of breeding occupancy—herbaceous alpine ecotones—were small and highly dispersed and are projected to shrink as treelines advance due to climate warming. Understanding spatial correlates in breeding distribution is a key requirement for species conservation in the face of climate change and can help identify priorities for landscape management and protection.
机译：保护计划通常通过居住地间接管理人口。从经验上讲，将繁殖成功与景观结构和人为变化联系起来是理解和管理影响繁殖的空间机制的第一步，但是这种联系没有得到足够的数据信息。分层的多州居住模型可以通过估算跨景观的生殖成功的空间模式来建立这些联系。为了说明这一点，我们调查了加拿大加拿大洛矶山脉艾伯塔省的灰熊（Ursus arctos）的发生情况。我们在不同类型土地覆盖的54个调查地点部署了6个星期的相机陷阱。我们使用了分层的多州占用模型来估计每个站点的检测概率，灰熊占用和生殖成功的概率。灰熊的栖息地因覆盖类型而异，在草本高寒交错带中比在低海拔湿地或中海拔针叶林中更大。灰熊占据的条件下，繁殖成功的条件概率为30％（SE = 0.14）。有小熊的灰熊比没有小熊的灰熊有更高的检测概率，但根据原始数据，这些地点被正确地分类为育有雌性的动物有49％的时间，因此被低估了一半。重复的调查和多州建模减少了将育种者所占位点误分类为未被利用的可能性，低于2％。在整个景观中繁殖出灰熊的可能性不同。那些繁殖可能性最高的斑块（草类高山过渡带）很小且高度分散，随着气候变暖，随着树木的生长，这些斑块预计会缩小。面对气候变化，了解繁殖分布中的空间相关性是物种保护的关键要求，并且可以帮助确定景观管理和保护的重点。

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