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Landscape estimates of carrying capacity for grizzly bears using nutritional energy supply for management and conservation planning
Journal for Nature Conservation ( IF 2 ) Pub Date : 2021-05-10 , DOI: 10.1016/j.jnc.2021.126018
Cameron J.R. McClelland , Catherine K. Denny , Terrence A. Larsen , Gordon B. Stenhouse , Scott E. Nielsen

Successful recovery and management of threatened and endangered species requires an understanding of the capacity of the available habitat to support the species. Measuring habitat supply, or specific elements of that habitat, has been a key objective and challenge in wildlife management, especially for wide-ranging omnivorous species. In this study, we provide a framework for estimating the carrying capacity of a threatened grizzly bear population in Alberta, Canada. Specifically, we compare current patterns in abundance from recent population inventories to potential abundance from our habitat-based estimates of carrying capacity to determine where conservation actions would be most effective in recovery. To estimate carrying capacity, we used field data from 2001 to 2016 to measure abundance of vegetation, insects (ants), and ungulates. We predicted spatial patterns in abundance and biomass from these field data using generalized linear models and combined these into one of five categories used by bears: roots, fruits, herbs, ants, and ungulates. Models were then converted to digestible energy (kilocalorie content) and summarized for individual watersheds. We then used a protected population of grizzly bears (i.e., a reference area) to calculate kilocalorie relationships per bear, and from that potential carrying capacities for watersheds using two methods. First, we considered the ‘full resource’ approach using kilocalories of all key food items. Second, we simplified it to only fruit and meat resources, for which data are more widely available and known to correlate locally with grizzly bear density. Despite differences between the two approaches, density (bears per 1000 km2) estimates for carrying capacity were similar across most of the region for the two scenarios suggesting one can may be able to just use fruit and meat resources and thus other food items may not limit bear populations. Finally, we identified watersheds where differences between current bear densities and carrying capacity was large and road densities high (risk of bear mortality), and thus where management efforts are most needed. This study provides a comprehensive framework for estimating carrying capacity and demonstrates how these findings can be applied to support grizzly bear management and population recovery efforts.



中文翻译:

使用营养能源进行管理和保护规划的灰熊承载力的景观估算

成功地恢复和管理受威胁和濒危物种需要了解可用栖息地对物种的支持能力。测量栖息地供应或该栖息地的特定要素一直是野生动植物管理(尤其是广泛杂食物种)管理的主要目标和挑战。在这项研究中,我们提供了一个框架,用于估算加拿大艾伯塔省受威胁的灰熊种群的承载能力。具体来说,我们将近期人口清单中的当前数量模式与我们基于栖息地的承载能力估算值中的潜在数量模式进行比较,以确定在哪里恢复活动最有效的保护措施。为了估算承载能力,我们使用了2001年至2016年的现场数据来测量植被,昆虫(蚂蚁)和有蹄类动物的数量。我们使用广义线性模型从这些田间数据预测了丰度和生物量的空间格局,并将其组合为熊所使用的五种类别之一:根,果实,草药,蚂蚁和有蹄类动物。然后将模型转换为可消化的能量(千卡含量),并汇总各个流域。然后,我们使用受保护的灰熊种群(即参考区域)来计算每只熊的千卡关系,并使用两种方法根据该流域的潜在承载力来计算。首先,我们考虑了使用所有关键食品卡路里的“全部资源”方法。其次,我们将其简化为仅水果和肉类资源,这些数据的可用范围更广,并且已知与灰熊密度在本地相关。尽管两种方法存在差异,但密度(每千公里的承载量)2)在这两种情况下,该地区大部分地区的承载力估算值相似,这表明一个人可能只能够使用水果和肉类资源,因此其他食品可能不会限制熊的数量。最后,我们确定了分水岭,在这些分水岭中,当前熊的密度和承载能力之间的差异较大,而道路密度较高(熊的死亡率有风险),因此最需要管理工作。这项研究为估算承载力提供了一个全面的框架,并展示了如何将这些发现用于支持灰熊的管理和种群恢复工作。

更新日期:2021-05-12
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