Habitats drive population biology

在我职业生涯的早期，我专注于随机和季节性环境中的人口模型，但很明显，这种理论结构不足以用于保护应用。与栖息地的联系对于种群模型的有用应用至关重要，在我的实验室里，我们开发了许多定量工具来建立栖息地和种群之间的联系。在自然选择最强烈的栖息地中，动物数量更多，对于我们所研究的物种来说，在这些栖息地中，适应性最高。生境选择模型可以用于动物在景观上的分布，当种群处于承载能力时，可以使用资源选择函数（RSF）来估计丰度。如果种群不处于平衡状态，丰度可能受到与生境没有直接关系的因素的影响，从而可能（但不一定）损害生境选择模型用于预测种群大小的使用。栖息地之间的分布通常与密度有关，但很少遵循理想的自由分布。可能会出现许多其他复杂情况，例如生境的季节性使用，但这种季节性可以在模型结构中得到适应。应用基于栖息地的种群模型原理的实地研究将集中在我对麋鹿、灰熊和狼獾的持续研究上。我们对麋鹿的长期研究是基于有史以来对该物种进行的最大的无线电遥测研究之一，在过去的8年里，在艾伯塔省西南部的季节性范围内积累了超过100万次GPS重新定位。我们已经开发了步骤选择函数（SSF）方法，使我们能够分析栖息地背景下的运动模式。来自欧洲的合作者分享了马鹿的可比数据，揭示了相似的栖息地选择和人口统计模式。我们提出的灰熊和黑熊研究同样扩展了基于无线电遥测数据的栖息地选择，以及最近基于从摩擦树收集的毛发样本的DNA推断种群。我们建议发展空间明确捕获（SECR）种群估计和监测熊的方法。阿尔伯塔省西南部的熊种群结构表明了一种源-汇平衡，而汇通常是由与农业的冲突驱动的。最后，我们很高兴能够利用无线电遥测和非侵入性采样技术研究阿尔伯塔省北部和山区的狼獾。现代技术使我们能够收集有关西北艾伯塔省北方森林中狼獾栖息地选择和人口统计学的新发现。这种典型的荒野物种与人类共存于工业景观中，其密度远高于之前的记录。同样，SECR， RSF和ssf的工具使我们能够以新颖的方式将栖息地生态学与种群生态学结合起来，并应用于保护生物学和管理。

Early in my career I focused on demographic models in stochastic and seasonal environments, but it became clear that such theoretical constructs were insufficient for conservation applications. Links with habitats are essential for useful applications of population models, and in my lab we have developed many quantitative tools for building connections between habitats and populations. Animals are more abundant in habitats that are selected most strongly and, for the species that we have studied, fitness is highest in these habitats. Models of habitat selection can be used to distribute animals on the landscape and when the population is at carrying capacity, resource selection functions (RSF) can be used to estimate abundance. If the population is not at equilibrium, abundance might be influenced by factors not directly related to habitats thereby potentially (but not necessarily) compromising the use of habitat selection models for predicting population size. Distribution among habitats often is density dependent, but seldom follows an ideal free distribution. Many other complications can occur such as seasonal use of habitats, but such seasonality can be accommodated in model structure. Field studies to apply the principles of habitat-based population models will focus on my continuing studies of elk, grizzly bears, and wolverines. Our long-term elk studies are grounded in one of the largest radiotelemetry studies ever conducted on this species with substantially over 1 million GPS relocations accumulated during the past 8 years on seasonal ranges in SW Alberta. We have developed methods for step-selection functions (SSF) that allow us to analyze patterns of movement in context of habitats. Collaborators from Europe have shared comparable data on red deer revealing similar patterns of habitat selection and demography. Our proposed grizzly bear and black bear studies likewise extend habitat selection based on radiotelemetry data and more recently population inferences based on DNA from hair samples collected at rub trees. We are proposing to develop methods for Spatially Explicit Capture Recapture (SECR) population estimation and monitoring for bears. The structure of bear populations in SW Alberta suggests a source-sink balance with sinks often driven by conflicts with agriculture. Finally, we are excited about our ability to study wolverines in boreal and montane regions of Alberta using radiotelemetry and noninvasive sampling. Modern technology has allowed us to assemble new findings about habitat selection and demography of wolverines in the boreal forests of NW Alberta. This archetypical wilderness species coexists with humans in an industrial landscape at densities much higher than previously documented. Again, tools of SECR, RSF, and SSFs allow us to marry habitat ecology with population ecology in novel ways with applications for conservation biology and management.