A clear connection between basic research and applied management is often missing or difficult to discern. We present a case study of integration of basic research with applied management for estimating abundance of gray wolves (Canis lupus) in Montana, USA. Estimating wolf abundance is a key component of wolf management but is costly and time intensive as wolf populations continue to grow. We developed a multimodel approach using an occupancy model, mechanistic territory model, and empirical group size model to improve abundance estimates while reducing monitoring effort. Whereas field-based wolf counts generally rely on costly, difficult-to-collect monitoring data, especially for larger areas or population sizes, our approach efficiently uses readily available wolf observation data and introduces models focused on biological mechanisms underlying territorial and social behavior. In a three-part process, the occupancy model first estimates the extent of wolf distribution in Montana, based on environmental covariates and wolf observations. The spatially explicit mechanistic territory model predicts territory sizes using simple behavioral rules and data on prey resources, terrain ruggedness, and human density. Together, these models predict the number of packs. An empirical pack size model based on 14 years of data demonstrates that pack sizes are positively related to local densities of packs, and negatively related to terrain ruggedness, local mortalities, and intensity of harvest management. Total abundance estimates for given areas are derived by combining estimated numbers of packs and pack sizes. We estimated the Montana wolf population to be smallest in the first year of our study, with 91 packs and 654 wolves in 2007, followed by a population peak in 2011 with 1252 wolves. The population declined ~6% thereafter, coincident with implementation of legal harvest in Montana. Recent numbers have largely stabilized at an average of 191 packs and 1141 wolves from 2016 to 2020. This new approach accounts for biologically based, spatially explicit predictions of behavior to provide more accurate estimates of carnivore abundance at finer spatial scales. By integrating basic and applied research, our approach can therefore better inform decision-making and meet management needs.

中文翻译：

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整合基础研究和应用研究来估计食肉动物的丰度

基础研究和应用管理之间的明确联系往往缺失或难以辨别。我们提出了一个将基础研究与应用管理相结合以估计灰狼 ( Canis lupus ) 数量的案例研究) 在美国蒙大拿州。估计狼群数量是狼群管理的一个关键组成部分，但随着狼群数量的持续增长，成本和时间都非常密集。我们开发了一种多模型方法，使用占用模型、机械领土模型和经验群体规模模型来改进丰度估计，同时减少监测工作。基于野外的狼数量通常依赖于昂贵、难以收集的监测数据，尤其是对于较大的区域或人口规模，我们的方法有效地使用现成的狼观察数据，并引入侧重于领土和社会行为背后的生物学机制的模型。在一个分为三部分的过程中，占有率模型首先根据环境协变量和狼的观察来估计蒙大拿州的狼分布范围。空间明确的机械领土模型使用简单的行为规则和有关猎物资源、地形崎岖性和人类密度的数据来预测领土大小。这些模型一起预测包的数量。基于 14 年数据的经验包大小模型表明，包大小与当地包的密度正相关，与地形崎岖、当地死亡率和收获管理强度负相关。给定区域的总丰度估计是通过组合估计的包数和包大小得出的。我们估计蒙大拿州的狼群数量在我们研究的第一年最少，2007 年有 91 狼群和 654 只狼，随后在 2011 年达到 1252 只狼的数量高峰。此后，人口下降了约 6%，恰逢蒙大拿州实施合法采伐。从 2016 年到 2020 年，最近的数字基本稳定在平均 191 只狼群和 1141 只狼。这种新方法考虑了基于生物学的、空间明确的行为预测，以在更精细的空间尺度上更准确地估计食肉动物的丰度。通过整合基础研究和应用研究，我们的方法可以更好地为决策​​提供信息并满足管理需求。