Department of Ecology, Montana State University, Bozeman, Montana, United States of America.
Montana Department of Fish, Wildlife and Parks, Bozeman, Montana, United States of America.
PLoS One. 2019 Dec 23;14(12):e0226492. doi: 10.1371/journal.pone.0226492. eCollection 2019.
Understanding the dynamics of ungulate populations is critical given their ecological and economic importance. In particular, the ability to evaluate the evidence for potential drivers of variation in population trajectories is important for informed management. However, the use of age ratio data (e.g., juveniles:adult females) as an index of variation in population dynamics is hindered by a lack of statistical power and difficult interpretation. Here, we show that the use of a population model based on count, classification and harvest data can dramatically improve the understanding of ungulate population dynamics by: 1) providing estimates of vital rates (e.g., per capita recruitment and population growth) that are easier to interpret and more useful to managers than age ratios and 2) increasing the power to assess potential sources of variation in key vital rates. We used a time series of elk (Cervus canadensis) spring count and classification data (2004 to 2016) and fall harvest data from hunting districts in western Montana to construct a population model to estimate vital rates and assess evidence for an association between a series of environmental covariates and indices of predator abundance on per capita recruitment rates of elk calves. Our results suggest that per capita recruitment rates were negatively associated with cold and wet springs, and severe winters, and positively associated with summer precipitation. In contrast, an analysis of the raw age ratio data failed to detect these relationships. Our approach based on a population model provided estimates of the region-wide mean per capita recruitment rate (mean = 0.25, 90% CI = 0.21, 0.29), temporal variation in hunting-district-specific recruitment rates (minimum = 0.09; 90% CI = [0.07, 0.11], maximum = 0.43; 90% CI = [0.38, 0.48]), and annual population growth rates (minimum = 0.83; 90% CI = [0.78, 0.87], maximum = 1.20; 90% CI = [1.11, 1.29]). We recommend using routinely collected population count and classification data and a population modeling approach rather than interpreting estimated age ratios as a substantial improvement in understanding population dynamics.
了解有蹄类动物种群的动态变化至关重要，因为它们具有生态和经济方面的重要性。特别是，评估种群轨迹变化的潜在驱动因素的证据的能力对于明智的管理至关重要。然而，由于缺乏统计能力和难以解释，使用年龄比例数据（例如，幼体：成年雌性）作为种群动态变化的指标受到了阻碍。在这里，我们通过以下方式表明，使用基于计数、分类和收获数据的种群模型可以通过以下方式极大地提高对有蹄类动物种群动态的理解：1）提供更容易解释和对管理者更有用的关键生活率（例如，每头个体的补充和种群增长率）的估计，而不是年龄比例，2）增加评估关键生活率潜在变化来源的能力。我们使用了 2004 年至 2016 年的麋鹿（Cervus canadensis）春季计数和分类数据以及来自蒙大拿州西部狩猎区的秋季收获数据的时间序列来构建一个种群模型，以估计关键生活率，并评估一系列环境协变量与麋鹿幼体每头个体补充率之间的捕食者丰度指数之间的关联的证据。我们的结果表明，每头个体的补充率与寒冷和潮湿的春天、恶劣的冬天呈负相关，与夏季降水呈正相关。相比之下，对原始年龄比例数据的分析未能检测到这些关系。我们基于种群模型的方法提供了该地区范围内的平均每头个体补充率（平均值=0.25，90%置信区间=0.21，0.29）、狩猎区特定补充率的时间变化（最小值=0.09；90%置信区间=[0.07，0.11]，最大值=0.43；90%置信区间=[0.38，0.48]）和年度种群增长率（最小值=0.83；90%置信区间=[0.78，0.87]，最大值=1.20；90%置信区间=[1.11，1.29]）的估计。我们建议使用常规收集的种群计数和分类数据以及种群建模方法，而不是将估计的年龄比例解释为对种群动态理解的重大改进。
