Pradel R, Renaud P-C, Pays O, Scholte P, Ogutu J O, Hibert F, Casajus N, Mialhe F, Fritz H
CEFE, Univ Montpellier, CNRS, EPHE, IRD Montpellier France.
Sustainability Research Unit, Faculty of Science, George Campus Nelson Mandela University George South Africa.
Ecol Evol. 2024 Aug 22;14(8):e70193. doi: 10.1002/ece3.70193. eCollection 2024 Aug.
Monitoring population trends is pivotal to effective wildlife conservation and management. However, wildlife managers often face many challenges when analyzing time series of census data due to heterogeneities in sampling methodology, strategy, or frequency. We present a three-step method for modeling trends from time series of count data obtained through multiple census methods (aerial or ground census and expert estimates). First, we design a heuristic for constructing credible intervals for all types of animal counts including those which come with no precision measure. Then, we define conversion factors for rendering aerial and ground counts comparable and provide values for broad classes of animals from an extant series of parallel aerial and ground censuses. Lastly, we construct a Bayesian model that takes the reconciled counts as input and estimates the relative growth rates between successive dates while accounting for their precisions. Importantly, we bound the rate of increase to account for the demographic potential of a species. We propose a flow chart for constructing credible intervals for various types of animal counts. We provide estimates of conversion factors for 5 broad classes of species. We describe the Bayesian model for calculating trends, annual rates of population increase, and the associated credible intervals. We develop a bespoke R CRAN package, popbayes, for implementing all the calculations that take the raw counts as input. It produces consistent and reliable estimates of population trends and annual rates of increase. Several examples from real populations of large African mammals illustrate the different features of our method. The approach is well-suited for analyzing population trends for heterogeneous time series and allows a principled use of all the available historical census data. The method is general and flexible and applicable to various other animal species besides African large mammals. It can readily be adapted to test predictions of various hypotheses about drivers of rates of population increase.
监测种群趋势对于有效的野生动物保护和管理至关重要。然而，由于抽样方法、策略或频率的异质性，野生动物管理者在分析普查数据的时间序列时往往面临许多挑战。我们提出了一种三步法，用于对通过多种普查方法（空中或地面普查以及专家估计）获得的计数数据时间序列进行趋势建模。首先，我们设计了一种启发式方法，用于为所有类型的动物计数构建可信区间，包括那些没有精度测量的计数。然后，我们定义转换因子，以使空中和地面计数具有可比性，并从现有的一系列平行空中和地面普查中为广泛的动物类别提供值。最后，我们构建一个贝叶斯模型，该模型将调和后的计数作为输入，并在考虑其精度的同时估计连续日期之间的相对增长率。重要的是，我们对增长率进行了限制，以考虑物种的人口统计学潜力。我们提出了一个用于为各种类型的动物计数构建可信区间的流程图。我们提供了5大类物种的转换因子估计值。我们描述了用于计算趋势、种群年增长率以及相关可信区间的贝叶斯模型。我们开发了一个定制的R CRAN包popbayes，用于实现所有以原始计数为输入的计算。它产生一致且可靠的种群趋势和年增长率估计值。来自非洲大型哺乳动物实际种群的几个例子说明了我们方法的不同特点。该方法非常适合分析异质时间序列的种群趋势，并允许有原则地使用所有可用的历史普查数据。该方法通用且灵活，适用于除非洲大型哺乳动物之外的各种其他动物物种。它可以很容易地进行调整，以测试关于种群增长率驱动因素的各种假设的预测。
