Data from: Improving population analysis using indirect count data: A case study of chimpanzees and elephants

Estimating spatiotemporal patterns of population density is a primary
objective of wildlife monitoring programs. However, estimating density is
challenging for species that are elusive and/or occur in habitats with
limited visibility. In such situations, indirect measures (e.g., nests,
dung) can serve as proxies for counts of individuals. Scientists have
developed approaches to estimate population density using these “indirect
count” data, although current methods do not adequately account for
variation in sign production and spatial patterns of animal density. In
this study, we describe a modified hierarchical distance-sampling model
that maximizes the information content of indirect count data using
Bayesian inference. We apply our model to assess the status of chimpanzee
and elephant populations using counts of nests and dung, respectively,
that were collected along transects in 2007 and 2021 in western Uganda.
Compared to conventional methods, our modeling framework produced more
precise estimates of covariate effects on expected animal density by
accounting for both long-term and recent variations in animal abundance
and enabled the estimation of the number of days that animal signs
remained visible. We estimated a 0.98 probability that chimpanzee density
in the region had declined by at least 10% and a 0.99 probability that
elephant density had increased by 50% from 2007 to 2021. We recommend
applying our modified hierarchical distance sampling model in the analysis
of indirect count data to account for spatial variation in animal density,
assess population change between survey periods, estimate the decay rate
of animal signs, and obtain more precise density estimates than achievable
with traditional methods.

估算种群密度的时空分布模式，是野生动物监测项目的核心目标之一。然而，对于行踪隐秘，或栖息于可视条件受限生境中的物种而言，种群密度估算极具挑战性。在此类场景下，诸如巢穴、粪便等间接监测指标，可作为个体数量的替代计数依据。学界已开发出基于这类“间接计数”数据估算种群密度的方法，但现有方法未能充分兼顾痕迹产生速率的变异，以及动物密度的空间分布特征。本研究提出一种改进的分层距离抽样模型，借助贝叶斯推断（Bayesian inference）最大化间接计数数据的信息利用效能。我们将该模型应用于评估黑猩猩与大象种群的现状：分别依托乌干达西部地区2007年与2021年沿样线采集的巢穴计数数据与粪便计数数据。相较于传统方法，本研究的建模框架同时兼顾了动物丰度的长期与近期波动，通过纳入协变量对预期种群密度的影响，获得了精度更高的估算结果，同时还可实现动物痕迹可视留存天数的估算。经估算，该区域黑猩猩种群密度在2007至2021年间下降至少10%的概率为0.98，而大象种群密度同期增长50%以上的概率为0.99。我们建议在间接计数数据的分析中应用本研究改进的分层距离抽样模型，以兼顾动物密度的空间变异、评估不同调查时段的种群变化、估算动物痕迹的衰减速率，并获得比传统方法精度更高的种群密度估算结果。