Accounting for viewshed area and animal availability when estimating density and recruitment of unmarked white-tailed deer

Quantifying demography of wildlife is vital to population monitoring; however, studies using physical capture methods can prove challenging. Camera traps have gained popularity as a density estimator tool in recent decades due to noninvasive data collection, reduced labor, cost efficiency, and large-scale monitoring capabilities. Many wildlife populations are comprised of individuals with no unique natural markers for individual identification, resulting in the need for unmarked abundance models. The recently developed Space-to-Event (STE) model offers a method for density estimation of unmarked populations using timelapse photography. STE relates detections of animals to camera sampling area (i.e., viewshed), resulting in density estimates that can be extrapolated to abundance over large areas. Consequently, this makes STE sensitive to estimates of viewshed area as small changes in viewshed could significantly affect density estimation. Using STE, we estimated density and recruitment of white-tailed deer (Odocoileus virginianus) in a densely forested landscape using measurements of viewshed per camera. We compared estimates of abundance derived from uniquely measured viewshed to estimates of abundance derived from an assumed viewshed area held constant across all cameras. When using a constant viewshed across all cameras, our point estimates of abundance shifted away from uniquely measured viewshed estimates in predictable ways, depending upon how much area was sampled. Additionally, we demonstrated the need for further exploration of animal availability at fine temporal scales by comparing estimates of density derived from sampling the full diel period to estimates derived from periods of peak activity (i.e., crepuscular periods). Finally, we extended the usefulness of the STE model by using densities of fawns and adult females to derive estimates of fawn recruitment.

对野生动物种群动态的量化研究是种群监测的核心环节，然而采用物理捕捉方法开展相关研究往往面临诸多挑战。近几十年来，相机陷阱（camera traps）凭借非侵入式的数据采集方式、更低的人力投入、优异的成本效益以及大规模监测能力，逐渐成为种群密度估算的主流工具之一。许多野生动物种群中的个体缺乏可用于个体识别的天然独特标记，这使得研究人员亟需针对无标记种群的丰度估算模型。近期提出的空间-事件（Space-to-Event, STE）模型为利用延时摄影开展无标记种群的密度估算提供了可行方案。该模型将动物的检测事件与相机采样视域范围（viewshed）相关联，由此得到的密度估算结果可外推至大区域尺度的种群丰度。但正因如此，STE模型对视域范围的估算极为敏感——视域范围的微小变动都可能对密度估算结果产生显著影响。本研究依托STE模型，基于每台相机的实测视域范围数据，在植被茂密的景观中对白尾鹿（Odocoileus virginianus）的种群密度和种群补充率进行了估算。我们将基于实测视域范围得到的丰度估算结果，与假设所有相机的视域范围均为固定常数得到的丰度估算结果进行了对比。结果显示，当所有相机采用统一的固定视域范围时，丰度的点估计值会按照可预测的方式偏离基于实测视域范围的估算结果，偏离程度取决于实际采样的区域大小。此外，我们将全昼夜采样周期得到的密度估算结果与仅基于活动高峰期（即晨昏活动期crepuscular periods）采样得到的结果进行了对比，证实了在精细时间尺度上开展动物可检测性相关研究的必要性。最后，本研究通过利用幼鹿与成年雌性的种群密度数据推导幼鹿补充率，进一步拓展了STE模型的应用场景。