Context: Ungulate populations are subject to fluctuations caused by extrinsic factors and require efficient and frequent surveying to monitor population sizes and demographics. Unmanned aerial systems (UAS) have become increasingly popular for ungulate research; however, little is understood about how this novel technology compares with conventional methodologies for surveying wild populations.

Aims: We examined the feasibility of using a fixed-wing UAS equipped with a thermal infrared sensor for estimating the population density of wild white-tailed deer (Odocoileus virginianus) at the Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota, USA. We compared UAS density estimates with those derived from faecal pellet-group counts.

Methods: We conducted UAS thermal survey flights from March to April of 2018 and January to March of 2019. Faecal pellet-group counts were conducted from April to May in 2018 and 2019. We modelled deer counts and detection probabilities and used these results to calculate point estimates and bootstrapped prediction intervals for deer density from UAS and pellet-group count data. We compared results of each survey approach to evaluate the relative efficacy of these two methodologies.

Key results: Our best-fitting model of certain deer detections derived from our UAS-collected thermal imagery produced deer density estimates (, 95% prediction interval = 4.32–17.84 deer km⁻²) that overlapped with the pellet-group count model when using our mean pellet deposition rate assumption (, 95% prediction interval = 4.14–11.29 deer km⁻²). Estimates from our top UAS model using both certain and potential deer detections resulted in a mean density of 13.77 deer km⁻² (95% prediction interval = 6.64–24.35 deer km⁻²), which was similar to our pellet-group count model that used a lower rate of pellet deposition (, 95% prediction interval = 6.46–17.65 deer km⁻²). The mean point estimates from our top UAS model predicted a range of 136.68–273.81 deer, and abundance point estimates using our pellet-group data ranged from 112.79 to 239.67 deer throughout the CCESR.

Conclusions: Overall, UAS yielded results similar to pellet-group counts for estimating population densities of wild ungulates; however, UAS surveys were more efficient and could be conducted at multiple times throughout the winter.

Implications: We demonstrated how UAS could be applied for regularly monitoring changes in population density. We encourage researchers and managers to consider the merits of UAS and how they could be used to enhance the efficiency of wildlife surveys.

背景：有蹄类动物种群易受外在因素引起的波动的影响，需要有效和频繁的调查来监测种群规模和人口统计数据。无人机系统 (UAS) 在有蹄类动物研究中越来越受欢迎。然而，人们对这项新技术与传统的野生种群调查方法相比如何进行比较了解甚少。

目的：我们研究了在美国明尼苏达州雪松溪生态系统科学保护区 (CCESR)使用配备有热红外传感器的固定翼 UAS 来估计野生白尾鹿 ( Odocoileus virginianus )种群密度的可行性。我们将 UAS 密度估计值与粪便颗粒组计数得出的值进行了比较。

方法：我们在 2018 年 3 月至 4 月和 2019 年 1 月至 3 月进行了 UAS 热调查飞行。2018 年和 2019 年 4 月至 5 月进行了粪便颗粒组计数。我们对鹿的数量和检测概率进行了建模，并使用这些结果来计算来自 UAS 和颗粒群计数数据的鹿密度的点估计和自举预测区间。我们比较了每种调查方法的结果，以评估这两种方法的相对有效性。

主要结果：我们从 UAS 收集的热成像中得出的某些鹿检测的最佳拟合模型产生了鹿密度估计（，95% 预测区间 = 4.32–17.84 鹿 km ^-2），当使用时与颗粒群计数模型重叠我们的平均颗粒沉积率假设 ( , 95% 预测区间 = 4.14–11.29 deer km ^-2 )。我们的顶级 UAS 模型使用某些和潜在的鹿检测得出的平均密度为 13.77 鹿 km ^-2（95% 预测区间 = 6.64–24.35 鹿 km ^-2），这类似于我们的颗粒组计数模型使用了较低的颗粒沉积率 ( , 95% 预测区间 = 6.46–17.65 鹿 km ^-2）。我们的顶级 UAS 模型的平均点估计值预测了 136.68-273.81 头鹿的范围，使用我们的颗粒组数据估计的丰度点估计值在整个 CCESR 范围从 112.79 到 239.67 头鹿。

结论：总体而言，UAS 产生的结果与用于估计野生有蹄类动物种群密度的颗粒组计数相似；然而，UAS 调查效率更高，可以在整个冬季多次进行。

启示：我们展示了如何应用 UAS 来定期监测人口密度的变化。我们鼓励研究人员和管理人员考虑 UAS 的优点以及如何使用它们来提高野生动物调查的效率。