Globally, many wild deer populations are actively studied or managed for conservation, hunting, or damage mitigation purposes. These studies require reliable estimates of population state parameters, such as density or abundance, with a level of precision that is fit for purpose. Such estimates can be difficult to attain for many populations that occur in situations that are poorly suited to common survey methods. We evaluated the utility of combining camera trap survey data, in which a small proportion of the sample is individually recognizable using natural markings, with spatial mark–resight (SMR) models to estimate deer density in a variety of situations. We surveyed 13 deer populations comprising four deer species (Cervus unicolor, C. timorensis, C. elaphus, Dama dama) at nine widely separated sites, and used Bayesian SMR models to estimate population densities and abundances. Twelve surveys provided sufficient data for analysis and seven produced density estimates with coefficients of variation (CVs) ≤ 0.25. Estimated densities ranged from 0.3 to 24.6 deer km−2. Camera trap surveys and SMR models provided a powerful and flexible approach for estimating deer densities in populations in which many detections were not individually identifiable, and they should provide useful density estimates under a wide range of conditions that are not amenable to more widely used methods. In the absence of specific local information on deer detectability and movement patterns, we recommend that at least 30 cameras be spaced at 500–1,000 m and set for 90 days. This approach could also be applied to large mammals other than deer.

中文翻译：

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使用带有相机陷阱数据的空间标记重新校正模型来估计鹿的密度和丰度


在全球范围内，出于保护、狩猎或减轻损害的目的，许多野鹿种群正在被积极研究或管理。这些研究需要对种群状态参数（例如密度或丰度）进行可靠的估计，并具有适合目的的精确度。对于许多在不太适合通用调查方法的情况下发生的人群来说，这样的估计可能很难获得。我们评估了将相机陷阱调查数据（其中一小部分样本可以使用自然标记单独识别）与空间标记重视（SMR）模型相结合的效用，以估计各种情况下的鹿密度。我们在 9 个相距较远的地点调查了 13 个鹿群，包括 4 种鹿（鹿属鹿、帝汶鹿、马鹿、大马鹿），并使用贝叶斯 SMR 模型来估计鹿群密度和丰度。十二项调查提供了足够的数据进行分析，七项得出的密度估计值的变异系数 (CV) ≤ 0.25。估计密度范围为 0.3 至 24.6 鹿 km−2。相机陷阱调查和 SMR 模型提供了一种强大而灵活的方法来估计鹿群中的鹿密度，其中许多检测结果无法单独识别，并且它们应该在不适合更广泛使用的方法的广泛条件下提供有用的密度估计。由于缺乏有关鹿的可检测性和运动模式的具体本地信息，我们建议至少 30 个摄像头间隔 500-1,000 m，并设置 90 天。这种方法也可以应用于鹿以外的大型哺乳动物。