DICAM Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, 38123, Trento, Italy.
Tropical Biodiversity Section, MUSE-Museo delle Scienze, Corso del Lavoro e della Scienza 3, 38122, Trento, Italy.
Ecol Appl. 2017 Jan;27(1):235-243. doi: 10.1002/eap.1438.
Spatially explicit models of animal abundance are a critical tool to inform conservation planning and management. However, they require the availability of spatially diffuse environmental predictors of abundance, which may be challenging, especially in complex and heterogeneous habitats. This is particularly the case for tropical mammals, such as nonhuman primates, that depend on multi-layered and species-rich tree canopy coverage, which is usually measured through a limited sample of ground plots. We developed an approach that calibrates remote-sensing imagery to ground measurements of tree density to derive basal area, in turn used as a predictor of primate density based on published models. We applied generalized linear models (GLM) to relate 9.8-ha ground samples of tree basal area to various metrics extracted from Landsat 8 imagery. We tested the potential of this approach for spatial inference of animal density by comparing the density predictions for an endangered colobus monkey, to previous estimates from field transect counts, measured basal area, and other predictors of abundance. The best GLM had high accuracy and showed no significant difference between predicted and observed values of basal area. Our species distribution model yielded predicted primate densities that matched those based on field measurements. Results show the potential of using open-access and global remote-sensing data to derive an important predictor of animal abundance in tropical forests and in turn to make spatially explicit inference on animal density. This approach has important, inherent applications as it greatly magnifies the relevance of abundance modeling for informing conservation. This is especially true for threatened species living in heterogeneous habitats where spatial patterns of abundance, in relation to habitat and/or human disturbance factors, are often complex and, management decisions, such as improving forest protection, may need to be focused on priority areas.
空间显式动物丰度模型是为保护规划和管理提供信息的关键工具。然而，它们需要具有丰富的空间扩散的丰度环境预测因子，这在复杂和异质的栖息地中可能具有挑战性。这在热带哺乳动物中尤其如此，例如非人类灵长类动物，它们依赖于多层次和物种丰富的树冠覆盖，而树冠覆盖通常是通过有限的地面样本来测量的。我们开发了一种方法，通过将遥感图像校准到地面树木密度测量值，从而得出基面积，进而根据已发表的模型将基面积用作灵长类动物密度的预测因子。我们应用广义线性模型（GLM）将 9.8 公顷的地面树木基面积样本与从 Landsat 8 图像中提取的各种指标联系起来。我们通过将濒危疣猴的密度预测值与来自实地样带计数、实测基面积和其他丰度预测因子的先前估计值进行比较，测试了这种方法在空间推断动物密度方面的潜力。最佳的 GLM 具有很高的准确性，并且基面积的预测值和实测值之间没有显著差异。我们的物种分布模型得出的预测灵长类动物密度与基于实地测量的密度相匹配。结果表明，利用开放获取和全球遥感数据来推导热带森林中动物丰度的重要预测因子，并进一步对动物密度进行空间显式推断具有潜力。这种方法具有重要的内在应用，因为它极大地提高了丰度模型在为保护提供信息方面的相关性。对于生活在异质栖息地中的受威胁物种来说尤其如此，因为与栖息地和/或人类干扰因素有关的丰度空间模式通常很复杂，并且管理决策（例如，改善森林保护）可能需要集中在优先区域。
