A spatially explicit, multi-scale occupancy model for large-scale population monitoring
A spatially explicit, multi-scale occupancy model for large-scale population monitoring
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DOI：
10.1002/jwmg.21466
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发表时间：
2018-08-01
影响因子：
2.3
通讯作者：
Porter, William F.
中科院分区：
文献类型：
--
作者：
Crosby, Andrew D.;Porter, William F.
野生动物生态学和管理的持续挑战之一是获得大空间范围内物种分布的可靠估计的能力。使用聚类抽样设计的多尺度占用模型提供了提高多个空间尺度的估计和模型过程的分辨率的机会，提高了大规模监测的效率，减轻了范围和粒度之间的权衡。然而，以允许添加协变量的方式说明子样本之间的空间相关性仍然是一个问题。以食肉动物的跟踪样带调查为例，我们描述和评估了一个分层的，多尺度的占用模型，该模型集成了现有的方法来估计占用在多个空间尺度同时，并使用条件自回归（CAR）过程来考虑子样本之间的空间相关性。我们在单次调查和多次调查抽样设计下评估了3个版本的模型：一个非空间模型，一个考虑样带片段之间使用的空间相关性的模型，以及一个在检测过程中也考虑空间相关性的模型。模拟结果表明，占空间相关性得到更好的估计横断面水平占用下两个抽样设计，而准确估计段级使用需要一个多调查设计。当应用于历史雪迹数据时，模型之间的估计差异遵循模拟中发现的相同模式。多项调查设计能够检测到相当的下降段使用少得多的调查工作比单项调查设计。这里提出的建模框架为研究人员和管理人员提供了一个强大的工具，用于在大的空间范围内监测种群，同时能够在更精细的空间尺度上检测生态重要的动态。(c)2018野生动物协会
One of the continuing challenges in wildlife ecology and management is the ability to obtain reliable estimates of species' distributions at large spatial extents. Multi-scale occupancy models using a cluster sampling design offer the opportunity to increase the resolution of estimates and model processes occurring at multiple spatial scales, increasing the efficiency of large-scale monitoring and mitigating the tradeoff between extent and grain. However, accounting for spatial correlation among subsamples in a way that allows for the addition of covariates remains an issue. Using tracking transect surveys for carnivores as an example, we describe and evaluate a hierarchical, multi-scale occupancy model that integrates existing approaches to estimate occupancy at multiple spatial scales simultaneously, and uses a conditional autoregressive (CAR) process to account for spatial correlation in use between subsamples. We evaluated 3 versions of the model under a single-survey and a multi-survey sampling design: a non-spatial model, a model that accounted for spatial correlation in use between transect segments, and a model that also accounted for spatial correlation in the detection process. Simulations showed that accounting for spatial correlation gave better estimates of transect-level occupancy under both sampling designs, whereas accurate estimates of segment-level use required a multi-survey design. When applied to historical snow track data, the differences in estimates among models followed the same pattern found in the simulations. The multi-survey design was able to detect equivalent declines in segment use with much less survey effort than the single-survey design. The modeling framework presented here offers researchers and managers a powerful tool for monitoring populations at large spatial extents while being able to detect ecologically important dynamics at finer spatial scales. (c) 2018 The Wildlife Society.