The characterization of species’ environmental niches and spatial distribution predictions based on them are now central to much of ecology and conservation, but implicitly requires decisions about the appropriate spatial scale (i.e., grain) of analysis. Ecological theory and empirical evidence suggest that range‐resident species respond to their environment at two characteristic, hierarchical spatial grains: (1) response grain, the (relatively fine) grain at which an individual uses environmental resources, and (2) occupancy grain, the (relatively coarse) grain equivalent to a typical home range. We use a multi‐grain (MG) occupancy model, aided by fine‐grain remotely sensed imagery, to simultaneously estimate species–environment associations at both grains, conduct grain optimization to measure response grain, and apply this analysis framework to an example species: a medium‐sized bird (Tockus deckeni) in a heterogeneous East African landscape. Based on home range analysis of movement data, we calculate an occupancy grain of 1 km for T. deckeni. Using a grain optimization procedure across 32 grains from 10 to 500 m, we identify 60 m as the most strongly supported response grain for a suite of environmental variables, slightly coarser than opportunistic behavioral observations would have suggested. Validation confirms that the accuracy of the optimized MG occupancy model substantially exceeds that of equivalent single‐grain (SG) occupancy models. We further use a simulation approach to assess the potential impacts of accounting for the multi‐scale structure of species’ environmental requirements on estimates of population size. We find that the more strongly supported MG approach consistently predicts a minimum population size in the study landscape that is much lower than that provided by the SG model. This suggests that SG approaches commonly used in conservation applications could lead to overly optimistic abundance and population estimates, and that the MG approach may be more appropriate for supporting species conservation goals. More generally, we conclude that multi‐grain approaches of the sort presented, and increasingly enabled by growing high‐resolution remotely sensed data, hold great promise for offering a more mechanistic framework for assessing the appropriate grain(s) for population monitoring and management and enable more reliable estimates of abundances and species’ distributions.

中文翻译：

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多层多粒度模型改进了物种环境关联，分布和丰度的描述。

物种环境生态位的表征和基于它们的空间分布预测现在对于许多生态和保护至关重要，但是隐含地需要对分析的适当空间规模（即谷物）做出决定。生态学理论和经验证据表明，常驻物种对环境的响应具有两个特征，即等级的空间颗粒：（1）反应颗粒，即个人使用环境资源的（相对较细的）颗粒，以及（2）占用颗粒。，即（相对粗略）的谷物，相当于典型的起始范围。我们使用多颗粒（MG）占用模型，并辅以细颗粒遥感图像，来同时估算两个谷物的物种-环境关联，进行谷物优化以测量响应谷物，并将此分析框架应用于示例物种：一只东非景观中的中型鸟类（Tockus deckeni）。基于对运动数据的原位范围分析，我们计算出T. deckeni的占用距离为1 km。使用从10到500 m的32个晶粒的晶粒优化程序，我们将60 m确定为一组环境变量的最有力支持的响应晶粒，这比机会性行为观察建议的略粗。验证确认，优化后的MG占用模型的准确性大大超过了等效单粒（SG）占用模型的准确性。我们进一步使用一种模拟方法来评估会计环境需求的多尺度结构对人口规模估计的潜在影响。我们发现，受到更强有力支持的MG方法始终能够预测研究环境中的最小人口规模，其远低于SG模型提供的最小人口规模。这表明在保护应用中常用的SG方法可能会导致过度乐观的数量和种群估计，而MG方法可能更适合于支持物种保护目标。更广泛地说，我们得出这样的结论：随着越来越多的高分辨率遥感数据的出现，这种多谷物方法越来越多地被采用，这为提供一种更机械的框架来评估用于人口监测和管理的适当谷物提供了广阔的前景。可以更可靠地估算丰度和物种分布。