Scale of inference: on the sensitivity of habitat models for wide‐ranging marine predators to the resolution of environmental data
Scale of inference: on the sensitivity of habitat models for wide‐ranging marine predators to the resolution of environmental data
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推理范围：广泛海洋捕食者栖息地模型对环境数据分辨率的敏感性
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
S. Bograd
中科院分区：
文献类型：
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作者：
K. Scales;E. Hazen;M. Jacox;C. Edwards;Andre M. Boustany;M. Oliver;S. Bograd
了解和预测鲸目动物、海鸟、鲨鱼、海龟、鳍足动物和大型迁徙鱼类等广泛海洋捕食者对动态海洋条件的反应，需要能够充分捕捉它们的环境偏好的基于生境的模型。海洋生态系统本质上是动态的，动物-环境的相互作用已知发生在多个嵌套的空间和时间尺度上。因此，环境数据层的空间分辨率和时间平均化是对生境选择的环境决定因素进行建模的关键考虑因素。同时的环境数据对动物的存在或运动(如每天、每周)与天气产品(每月、季节、气候)的效用目前存在争议，以及近实时、高分辨率和复合(即天气、无云)数据场之间的权衡。利用内置环境偏好的运动模拟，结合模型和遥感(ROMS，MODIS-AQUA)海表面温度(SST)场，我们探索了空间和时间分辨率(3-111公里，日气候)在预测栖息地模型中的影响。结果表明，使用季节或气候数据场拟合的模型可能会在基于动物运动数据集的在场可用性设计中引入偏差，特别是在高度动态的海洋领域。在使用粗略(0.25度)空间分辨率的季节或气候场构建模型时，这些影响非常明显。然而，云层遮挡可能会导致遥感数据领域的重大信息丢失。我们发现，模型精度大幅下降，超过70%的数据丢失。因此，在多云地区，每周或每月的环境数据字段可能更可取。这些发现对海洋资源管理具有重要意义，特别是在确定受养护关注种群的关键栖息地和预测气候调节的生态系统变化方面。
&NA; Understanding and predicting the responses of wide‐ranging marine predators such as cetaceans, seabirds, sharks, turtles, pinnipeds and large migratory fish to dynamic oceanographic conditions requires habitat‐based models that can sufficiently capture their environmental preferences. Marine ecosystems are inherently dynamic, and animal–environment interactions are known to occur over multiple, nested spatial and temporal scales. The spatial resolution and temporal averaging of environmental data layers are therefore key considerations in modelling the environmental determinants of habitat selection. The utility of environmental data contemporaneous to animal presence or movement (e.g. daily, weekly), versus synoptic products (monthly, seasonal, climatological) is currently debated, as are the trade‐offs between near real‐time, high resolution and composite (i.e. synoptic, cloud‐free) data fields. Using movement simulations with built‐in environmental preferences in combination with both modelled and remotely‐sensed (ROMS, MODIS‐Aqua) sea surface temperature (SST) fields, we explore the effects of spatial and temporal resolution (3–111 km, daily–climatological) in predictive habitat models. Results indicate that models fitted using seasonal or climatological data fields can introduce bias in presence‐availability designs based upon animal movement datasets, particularly in highly dynamic oceanographic domains. These effects were pronounced where models were constructed using seasonal or climatological fields of coarse (> 0.25 degree) spatial resolution. However, cloud obstruction can lead to significant information loss in remotely‐sensed data fields. We found that model accuracy decreased substantially above 70% data loss. In cloudy regions, weekly or monthly environmental data fields may therefore be preferable. These findings have important implications for marine resource management, particularly in identifying key habitats for populations of conservation concern, and in forecasting climate‐mediated ecosystem changes.
DOI：
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发表时间：
2015-10
期刊：
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影响因子：
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作者：
Douglas M. Bates;M. Maechler;Ben Bolker;Steven C. Walker
通讯作者：
Douglas M. Bates;M. Maechler;Ben Bolker;Steven C. Walker