Ecological models are constrained by the availability of high-quality data at biologically appropriate resolutions and extents. Modeling a species' affinity or aversion with a particular land cover class requires data detailing that class across the full study area. Data sets with detailed legends (i.e., high thematic resolution) and/or high accuracy often sacrifice geographic extent, while large-area data sets often compromise on the number of classes and local accuracy. Consequently, ecologists must often restrict their study extent to match that of the more precise data set, or ignore potentially key land cover associations to study a larger area. We introduce a hierarchical Bayesian model to capitalize on the thematic resolution and accuracy of a regional land cover data set, and on the geographic breadth of a large area land cover data set. For the full extent (i.e., beyond the regional data set), the model predicts systematic discrepancies of the large-area data set with the regional data set, and divides an aggregated class into two more specific classes detailed by the regional data set. We illustrate the application of our model for mapping eastern white pine (Pinus strobus) forests, an important timber species that also provides habitat for an invasive shrub in the northeastern United States. We use the National Land Cover Database (NLCD), which covers the full study area but includes only generalized forest classes, and the NH GRANIT land cover data set, which maps White Pine Forest and has high accuracy, but only exists within New Hampshire. We evaluate the model at coarse (20 km²) and fine (2 km²) resolutions, with and without spatial random effects. The hierarchical model produced improved maps of compositional land cover for the full extent, reducing inaccuracy relative to NLCD while partitioning a White Pine Forest class out of the Evergreen Forest class. Accuracy was higher with spatial random effects and at the coarse resolution. All models improved upon simply partitioning Evergreen Forest in NLCD based on the predicted distribution of white pine. This flexible statistical method helps ecologists leverage localized mapping efforts to expand models of species distributions, population dynamics, and management strategies beyond the political boundaries that frequently delineate land cover data sets.

中文翻译：

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在分层贝叶斯框架中扩展组成土地覆盖图的覆盖范围和主题分辨率

生态模型受到在生物学上合适的分辨率和范围的高质量数据的可用性的限制。模拟一个物种对特定土地覆盖类别的亲和力或厌恶程度，需要在整个研究区域内详细说明该类别的数据。具有详细图例（即，高专题分辨率）和/或高精度的数据集通常会牺牲地理范围，而大面积数据集通常会在类别数量和局部精度方面做出妥协。因此，生态学家必须经常限制他们的研究范围以匹配更精确的数据集，或者忽略潜在的关键土地覆盖关联来研究更大的区域。我们引入了分层贝叶斯模型，以利用区域土地覆盖数据集的主题分辨率和准确性，以及大面积土地覆盖数据集的地理广度。对于整个范围（即超出区域数据集），该模型预测大区域数据集与区域数据集的系统差异，并将聚合类划分为由​​区域数据集详述的两个更具体的类。我们说明了我们的模型在绘制东部白松（Pinus strobus ) 森林，一种重要的木材物种，也为美国东北部的入侵灌木提供栖息地。我们使用国家土地覆盖数据库 (NLCD)，它覆盖了整个研究区域，但只包括广义的森林类别，以及 NH GRANIT 土地覆盖数据集，它绘制了白松林并具有很高的准确性，但仅存在于新罕布什尔州。我们以粗（20 km ²）和精细（2 km ²）分辨率评估模型，有和没有空间随机效应。分层模型产生改善组成土地覆盖的地图的全部范围，相对误差减少到NLCD而分隔一白色松林类出的常绿森林班级。空间随机效应和粗分辨率下的精度更高。所有模型都基于白松的预测分布对 NLCD 中的常绿森林进行了简单划分。这种灵活的统计方法可帮助生态学家利用局部制图工作来扩展物种分布、种群动态和管理策略的模型，超越经常描绘土地覆盖数据集的政治边界。