A common goal in ecology and its applications is to better understand how species' distributions change over space and time, yet many conventional summary metrics (e.g. center of gravity) of distribution shifts may offer limited inference because such changes may not be spatially homogenous. We develop a modeling approach to estimate a spatially explicit temporal trend (i.e. local trend), alongside spatial (temporally constant) and spatiotemporal (time‐varying) components, to compare inferred spatial shifts to those indicated by conventional metrics. This method is generalizable to many data types including presence–absence data, count data and continuous data types such as density. To demonstrate the utility of this new approach, we focus on the application of this model to a community of well‐studied marine fish species on the US west coast (19 species, representing a wide range of presence–absence and densities). Results from conventional model selection indicate that the use of the model accounting for local trends is clearly justified for over 89% of these species. In addition to making more parsimonious and accurate predictions, we illustrate how estimated spatial fields from the local trend model can be used to classify regions within the species range where change is relatively homogenous. Conventional summary metrics, such as center of gravity, can then be calculated on each such region or within previously defined biogeographic boundaries. We use this approach to illustrate that change is more nuanced than what is expressed via global metrics. Using arrowtooth flounder Atheresthes stomias as an example, the observed southward shift over time in the center of gravity is not reflective of a uniform shift in densities but local trends of decreasing density in the northern region and rapidly increasing density at the southern edge of the species' range. Thus, estimating local trends with spatiotemporal models improves interpretation of species distribution change.

中文翻译：

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通过结合当地趋势来改进对物种分布变化的估计

生态学及其应用的一个共同目标是更好地理解物种的分布如何随时间和空间变化，但是许多常规的分布变化摘要指标（例如重心）可能提供有限的推论，因为这种变化可能在空间上不是同质的。我们开发了一种建模方法来估计空间上明确的时间趋势（即局部趋势），以及空间（时间上恒定）和时空（时变）分量，以将推断的空间平移与常规指标所指示的空间平移进行比较。此方法可推广到许多数据类型，包括在场数据，缺勤数据，计数数据和连续数据类型（例如密度）。为了证明这种新方法的实用性，我们专注于将此模型应用于美国西海岸的一个经过深入研究的海洋鱼类物种（19种，代表着广泛的存在与缺乏）。常规模型选择的结果表明，对于这些物种中超过89％的物种，使用考虑当地趋势的模型显然是合理的。除了做出更简约和准确的预测之外，我们还说明了如何使用局部趋势模型估算的空间场来对物种范围内变化相对均匀的区域进行分类。然后可以在每个这样的区域上或在先前定义的生物地理边界内计算常规的摘要度量，例如重心。我们使用这种方法来说明变化比通过全局指标表达的变化更加细微。作为一个例子，以花椰菜气孔为例，在重心处观察到的随时间的南移并不反映出密度的均匀变化，而是反映了北部地区密度下降和物种范围南部边缘密度迅速增加的局部趋势。因此，用时空模型估算局部趋势可以改善对物种分布变化的解释。