megaSDM: integrating dispersal and time‐step analyses into species distribution models
megaSDM: integrating dispersal and time‐step analyses into species distribution models
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DOI：
10.1111/ecog.05450
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发表时间：
2021-10
期刊：
影响因子：
5.9
通讯作者：
Benjamin R. Shipley;Renee Bach;Younje Do;Heather Strathearn;Jenny L. McGuire;B. Dilkina
中科院分区：
文献类型：
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作者：
Benjamin R. Shipley;Renee Bach;Younje Do;Heather Strathearn;Jenny L. McGuire;B. Dilkina
了解物种范围如何随着气候的迅速变化而变化，可以告诉我们如何有效地保护脆弱的物种。物种分布模型（SDM）是研究这些范围变化的重要方法。执行SDM的工具不断改进。在这里，我们介绍megaSDM R软件包。该软件包通过整合分散概率、创建范围变化动态的时间步长图以及有效处理大型数据集和计算密集型环境子采样技术，促进了现实的时空SDM分析。megaSDM提供了一份物种和环境数据清单，综合了地理信息系统处理、二次抽样方法、MaxEnt建模、扩散率限制和其他统计工具，以便为所要求的每一物种、时间段和气候情景创造各种输出。对于其中的每一个，megaSDM生成一系列分布图，并输出统计数据的可视化表示。megaSDM与其他常用的SDM工具相比具有多项优势。首先，megaSDM中的许多函数本身就实现了并行化，使包能够有效地处理大量数据，而不需要额外的编码。megaSDM还实现了事件的环境子采样，使该技术以一种由于计算考虑而在以前不可能的方式广泛使用。独特的是，megaSDM生成的地图显示了在所有考虑的时间段内物种范围的扩张和收缩（时间地图），并根据物种特定的扩散约束约束物种范围的存在/不存在和连续适用性地图。然后，用户可以直接比较非分散和分散限制分布预测。本文讨论了megaSDM的独特功能和亮点，描述了该包的结构，并通过实例演示了该包的功能和模型流程。
Understanding how species ranges shift as climates rapidly change informs us how to effectively conserve vulnerable species. Species distribution models (SDMs) are an important method for examining these range shifts. The tools for performing SDMs are ever improving. Here, we present the megaSDM R package. This package facilitates realistic spatiotemporal SDM analyses by incorporating dispersal probabilities, creating time‐step maps of range change dynamics and efficiently handling large datasets and computationally intensive environmental subsampling techniques. Provided a list of species and environmental data, megaSDM synthesizes GIS processing, subsampling methods, MaxEnt modelling, dispersal rate restrictions and additional statistical tools to create a variety of outputs for each species, time period and climate scenario requested. For each of these, megaSDM generates a series of distribution maps and outputs visual representations of statistical data. megaSDM offers several advantages over other commonly used SDM tools. First, many of the functions in megaSDM natively implement parallelization, enabling the package to handle large amounts of data efficiently without the need for additional coding. megaSDM also implements environmental subsampling of occurrences, making the technique broadly available in a way that was not possible before due to computational considerations. Uniquely, megaSDM generates maps showing the expansion and contraction of a species range across all considered time periods (time‐maps), and constrains both presence/absence and continuous suitability maps of species ranges according to species‐specific dispersal constraints. The user can then directly compare non‐dispersal and dispersal‐limited distribution predictions. This paper discusses the unique features and highlights of megaSDM, describes the structure of the package and demonstrates the package's features and the model flow through examples.