LOVE Is in the R--用于本地植被重建的两个 R 工具

IF 2.3 Q2 GEOSCIENCES, MULTIDISCIPLINARY Quaternary Pub Date : 2024-03-25 DOI:10.3390/quat7020018
M. Theuerkauf, J. Couwenberg
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引用次数: 0

摘要

小湖泊和泥炭地的花粉沉积由来自附近植被的本地花粉沉积和来自更远地方的区域花粉沉积组成。LOVE 模型的目的是仅提取当地的花粉信号,重建小范围内过去的植被。为此,我们将小地点的花粉沉积与以区域花粉沉积为主的大湖泊的花粉沉积联系起来。我们在此介绍了 LOVE 模型在 R 统计计算环境中的新实施方案,与现有实施方案相比,该方案更加方便用户使用。它可以更方便地应用化石花粉数据,并有助于在模拟景观中对该方法进行广泛测试。LOVE 模型源自关键的数学假设,这些假设极大地限制了它的应用。此外,我们还将 LOVEoptim 作为一种局部尺度重建的调整方法。除 LOVE 模型外,过去的局部植物丰度是通过数值优化近似得到的。在模拟景观(基于数字地图的真实景观模式)中进行的测试表明,这两种方法都是有效的。由于数学假设较少,LOVEoptim 的适用范围更广。建模结果有助于更好地解释 LOVE/LOVEoptim 重建所反映的植被空间尺度。
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LOVE Is in the R—Two R Tools for Local Vegetation Reconstruction
Pollen deposition in small lakes and peatlands is composed of local pollen deposition arriving from the nearby vegetation and of regional pollen deposition arriving from farther away. The LOVE model aims to reconstruct past vegetation on a small local scale by extracting only the local pollen signal. To this end, pollen deposition from small sites is related to pollen deposition from large lakes with predominantly regional pollen deposition. We here present a new implementation of the LOVE model in the R environment for statistical computing that is more user friendly than existing implementations. It allows more readily application with fossil pollen data and facilitates extensive testing of the approach in simulated landscapes. The LOVE model derives from critical mathematical assumptions that strongly limit its application. We additionally present LOVEoptim as an adjusted approach to local-scale reconstructions. Other than in LOVE, past local plant abundances are approximated using numerical optimization. Tests in a simulated landscape, which is based on true landscape patterns from digital maps, show that both approaches are valid. Due to fewer mathematical assumptions, LOVEoptim is more widely applicable. The modeling results help to better interpret the spatial scale of vegetation reflected in LOVE/LOVEoptim reconstructions.
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来源期刊
Quaternary
Quaternary GEOSCIENCES, MULTIDISCIPLINARY-
CiteScore
3.30
自引率
4.30%
发文量
44
审稿时长
11 weeks
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