生态位模型考虑了变量之间的局部关系:环境字符串模型

IF 2.7 3区 环境科学与生态学 Q2 ECOLOGY Ecosphere Pub Date : 2024-10-08 DOI:10.1002/ecs2.70015
Grégory Beaugrand
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引用次数: 0

摘要

人们提出了许多方法来模拟物种的空间分布。有些方法是专门为此目的而设计的,有些则是众所周知的统计工具,可用于许多科学领域。在本文中,我提出了一种新的生态位模型,称为环境字符串模型(ESM),它基于环境字符串的概念,环境字符串被定义为环境变量的组合,节点数与环境变量数相同。环境串有两种类型:(1)丰度已知串;(2)丰度未知串(或目标串),对其进行丰度估计。该模型的新颖之处在于,它从附近的丰度已知串评估与目标串相关的丰度,这些丰度已知串保留了与目标串的局部多维关系。在没有类似环境数据的情况下,该模型不会提供丰度估算,因此它可以处理截断的空间分布或龛位。该模型在北大西洋的两个关键桡足类物种(Calanus finmarchicus 和 Calanus helgolandicus)上进行了测试。我研究了变量对模型性能的影响。结果表明,该模型能很好地重建钙华鲑和钙华 helgolandicus 的平均空间分布和季节性波动。与广义线性模型(GLMs)、广义加法模型(GAMs)和广义回归神经网络(GRNN)相比,ESM 的性能最佳。我提出了一些指标来评估估计丰度在空间和时间上的稳健性,并展示了如何将该模型扩展到存在/不存在或仅存在数据。我认为,ESM 可用来填补 CPR 调查等任何取样计划和许多卫星数据库(如海洋颜色和光合有效辐射)中的空白。
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An ecological niche model that considers local relationships among variables: The Environmental String Model

Many methods have been proposed to model the spatial distribution of a species. While some methods have been specifically designed for this purpose, others are well-known statistical tools that can be used in many scientific fields. In this paper, I propose a new ecological niche model, called the Environmental String Model (ESM), that is based on the concept of environmental string, which is defined as being a combination of environmental variables, with as many nodes as environmental variables. There are two types of environmental strings: (1) the abundance-known string and (2) the abundance-unknown string (or target string) for which an estimation of abundance is searched. The novelty of the model is that it assesses the abundance associated with a target string from nearby abundance-known strings, which preserve the local multidimensional relationships with the target string. The model does not provide an abundance estimate in the absence of data from a similar environment and it can therefore deal with truncated spatial distributions or niches. It is tested in the North Atlantic Ocean on two key copepod species, Calanus finmarchicus and Calanus helgolandicus, which have been monitored by the Continuous Plankton Recorder (CPR) survey for decades. I investigate the influence of variables on model performance. I show that the model reconstructs the mean spatial distribution and seasonal fluctuations in both Calanus well. When compared with generalized linear models (GLMs), generalized additive models (GAMs) and generalized regression neural network (GRNN), the ESM gives the best performance. I propose a number of indicators to evaluate the robustness of estimated abundance in space and time and show how the model may be extended to presence/absence or presence-only data. I think that the ESM could be used to fill gaps in any sampling program such as the CPR survey and many satellite databases (e.g., ocean color and photosynthetically active radiation).

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来源期刊
Ecosphere
Ecosphere ECOLOGY-
CiteScore
4.70
自引率
3.70%
发文量
378
审稿时长
15 weeks
期刊介绍: The scope of Ecosphere is as broad as the science of ecology itself. The journal welcomes submissions from all sub-disciplines of ecological science, as well as interdisciplinary studies relating to ecology. The journal''s goal is to provide a rapid-publication, online-only, open-access alternative to ESA''s other journals, while maintaining the rigorous standards of peer review for which ESA publications are renowned.
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