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Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence

机译:海洋环境中的生态利基模型和物种分布模型:文献综述及证据空间分析

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In recent years, the use of ecological niche models (ENMs) and species distribution models (SDMs) to explore the patterns and processes behind observed distribution of species has experienced an explosive growth. Although the use of these methods has been less common and more recent in marine ecosystems than in a terrestrial context, they have shown significant increases in use and applications. Herein, we provide a systematic review of 328 articles on marine ENMs and SDMs published between 1990 and 2016, aiming to identify their main applications and the diversity of methodological frameworks in which they are developed, including spatial scale, geographic realm, taxonomic groups assessed, algorithms implemented, and data sources. Of the 328 studies, 48 % were at local scales, with a hotspot of research effort in the North Atlantic Ocean. Most studies were based on correlative approaches and were used to answer ecological or biogeographic questions about mechanisms underlying geographic ranges (64 %). A few attempted to evaluate impacts of climate change (19 %) or to develop strategies for conservation (11 %). Several correlative techniques have been used, but most common was the machine-learning approach Maxent (46 %) and statistical approaches such as generalized additive models GAMS (22 %) and generalized linear models, GLMs (14 %). The groups most studied were fish (23 %), molluscs (16 %), and marine mammals (14 %), the first two with commercial importance and the last important for conservation. We noted a lack of clarity regarding the definitions of ENMs versus SDMs, and a rather consistent failure to differentiate between them. This review exposed a need to know, reduce, and report error and uncertainty associated with species' occurrence records and environmental data. In addition, particular to marine realms, a third dimension should be incorporated into the modelling process, referring to the vertical position of the species, which will improve the precision and utility of these models. So too is of paramount importance the consideration of temporal and spatial resolution of environmental layers to adequately represent the dynamic nature of marine ecosystems, especially in the case of highly mobile species.
机译:近年来,使用生态利基模型(eNM)和物种分布模型(SDMS)来探索所观察到的物种分布背后的模式和流程,经历了爆炸性的生长。虽然使用这些方法的使用较少,但在海洋生态系统中的使用不那么常见,但在陆地背景下,它们已经显示出使用和应用的显着增加。在此,我们对1990年至2016年间发布的海洋遗传和SDMS的328篇文章提供了系统审查,旨在确定其主要申请和制定方法的方法框架的多样性,包括空间规模,地理领域,评估的分类学团组,实现的算法和数据源。在328项研究中,48%在当地秤上,北大西洋的研究努力热点。大多数研究基于相关方法,用于回答关于地理范围的机制的生态或生物地理问题(64%)。一些试图评估气候变化的影响(19%)或制定保护策略(11%)。已经使用了几种相关性,但最常见的是机器学习方法MaxEnt(46%)和统计方法,如广义添加剂模型Gam(22%)和广义的线性模型,GLM(14%)。学习的群体是鱼(23%),软体动物(16%)和海洋哺乳动物(14%),前两个具有商业重要性,并对保护的最后一个重要。我们注意到enms与sdms的定义缺乏清晰度,并且在它们之间区分相当一致的失败。此审查暴露了需要了解,减少和报告与物种发生记录和环境数据相关的错误和不确定性。此外,特别是海洋领域,应将第三维应纳入建模过程中,参考物种的垂直位置,这将提高这些模型的精度和效用。因此,对环境层的时间和空间分辨率来说太重要了,以充分代表海洋生态系统的动态性质,特别是在高度移动物种的情况下。

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