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Predicting marine species distribution and identifying priority areas for conservation – comparing and coupling food web and Bayesian hierarchical modelling approaches

预测海洋物种分布并确定优先保护区域——比较和耦合食物网和贝叶斯分层建模方法

基本信息

批准号：
414356701
负责人：
Dr. Jennifer Rehren
金额：
--
依托单位：
Institut de Ciències del Mar (ICM)
依托单位国家：
德国
项目类别：
Research Fellowships
财政年份：
2019
资助国家：
德国
起止时间：
2018-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/414356701?language=en
关键词：
Predicting marine species distribution identifying

项目摘要

Marine ecosystems are impacted by numerous anthropogenic stressors, with overexploitation of resources being among the central threats. A prominent tool to mitigate anthropoogenic pressures and to protect marine biodiversity and ecosystem functioning is the implementation of marine protected areas. For a successful identification and prioritization of suitable conservation areas it is crucial to understand the geographic distribution of biodiversity, target species and essential habitats. A wide variety of statistical modelling approaches for understanding and predicting species distribution have been developed along with different conservation planning algorithms. However, the resulting predictions are always associated with varying degrees of uncertainty; and although the knowledge on the level of uncertainty facilitates decision-making in the face of high risks involved and allows for the adaption of management plans, uncertainty is rarely accounted for in conservation planning tools. Furthermore, most correlative species distribution models and reserve selection algorithms only account for associated environmental factors, while ecological processes and human activities can also drive species distribution. A crucial step towards better predicting the distribution of species and towards an enhanced identification of conservation areas, therefore, encompasses the development of approaches that explicitly account for ecological processes and human activities and that quantify uncertainties associated with resulting conservation plans. Bayesian hierarchical species distribution (B-HSD) modelling is a novel technique that allows the incorporation of spatial random-effect terms, spatial correlation of the variables and the uncertainty of the parameters in the modelling process, resulting in a more realistic and accurate estimation of uncertainty. Ecological processes and human activities can be explicitly considered in mechanistic modelling such as the Ecospace habitat capacity (E-HFC) model. E-HFC allows to spatially drive foraging capacity of species from cumulative physical, oceanographic and environmental effects in conjunction with food web dynamics and fisheries impacts. Furthermore, the ‘objective function’ in Ecospace allows for the identification of effective conservation areas. However, as most ecosystem models and conservation planning tools the E-HFC model does not incorporate the uncertainty related to species distribution. The objective of the project is, thus, to explore the complementarity and applicability of B-HSD models and the E-HFC model for the prediction of species distribution and the identification of priority areas for conservation. For this purpose I use the tropical bay system Chwaka Bay (Tanzania) and its recently developed Ecopath model as case study. With this I attempt to further develop the E-HFC model applicability and to contribute to the development of hybrid approaches for the prediction of species distribution.

海洋生态系统受到许多人为压力因素的影响，资源过度开发是主要威胁之一。建立海洋保护区是减轻海洋生物压力和保护海洋生物多样性和生态系统功能的一个重要工具。为了成功地确定和优先考虑适当的保护区，了解生物多样性、目标物种和基本生境的地理分布至关重要。沿着发展了各种各样的统计建模方法来理解和预测物种分布，并采用了不同的保护规划算法。然而，由此产生的预测总是与不同程度的不确定性;虽然知识的不确定性的水平，促进决策面临的高风险，并允许调整管理计划，不确定性很少占保护规划工具。此外，大多数相关的物种分布模型和保护区选择算法只考虑了相关的环境因素，而生态过程和人类活动也可以驱动物种分布。因此，更好地预测物种分布和更好地确定保护区的一个关键步骤包括制定明确说明生态过程和人类活动的方法，并量化与最终保护计划有关的不确定性。贝叶斯层次物种分布（B-HSD）模型是一种新的技术，它允许在建模过程中纳入空间随机效应项、变量的空间相关性和参数的不确定性，从而更真实和准确地估计不确定性。生态过程和人类活动可以明确地考虑在机械建模，如生态空间栖息地容量（E-HFC）模型。电子氢氟碳化合物可以从累积的物理、海洋和环境影响以及食物网动态和渔业影响中，在空间上驱动物种的觅食能力。此外，生态空间中的“目标函数”允许确定有效的保护区。然而，作为大多数生态系统模型和保护规划工具，E-HFC模型没有纳入与物种分布相关的不确定性。因此，该项目的目标是探索B-HSD模型和E-HFC模型在预测物种分布和确定优先保护区方面的互补性和适用性。为此，我使用的热带海湾系统Chwaka湾（坦桑尼亚）和最近开发的Ecopath模型作为案例研究。有了这个，我试图进一步发展的E-HFC模型的适用性，并有助于混合方法的发展预测的物种分布。