Senecio inaequidens DC. 将在未来气候中茁壮成长：地中海生物多样性热点地区的案例研究

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2024-08-19 DOI:10.1016/j.ecoinf.2024.102783

Erika Bazzato, Giacomo Calvia, Michela Marignani, Alessandro Ruggero, Vanessa Lozano

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引用次数: 0

摘要

监测入侵非本地植物在当前和未来气候条件下的扩展情况，对于了解生物多样性威胁、应对生态影响和制定有效的管理策略至关重要。本研究的重点是模拟撒丁岛（意大利）的 DC.扩展和分布情况，以应对这些环境挑战。目标是确定在当前条件下适合生物气候的区域，预测潜在的未来分布，并评估岛上的入侵动态，以确定适合有效管理策略的区域。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Senecio inaequidens DC. will thrive in future climate: A case study in a Mediterranean biodiversity hotspot

Monitoring the expansion of invasive non-native plants under current and future climatic conditions is crucial for understanding biodiversity threats, addressing the ecological impact, and developing effective management strategies. This study focuses on modelling the expansion and distribution of DC. on the island of Sardinia (Italy) to address these environmental challenges. The objectives were to identify bio-climatically suitable areas under current conditions, project potential future distribution, and evaluate invasion dynamics on the island to localize suitable areas for effective management strategies.

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.