Using PLS-SEM models to explore the interactions of meteorology and landscape pattern changes on waterbird diversity: A case of the Liaohe Estuary

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY Ecological Informatics Pub Date : 2025-01-19 DOI:10.1016/j.ecoinf.2025.103022
Xiuzhong Li , Baocun Ji , Na Li , Christopher J. Anderson , Qiuying Chen
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Abstract

Waterbirds are highly sensitive to environmental quality, with climate and landscape patterns being the two most important factors for influencing waterbird diversity. Understanding the effects of climate and landscape may lead to more effective policies and management strategies. This study focused on theinteractions of meteorological factors and landscape patterns on waterbird diversity in the Liaohe Estuary, an internationally important wetland system in northeast China and an important habitat for waterbirds on the East-Asian and Australasian flyway. Waterbird abundance and species richness (2010−2022) were related to meteorological factors represented by annual mean temperature and annual precipitation and various measures of landscape fragmentation caused by human land uses and natural landscapes. Structural equation models were constructed using four latent variables: waterbird abundance or richness, human activities, natural landscape, and meteorology, and the models were estimated through uncertainty and sensitivity analysis. The results showed that (1) landscape fragmentation of human activities (abundance model = 0.606, richness model = 0.719) was higher than the natural landscape (abundance model = 0.596, richness model = 0.703) with climate warming and precipitation decreasing, human activities were the strongest factors for natural landscape fragmentation (abundance model = 0.807, richness model = 0.803). (2) Meteorology (0.647) and human activities (0.679) showed nearly identical effects on waterbird abundance, while the natural landscape showed the largest effects (0.908) on waterbird richness, meteorology still showed similar effects (0.665), climate and landscape finally observed positive influences. (3) The combined effects of climate and landscape on abundance were higher than richness, the Charadriiformers and Lariformes groups showing a stronger response in both abundance and richness compared to the Podicipediformes, Pelecaniformes, and Anseriformes group. Based on these findings, we suggest that climate had more consistent total effects on waterbird abundance and richness than landscape. As long as landscape fragmentation remains below the waterbirds' tolerance threshold, it can benefit both waterbird abundance and richness by providing more ecotones and wider inches for the waterbirds adapting to climate changes. Moderate human activities leading to natural landscape fragmentation may also have direct and indirect benefits for waterbird abundance. However, this study doesn't clarify the different waterbird tolerance values or the mechanism through which climate and landscape changes affect different orders of waterbirds, and the spatial ecological corridor and the ecological flow among different sample points were ignored, both of which will be well worth exploring in the future.
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来源期刊
Ecological Informatics
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.
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