Published on 24 September 2025
Activities and impacts in Sardinian wetlands
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Wetlands play a crucial role in climate regulation, water cycling, biodiversity conservation, and human well-being. Yet, they have faced centuries of degradation. This study aimed to analyze the relationship between human activities and their impacts across various wetland typologies, identify the most vulnerable systems, and investigate the main drivers of degradation. The focus was on Sardinian wetlands, located on the second-largest island in the Mediterranean. Our analysis revealed significative spatial co-occurrences between human activities and ecological impacts. The most frequent impacts included vegetation and habitat degradation, pollution, and hydrological alterations. Activities such as mining, urbanization, transportation, and tourism were particularly associated with these negative effects. While management practices yielded mixed outcomes—showing both positive and negative aspects—the extent of degradation was more closely linked to wetland size and ownership structure than to typology alone. In particular, larger wetlands and those with mixed ownership were more affected, suggesting that conventional inland vs. coastal classifications may no longer be sufficient to capture vulnerability patterns. Freshwater wetlands—often associated with small-scale agricultural use—showed lower levels of impact, highlighting the potential compatibility of traditional practices with conservation. Overall, our findings underscore the strong, often detrimental, connections between specific human activities and wetland degradation, while also pointing to the promising role of well-targeted management and conservation actions in supporting ecological resilience. This is a supplemental material of a research paper, please use the following citation when using this dataset, or possible future updated versions:Cuena-Lombraña A, Bacchetta G, Marignani M, Fois M (2025) Activities and impacts in wetlands: challenges for Sardinia. Regional Environmental Change, https://doi.org/10.1007/s10113-025-02462-1
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Publication Details
Subfield
Artificial Intelligence
Field
Computer Science
Domain
Physical Sciences
Confidence Score
66%
Source
Open Alex