Assessing risk of collapse of Lake Burullus Ramsar site in Egypt using IUCN Red List of Ecosystems

摘要

The IUCN Red List of Ecosystems (RLE) assessment is a new powerful scheme that identifies ecosystems that are at risk of collapse due to global environmental changes, which can help any mitigation actions aimed at conserving and rescuing these natural ecosystems. The current study applied RLE assessment protocol on Lake Burullus ecosystem, a highly productive wetland ecosystem, Ramsar site and International Bird Area (IBA) of Egypt. A conceptual model of the key ecosystem processes for Burullus wetland was constructed. The application of RLE followed the standard protocol for the first four criteria; (A) declines in distribution, (B) restricted distribution, (C) degradation of the abiotic components, and (D) regarding the assessment of the disruption of biotic processes and interactions. However, data were insufficient for assessing Criterion (E) that entails conducting quantitative estimates of the risk of collapse. Multi-date satellite images were obtained and processed to estimate the changes in the spatial distribution under criteria (A) and (B) of the study area. For evaluating Criteria (C) and (D), long-term data were collected from literature and previous works to cover the time frame of the assessment as much as possible. The results from the RLE assessment revealed that the status of the ecosystem is Critically Endangered (CR), which was attributed to various types of threats that caused degradation of the natural quality and integrity of the ecosystem. Although the RLE assessment provides a coherent approach for identifying ecosystems vulnerable to human-induced changes; data insufficiency can be an impediment for the efficient application of the RLE assessment. Remotely-sensed data can help in derivation of suitable spectral indicators of the status of the ecosystems structure and functioning that might overcome some of the challenges of data adequacy and relevance to the RLE assessment especially for the understudied and data-deficient ecosystems.