Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula. Issue 17 (19th August 2018)

• Record Type: Journal Article
• Authors: Kotta, Jonne; Valdivia, Nelson; Kutser, Tiit; Toming, Kaire; Rätsep, Merli; Orav‐Kotta, Helen
• Is Part Of: Ecology and evolution. Volume 8:Issue 17(2018)
• Page Start: 9086
• Page End: 9094
• Publication Date: 2018-08-19
• Subjects: Antarctica -- benthic vegetation -- biotic patterns -- climate change -- remote sensing -- Sentinel‐2A; Ecology -- Periodicals; Evolution -- Periodicals; 577.05
• DOI: 10.1002/ece3.4463
• Languages: English
• ISSNs: 2045-7758

Abstract

Abstract : Antarctica is an iconic region for scientific explorations as it is remote and a critical component of the global climate system. Satellite‐based remote sensing, when used jointly with in situ ground‐truthing and machine learning algorithms, provides a powerful tool to predict the cover and richness of intertidal macroalgae. This is a significant milestone as logistical constraints are an integral part of the Antarctic expeditions. When continuous fine‐resolution large‐scale observations become available, a series of novel hypotheses can be formulated and tested, which can be considered as directions for future research with important potential. Abstract: Antarctica is an iconic region for scientific explorations as it is remote and a critical component of the global climate system. Recent climate change causes a dramatic retreat of ice in Antarctica with associated impacts to its coastal ecosystem. These anthropogenic impacts have a potential to increase habitat availability for Antarctic intertidal assemblages. Assessing the extent and ecological consequences of these changes requires us to develop accurate biotic baselines and quantitative predictive tools. In this study, we demonstrated that satellite‐based remote sensing, when used jointly with in situ ground‐truthing and machine learning algorithms, provides a powerful tool to predict the cover and richness of intertidal macroalgae. The salient finding was that the Sentinel‐based remote sensing described a significant proportion of variability in the cover and richness of Antarctic macroalgae. The highest performing models were for macroalgal richness and the cover of green algae as opposed to the model of brown and red algal cover. When expanding the geographical range of the ground‐truthing, even involving only a few sample points, it becomes possible to potentially map other Antarctic intertidal macroalgal habitats and monitor their dynamics. This is a significant milestone as logistical constraints are an integral part of the Antarctic expeditions. The method has also a potential in other remote coastal areas where extensive in situ mapping is not feasible.