Data from: Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula

• Main Authors: Kotta, Jonne, Valdivia, Nelson, Kutser, Tiit, Toming, Kaire, Rätsep, Merli, Orav-Kotta, Helen
• Format: Dataset
• Language: unknown
• Published: Data Archiving and Networked Services (DANS) 2019
• Subjects: Life sciences; medicine and health care; biotic patterns; Remote sensing; benthic vegetation; Adenocystis utricularis; Sentinel-2A; Iridaea cordata; climate change; Urospora penicilliformis; (:tba); geo; envir; Antarctic; The Antarctic; Antarctic Peninsula; The Sentinel; Antarc*; Antarctica
• Online Access: https://doi.org/10.5061/dryad.33cd137

1. 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 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. 2. 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. 3. 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 brown and green algae as opposed to the model of red algal cover. 4. 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. dataSHEET DATA Cover of substrate types and cover (%) and richness of macroalgal taxonomic groups along with values of the bottom of atmosphere reflectance at different spatial resolution (in metres) at an indicative wavelength. SHEET EXTERNAL VALIDATION DATASET Percent coverage of different macroalgal taxonomic group or macroalgal richness estimated by our BRT modelling together with that estimated during a separate field survey.