Adaptation of Coccomyxa sp. to Extremely Low Light Conditions Causes Deep Chlorophyll and Oxygen Maxima in Acidic Pit Lakes

Deep chlorophyll maxima (DCM) and metalimnetic oxygen maxima (MOM) are outstanding biogeochemical features of acidic pit lakes (APL). However, knowledge of the eukaryotic phototrophs responsible for their formation is limited. We aimed at linking the dynamics of phototrophic communities inhabiting meromictic APL in Spain with the formation of these characteristic layers. Firstly, the dynamics of DCM and MOM and their relation to physico-chemical parameters (photosynthetically active radiation (PAR), pH, dissolved ferric iron concentration, temperature), pigments and nutrient distribution is described; secondly, the phototrophic community composition is studied through a combination of microscopy, biomolecular and “omics” tools. Phototrophic communities of the studied APL show a low diversity dominated by green microalgae, specifically Coccomyxa sp., which have been successfully adapted to the chemically harsh conditions. DCM and MOM are usually non-coincident. DCM correspond to layers where phototrophs have higher chlorophyll content per cell to cope with extremely low PAR (<1 µmol m−2 s−1), but where photosynthetic oxygen production is limited. MOM correspond to shallower waters with more light, higher phytoplankton biomass and intense photosynthetic activity, which affects both oxygen concentration and water temperature. The main drivers of DCM formation in these APL are likely the need for nutrient uptake and photo-acclimation.

BEYOND FLOOD HAZARD MAPS: DETAILED FLOOD CHARACTERIZATION WITH REMOTE SENSING, GIS AND 2D MODELLING

Flooding is considered to be one of the most destructive among many natural disasters such that understanding floods and assessing the risks associated to it are becoming more important nowadays. In the Philippines, Remote Sensing (RS) and Geographic Information System (GIS) are two main technologies used in the nationwide modelling and mapping of flood hazards. Although the currently available high resolution flood hazard maps have become very valuable, their use for flood preparedness and mitigation can be maximized by enhancing the layers of information these maps portrays. In this paper, we present an approach based on RS, GIS and two-dimensional (2D) flood modelling to generate new flood layers (in addition to the usual flood depths and hazard layers) that are also very useful in flood disaster management such as flood arrival times, flood velocities, flood duration, flood recession times, and the percentage within a given flood event period a particular location is inundated. The availability of these new layers of flood information are crucial for better decision making before, during, and after occurrence of a flood disaster. The generation of these new flood characteristic layers is illustrated using the Cabadbaran River Basin in Mindanao, Philippines as case study area. It is envisioned that these detailed maps can be considered as additional inputs in flood disaster risk reduction and management in the Philippines.