Effect of canopy removal on a rocky shore community metabolism and structure

The effects of the permanent removal of the canopy-forming alga Fucus serratus was studied in terms of both functioning and diversity on a mid–low rocky shore ecosystem in the south-west English Channel (48°N 43.686′, 3°W 59.282′). Ecosystem functioning was examined as net or gross primary productivity (NPP or GPP) and respiration (Resp) measured through CO₂ fluxes. Diversity was examined as number and composition of species. Measurements were performed in situ, during emersion times, without altering target assemblages. The experiment was designed with two treatments [control (C) or canopy removed (CR)] and five replicates, and was conducted over an 18-month period (from February 2006 to August 2007) to integrate the seasonal variability. The mean GPP and Resp were severely reduced in CR treatment compared to control throughout the survey. The mean NPP was not affected at first, due to the development of opportunistic green algae, but was drastically reduced after 9 months of experiment. The canopy removal affected neither the number of species nor their distribution among trophic groups, and the algal community was only slightly affected. The abundance and biomass of mobile invertebrates, however, were greatly reduced in the absence of canopy. This indicates an important effect of the dominant alga on the higher trophic levels of the community. At this tidal level, the canopy did not seem to affect the community by dampening the environmental stress but by providing food, habitat or both.     

A geostatistical and probabilistic spectral image processing methodology for monitoring potential CO2 leakages on the surface

Remote sensing has demonstrated success in various environmental applications over the past three decades. This is largely attributed to its ability for good areal coverage and continued development in sensor technologies. Carbon dioxide Capture and Storage (CCS) is an emerging climate change mitigation technology where monitoring is vital for its sustainability. This research investigates the use of spectral remote sensing imagery in detecting potential CO₂ occurrences at the surface, should a leakage occur from subsurface reservoirs where CO₂ is stored. Currently, there are no known leakages of CO₂ at industrial storage sites, therefore, this research was carried out at the Latera natural analogue site in Italy, in order to develop the methodology described. This paper describes the use of a popular probabilistic information fusion theory, referred to as the Dempster–Shafer theory of evidence, to analyse outlier pixels (anomalies). Outlier pixels are first determined using a new geostatistical image filtering methodology based on Intrinsic Random Function (IRF), Independent Component Analysis (ICA), and the industry standard parametric Reed–Xiaoli (RX) anomaly detection. Information fusion of detected outlier pixels and indirect surface effects of CO₂ leakage over time, such as stressed vegetation or mineral alterations, assigns a confidence measure per outlier pixel in order to identify potential leakage points. After visual validation using direct field measurements, it was demonstrated that the proposed methodology is able to detect majority of the seepage points at Latera, and holds promise as a new unsupervised CO₂ monitoring methodology.