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- First quantification of subtidal community structure at Tristan da Cunha Islands in the remote South Atlantic from kelp forests to the deep sea
- Tristan da Cunha Islands, an archipelago of four rocky volcanic islands situated in the South Atlantic Ocean and part of the United Kingdom Overseas Territories (UKOTs), present a rare example of a relatively unimpacted temperate marine ecosystem. We conducted the first quantitative surveys of nearshore kelp forests, offshore pelagic waters and deep sea habitats. Kelp forests had very low biodiversity and species richness, but high biomass and abundance of those species present. Spatial variation in assemblage structure for both nearshore fish and invertebrates/algae was greatest between the three northern islands and the southern island of Gough, where sea temperatures were on average 3-4o colder. Despite a lobster fishery that provides the bulk of the income to the Tristan islands, lobster abundance and biomass are comparable to or greater than many Marine Protected Areas in other parts of the world. Pelagic camera surveys documented a rich biodiversity offshore, including large numbers of juvenile blue sharks, Prionace glauca. Species richness and abundance in the deep sea is positively related to hard rocky substrate and biogenic habitats such as sea pens, crinoids, whip corals, and gorgonians were present at 40% of the deep camera deployments. We observed distinct differences in the deep fish community above and below ~750 m depth. Concurrent oceanographic sampling showed a discontinuity in temperature and salinity at this depth. While currently healthy, Tristan's marine ecosystem is not without potential threats: shipping traffic leading to wrecks and species introductions, pressure to increase fishing effort beyond sustainable levels and the impacts of climate change all could potentially increase in the coming years. The United Kingdom has committed to protection of marine environments across the UKOTs, including Tristan da Cunha and these results can be used to inform future management decisions as well as provide a baseline against which future monitoring can be based.
- Caselle, Hamilton, Davis, Thompson, Turchik, Jenkinson, Simpson, Sala
- Long-term variation in concentrations and mass loads in a semi-arid watershed influenced by historic mercury mining and urban pollutant sources
- Urban watersheds are significantly anthropogenically-altered landscapes. Most previous studies cover relatively short periods, without addressing concentrations, loads, and yields in relation to annual climate fluctuations, and datasets on Ag, Se, PBDEs, and PCDD/Fs are rare. Intensive storm-focused sampling and continuous turbidity monitoring were employed to quantify pollution at two locations in the Guadalupe River (California, USA). At a downstream location, we determined loads of suspended sediment (SS) for 14 yrs., mercury (HgT), PCBs, and total organic carbon (TOC) (8 yrs), total methylmercury (MeHgT) (6 yrs), nutrients, and trace elements including Ag and Se (3 yrs), DDTs, chlordanes, dieldrin, and PBDEs (2 yrs), and PCDD/Fs (1 yr). At an upstream location, we determined loads of SS for 4 yrs. and HgT, MeHgT, PCBs and PCDD/Fs for 1 yr. These data were compared to previous studies, climatically adjusted, and used to critically assess the use of small datasets for estimating annual average conditions. Concentrations and yields in the Guadalupe River appear to be atypical for total phosphorus, DDTs, dieldrin, HgT, MeHgT, Cr, Ni, and possibly Se due to local conditions. Other pollutants appear to be similar to other urban systems. On average, wet season flow varied by 6.5-fold and flow-weighted mean (FWM) concentrations varied 4.4-fold, with an average 7.1-fold difference between minimum and maximum annual loads. Loads for an average runoff year for each pollutant were usually less than the best estimate of long-term average. The arithmetic average of multiple years of load data or a FWM concentration combined with mean annual flow was also usually below the best estimate of long-term average load. Mean annual loads using sampled years were also less than the best estimate of long-term average by a mean of 2.2-fold. Climatic adjustment techniques are needed for computing estimates of long-term average annual loads.
- McKee, Bonnema, David, Davis, Franz, Grace, Greenfield, Gilbreath, Grosso, Heim