2 resultados para DIGITAL VIDEO

em Publishing Network for Geoscientific


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We investigated the effects of pH on movement behaviors of the harmful algal bloom causing raphidophyte Heterosigma akashiwo. Motility parameters from >8000 swimming tracks of individual cells were quantified using 3D digital video analysis over a 6-h period in 3 pH treatments reflecting marine carbonate chemistry during the pre-industrial era, currently, and the year 2100. Movement behaviors were investigated in two different acclimation-to-target-pH conditions: instantaneous exposure and acclimation of cells for at least 11 generations. There was no negative impairment of cell motility when exposed to elevated PCO2 (i.e., low pH) conditions but there were significant behavioral responses. Irrespective of acclimation condition, lower pH significantly increased downward velocity and frequency of downward swimming cells (p < 0.001). Rapid exposure to lower pH resulted in 9% faster downward vertical velocity and up to 19% more cells swimming downwards (p < 0.001). Compared to pH-shock experiments, pre-acclimation of cells to target pH resulted in ~30% faster swimming speed and up to 46% faster downward velocities (all p < 0.001). The effect of year 2100 PCO2 levels on population diffusivity in pre-acclimated cultures was >2-fold greater than in pH-shock treatments (2.2 × 105 µm**2/s vs. 8.4 × 104 µm**2/s). Predictions from an advection-diffusion model, suggest that as PCO2 increased the fraction of the population aggregated at the surface declined, and moved deeper in the water column. Enhanced downward swimming of H. akashiwo at low pH suggests that these behavioral responses to elevated PCO2 could reduce the likelihood of dense surface slick formation of H. akashiwo through reductions in light exposure or growth independent surface aggregations. We hypothesize that the HAB alga's response to higher PCO2 may exploit the signaling function of high PCO2 as indicative of net heterotrophy in the system, thus indicative of high predation rates or depletion of nutrients.

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Kelp forests represent a major habitat type in coastal waters worldwide and their structure and distribution is predicted to change due to global warming. Despite their ecological and economical importance, there is still a lack of reliable spatial information on their abundance and distribution. In recent years, various hydroacoustic mapping techniques for sublittoral environments evolved. However, in turbid coastal waters, such as off the island of Helgoland (Germany, North Sea), the kelp vegetation is present in shallow water depths normally excluded from hydroacoustic surveys. In this study, single beam survey data consisting of the two seafloor parameters roughness and hardness were obtained with RoxAnn from water depth between 2 and 18 m. Our primary aim was to reliably detect the kelp forest habitat with different densities and distinguish it from other vegetated zones. Five habitat classes were identified using underwater-video and were applied for classification of acoustic signatures. Subsequently, spatial prediction maps were produced via two classification approaches: Linear discriminant analysis (LDA) and manual classification routine (MC). LDA was able to distinguish dense kelp forest from other habitats (i.e. mixed seaweed vegetation, sand, and barren bedrock), but no variances in kelp density. In contrast, MC also provided information on medium dense kelp distribution which is characterized by intermediate roughness and hardness values evoked by reduced kelp abundances. The prediction maps reach accordance levels of 62% (LDA) and 68% (MC). The presence of vegetation (kelp and mixed seaweed vegetation) was determined with higher prediction abilities of 75% (LDA) and 76% (MC). Since the different habitat classes reveal acoustic signatures that strongly overlap, the manual classification method was more appropriate for separating different kelp forest densities and low-lying vegetation. It became evident that the occurrence of kelp in this area is not simply linked to water depth. Moreover, this study shows that the two seafloor parameters collected with RoxAnn are suitable indicators for the discrimination of different densely vegetated seafloor habitats in shallow environments.