Jennifer Smyth

Bio: Jennifer Smyth is an academic researcher from Ulster University. The author has contributed to research in topics: Biotope & Reef. The author has an hindex of 1, co-authored 1 publications receiving 21 citations.

The impact of scour processes on a smothered reef system in the Irish Sea

Abstract: The Irish Sea, like many marine areas, is threatened by anthropogenic activities. In particular the Pisces Reef system, a series of smothered rocky reefs are subject to fishing pressures as a result of their position within a Nephrops norvegicus fishery. In an area of sediment deposition and retention the reefs modify the environment by increasing the energy of near-bottom currents which results in localised scouring. This is the first study to attempt to characterise and investigate the ecological functioning of the Pisces Reef system. A multidisciplinary approach was essential for accurate investigation of the area. To facilitate more effective management of the benthic habitats of the Reef system, this study integrates acoustic, seismic, grab sampling and video ground-truthing methods for benthic habitat discrimination. Orientation of the scour hollows also suggest that seabed features could be used to infer dominant flow regimes such as the Irish Sea Gyre. The data revealed significant geology–benthos relationships. A unique biotope was described for the reef habitat and it was demonstrated that scouring may influence community composition through disturbance mechanisms. This study provides preliminary information required for management of a unique habitat within a uniform region.

Benthic habitat mapping: A review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques

Abstract: This review examines the various strategies and methods used to produce benthic habitat maps using acoustic remote sensing techniques, coupled with in situ sampling. The applications of three acoustic survey techniques are examined in detail: single-beam acoustic ground discrimination systems, sidescan sonar systems, and multi-beam echo sounders. Over the past decade we have witnessed the nascence of the field of benthic habitat mapping and, on the evidence of the literature reviewed in this paper, have seen a rapid evolution in the level of sophistication in our ability to image and thus map seafloor habitats. As acoustic survey tools have become ever more complex, new methods have been tested to segment, classify and combine these data with biological ground truth sample data. Although the specific methods used to derive habitat maps vary considerably, the review indicates that studies can generally be categorized into one of three over-arching strategies; 1) Abiotic surrogate mapping; 2) Assemble first, predict later (unsupervised classification); 3) Predict first, assemble later (supervised classification). Whilst there is still no widely accepted agreement on the best way to produce benthic habitat maps, all three strategies provide valuable map resources to support management objectives. Whilst there is still considerable work to be done before we can answer many of the outstanding technological, methodological, ecological and theoretical questions that have been raised here, the review concludes that the advent of spatial ecological studies founded on high-resolution environmental data sets will undoubtedly help us to examine patterns in community and species distributions. This is a vital first step in unraveling ecological complexities and thus providing improved spatial information for management of marine systems.

An evaluation of supervised and unsupervised classification techniques for marine benthic habitat mapping using multibeam echosounder data

Abstract: Marine habitat mapping provides information on seabed substrata and faunal community structure to users including research scientists, conservation organizations, and policy makers. Full-coverage acoustic data are frequently used for habitat mapping in combination with video groundtruth data in either a supervised or unsupervised classification. In this investigation, video ground-truth data with a camera footprint of 1 m2 were classified to level 4 of the European Nature Information System habitat classification scheme. Acoustic data with a horizontal resolution of 1 m2 were collected over an area of 130 km2 using a multibeam echosounder, and processed to provide bathymetry and backscatter data. Bathymetric derivatives including eastness, northness, slope, topographic roughness index, vector rugosity measure, and two measures of curvature were created. A feature selection process based on Kruskal–Wallis and post hoc pairwise testing was used to select environmental variables able to discriminate ground-truth classes. Subsequently, three datasets were formed: backscatter alone (BS), backscatter combined with bathymetry and derivatives (BSDER), and bathymetry and derivatives alone (DER). Two classifications were performed on each of the datasets to produce habitat maps: maximum likelihood supervised classification (MLC) and ISO Cluster unsupervised classification. Accuracy of the supervised habitat maps was assessed using total agreement, quantity disagreement, and allocation disagreement. Agreement in the unsupervised maps was assessed using the Cramer’s V coefficient. Choice of input data produced large differences in the accuracy of the supervised maps, but did not have the same effect on the unsupervised maps. Accuracies were 46, 56, and 49% when calculated using the sample and 52, 65, and 51% when using an unbiased estimate of the population for the BS, BSDER, and DER maps, respectively. Cramer’s V was 0.371, 0.417, and 0.366 for the BS, BSDER, and DER maps, respectively.

The use of multibeam backscatter and bathymetry as a means of identifying faunal assemblages in a deep-sea cold seep

Abstract: Deep-sea ecosystems have attracted considerable commercial interest in recent years because of their potential to sustain a diverse range of mankind's industrial needs. If these systems are to be preserved or exploited in a sustainable manner, mapping habitats and species distributions is critical. As biodiversity at cold-seeps or other deep-sea ecosystems is driven by habitat heterogeneity, imagery is the obvious choice for characterizing these systems and has indeed proven extremely valuable towards mapping biogenic habitats formed by dense aggregations of large sized species, such as coral reefs, tubeworm bushes or bivalve beds. However, the acquisition of detailed images with resolution sufficient for reliable identification is extremely time consuming, labor intensive and highly susceptible to logistical issues. We developed a novel method for quickly mapping cold seep fauna and habitats over large areas, at the scale of squares of kilometers. Our method uses multibeam echosounder bathymetry and acoustic backscatter data, both segmented and reclassified based on topographical features and then combined to obtain a raster containing unique values incorporating both backscatter and bathymetry data. Two datasets, obtained from 30 m and 8 m above the seafloor were used and the results from the two datasets were compared. The method was applied to a cold seep community located in a pockmark in the deep Congo channel and we were able to ground truth the accuracy of our method against images of the area. The two datasets, obtained from different altitudes gave varying results: the 8 m altitude dataset reliably predicted tubeworms and carbonate rock, while the 30 m altitude dataset predicted tubeworms and vesicomyid clams. The 30 m dataset was more accurate than the 8 m altitude dataset in predicting distributions of tubeworms. Overall, all the predictions were quite accurate, with at least 90% of predictions being within 5 m of real distributions.