{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/9272"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/9272","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Multivariate analyses of the impact of offshore marine mining on the benthic macrofauna off the west coast of Southern Africa","abstract":"The strategy for analysing multivariate data presented by Field et. al. (1982) was tested for its sensitivity in detecting the effects of offshore marine mining on macrobenthic communities. The technique has proven to be particularly sensitive and robust in elucidating changes in the structure of marine communities following organic pollution events. The primary aim of this study was to investigate its applicability in discerning community changes in an area exposed to physical disturbance of the seabed. Statistical testing, using analysis of similarities, reveals a highly significant difference between mined and unmined samples. Statistical testing also detects natural spatial heterogeneity across the 6 study areas. Aggregation of the data to higher taxonomic levels did not result in the loss of information, and in fact, improved the resolution of the community patterns. Multivariate analyses were therefore performed using the community data aggregated to genus-level. Hierarchical agglomerative clustering reveals two major groups of samples, the mined and the unmined samples.","abstract_html":"The strategy for analysing multivariate data presented by Field et. al. (1982) was tested for its sensitivity in detecting the effects of offshore marine mining on macrobenthic communities. The technique has proven to be particularly sensitive and robust in elucidating changes in the structure of marine communities following organic pollution events. The primary aim of this study was to investigate its applicability in discerning community changes in an area exposed to physical disturbance of the seabed. Statistical testing, using analysis of similarities, reveals a highly significant difference between mined and unmined samples. Statistical testing also detects natural spatial heterogeneity across the 6 study areas. Aggregation of the data to higher taxonomic levels did not result in the loss of information, and in fact, improved the resolution of the community patterns. Multivariate analyses were therefore performed using the community data aggregated to genus-level. Hierarchical agglomerative clustering reveals two major groups of samples, the mined and the unmined samples.","abstract_has_math":false,"creators":["Savage, Candida"],"institution":"Department of Biological Sciences","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Field, John G","Wickens, Patti"],"committee_chairs":[],"committee_members":[],"year":1996,"date_issued":"1996","date_published":"1996","updated_at":"2026-07-22T22:23:04Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/9272","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Field, John G","Wickens, Patti"]},{"key":"dc:creator","label":"Author","values":["Savage, Candida"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-11-05T17:41:22Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-11-05T17:41:22Z"]},{"key":"dc:date.issued","label":"Date","values":["1996"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Biological Sciences"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MSc"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/9272"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Bibliography: leaves 90-95."]},{"key":"dc:description.abstract","label":"Abstract","values":["The strategy for analysing multivariate data presented by Field et. al. (1982) was tested for its sensitivity in detecting the effects of offshore marine mining on macrobenthic communities. The technique has proven to be particularly sensitive and robust in elucidating changes in the structure of marine communities following organic pollution events. The primary aim of this study was to investigate its applicability in discerning community changes in an area exposed to physical disturbance of the seabed. Statistical testing, using analysis of similarities, reveals a highly significant difference between mined and unmined samples. Statistical testing also detects natural spatial heterogeneity across the 6 study areas. Aggregation of the data to higher taxonomic levels did not result in the loss of information, and in fact, improved the resolution of the community patterns. Multivariate analyses were therefore performed using the community data aggregated to genus-level. 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The primary aim of this study was to investigate its applicability in discerning community changes in an area exposed to physical disturbance of the seabed. Statistical testing, using analysis of similarities, reveals a highly significant difference between mined and unmined samples. Statistical testing also detects natural spatial heterogeneity across the 6 study areas. Aggregation of the data to higher taxonomic levels did not result in the loss of information, and in fact, improved the resolution of the community patterns. Multivariate analyses were therefore performed using the community data aggregated to genus-level. 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