Disturbance indicators for time series reconstruction and marine ecosystem health impact assessment
A systematic reconstruction of Multiple Marine Ecological Disturbances (MMEDs) involving disease occurrence, morbidity and mortality events has been undertaken so that a taxonomy of globally distributed marine disturbance types can be better quantified and common forcing factors identified. Combined...
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University of New Hampshire Scholars' Repository
2000
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| Online Access: | https://scholars.unh.edu/dissertation/2149 https://scholars.unh.edu/cgi/viewcontent.cgi?article=3148&context=dissertation |
| Summary: | A systematic reconstruction of Multiple Marine Ecological Disturbances (MMEDs) involving disease occurrence, morbidity and mortality events has been undertaken so that a taxonomy of globally distributed marine disturbance types can be better quantified and common forcing factors identified. Combined disturbance data include indices of morbidity, mortality and disease events affecting humans, marine invertebrates, flora and wildlife populations. In the search for the best disturbance indicators of ecosystem change, the unifying solution for joining data from disparate fields is to organize data into space/time/topic hierarchies that permit convergence of data due to shared and appropriate scaling. The scale of the data selects for compatible methodologies, leading to better data integration, dine series reconstruction and the discovery of new relationships. Information technology approaches designed to assist this process include bibliographic keyword searches, data-mining, data-modeling and geographic information system design. "Expert" consensus, spatial, temporal, categorical and statistical data reduction methods are used to reclassify thousands of independent anomaly observations into eight functional impact groups representing anoxic-hypoxic, biotoxin-exposure, disease, keystone-chronic, mass-lethal, new-novel-invasive, physically forced and trophic-magnification disturbances. Data extracted from the relational database and Internet (http://www.heedmd.org) geographic information system demonstrate non-random patterns relative to expected dependencies. When data are combined they better reflect response to exogenous forcing factors at larger scales (e.g. North Atlantic and Southern Ocean Oscillation index scales) than is apparent without grouping. New hypotheses have been generated linking MMEDs to climate system "forcing", variability and changes within the Northwestern Atlantic Ocean, Gulf of Mexico and Caribbean Sea. A more general global survey known collectively as the Health Ecological and Economic ... |
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