Colorado River Clams Provide Benchmark

Muro, Mark

doi:10.1126/science.290.5499.2045a

Cited by 1 publication

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“…Moreover, because of their location, coastal settings such as salt marshes link fully marine and terrestrial set tings. Marginal marine signals also inform us about the pre-anthropogenic state of environ ments and the response of communities to land and water use; coastal erosion, sedimen tation, and nutrient loads; biodiversity and community stability in response to natural and anthropogenic environmental distur bance; and the effective management of wet lands and other coastal ecosystems (e.g., Kowalewski et al 2000;Muro 2000;Murray 2000a;papers in Martin 2000).…”

Section: Resultsmentioning

confidence: 99%

Artificial time-averaging of marsh foraminiferal assemblages: linking the temporal scales of ecology and paleoecology

et al. 2002

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Multiple regression models were developed for seasonal test inputs to, and preservation of, marsh foraminiferal assemblages for a two-year period at Bombay Hook National Wildlife Refuge (BHNWR; Smyrna, Delaware). Seasonal assemblages were quite variable and yielded poor regression models. However, signal/noise ratios were amplified using artificially time-averaged (ATA) assemblages, in which separate dead and live abundances of the most abundant species were summed for all seasons. Regression models that used ATA species abundances to retrodict original sample depths accounted for up to ~99% (p < 0.0001) and ~91% (p < 0.023) of the variation of dead and live ATA assemblages, respectively, and usually retrodicted sample depths within 2–3 cm of actual depths.Artificially time-averaged assemblages were also used to extract multidecadal- to centennial-scale sea-level signals from near-surface assemblages at BHNWR formed during the past few centuries. The BHNWR sea-level curve closely resembles one previously published for marshes in Clinton, Connecticut (also based on foraminifera). The technique of artificial time-averaging therefore links the temporal scales of ecology and paleobiology by extracting high-resolution paleoenvironmental signals preserved in the fossil record.

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Section: Resultsmentioning

confidence: 99%