Long-term variability of meiobenthos: value, synopsis, hypothesis generation and predictive modelling
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Few long-term data sets exist for meiofauna. Such data sets are expensive to collect, sort and identify; continuous meiofauna data for a period of greater than two years are limited to one site in Belgium (7 yrs) and two sites (one mud, one sand) in South Carolina, USA (11 yrs). The Belgian study concentrates on benthic copepod abundances whereas data from South Carolina includes major taxa and benthic copepods as well as 4 years of concurrent macrofauna abundance and 3 years of nematode species abundances.
In South Carolina, the variance associated with meiofaunal abundance had 6 or 12 month recurrent cycles. Similar analyses on 4 years of macrofauna from the same 2 sites indicated the same cyclicity: one year. Seasonality of the South Carolina major taxa and the 6 most abundant mud copepod species was pronounced at the mud site, but absent or less pronounced at the sand site. Similar results were also found for the nematode species over three years. Variability in meiofaunal abundance was greater year-to-year than within a year.
Many such long-term data sets are analysed and abandoned. Herewith, I use our long-term results to hypothesize the causes of the high temporal variance in mud and the lower temporal variance in sand. Is it because the mud fauna is controlled by seasonal inputs of natant predators while at the hydrodynamically active sand site temporal variability is homogenized by constant physical activity?
By appropriate statistical modelling long-term data sets can also be used to assess the appropriateness of the sampling schedule (spatial and temporal) and as a predictor of future trends.
Keywordsmeiobenthos long-term synopsis hypothesis modelling
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- de Bovée, F. & J. Soyer, 1974. Cycle annuel quantitive du méiobenthos des vases terrigénes côtières. Distribution vertical. Vie Milieu 24: 141–157.Google Scholar
- Connell, J. H., 1975. Some mechanisms producing structure in natural communities. In M. L. Cody & J. M. Diamond, (eds.), Ecology and Evolution of Communities. Belknap Press, Cambridge, Mass.: 460–488.Google Scholar
- Dayton, P. K. & J. S. Oliver, 1980. An Evaluation of Experimental Analyses of Population and Community Patterns in Benthic Marine Environments. In K. R. Tenore & B. C. Coull, (eds.), Marine Benthic Dynamics. University of South Carolina Press, Columbia, S. C.: 93–120.Google Scholar
- Edwards, D. & B. C. Coull, Submitted Ms. Autoregression in trend analysis: An example using long-term ecological data. Oikos.Google Scholar
- Eskin, R. A., 1985. Population dynamics and ecology of the meiobenthic nematodes of North Inlet, South Carolina. Ph.D. Thesis, Univ. of South Carolina.Google Scholar
- Heip, C., 1980. The influence of competition and predation on production of meiobenthic copepods. In K. R. Tenore & B. C. Coull (eds.), Marine Benthic Dynamics. University of South Carolina Press, Columbia, S.C.: 167–177.Google Scholar
- Heip, C. & P. M. J. Herman, 1985. The stability of a benthic copepod community. In P. E. Gibbes (ed.), Proc. 19th European Marine Biology Symp. Cambridge Univ. Press, Cambridge, U.K.: 255–264.Google Scholar
- Herman, P. M. J. & C. Heip, 1983. Long-term dynamics of meiobenthic populations. Oceanol. Acta 1983: 109–112.Google Scholar
- Smith, L. D. & B. C. Coull, in press. Juvenile spot (pisces) and grass shrimp predation on meiofauna in muddy and sandy substrates. J. Exp. Mar. Biol. Ecol.Google Scholar
- Stripp, K., 1969. Jahreszeitliche Fluktuationen von Makrofauna und Meiofauna in der Helgoländer Bucht. Verröff. Inst. Meeresforsch. Bremerhaven 12: 65–94.Google Scholar
- Wiens, J. A., 1977. On competition and variable environments. Am. Sci. 65: 590–597.Google Scholar