de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Contribution analysis of disturbance-caused changes in phytoplankton diversity.

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons56867

Polishchuk,  Leonard V.
Department Ecophysiology, Max Planck Institute for Limnology, Max Planck Institute for Evolutionary Biology, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Polishchuk, L. V. (1999). Contribution analysis of disturbance-caused changes in phytoplankton diversity. Ecology, 80(2), 721-725.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-E0BE-4
Zusammenfassung
For an experimental system of marine planktonic algae, U. Sommer has published a set of regression equations linking species diversity to intensity and frequency of disturbance. I apply a simple version of contribution analysis to Sommer's equations pursuing a twofold goal: (1) to reveal a mechanism responsible for changes in species diversity along the gradient of disturbance and (2) to evaluate the potential of the method used. The relative importance (i.e., contributions) of changes in disturbance intensity and frequency to the resultant change in diversity is estimated, as disturbance regime is gradually shifting from high frequencies and low intensities to low frequencies and high intensities. Changes in intensity are found to be of primary importance under a high-frequency and low-intensity disturbance regime, while changes in frequency are of primary importance under a low-frequency and high-intensity disturbance regime. Competitive interactions appear to shape the algal community at both ends of the disturbance gradient-not only when disturbances are weak though common, but also when they are strong and rare. The results suggest that contribution analysis may allow one to extract information on the underlying mechanisms from purely descriptive regression relationships.