Various population fluctuation patterns of light-attracted beetles in a tropical lowland dipterocarp forest in Sarawak View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2000-04

AUTHORS

M. Kato, T. Itioka, S. Sakai, K. Momose, S. Yamane, A. A. Hamid, T. Inoue

ABSTRACT

The population fluctuation pattern of light-attracted beetles was studied from August 1992 to September 1998 (for 73 months) using ultraviolet light-traps set at three vertical levels in a tropical lowland dipterocarp forest in Sarawak, Malaysia. During our study, a general flowering occurred from April to July in 1996, and flowering on a small scale in 1997 and 1998. We analyzed the data for eight scarabaeid and six meloid species, some of which were anthophilous species. Various fluctuation patterns were observed among the beetle species in aspects of both seasonality and correlation with the supraannual phenological pattern. Three large chafer species (Scarabaeidae, Melolonthini) showed a clear seasonal fluctuation pattern with a peak once from March to May every year, the peak monthly catch greatly fluctuating annually. Other scarabaeid beetles did not show such a clear seasonal population pattern and hardly fluctuated annually. Populations of an anthophilous scarabaeid species, Parastasia bimaculata, a specific pollinator of Homalomena propinqua (Araceae), hardly fluctuated, probably because of its response to the constant flowering of its floral hosts. Monthly catches of an anthophilous scarabaeid, Anomala sp., and meloid beetles showed clear supraannual patterns in response to the general flowering and were significantly correlated with the flowering intensity with or without a lag of a month. The fluctuation pattern of meloids suggests a supraannual population fluctuation pattern of their hosts, i.e., megachilid/anthophorid bees. More... »

PAGES

97-104

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s101440050014

DOI

http://dx.doi.org/10.1007/s101440050014

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004400836


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