Patterns of cerambycid beetle species composition in relation to geographic features, climate and/or silvicultural treatments on different scales View Full Text


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Article Info

DATE

2017-09-11

AUTHORS

Kimiko Okabe, Motohiro Hasegawa, Hiroshi Makihara

ABSTRACT

Characterising geographic patterns of biodiversity generated by intrinsic distributions of organisms is essential for designing effective biodiversity conservation plans on both the project and sub-national to national scales. Species composition is generally similar within the same types of ecosystems located close to one another, but similarity also depends on the focal organism(s) as well as the scale of the analysis. To facilitate decision-making for environmental compensation projects such as “biodiversity offsets”, we examined whether Japanese ecoregions based on vegetation are correlated with the distribution of cerambycid beetles, using existing cerambycid data collected from Hokkaido to the Nansei Islands in both natural and plantation forests over 1 year in each area. At the national level, the species compositions of beetles were quite distinct in Hokkaido and the Nansei Islands but less so in other areas. The overall pattern of the observed sampling data fit that obtained by previously accumulated local inventories. At the area level (including plantation forests under different management regimes/successional stages and in some cases naturally regenerated mature to old growth forests), no consistent beetle composition patterns were observed, although compositions in natural forests and closed canopy/thinned/old growth plantation forests were sometimes distinct. Therefore, we conclude that when ecoregions are considered during decision-making for “no net-loss or net-gain” biodiversity conservation measures, it is important to examine multiple organisms on various scales using scientific approaches from several perspectives. More... »

PAGES

771-779

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10841-017-0020-1

DOI

http://dx.doi.org/10.1007/s10841-017-0020-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091587181


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