English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Aims Our objectives were to obtain typical plant association types in a 20 hm forest dynamic monitoring plot in Mt. Tiantong, and provide methodological references for determining transitional types and identifying typical types in vegetation classification. Methods We used two-way indicator species analysis (TWINSPAN) and detrended correspondence analysis (DCA) to remove transitional community and identify typical association types. DCA method was also used to analyze the relationship between the distributions of different plant associations and environmental variables. Important findings The results showed that the classification of typical plant associations was greatly improved by the removal of transitional communities. The forest was divided into three associations: 1) Viburnum erosumTernstroemia gymnanthera/Lithocarpus harlandii + Cyclobalanopsis sessilifolia Association, 2) Daphniphyllum oldhami-Lithocarpus glaber/Schima superba + Castanopsis carlesii Association, and 3) Illicium lanceolatumPhoebe sheareri/Choerospondias axillaries + Machilus leptophylla Association. The results of DCA revealed the importance of environmental variables (elevation, convexity, slope, and aspect) on spatial distribution of plant associations. Elevation and convexity had significant effects on the distribution of associations, while slope and aspect had weak effects. The results confirmed the high accuracy of the association classification we used.

Classification of plant associations based on a 20 hm2 dynamics plot of evergreen broad- leaved forest in Mt. Tiantong, Zhejiang, China