Using spatial analysis to monitor tree diversity at a large scale: a case study in Northeast China Transect

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Monitoring and assessing diversity change at a large scale is important for any meaningful biodiversity conservation and management. Spatial analysis techniques can provide information about different aspects of diversity distribution including change. We applied some common spatial analysis methods and additive partitioning of species diversity in the Northeast China Transect as a case study to show how to characterize the distribution and change of tree diversity in this area from different perspectives.


The field data were collected from the permanent plots conducted every 4 km. The additive partitioning of species diversity was used to characterize the diversity of tree species at different scales. Moran's I was used for identifying the spatial scale of autocorrelation, lacunarity was studied for diversity patch contagion and dispersion and spectral entropy was used for assessing the overall spatial distribution.

Important findings

Data collected from 1986 to 1994 indicate that the change of α diversity was not significant in the study area, but the change of β diversity was significant. The percentage of α diversity in total diversity (γ) increased from 14.2 to 17.2%, and the percentage of β diversity decreased from 85.8 to 82.8%. For both α and β diversities, the scale of spatial autocorrelation decreased at the scale of 25–40 km and increased around 15–20 and 200 km. The lacunarity of α diversity decreased significantly and there was a sudden change at the scale of 56–68 km, but the lacunarity of β diversity increased across scales. The spectral entropy decreased slightly in α diversity and remained similar for β diversity. By using spatial analysis, we can monitor the diversity change over a large area and also assess the effectiveness of the current conservation strategies.

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