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Species-Area Relationship and Its Scale-Dependent Effects in Natural Forests of North Eastern China
- Chen, Beibei, Jiang, Jun, Zhao, Xiuhai
- Forests 2019 v.10 no.5
- Pinus koraiensis, biodiversity, forest dynamics, forest types, logit analysis, mixed forests, models, sampling, China
- The Species-area relationship is one of the core issues in community ecology and an important basis for scale transformation of biodiversity. However, the effect of scale on this relationship, together with the selection of an optimal species-area model for different sampling methods, is still controversial. This study is based on the data from two sampling areas of 40 km2 in size, one in a Korean pine (Pinus koraiensis Sieb. et Zucc) broad-leaved mixed forest in Mt. Changbai and the other in Jiaohe, Jilin Province. The logarithmic, power, and logistic model were established on a scale of 10 km2, 20 km2, and 30 km2, respectively, using a nested sampling plot and random sampling plot. The goodness of the species-area model was tested by the Akaike information criterion (AIC). The results show that the sampling method affected the relationship between species and area, and the data were fitted better under random sampling compared with nested sampling. The construction of the relationship between species and area was closely related to the upper limit of the sampling area size. On a small scale (10 km2), the data were fitted best with the logarithmic and logistic model, whereas the logistic model was the best fit on a medium (20 km2) and large scale (30 km2). We evaluated the scale dependence of species-area relationship in two forests with nested and random sampling methods. We further showed that the logistic model based on the random sampling plot can explain most soundly the species-area relationship in Jiaohe and Mt. Changbai. More studies are needed in other regions to develop models to optimize sampling designs for different forest types under different density constraints at different spatial scales, and for a more accurate estimation of forest dynamics under long-term observations.