To maximize the ecological services of urban forests, a better understanding of the effects of urbanization on urban forest characteristics, landscape metrics, and their associations is needed for landscape-related regulations in space-limited green infrastructure of metropolitan regions. In this study, Harbin, a typical fast-growing provincial-capital city in Northeast China, is used as a case study. Based on remote sensing images, field surveys, and correlation and variation partitioning analyses, we conclude that landscape characteristics and forest attributes have large variations among different urbanization intensity (UI) regions. Forest patch density (PD), landscape shape index, woody plants species richness, and the Shannon–Wiener index (H′) increased linearly, while stem section area and tree height decreased linearly with the increasing of UIs. UI had a greater influence on tree size and forest community attributes than the forest landscape pattern. Accordingly, any landscape regulation on forest attributes should be implemented according to UIs. In addition, Euclidean nearest neighbor distance(ENN-MN), mean perimeter-area ratio (PARA-MN), fractal dimension index(FRAC-MN), and PD could probably indicate forest attributes the most, e.g., the increase of PARA-MN may be accompanied with taller trees in low and heavy UI regions, but lower woody plants species evenness in low and medium UI regions. More diversified woody plants species, and afforested areas should be advocated in a low UI region, while in a heavy UI region, the conservation of large trees should be implemented. Our results highlight that the implementation of urban forest management should vary according to different urbanization regions to maximize ecological services.
In this article, we propose a factor-adjusted multiple testing (FAT) procedure based on factor-adjusted p-values in a linear factor model involving some observable and unobservable factors, for the purpose of selecting skilled funds in empirical finance. The factor-adjusted p-values were obtained after extracting the latent common factors by the principal component method. Under some mild conditions, the false discovery proportion can be consistently estimated even if the idiosyncratic errors are allowed to be weakly correlated across units. Furthermore, by appropriately setting a sequence of threshold values approaching zero, the proposed FAT procedure enjoys model selection consistency. Extensive simulation studies and a real data analysis for selecting skilled funds in the U.S. financial market are presented to illustrate the practical utility of the proposed method. Supplementary materials for this article are available online. 相似文献
Social Indicators Research - Previous studies have documented the associations of objective class with either social capital or subjective social status, yet little attention has been paid to the... 相似文献
In this paper, a rumor blocking problem is studied with an objective function which is neither submodular or supermodular. We will prove that this problem is NP-hard and give a data-dependent approximation with sandwich method. In addition, we show that every set function has a pair of monotone nondecreasing modular functions as upper bound and lower bound, respectively. 相似文献