Grafius Darren R, Corstanje Ron, Harris Jim A
School of Water, Energy and Environment, Cranfield University, Cranfield, MK43 0AL Bedfordshire UK.
Landsc Ecol. 2018;33(4):557-573. doi: 10.1007/s10980-018-0618-z. Epub 2018 Feb 19.
Landscape metrics represent powerful tools for quantifying landscape structure, but uncertainties persist around their interpretation. Urban settings add unique considerations, containing habitat structures driven by the surrounding built-up environment. Understanding urban ecosystems, however, should focus on the habitats rather than the matrix.
We coupled a multivariate approach with landscape metric analysis to overcome existing shortcomings in interpretation. We then explored relationships between landscape characteristics and modelled ecosystem service provision.
We used principal component analysis and cluster analysis to isolate the most effective measures of landscape variability and then grouped habitat patches according to their attributes, independent of the surrounding urban form. We compared results to the modelled provision of three ecosystem services. Seven classes resulting from cluster analysis were separated primarily on patch area, and secondarily by measures of shape complexity and inter-patch distance.
When compared to modelled ecosystem services, larger patches up to 10 ha in size consistently stored more carbon per area and supported more pollinators, while exhibiting a greater risk of soil erosion. Smaller, isolated patches showed the opposite, and patches larger than 10 ha exhibited no additional areal benefit.
Multivariate landscape metric analysis offers greater confidence and consistency than analysing landscape metrics individually. Independent classification avoids the influence of the urban matrix surrounding habitats of interest, and allows patches to be grouped according to their own attributes. Such a grouping is useful as it may correlate more strongly with the characteristics of landscape structure that directly affect ecosystem function.
景观指标是量化景观结构的有力工具,但在其解读方面仍存在不确定性。城市环境有独特的考量因素,包含由周边建成环境驱动的栖息地结构。然而,理解城市生态系统应关注栖息地而非基质。
我们将多变量方法与景观指标分析相结合,以克服现有解读中的不足。然后,我们探究了景观特征与模拟的生态系统服务供给之间的关系。
我们使用主成分分析和聚类分析来分离景观变异性的最有效度量,然后根据栖息地斑块的属性对其进行分组,而不考虑周边城市形态。我们将结果与三种生态系统服务的模拟供给进行比较。聚类分析得出的七类主要根据斑块面积划分,其次根据形状复杂性和斑块间距离的度量划分。
与模拟的生态系统服务相比,面积达10公顷的较大斑块每单位面积始终储存更多碳并支持更多传粉者,同时表现出更大的土壤侵蚀风险。较小的孤立斑块则相反,面积大于10公顷的斑块没有额外的面积效益。
多变量景观指标分析比单独分析景观指标更具可信度和一致性。独立分类避免了感兴趣栖息地周边城市基质的影响,并允许根据斑块自身属性进行分组。这样的分组很有用,因为它可能与直接影响生态系统功能的景观结构特征有更强的相关性。