排序方式： 共有14条查询结果，搜索用时 15 毫秒
11.
Many soil properties and processes vary at different spatial scales. As a result, relationships between soil properties often depend on scale. In this paper we show this for two soil properties of biological importance, by means of a nested analysis of covariance. The variables were urease activity (UA) and soil organic carbon (SOC), sampled on an unbalanced nested design at three sites with different land uses (arable, forest and pasture). The objective of this study was to investigate the scale‐dependent relationships of UA and SOC at these three sites to exemplify the phenomenon of scale‐dependency in the covariation of biogeochemical variables. At each site the variables showed different scale dependencies, expressed in their correlations at different scales. At the pasture site, UA and SOC were uncorrelated at all scales in the sampling design (0.2 m, 1 m, 6 m and ≥15 m), and the overall product moment correlation was 0.10. A significant positive scale dependent correlation (0.65) was found at the 1‐m scale for the forested site. The soil properties were not spatially correlated at any of the other scales and the associated product moment correlation for this site was 0.14. Urease activity and soil organic C were found not to be correlated at the shorter scales in the arable site. However, significant positive correlation coefficients of 0.89 and 0.82 were obtained at the longer scales of 6 and ≥15‐m respectively for the arable site. The product moment correlation at this site was 0.65. At both the arable and forest site, we found that correlations at particular scales were stronger than the overall product moment correlation. This approach allowed us to identify significant relationships between urease activity and soil organic carbon and the scales at which these relationships occur and to draw conclusions about the spatial scales, which must be resolved in further studies of these variables in these contrasting environments. This study highlights the pervasive effect of scale in soil biogeochemistry and shows that scale‐dependence must not be disregarded by soil scientists in their investigations of biogeochemical processes. 相似文献
12.
Context
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.Objectives
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.Methods
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.Results
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.Conclusions
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.13.
14.
There are various circumstances in which it is important to know whether we can treat a model of a soil process as linear with respect to its parameters. In particular this is necessary when we decide how to apply that model so as to generate outputs at different spatial scales. Very few, if any, interesting soil models are strictly linear. However, the assumption of linearity might not be unreasonable if, for the region of interest, the variation of a particular input is fairly limited, or is limited to certain regions of the model’s parameter space, or both. For this reason we propose the concept of effective linearity. We propose that the effective linearity of a model is quantified by the mean square deviation of the model output from a best linear approximation, given some distribution of inputs. This can be computed for a full set of inputs, or for one input with the other inputs assumed to have an independent non‐linear effect. We computed these measures of non‐linearity for a model of ammonia volatilization from the soil. We computed them for regions of different size, given a particular geostatistical model of the spatial covariation of the inputs of interest. This showed that, except for small regions, the model could not be regarded as effectively linear because of the model response to soil pH. As a result the mean square deviation of model predictions relative to a best linear approximation is large by comparison to the analytical variance of the model output. 相似文献