Abstract. The analysis of spatial pattern in plant ecology usually implies the solution of some edge effect problems. We present in this paper some explicit formulas of edge effect correction that should enable plant ecologists to analyse a wider range of real field data.
We consider the local correcting factor of edge effect for Ripley's K‐function, that can also be used for other statistics of spatial analysis based on the counting of neighbours within a given distance. For both circular and rectangular study areas, we provide a review of explicit formulas and an extension of these formulas for long and narrow plots. In the case of irregular‐shaped study plots, we propose a generalization of the method that computes edge effect correction by excluding triangular surfaces from a simple (rectangular or circular) initial shape.
An example in forest ecology, where the soil characteristics determine a study plot of complex shape, illustrates how this edge effect correction can be effective in avoiding misinterpretations.
Abstract. The interactions between plants of different species, age or size play an important role in the dynamics of an ecosystem and can induce specific structures. These interactions can be studied by analysing the spatial structure of the corresponding bivariate patterns. The intertype L12‐function has recently been successfully used in many papers for that purpose. However, when interpreting the results obtained with ecological data, at least two different null hypotheses – independence or random labelling – can be appropriate, depending on the context of the study and the nature of the data. As these two hypotheses correspond to different confidence intervals, an inappropriate choice of the null hypothesis can lead to misinterpretations of biotic interactions when studying ecological data. This problem has rarely been mentioned in the literature.
In this paper we clarify the differences between these two null hypotheses, and illustrate the risk of misinterpretation when using an inappropriate null hypothesis. We review the main characteristics of these two hypotheses, and analyse the spatial structure of both real data from forest stands and simulated virtual stands of different structures. We demonstrate that the risk of misinterpretation is quite high, and that extreme misinterpretations, i.e. cases leading to opposite conclusions in terms of spatial interaction, can occur in a significant number of cases. We therefore propose some guidelines to help ecologists avoid such misinterpretations.
Abstract. Spatial heterogeneity is a characteristic of most natural ecosystems which is difficult to handle analytically, particularly in the absence of knowledge about the exogenous factors responsible for this heterogeneity. While classical methods for analysis of spatial point patterns usually require the hypothesis of homogeneity, we present a practical approach for partitioning heterogeneous vegetation plots into homogeneous subplots in simple cases of heterogeneity without drastically reducing the data. It is based on the detection of endogenous variations of the pattern using local density and second‐order local neighbour density functions that allow delineation of irregularly shaped subplots that could be considered as internally homogeneous. Spatial statistics, such as Ripley's K‐function adapted to analyse plots of irregular shape, can then be computed for each of the homogeneous subplots. Two applications to forest ecological field data demonstrate that the method, addressed to ecologists, can avoid misinterpretations of the spatial structure of heterogeneous vegetation stands.
The article describes a riverscape approach based on landscape ecology concepts, which aims at studying the multiscale relationships between the spatial pattern of stream fish habitat patches and processes depending on fish movements. A review of the literature shows that few operational methods are available to study this relationship due to multiple methodological and practical challenges inherent to underwater environments. We illustrated the approach with literature data on a cyprinid species (Barbus barbus) and an actual riverscape of the Seine River, France. We represented the underwater environment of fishes for different discharges using two-dimensional geographic information system-based maps of the resource habitat patches, defined according to activities (feeding, resting, and spawning). To quantify spatial patterns at nested levels (resource habitat patch, daily activities area, subpopulation area), we calculated their composition, configuration, complementation, and connectivity with multiple spatial analysis methods: patch metrics, moving-window analysis, and least cost modeling. The proximity index allowed us to evaluate habitat patches of relatively great value, depending on their spatial context, which contributes to the setting of preservation policies. The methods presented to delimit potential daily activities areas and subpopulation areas showed the potential gaps in the biological connectivity of the reach. These methods provided some space for action in restoration schemes.
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