A small-scale analysis of the spatial structure of a Posidonia oceanica meadow off the Island of Ischia (Gulf of Naples,Italy): Relationship with the seafloor morphology

Posidonia oceanica L. (Delile), an endemic species of the Mediterranean, forms extensive meadows which are continuously endangered by anthropic impacts. The availability of up-to-date information about interannual changes in shoot density of meadows and the knowledge of its expansion capabilities are crucial elements for the development of effective protection plans. Conversely, spatial ecology is becoming an increasingly important component of resource management, and the use of quantitative data for constructing prognosis maps of the dynamics of ecosystem degradation and restoration by nonlinear simulation methods is a topical field of landscape ecology. Unfortunately, little is known on spatial patterns of shoot density of P. oceanica on a small scale, despite their increasing use as indicators of the status and/or trends of meadows. The spatial structure of a continuous P. oceanica meadow, extending from 1 to 33 m depth in Lacco Ameno (Gulf of Naples, Italy), is investigated here by the “kriging” technique, a method widely used for geostatistical purposes. The analysis detected peculiar spatial patterns of shoot density and facilitated a small-scale (square meters) model of the distribution of P. oceanica. The highest shoot densities were found at the shallow stand (430 shoots m⁻², on the average, with a peak of 1000 shoots m⁻² in a relatively small area, at 1 m depth) and the lowest at the deep stand (average density <300 shoots m⁻² below 15 m depth). A high degree of patchiness was found in the shallow stand, down to 10 m depth. Nestlike patterns with a rounded shape, characterized by shoot density radially decreasing from the center, were demonstrated mainly in the shallow stand. An opposite trend was detected in one case, where the main nestlike pattern was characterized by a decrease of density towards the center. Nestlike patterns may be generated by the confluence and overlap of stolons expanding from proximal areas. The comparison of density and depth models indicated that the shape of the seafloor influences the density structure of meadows and the growth patterns of P. oceanica.     

Trajectory Data Analyses for Pedestrian Space-time Activity Study

It is well recognized that human movement in the spatial and temporal dimensions has direct influence on disease transmission^1-3. An infectious disease typically spreads via contact between infected and susceptible individuals in their overlapped activity spaces. Therefore, daily mobility-activity information can be used as an indicator to measure exposures to risk factors of infection. However, a major difficulty and thus the reason for paucity of studies of infectious disease transmission at the micro scale arise from the lack of detailed individual mobility data. Previously in transportation and tourism research detailed space-time activity data often relied on the time-space diary technique, which requires subjects to actively record their activities in time and space. This is highly demanding for the participants and collaboration from the participants greatly affects the quality of data⁴.Modern technologies such as GPS and mobile communications have made possible the automatic collection of trajectory data. The data collected, however, is not ideal for modeling human space-time activities, limited by the accuracies of existing devices. There is also no readily available tool for efficient processing of the data for human behavior study. We present here a suite of methods and an integrated ArcGIS desktop-based visual interface for the pre-processing and spatiotemporal analyses of trajectory data. We provide examples of how such processing may be used to model human space-time activities, especially with error-rich pedestrian trajectory data, that could be useful in public health studies such as infectious disease transmission modeling.The procedure presented includes pre-processing, trajectory segmentation, activity space characterization, density estimation and visualization, and a few other exploratory analysis methods. Pre-processing is the cleaning of noisy raw trajectory data. We introduce an interactive visual pre-processing interface as well as an automatic module. Trajectory segmentation⁵ involves the identification of indoor and outdoor parts from pre-processed space-time tracks. Again, both interactive visual segmentation and automatic segmentation are supported. Segmented space-time tracks are then analyzed to derive characteristics of one''s activity space such as activity radius etc. Density estimation and visualization are used to examine large amount of trajectory data to model hot spots and interactions. We demonstrate both density surface mapping⁶ and density volume rendering⁷. We also include a couple of other exploratory data analyses (EDA) and visualizations tools, such as Google Earth animation support and connection analysis. The suite of analytical as well as visual methods presented in this paper may be applied to any trajectory data for space-time activity studies.     

Evaluating landscape quality with vegetation naturalness maps: an index and some inferences

Question: Can landscape quality be evaluated and compared with a single numerical value using vegetation maps? Location: Northern Apennines (Italy), ca. 44° N,10–11° E. Methods: Seven phytosociological vegetation maps (1:25000), which correspond to man's different impact on mountain landscapes, were considered. Syntaxa were classified into five degrees of naturalness: urbanized, agricultural, semi‐natural, sub‐natural, and natural. Vegetation maps showing naturalness were derived in a vectorial GIS. The degrees of naturalness were ordered according to increasing naturalness. If c₁ is the cumulative relative value of every mapped area of the degrees of naturalness, the sum of these cumulative values A =∑ c_i is is a measure of vegetation artificiality. Its maximum value is A_max= n‐1. The Index of Vegetation Naturalness IVN = 1 ‐A/A_max, ranging from 0 to 1. Our IVN is an extension of the ILC by Pizzolotto & Brandmayr (1996) due to the ordinal character of the vegetation classification into degrees of naturalness. The maps of vegetation naturalness were also analysed by two known metrics for the evaluation of landscape quality: TECI (Total Edge Contrast Index) and MSI (Mean Shape Index). Results: The case studies show that IVN is linearly correlated with decreasing area of urbanized and agricultural vegetation types as well as with increasing area of the highest degree of naturalness. Conclusions: IVN can be joined with the TECI for the evaluation of naturalness of landscapes. TECI can supply additional information about the importance of landscape ecotones. Our case studies suggest that an urbanized landscape should correspond to IVN values lower than 0.20. A natural landscape will have IVN values higher than 0.80.     

Recent dynamics of the wet pastures at Oukaimeden plateau (High Atlas mountains, Morroco)

Wetlands are a very important universal heritage; in the Moroccan High Atlas mountains the geomorphology and the slopes evolution determine the existence of the numerous projecting ledges of pozzines on plateaus and oozing. This is particularly the case in Oukaimeden site located at 75 km south of Marrakesh. These wet grasslands have been developed on small surface areas supported by soils water accumulation; and close to the sources and on some banks constitute wetlands of high floristic richness, which makes them very coveted pastures by local populations. These wet pastures are highly sensitive to climatic factors. Recent climatic changes, the anthropic and pastoral pressures and the global environmental changes, affect these zones at the level of their structure (areas, soil characteristics…) and their ecological functions. In the Oukaimeden plateau, the use of aerial photography made it possible to carry out a detailed follow up of the hydraulic adjustments (dam construction in the 1970s) as well as the setting up of new adjustments (tracks, paths, drain channel) on the structure and functioning of these grasslands.     

Ginkgo,a multivariate analysis package

Abstract. The Ginkgo software is a subset of the VegAna (for Vegetation edition and Analysis) package that contains three programs named Quercus, Fagus and Yucca. Ginkgo is a multivariate analysis tool; it is oriented mainly towards ordination and classification of ecological data. Quercus is a relevé table editor; it handles community data to perform a phytosociological analysis. Fagus is a floristic citation editor; it can handle data coming from field surveys, bibliographic sources or collections. Yucca is a cartographic tool; it allows plotting distributions of taxa or syntaxa. VegAna is produced by the Department of Vegetal Biology, University of Barcelona. The general project is directed by Xavier Font I Castell, the Ginkgo module by Francesc Oliva I Cuyàs. Programmers are Miquel De Cáceres and Richard Garcia. This review deals primarily with Ginkgo.     

Detecting complex relations among vegetation,soil and geomorphology. An in-depth method applied to a case study in the Apennines (Italy)

Physical environment is the ruling factor of vegetation patterns in mountain areas, where vegetation mosaics are determined by a complex interplay among topography, geomorphology and soil. A deep analysis of such interplay is pivotal in order to build vegetation anamnesis and make sound projections. Instead, even recent cartographic models are still linked to standard statistical methods which are not on top of an efficient uncovering of knotty associations among these kinds of data. To this aim, in this study we propose a novel approach for: (a) assessing the associations among vegetation, soil, topography and geomorphology; (b) measuring the frequency and strength of these associations; (c) define in a rigorous way land units based on vegetation–soil–geomorphology associations; (d) advance hypotheses on the causes and prospects of the existing spatial pattern. In order to test the strength of the proposed methodology we applied it to a case study in the above-tree-line glacial cirque of Mount Prado (Northern Apennines, N Italy). In this area, the vegetation mosaic is still strongly conditioned by physical features but in a lower measure with respect to the higher alpine sites. We have been able to detect and weight 168 kinds of associations among vegetation, soil and geomorphological types, 1092 kinds of associations among vegetation and topographic variables and 12 land units with inner dominance of a particular association. The analysis of associations between vegetation types, soils, topography and landforms produced considerable insights into the ecology of the occurring plant communities. This proposed analytic methodology can be extended to other regions (e.g. mountain and alpine areas) and can also be considered a tool for interpreting present landscape heterogeneity also in a historical perspective.