Relevance of the cell neighborhood size in landscape metrics evaluation and free or open source software implementations

Landscape metrics constitute one of the main tools for the study of the changes of the landscape and of the ecological structure of a region. The most popular software for landscape metrics evaluation is FRAGSTATS, which is free to use but does not have free or open source software (FOSS). Therefore, FOSS implementations, such as QGIS’s LecoS plugin and GRASS’ r.li modules suite, were developed. While metrics are defined in the same way, the “cell neighborhood” parameter, specifying the configuration of the moving window used for the analysis, is managed differently: FRAGSTATS can use values of 4 or 8 (8 is default), LecoS uses 8 and r.li 4. Tests were performed to evaluate the landscape metrics variability depending on the “cell neighborhood” values: some metrics, such as “edge density” and “landscape shape index”, do not change, other, for example “patch number”, “patch density”, and “mean patch area”, vary up to 100% for real maps and 500% for maps built to highlight this variation. A review of the scientific literature was carried out to check how often the value of the “cell neighborhood” parameter is explicitly declared. A method based on the “aggregation index” is proposed to estimate the effect of the uncertainty on the “cell neighborhood” parameter on landscape metrics for different map

ORTHORECTIFICATION OF A LARGE DATASET OF HISTORICAL AERIAL IMAGES: PROCEDURE AND PRECISION ASSESSMENT IN AN OPEN SOURCE ENVIRONMENT

The availability of data time series spanning a long period is crucial for landscape change analysis. A suitable dataset, both in terms of time span and information content, must be available for the use with a GIS.In Italy, one of the most important historical source of land cover analysis is the GAI (Gruppo Aereo Italiano) photogrammetric survey (“Volo GAI”) commissioned in 1954 by the Italian national mapping agency, Istituto Geografico Militare Italiano (IGMI).The survey covers the whole Italy, but so far only some Regions, namely Lombardia and Veneto, have carried out the image rectification and the successive analyses to map land cover and use.This work describes the process of image orthorectification of the Volo GAI images for the Province of Trento (Provincia Autonoma di Trento).Image orthorectification must be performed to transform the images in maps available for analysis. This procedure corrects the geometry according to the terrain surface described by a Digital Terrain Model (DTM) to create an image compatible with the cartographic projection in use.To this end, the orthorectification modules available in GRASS GIS have been used, with the advantage of using the same GIS environment which will be used for the landscape analysis. The dataset covering the whole Province contains almost 100 images, this paper presents the preliminary results of the orthorectification of a quarter of the images. A reduced dataset has been used to test the results obtained using different settings with respect to: digital image resolution, DTM resolution and number of Ground Control Points (GCPs) used for the external orientation.These preliminary tests show that for the average quality of the Volo GAI images scan resolution beyond 600 DPI and DTM resolution above 10 m do not provide significant improvements for orthorectification images. The minimum number of GCPs to guarantee the requested accuracy can vary from image to image, depending on the image quality and recognizable features position, but it is usually in the 15–20 points range

The underlying mechanisms for development of hypertension in the metabolic syndrome

High blood pressure is an important constituent of the metabolic syndrome. However, the underlying mechanisms for development of hypertension in the metabolic syndrome are very complicated and remain still obscure. Visceral/central obesity, insulin resistance, sympathetic overactivity, oxidative stress, endothelial dysfunction, activated renin-angiotensin system, increased inflammatory mediators, and obstructive sleep apnea have been suggested to be possible factors to develop hypertension in the metabolic syndrome. Here, we will discuss how these factors influence on development of hypertension in the metabolic syndrome

Renoprotective RAAS inhibition does not affect the association between worse renal function and higher plasma aldosterone levels

Abstract Background Aldosterone is elevated in chronic kidney disease (CKD) and may be involved in hypertension. Surprisingly, the determinants of the plasma aldosterone concentration (PAC) and its role in hypertension are not well studied in CKD. Therefore, we studied the determinants of aldosterone and its association with blood pressure in CKD patients. We also studied this during renin-angiotensin-aldosterone system inhibition (RAASi) to establish clinical relevance, as RAASi is the treatment of choice in CKD with albuminuria. Methods We performed a post-hoc analysis on data from a randomized controlled double blind cross-over trial in non-diabetic CKD patients (n = 33, creatinine clearance (CrCl) 85 (75–95) ml/min, proteinuria 3.2 (2.5–4.0) g/day). Patients were treated with losartan 100 mg (ARB), and ARB + hydrochlorothiazide 25 mg (HCT), during both a regular (200 ± 10 mmol Na+/day) and low (89 ± 8 mmol Na+/day) dietary sodium intake, in 6-week study periods. PAC data at the end of each study period were analyzed. The association between PAC and blood pressure was analyzed continuously, and according to PAC above or below the median. Results Lower CrCl was correlated with higher PAC during placebo as well as during ARB (β = −1.213, P = 0.008 and β = −1.090, P = 0.010). Higher PAC was not explained by high renin, illustrated by a comparable association between CrCl and the aldosterone-to-renin ratio. The association between lower CrCl and higher PAC was also found in a second study with single RAASi with ACE inhibition (ACEi; lisinopril 40 mg/day), and dual RAASi (lisinopril 40 mg/day + valsartan 320 mg/day). Higher PAC was associated with a higher systolic blood pressure (P = 0.010) during different study periods. Only during maximal treatment with ARB + HCT + dietary sodium restriction, blood pressure was no longer different in subjects with a PAC above and below the median. Conclusions In CKD patients with a standardized regular sodium intake, worse renal function is associated with a higher aldosterone, untreated and during RAASi with either ARB, ACEi, or both. Furthermore, higher aldosterone is associated with higher blood pressure, which can be treated with the combination of RAASi, HCT and dietary sodium restriction. The first study was performed before it was standard to register trials and the study was not retrospectively registered. The second study was registered in the Netherlands Trial Register on the 5th of May 2006 (NTR675)

Data from: Climate-related adaptive genetic variation and population structure in natural stands of Norway spruce in the South-Eastern Alps

Forest trees dominate many Alpine landscapes that are currently exposed to changing climate. Norway spruce is one of the most important conifer species of the Italian Alps, and natural populations are found across steep environmental gradients with large differences in temperature and moisture availability. This study seeks to determine and quantify patterns of genetic diversity in natural populations toward understanding adaptive responses to changing climate. Across the Italian species range, 24 natural stands were sampled with a major focus on the Eastern Italian Alps. Sampled trees were genotyped for 384 selected single nucleotide polymorphisms (SNPs) from 285 genes. A wide array of potential candidate genes was tested for correlation with climatic parameters. To minimize false-positive association between genotype and climate, population structure was investigated. Pairwise F ST estimates between sampled populations ranged between 0.000 and 0.075, with the highest values involving the two disjoint populations, Valdieri, on the western Italian Alps, and Campolino, the most southern population on the Apennines. Despite considerable genetic admixture among populations, both Bayesian and multivariate approach identified four genetic clusters. Selection scans revealed five F ST outliers, and the environmental association analysis detected ten SNPs associated to one or more climatic variables. Overall, 13 potentially adaptive loci were identified, three of which have been reported in a previous study on the same species conducted on a broader geographical scale. In our study, precipitation, more than temperature, was often associated with genotype; therefore, it appears as the most important environmental variable associated with the high sensitivity of Norway spruce to soil water supply. These findings provide relevant information for understanding and quantifying climate change effects on this species and its ability to genetically adapt

A multi-temporal approach in MaxEnt modelling: a new frontier for land use/land cover change detection

Land-cover change, a major driver of the distribution and functioning of ecosystems, is characterized by a high diversity of patterns of change across space and time. Thus, a large amount of information is necessary to analyse change and develop plans for proper management of natural resources. In this work we tested MaxEnt algorithm in a completely remote land-cover classification and change analysis. In order to provide an empirical example, we selected south-eastern Italian Alps, manly Trentino-South Tyrol, as test region. We classified two Landsat images (1976 and 2001) in order to forecast probability of occurrence for unsampled locations and to determine the best subset of predictors (spectral bands). A difference map for each land cover class, representing the difference between 1976 and 2001 probability of occurrence values, was built. In order to better address the patterns of change analysis, we put together difference maps and topographic variables. The latter are considered, at least in the study area, as the main environmental drivers of land-use change, in connection with climate change. Our results indicate that the selected algorithm, applied to land cover classes, can provide reliable data, especially when referring to classes with homogeneous texture properties and surface reflectance. The performed models had satisfactory predictive performance, showing relatively clear patterns of difference between the two considered time steps. The development of a methodology that, in the absence of field data, allow to obtain data on land use change dynamics, is of extreme importance for land planning and management