Modelling biodiversity trends in the montado (wood pasture) landscapes of the Alentejo, Portugal

Abstract Context Montados are dynamic agroforestry systems of southern Portugal, with high economic and ecological values. Changes in land use and cover have important implications for landscape-level biodiversity and its conservation. Objectives Our objectives were to evaluate the biodiversity values and trends in a montado system in the Alentejo, Portugal so as to inform landscape level conservation approaches. In doing so, we aimed to develop a replicable and robust approach drawing together field observation, expert opinion, and remote sensing to produce predictions relevant to land management planning. Methods Field sampling and subsequent analysis of data on the birds, butterflies and plants in eight distinct land covers allowed the identification of two principal habitat groupings of importance: ‘montado mosaic’ and ‘shrubland’. Morphological spatial pattern analysis was performed on Landsat-derived GIS habitat layers for 1984 and 2009, generating maps and statistics for change in the different landscape functional classes. In addition, we demonstrated how the modelling of ecotones between open and closed biomes can identify the preferred hunting grounds of the threatened Iberian lynx and black vulture, flagship species whose conservation provides benefits to the area’s wider biodiversity values. Results Total and core area of montado mosaics and shrubland increased over the 25 year period, whilst the amount of habitat connectivity declined in the case of shrubland. Considerable local variation in these trends highlighted targetable areas for conservation action (e.g. through agri-environment spending). Conclusions A rapid and robust approach was demonstrated, with potentially wider utility for biodiversity assessment and planning

Below the canopy: global trends in forest vertebrate populations and their drivers

Global forest assessments use forest area as an indicator of biodiversity status, which may mask below-canopy pressures driving forest biodiversity loss and 'empty forest' syndrome. The status of forest biodiversity is important not only for species conservation but also because species loss can have consequences for forest health and carbon storage. We aimed to develop a global indicator of forest specialist vertebrate populations to improve assessments of forest biodiversity status. Using the Living Planet Index methodology, we developed a weighted composite Forest Specialist Index for the period 1970-2014. We then investigated potential correlates of forest vertebrate population change. We analysed the relationship between the average rate of change of forest vertebrate populations and satellite-derived tree cover trends, as well as other pressures. On average, forest vertebrate populations declined by 53% between 1970 and 2014. We found little evidence of a consistent global effect of tree cover change on forest vertebrate populations, but a significant negative effect of exploitation threat on forest specialists. In conclusion, we found that the forest area is a poor indicator of forest biodiversity status. For forest biodiversity to recover, conservation management needs to be informed by monitoring all threats to vertebrates, including those below the canopy

Rapid characterisation of vegetation structure to predict refugia and climate change impacts across a global biodiversity hotspot

Identification of refugia is an increasingly important adaptation strategy in conservation planning under rapid anthropogenic climate change. Granite outcrops (GOs) provide extraordinary diversity, including a wide range of taxa, vegetation types and habitats in the Southwest Australian Floristic Region (SWAFR). However, poor characterization of GOs limits the capacity of conservation planning for refugia under climate change. A novel means for the rapid identification of potential refugia is presented, based on the assessment of local-scale environment and vegetation structure in a wider region. This approach was tested on GOs across the SWAFR. Airborne discrete return Light Detection And Ranging (LiDAR) data and Red Green and Blue (RGB) imagery were acquired. Vertical vegetation profiles were used to derive 54 structural classes. Structural vegetation types were described in three areas for supervised classification of a further 13 GOs across the region.Habitat descriptions based on 494 vegetation plots on and around these GOs were used to quantify relationships between environmental variables, ground cover and canopy height. The vegetation surrounding GOs is strongly related to structural vegetation types (Kappa = 0.8) and to its spatial context. Water gaining sites around GOs are characterized by taller and denser vegetation in all areas. The strong relationship between rainfall, soil-depth, and vegetation structure (R2 of 0.8–0.9) allowed comparisons of vegetation structure between current and future climate. Significant shifts in vegetation structural types were predicted and mapped for future climates. Water gaining areas below granite outcrops were identified as important putative refugia. A reduction in rainfall may be offset by the occurrence of deeper soil elsewhere on the outcrop. However, climate change interactions with fire and water table declines may render our conclusions conservative. The LiDAR-based mapping approach presented enables the integration of site-based biotic assessment with structural vegetation types for the rapid delineation and prioritization of key refugia

Effect of blood glucose level on standardized uptake value (SUV) in F-18- FDG PET-scan : a systematic review and meta-analysis of 20,807 individual SUV measurements

Objectives To evaluate the effect of pre-scan blood glucose levels (BGL) on standardized uptake value (SUV) in F-18-FDG-PET scan. Methods A literature review was performed in the MEDLINE, Embase, and Cochrane library databases. Multivariate regression analysis was performed on individual datum to investigate the correlation of BGL with SUVmax and SUVmean adjusting for sex, age, body mass index (BMI), diabetes mellitus diagnosis, F-18-FDG injected dose, and time interval. The ANOVA test was done to evaluate differences in SUVmax or SUVmean among five different BGL groups (200 mg/dl). Results Individual data for a total of 20,807 SUVmax and SUVmean measurements from 29 studies with 8380 patients was included in the analysis. Increased BGL is significantly correlated with decreased SUVmax and SUVmean in brain (p <0.001, p <0.001,) and muscle (p <0.001, p <0.001) and increased SUVmax and SUVmean in liver (p = 0.001, p = 0004) and blood pool (p=0.008, p200 mg/dl had significantly lower SUVmax. Conclusion If BGL is lower than 200mg/dl no interventions are needed for lowering BGL, unless the liver is the organ of interest. Future studies are needed to evaluate sensitivity and specificity of FDG-PET scan in diagnosis of malignant lesions in hyperglycemia.Peer reviewe

Applications of Free Energy Calculations to Chemistry and Biology.

International audienc

A Little Something for Me and Maybe for You, Too: Promotions that Relieve Guilt

Consumer promotions, Gift-with-purchase promotions, Consumption guilt,

Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment

Conservation and monitoring of forest biodiversity requires reliable information about forest structure and composition at multiple spatial scales. However, detailed data about forest habitat characteristics across large areas are often incomplete due to difficulties associated with field sampling methods. To overcome this limitation we employed a nationally available light detection and ranging (LiDAR) remote sensing dataset to develop variables describing forest landscape structure across a large environmental gradient in Switzerland. Using a model species indicative of structurally rich mountain forests (hazel grouse Bonasa bonasia), we tested the potential of such variables to predict species occurrence and evaluated the additional benefit of LiDAR data when used in combination with traditional, sample plot-based field variables. We calibrated boosted regression trees (BRT) models for both variable sets separately and in combination, and compared the models’ accuracies. While both field-based and LiDAR models performed well, combining the two data sources improved the accuracy of the species’ habitat model. The variables retained from the two datasets held different types of information: field variables mostly quantified food resources and cover in the field and shrub layer, LiDAR variables characterized heterogeneity of vegetation structure which correlated with field variables describing the understory and ground vegetation. When combined with data on forest vegetation composition from field surveys, LiDAR provides valuable complementary information for encompassing species niches more comprehensively. Thus, LiDAR bridges the gap between precise, locally restricted field-data and coarse digital land cover information by reliably identifying habitat structure and quality across large areas