A multiscale analysis of ecosystem services supply in the NW Iberian Peninsula from a functional perspective

In recent years, the assessment of ecosystem services (ES) supply has been based on the use of Land Use/Land Cover (LULC) data as proxies for spatial representation of ecosystems. Nevertheless, some shortcomings of this method, such as uncertainties derived from generalization of the ecosystem types and assumptions of invariance across spatial scales, indicate the need for new approaches. Such approaches could be aimed at improving knowledge of the relationships between ecosystem services and landscape structure and the spatial characteristics of ES patterns. In this study, we propose an integrative approach that involves the generation and analysis of continuous maps representing the supply of five ES potentially related to the amount of biomass. Five remote sensing images of the Northwestern Iberian Peninsula, obtained with Landsat-5 TM, were used to generate a proxy for net primary production by combining the normalized difference vegetation index (NDVI) of each image to calculate a ΣNDVI index that could act as a potential indicator of some ecosystem services. This information was combined with three variables – terrain slope, population density and occurrence of protected areas – to produce spatial models for the five ES and eventually a series of five supply maps. Food, materials and energy provision services showed a clustered pattern, with high supply values in flat zones and areas with high population densities. In contrast, mass flow and climate regulation services were more widely distributed throughout the study area. The five ecosystem service patterns were analyzed at different scales by two methods: lacunarity and four term local quadrat variance (4TLQV) analysis. These methods revealed differences in the spatial pattern: lacunarity analysis was useful for detection of scale thresholds at the local level, whereas 4TLQV was more sensitive to scale thresholds at larger spatial levels. Thus, the variance analysis yielded higher values for larger windows sizes, particularly for provisioning services. The results demonstrated the suitability of the proposed approach for the spatially explicit modeling of ecosystem services, avoiding the uncertainty of other assessments such as those based on LULC data, and for the exploratory analysis of ES supply from a spatial point of view.     

Modelling biomass of mountainous grasslands by including a species composition map

High mountain grasslands offer multiple goods and services to society but are severely threatened by improper land use practices such as abandonment or rapid intensification. In order to reduce abandonment and strengthen the common extensive agricultural practice a sustainable land use management of high mountain grasslands is needed. A spatially detailed yield assessment helps to identify possible meadows or, on the contrary, areas with a low carrying capacity in a region, making it easier to manage these sites. Such assessments are rarely available for remote and inaccessible areas. Remotely sensed vegetation indices are able to provide valuable information on grassland properties. These indices tend, however, to saturate for high biomass. This affects their applicability to assessments of high-yield grasslands.The main aim of this study was to model a spatially explicit grassland yield map and to test whether saturation issues can be tackled by consideration of plant species composition in the modelling process. The high mountain grassland of the subalpine belt (1800 – 2500 m a.s.l.) in the Kazbegi region, Greater Caucasus, Georgia, was chosen as test site for its strong species composition and yield gradients.We first modelled the species composition of the grassland described as metrically scaled gradients in the form of ordination axes by random forest regression. We then derived vegetation indices from Rapid Eye imagery, and topographic variables from a digital elevation model, which we used together with the multispectral bands as predictive variables. For comparison, we performed two yield models, one excluding the species composition maps and one including the species composition map as predictors. Moreover, we performed a third individual model, with species composition as predictors and a split dataset, to produce the final yield map.Three main grassland types were found in the vegetation analysis: Hordeum violaceum-meadows, Gentianella caucasea-grassland and Astragalus captiosus-grassland. The three random forest regression models for the ordination axes explained 64%, 33% and 46% of the variance in species composition. Independent validation of modelled ordination scores against a validation data set resulted in an R² of 0.64, 0.32 and 0.46 for the first, second and third axes, respectively. The model based on species composition resulted in a R² = 0.55, whereas the benchmark model showed weaker relationships between yield and the multispectral reflectance, vegetation indices, and topographical parameters (R² = 0.42). The final random forest yield model used to derive the yield map resulted in 62% variance explained and an R² = 0.64 between predicted and observed biomass. The results further indicate that high yields are generally difficult to predict with both models.The benefit of including a species composition map as a predictor variable for grassland yield lies in the preservation of ecologically meaningful features, especially the occurrence of high yielding vegetation type of Hordeum violaceum meadows is depicted accurately in the map. Even though we used a gradient based design, sharp boundaries or immediate changes in productivity were visible, especially in small structures such as arable fields or roads (Fig. 6b), making it a valuable tool for sustainable land use management. The saturation effect however, was mitigated by using species composition as predictor variables but is still present at high yields.     

Design and Analysis of Field Experiments Incorporating Local and Remote Effects of Treatments

Design and analysis of field experiments in the presence of local and remote treatment effects are considered. The design criteria are optimality and balance for estimating local and remote effects. A nonlinear model, where remote effects are assumed to be proportional to the local effects, has been discussed.     

GLUT2 in pancreatic and extra-pancreatic gluco-detection

Detection of variations in blood glucose concentrations by pancreatic &#103 -cells and a subsequent appropriate secretion of insulin are key events in the control of glucose homeostasis. Because a decreased capability to sense glycemic changes is a hallmark of type 2 diabetes, the glucose signalling pathway leading to insulin secretion in pancreatic &#103 -cells has been extensively studied. This signalling mechanism depends on glucose metabolism and requires the presence of specific molecules such as GLUT2, glucokinase and the K ATP channel subunits Kir6.2 and SUR1. Other cells are also able to sense variations in glycemia or in local glucose concentrations and to modulate different physiological functions participating in the general control of glucose and energy homeostasis. These include cells forming the hepatoportal vein glucose sensor, which controls glucose storage in the liver, counterregulation, food intake and glucose utilization by peripheral tissues and neurons in the hypothalamus and brainstem whose firing rates are modulated by local variations in glucose concentrations or, when not protected by a blood-brain barrier, directly by changes in blood glucose levels. These glucose-sensing neurons are involved in the control of insulin and glucagon secretion, food intake and energy expenditure. Here, recent physiological studies performed with GLUT2 -/- mice will be described, which indicate that this transporter is ess ential for glucose sensing by pancreatic &#103 -cells, by the hepatoportal sensor and by sensors, probably located centrally, which control activity of the autonomic nervous system and stimulate glucagon secretion. These studies may pave the way to a fine dissection of the molecular and cellular components of extra-pancreatic glucose sensors involved in the control of glucose and energy homeostasis.     

Snap,crackle, and pop: Acoustic-based model estimation of snapping shrimp populations in healthy and degraded hard-bottom habitats

Human use of the ocean and its ecosystems continues to degrade coastal habitats around the world. Assessing anthropogenic impacts on these environments can be costly and manpower-intensive; thus, the development of rapid, remote techniques to assess habitat quality is important. We employed autonomous hydrophone receivers to record the soundscapes of healthy, sponge-rich hard-bottom habitat in Florida Bay, Florida (USA) and hard-bottom areas impacted by sponge die-offs. We also recorded sounds emanating from individual sponges of three species that were isolated in underwater sound booths, and then enumerated the invertebrates (mostly snapping shrimp) dwelling within the canals of each sponge. From these recordings, a modified cylindrical sound propagation model was used to estimate distances to individual snapping shrimp snaps. Using the program Distance, we estimated snapping shrimp population density and abundance within both habitat types. More snapping shrimp snaps per unit time were recorded in healthy hard-bottom areas as compared to degraded hard-bottom areas. In addition, the average distance to a snap source was greater within degraded hard-bottom areas than within healthy hard-bottom areas. As a consequence, the estimated density and abundance of snapping shrimp were one to two orders of magnitude greater within healthy habitat than within degraded habitat. This study demonstrates the feasibility of using acoustic sampling and modeling to rapidly assess populations of soniferous benthic indicator species, whose vocalizations may yield indirect estimates of habitat quality.     

Zoning eco-environmental vulnerability for environmental management and protection

Eco-environmental vulnerability assessment is crucial for environmental and resource management. However, evaluation of eco-environmental vulnerability over large areas is a difficult and complex process because it is affected by many variables including hydro-meteorology, topography, land resources, and human activities. The Thua Thien – Hue Province and its largest river system, the Perfume River, are vital to the social-economic development of the north central coastal region of Vietnam, but there is no zoning system for environmental protection in this region. An assessment framework is proposed to evaluate the vulnerable eco-environment in association with 16 variables with 6 of them constructed from Landsat 8 satellite image products. The remaining variables were extracted from digital maps. Each variable was evaluated and spatially mapped with the aid of an analytical hierarchy process (AHP) and geographical information system (GIS). An eco-environmental vulnerability map is assorted into six vulnerability levels consisting of potential, slight, light, medium, heavy, and very heavy vulnerabilities, representing 14%, 27%, 17%, 26%, 13%, 3% of the study area, respectively. It is found that heavy and very heavy vulnerable areas appear mainly in the low and medium lands where social-economic activities have been developing rapidly. Tiny percentages of medium and heavy vulnerable levels occur in high land areas probably caused by agricultural practices in highlands, slash and burn cultivation and removal of natural forests with new plantation forests. Based on our results, three ecological zones requiring different development and protection solutions are proposed to restore local eco-environment toward sustainable development. The proposed integrated method of remote sensing (RS), GIS, and AHP to evaluate the eco-environmental vulnerability is useful for environmental protection and proper planning for land use and construction in the future.     

Acoustic mapping using a multibeam echosounder reveals cold-water coral reefs and surrounding habitats

Multibeam surveys were carried out in four areas to the west of Scotland where the coral Lophelia pertusa had previously been recorded. Distinctive seabed mounds were found in one area; video images from the mounds showed coral reef formation, and grab samples recovered L. pertusa reef framework and rubble. Skeleton samples were dated to 3,800 years BP. Grab samples contained 123 species of fauna. The reef structures, termed the Mingulay Reef Complex, were identified as topographic mound-like structures from the bathymetric data and were also visible on the backscatter images. The location of the reefs coincides with Atlantic bottom waters, close to a primary productivity centre and mixing zone, in an area where currents are likely to be accelerated by rocky seafloor ridges. This study shows that multibeam echosounders are powerful tools to locate and map deep-water coral reefs irrespective of water depth. Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.