Quantifying spatial non-stationarity in the relationship between landscape structure and the provision of ecosystem services: An example in the New Zealand hill country.

Knowing how landscape structure affects the provision of ecosystem services (ES) is an important first step toward better landscape planning. Because landscape structure is often heterogenous across space, modelling the relationship between landscape structure and the provision of ES must account for spatial non-stationarity. This paper examines the relationship between landscape structure and the provision of ES using a hill country and steep-land case farm in New Zealand. Indicators derived from land cover and topographical data such as Largest Patch Index (LPI), Contrast Class Edge (CCE), Edge Density (ED), and Terrain slope (SLOPE) were used to examine the landscape's structure and pattern. Measures of pasture productivity, soil erosion control, and water supply were derived with InVEST tools and spatial analysis in a GIS. Multiscale Geographically Weighted Regression (MGWR) was used to evaluate the relationship between indicators of landscape structure and the provisioning of ES. Other regression models, including Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR), were carried out to evaluate the performance of MGWR. Results showed that landscape patterns significantly affect the supply of all mapped ES, and this varies across the landscape, dependent on the pattern of topographical features and land cover pattern and structure. MWGR outperformed other OLS and GWR in terms of explanatory power of the ES determinants and had a better ability to deal with the presence of spatial autocorrelation. Spatially and quantitatively detailed variations of the relationship between landscape structure and the provision of ES provide a scientific basis to inform the design of sustainable multifunctional landscapes. Information derived from this analysis can be used for spatial planning of farmed landscapes to promote multiple ES which meet multiple sustainable development objectives.

Impact of a hypoglycemia reduction bundle and a systems approach to inpatient glycemic management.

OBJECTIVE: Uncontrolled hyperglycemia and iatrogenic hypoglycemia represent common and frequently preventable quality and safety issues. We sought to demonstrate the effectiveness of a hypoglycemia reduction bundle, proactive surveillance of glycemic outliers, and an interdisciplinary data-driven approach to glycemic management. POPULATION: all hospitalized adult non-intensive care unit (non-ICU) patients with hyperglycemia and/or a diagnosis of diabetes admitted to our 550-bed academic center across 5 calendar years (CYs). INTERVENTIONS: hypoglycemia reduction bundle targeting most common remediable contributors to iatrogenic hypoglycemia; clinical decision support in standardized order sets and glucose management pages; measure-vention (daily measurement of glycemic outliers with concurrent intervention by the inpatient diabetes team); educational programs. MEASURES AND ANALYSIS: Pearson chi-square value with relative risks (RRs) and 95% confidence intervals (CIs) were calculated to compare glycemic control, hypoglycemia, and hypoglycemia management parameters across the baseline time period (TP1, CY 2009-2010), transitional (TP2, CY 2011-2012), and mature postintervention phase (TP3, CY 2013). Hypoglycemia defined as blood glucose <70 mg/dL, severe hypoglycemia as <40 mg/dL, and severe hyperglycemia >299 mg/dL. RESULTS: A total of 22,990 non-ICU patients, representing 94,900 patient-days of observation were included over the 5-year study. The RR TP3:TP1 for glycemic excursions was reduced significantly: hypoglycemic stay, 0.71 (95% CI, 0.65 to 0.79); severe hypoglycemic stay, 0.44 (95% CI, 0.34 to 0.58); recurrent hypoglycemic day during stay, 0.78 (95% CI, 0.64 to 0.94); severe hypoglycemic day, 0.48 (95% CI, 0.37 to 0.62); severe hyperglycemic day (>299 mg/dL), 0.76 (95% CI, 0.73 to 0.80). CONCLUSION: Hyperglycemia and hypoglycemia event rates were both improved, with the most marked effect on severe hypoglycemic events. Most of these interventions should be portable to other hospitals.

The use of oral sucrose for procedural pain relief in infants up to six months of age: a randomized controlled trial.

The aim of this study was to evaluate the effectiveness of oral sucrose in decreasing pain during minor procedures in infants of 1-6 months corrected age. A blinded randomized controlled trial with infants aged 4-26 weeks who underwent venipuncture, heel lance or intravenous cannulation were stratified by corrected age into > 4-12 weeks and > 12-26 weeks. They received 2 mL of either 25% sucrose or sterile water orally 2 minutes before the painful procedure. Nonnutritional sucking and parental comfort, provided in adherence to hospital guidelines, were recorded. Pain behavior was recorded using a validated 10 point scale at baseline, during and following the procedure. Data collectors were blinded to the intervention. A total of 21 and 20 infants received sucrose and water, respectively, in the > 4-12-week age group, and 21 and 22, respectively, in the > 12-26-week age group. No statistical differences were found in pain scores between treatment and control groups at any data collection points in either age group. Infants aged > 4-12 weeks who did nonnutritional sucking showed statistically significantly lower median pain scores at 1, 2, and 3 minutes after the procedure than those who did not suck. Infants aged > 4-26 weeks exhibited pain behavior scores that indicated moderate to large pain during painful procedures; however, there was insufficient evidence to show that 2 mL 25% sucrose had a statistically significant effect in decreasing pain. Infants should be offered nonnutritional sucking in compliance with the Baby Friendly Health Initiative during painful procedures.

Assisting Australian indigenous resource management and sustainable utilization of species through the use of GIS and environmental modeling techniques.

Information on distribution and relative abundance of species is integral to sustainable management, especially if they are to be harvested for subsistence or commerce. In northern Australia, natural landscapes are vast, centers of population few, access is difficult, and Aboriginal resource centers and communities have limited funds and infrastructure. Consequently defining distribution and relative abundance by comprehensive ground survey is difficult and expensive. This highlights the need for simple, cheap, automated methodologies to predict the distribution of species in use, or having potential for use, in commercial enterprise. The technique applied here uses a Geographic Information System (GIS) to make predictions of probability of occurrence using an inductive modeling technique based on Bayes' theorem. The study area is in the Maningrida region, central Arnhem Land, in the Northern Territory, Australia. The species examined, Cycas arnhemica and Brachychiton diversifolius, are currently being 'wild harvested' in commercial trials, involving sale of decorative plants and use as carving wood, respectively. This study involved limited and relatively simple ground surveys requiring approximately 7 days of effort for each species. The overall model performance was evaluated using Cohen's kappa statistics. The predictive ability of the model for C. arnhemica was classified as moderate and for B. diversifolius as fair. The difference in model performance can be attributed to the pattern of distribution of these species. C. arnhemica tends to occur in a clumped distribution due to relatively short distance dispersal of its large seeds and vegetative growth from long-lived rhizomes, while B. diversifolius seeds are smaller and more widely dispersed across the landscape. The output from analysis predicts trends in species distribution that are consistent with independent on-site sampling for each species and therefore should prove useful in gauging the extent of resource availability. However, some caution needs to be applied as the models tend to over predict presence which is a function of distribution patterns and of other variables operating in the landscape such as fire histories which were not included in the model due to limited availability of data.

The application of local measures of spatial autocorrelation for describing pattern in north Australian landscapes.

This paper tests the use of a spatial analysis technique, based on the calculation of local spatial autocorrelation, as a possible approach for modelling and quantifying structure in northern Australian savanna landscapes. Unlike many landscapes in the world, northern Australian savanna landscapes appear on the surface to be intact. They have not experienced the same large-scale land clearance and intensive land management as other landscapes across Australia. Despite this, natural resource managers are beginning to notice that processes are breaking down and declines in species are becoming more evident. With future declines of species looking more imminent it is particularly important that models are available that can help to assess landscape health, and quantify any structural change that takes place. GIS and landscape ecology provide a useful way of describing landscapes both spatially and temporally and have proved to be particularly useful for understanding vegetation structure or pattern in landscapes across the world. There are many measures that examine spatial structure in the landscape and most of these are now available in a GIS environment (e.g. FRAGSTATS* ARC, r.le, and Patch Analyst). All these methods depend on a landscape described in terms of patches, corridors and matrix. However, since landscapes in northern Australia appear to be relatively intact they tend to exist as surfaces of continuous variation rather than in clearly defined homogeneous units. As a result they cannot be easily described using entity-based models requiring patches and other essentially cartographic approaches. This means that more appropriate methods need to be developed and explored. The approach examined in this paper enables clustering and local pattern in the data to be identified and forms a generic method for conceptualising the landscape structure where patches are not obvious and where boundaries between landscape features are difficult to determine. Two sites are examined using this approach. They have been exposed to different degrees of disturbance by fire and grazing. The results show that savanna landscapes are very complex and that even where there is a high degree of disturbance the landscape is still relatively heterogeneous. This means that treating savanna landscapes as being made up of homogeneous units can limit analysis of pattern, as it can over simplify the structure present, and that methods such as the autocorrelation approach are useful tools for quantifying the variable nature of these landscapes.