Sensitivity of predictive species distribution models to change in grain size

Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.

A new automated method versus continuous positive airway pressure method for measuring pressure-volume curves in patients with acute lung injury

Objective: To compare pressure–volume (P–V) curves obtained with the Galileo ventilator with those obtained with the CPAP method in patients with ALI or ARDS receiving mechanical ventilation. P–V curves were fitted to a sigmoidal equation with a mean R2 of 0.994 ± 0.003. Lower (LIP) and upper inflection (UIP), and deflation maximum curvature (PMC) points calculated from the fitted variables showed a good correlation between methods with high intraclass correlation coefficients. Bias and limits of agreement for LIP, UIP and PMC obtained with the two methods in the same patient were clinically acceptable.

Final Report of the Empowerment of Nurses and Midwives Steering Group - An Agenda for Change

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Adoption legislation: 2003 consultation and proposals for change

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Where will conflicts between alien and rare species occur after climate and land-use change? A test with a novel combined modelling approach

Protecting native biodiversity against alien invasive species requires powerful methods to anticipate these invasions and to protect native species assumed to be at risk. Here, we describe how species distribution models (SDMs) can be used to identify areas predicted as suitable for rare native species and also predicted as highly susceptible to invasion by alien species, at present and under future climate and land-use scenarios. To assess the condition and dynamics of such conflicts, we developed a combined predictive modelling (CPM) approach, which predicts species distributions by combining two SDMs fitted using subsets of predictors classified as acting at either regional or local scales. We illustrate the CPM approach for an alien invader and a rare species associated to similar habitats in northwest Portugal. Combined models predict a wider variety of potential species responses, providing more informative projections of species distributions and future dynamics than traditional, non-combined models. They also provide more informative insight regarding current and future rare-invasive conflict areas. For our studied species, conflict areas of highest conservation relevance are predicted to decrease over the next decade, supporting previous reports that some invasive species may contract their geographic range and impact due to climate change. More generally, our results highlight the more informative character of the combined approach to address practical issues in conservation and management programs, especially those aimed at mitigating the impact of invasive plants, land-use and climate changes in sensitive regions

Perfusion ct based thrombolysis in acute ischaemic stroke

Background Despite use in clinical practice and trials of thrombolysis, a non-contrast CT is not sensitive for identifying penumbral tissue in acute stroke. This study evaluated how it compares with physiological imaging using CT perfusion.Methods 40 imaging datasets with non-contrast CT (NCCT) and perfusion CT (CTP) were retrospectively identified. 2 sets of observers (n¼6) and a neuroradiologist made a blind evaluation of the images. Inter-observer agreement was calculated for identifying ischaemic change on NCCT, and abnormalities on cerebral blood flow, time to peak and cerebral blood volume maps. A prospective cohort of 73 patients with anterior circulation cortical strokes were thrombolysed based on qualitative assessment of penumbral tissue on CTP within 3 h of stroke onset. Functional outcome was assessed at 3 months.Results Inter-rater agreement was moderate (k¼0.54) for early ischaemic change on NCCT. Perfusion maps improved this to substantial for deficit in cerebral blood volume (k¼0.67) and almost perfect for time to peak and cerebral blood flow (both k¼0.87). In the prospective arm, 58.9% of patients with cortical strokes were thrombolysed. There was no significant difference in attainment of complete recovery (p¼0.184) between the thrombolysed and nonthrombolysed group.Conclusions We demonstrate how perfusion CT aids clinical decision- making in acute stroke. Good functional outcomes from thrombolysis can be safely achieved using this physiologically informed approach.

A Vision for Change - Report of the Expert Group on Mental Health Policy

This Report proposes a framework of mental health service delivery with the service user at its centre. The emphasis is firmly on recovery and on facilitating active partnerships between service users, carers and mental health  professionals. Its recommendations are innovative and some of them are challenging. However, I have nodoubt that their implementation will bring about farreaching change and modernisation in the Irish mentalhealth services, which will be to the benefit of everyone concerned.” Tim O'Malley T.D.Minister of State at the Department of Health & Childrenwith special responsibility for mental health Download the Report (PDF, 1mb)

First Annual Report of the Independent Monitoring Group on "A Vision for Change"

First Annual Report of the Independent Monitoring Group on “A Vision for Change” In January 2006, the Government adopted the Report of the Expert Group on Mental Health Policy "A Vision for Change"Âù as the basis for the future development of mental health services. In March 2006, the Minister of State at the Department of Health and Children, Mr Tim Oâ?TMalley, T.D., with special responsibility for mental health services, established the independent Monitoring Group to monitor progress on the implementation of the report recommendations. Click here to download PDF 255kb

2nd Report of the Independent Monitoring Group on A Vision For Change

This is the Second Annual Report of the Independent Monitoring Group for A Vision for Change the Report of the Expert Group on Mental Health Policy. The Monitoring Group was established in March 2006 to monitor and assess progress on the implementation of A Vision for Change. In this Second Report, the Monitoring Group has found that by and large the recommendations in its first report were not addressed in 2007, although some have been prioritised for implementation in 2008. Download document here

Third Annual Report of the Independent Monitoring Group for A Vision for Change - the Report of the Expert Group on Mental Health Policy - April 2009

Third Annual Report of the Independent Monitoring Group for A Vision for Change – the Report of the Expert Group on Mental Health Policy – April 2009 Click here to download PDF 322kb

Fourth Annual Report of the Independent Monitoring Group for A Vision for Change - the Report of the Expert Group on Mental Health Policy - June 2010

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Facilitation Process Concerning the Difficulties in Implementing A Vision For Change in the South Tipperary and Carlow Kilkenny Catchment Area Mental Health Service

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Combining probabilistic land-use change and tree population dynamics modelling to simulate responses in mountain forests

Altitudinal tree lines are mainly constrained by temperature, but can also be influenced by factors such as human activity, particularly in the European Alps, where centuries of agricultural use have affected the tree-line. Over the last decades this trend has been reversed due to changing agricultural practices and land-abandonment. We aimed to combine a statistical land-abandonment model with a forest dynamics model, to take into account the combined effects of climate and human land-use on the Alpine tree-line in Switzerland. Land-abandonment probability was expressed by a logistic regression function of degree-day sum, distance from forest edge, soil stoniness, slope, proportion of employees in the secondary and tertiary sectors, proportion of commuters and proportion of full-time farms. This was implemented in the TreeMig spatio-temporal forest model. Distance from forest edge and degree-day sum vary through feed-back from the dynamics part of TreeMig and climate change scenarios, while the other variables remain constant for each grid cell over time. The new model, TreeMig-LAb, was tested on theoretical landscapes, where the variables in the land-abandonment model were varied one by one. This confirmed the strong influence of distance from forest and slope on the abandonment probability. Degree-day sum has a more complex role, with opposite influences on land-abandonment and forest growth. TreeMig-LAb was also applied to a case study area in the Upper Engadine (Swiss Alps), along with a model where abandonment probability was a constant. Two scenarios were used: natural succession only (100% probability) and a probability of abandonment based on past transition proportions in that area (2.1% per decade). The former showed new forest growing in all but the highest-altitude locations. The latter was more realistic as to numbers of newly forested cells, but their location was random and the resulting landscape heterogeneous. Using the logistic regression model gave results consistent with observed patterns of land-abandonment: existing forests expanded and gaps closed, leading to an increasingly homogeneous landscape.