Regional patterns of agricultural land use and deforestation in Colombia

An expanding human population and associated demands for goods and services continues to exert an increasing pressure on ecological systems. Although the rate of expansion of agricultural lands has slowed since 1960, rapid deforestation still occurs in many tropical countries, including Colombia. However, the location and extent of deforestation and associated ecological impacts within tropical countries is often not well known. The primary aim of this study was to obtain an understanding of the spatial patterns of forest conversion for agricultural land uses in Colombia. We modeled native forest conversion in Colombia at regional and national-levels using logistic regression and classification trees. We investigated the impact of ignoring the regional variability of model parameters, and identified biophysical and socioeconomic factors that best explain the current spatial pattern and inter-regional variation in forest cover. We validated our predictions for the Amazon region using MODIS satellite imagery. The regional-level classification tree that accounted for regional heterogeneity had the greatest discrimination ability. Factors related to accessibility (distance to roads and towns) were related to the presence of forest cover, although this relationship varied regionally. In order to identify areas with a high risk of deforestation, we used predictions from the best model, refined by areas with rural population growth rates of > 2%. We ranked forest ecosystem types in terms of levels of threat of conversion. Our results provide useful inputs to planning for biodiversity conservation in Colombia, by identifying areas and ecosystem types that are vulnerable to deforestation. Several of the predicted deforestation hotspots coincide with areas that are outstanding in terms of biodiversity value.

Biodiversity conservation planning tools: Present status and challenges for the future

Species extinctions and the deterioration of other biodiversity features worldwide have led to the adoption of systematic conservation planning in many regions of the world. As a consequence, various software tools for conservation planning have been developed over the past twenty years. These, tools implement algorithms designed to identify conservation area networks for the representation and persistence of biodiversity features. Budgetary, ethical, and other sociopolitical constraints dictate that the prioritized sites represent biodiversity with minimum impact on human interests. Planning tools are typically also used to satisfy these criteria. This chapter reviews both the concepts and technical choices that underlie the development of these tools. Conservation planning problems can be formulated as optimization problems, and we evaluate the suitability of different algorithms for their solution. Finally, we also review some key issues associated with the use of these tools, such as computational efficiency, the effectiveness of taxa and abiotic parameters at choosing surrogates for biodiversity, the process of setting explicit targets of representation for biodiversity surrogates, and

Situating Learning: (Re)examining the Notion of apprenticeship in Coach Education

The last two decades has seen a proliferation in the provision of and importance attached to coach education in many Western countries. Pivotal to many coach education programmes is the notion of apprenticeship. Increasingly, mentoring is being positioned as a possible tool for enhancing coach education and professional expertise. However, there is a paucity of empirical data on interventions in and evaluations of coach education programmes. In their recent evaluation of a coach education programme, Cassidy, Potrac & McKenzie conclude that the situated learning literature could provide coach educators with a generative platform for the (re)examination of apprenticeships and mentoring in a coach education context. This paper discusses the merits of using Situated Learning theory and the associated concept of Communities of Practice (CoP) to stimulate discussion on developing new understandings of the practices of apprenticeship and mentoring in coach education.

Conservation planning with irreplaceability: does the method matter?

A number of systematic conservation planning tools are available to aid in making land use decisions. Given the increasing worldwide use and application of reserve design tools, including measures of site irreplaceability, it is essential that methodological differences and their potential effect on conservation planning outcomes are understood. We compared the irreplaceability of sites for protecting ecosystems within the Brigalow Belt Bioregion, Queensland, Australia, using two alternative reserve system design tools, Marxan and C-Plan. We set Marxan to generate multiple reserve systems that met targets with minimal area; the first scenario ignored spatial objectives, while the second selected compact groups of areas. Marxan calculates the irreplaceability of each site as the proportion of solutions in which it occurs for each of these set scenarios. In contrast, C-Plan uses a statistical estimate of irreplaceability as the likelihood that each site is needed in all combinations of sites that satisfy the targets. We found that sites containing rare ecosystems are almost always irreplaceable regardless of the method. Importantly, Marxan and C-Plan gave similar outcomes when spatial objectives were ignored. Marxan with a compactness objective defined twice as much area as irreplaceable, including many sites with relatively common ecosystems. However, targets for all ecosystems were met using a similar amount of area in C-Plan and Marxan, even with compactness. The importance of differences in the outcomes of using the two methods will depend on the question being addressed; in general, the use of two or more complementary tools is beneficial.

Assessment of two arsenic-contaminated drinking water mitigation interventions in Bangladesh

Arsenic is a carcinogen. In Bangladesh, there are over 10 million tube-wells of which about 50% have arsenic concentrations exceeding the WHO recommended guideline value of 10 μg/L for drinking water. This study aimed to evaluate the efficacy of two relatively inexpensive mitigation interventions, three-pitcher filters and dug-wells. A randomised controlled field trial was conducted in Natore. Six Hundred and forty participants, 60 clusters of 47 villages were included in the trial. Two hundred and six participants were selected for the control group, 218 participants for the dug-wells, and 216 participants for the three-pitcher filters. The average arsenic in the drinking water was 128 μg/L in the three-pitcher trial. Twelve months post intervention, about 30% of the filtered water samples were >50 μg/L whereas dug-well water was

Development of a clinical and echocardiographic score for assigning risk of major events after exercise and dobutamine echocardiograms

The prognostic value of exercise (EXE) and dobutamine echocardiograms (DbE) has been well defined in large studies. However, while risk is determined by both clinical and echo features, no simple means of combining these data has been defined. We sought to combine these data into risk scores. Methods. At 3 expert centers, 7650 pts underwent standard EXE (n=5211) and DbE (w2439) for evaluation of known or suspected CAD and were followed for up to 10 years (mean 5-2) for major events (death or myocardial infarction). A subgroup of 2953 EXE and 1025 DbE pts was randomly selected to develop separate multivariate models for prediction of events. After simplication of each model for clinical use, models were validated in the remaining EXE and DbE pts. ResuI1s. The total number of events was 200 in the EXE and 225 in the DbE pts, of which 58 and 99 events occurred in the respective testing groups. The following regression equations gave equivalent results I” the testing and validation groups for both EXE and DbE; DbE = (Age’O.02) + (DM’l .O) + (Low RPP’0.6) + ([CHF+lschemia+Scar]‘O.7) EXE = ([DM+CHF]‘O.S) + O.S(lschemla #) + l.B(Scar#) - (METS0.19) (where each categorical variable scored 1 when present and 0 when absent, Ischemia# = 1 for l-2 VD. 6 for 3 VD; Scar# = 1 for 1-2 VD, 1.7 for 3 VD). The table summarizes the scores and equivalent outcomes for EXE and DbE. Conclusions. Risk scores based on clinical and EXE or DbE results may be used to quantify the risk of events during follow-up.