Suchergebnisse

The purpose of this study is to evaluate the quality of African media coverage of the extractive industries. This sector plays prominently on the African political, economic, social and journalistic landscape, yet coverage of these industries remains a challenge for African journalists. The financial and technical aspects of the extractive sector are complex, and both governments and companies often have a vested interest in withholding information from journalists. Many reporters lack sufficient training, resources and/or journalistic freedom to publish accurate, well‐researched, in‐depth coverage. As a result, what are arguably some of the most critical industries on the continent operate in relative freedom from public scrutiny. By making a careful study of African media coverage of the extractive sector in three countries, this report hopes to identify key strengths and weaknesses in extractive industry reporting as well as opportunities for media support and the expansion or revision of current media training efforts. The discovery of oil in Uganda in 2006 and in Ghana in 2007 brought both a great deal of excitement and a considerable level of worry to these countries. While extractive resources (oil, gas and mining) are a leading source of wealth for many African countries, this wealth often leads to corruption and conflict. In fact, countries with abundant natural resources tend to fall below less resource‐wealthy countries in terms of human development, a paradox economist Richard Auty and subsequent researchers have labeled the "resource curse." This report is based on two premises: first, that if Africans are to benefit from the immense resource stores that lie beneath their soil, great efforts must be made toward transparency in how these resources are handled. Second, the call for transparency cannot come from non‐governmental organizations and individual political leaders alone. The African media have a critical responsibility to push toward government and corporate openness in the extractive industries. African media have made great strides since the latter half of the twentieth century, but they continue to struggle to fulfill their role as society's watchdog. Instead of keeping governments in check through enterprising stories or investigative pieces, most African media have instead been leashed by a confluence of factors. Lack of resources, government intimidation and interference, media ownership, revenue structures and declining educational quality all make the job of the African journalist difficult. To mitigate some of these constraints, local and international non‐governmental organizations (NGOs), media outlets and in some cases government agencies are training journalists to more fully understand the intricacies of the areas that they cover.

Due to socioeconomic differences, the accuracy and extent of reporting on the occurrence of native species differs among countries, which can impact the performance of species distribution models. We assessed the importance of geographical biases in occurrence data on model performance using Hydrilla verticillata as a case study. We used Maxent to predict potential North American distribution of the aquatic invasive macrophyte based upon training data from its native range. We produced a model using all available native range occurrence data, then explored the change in model performance produced by omitting subsets of training data based on political boundaries. We also compared those results with models trained on data from which a random sample of occurrence data was omitted from across the native range. Although most models accurately predicted the occurrence of H. verticillata in North America (AUC > 0.7600), data omissions influenced model predictions. Omitting data based on political boundaries resulted in larger shifts in model accuracy than omitting randomly selected occurrence data. For well-documented species like H. verticillata, missing records from single countries or ecoregions may minimally influence model predictions, but for species with fewer documented occurrences or poorly understood ranges, geographic biases could misguide predictions. Regardless of focal species, we recommend that future species distribution modeling efforts begin with a reflection on potential spatial biases of available occurrence data. Improved biodiversity surveillance and reporting will provide benefit not only in invaded ranges but also within under-reported and unexplored native ranges.