Source and Source Relations

In seeking to understand the construction of news, how journalist-source relationships function is a core concern of journalism studies. These relationships are not necessarily a simple one-way transfer of information but can be a complex interaction that may require understanding of journalism practices, journalism ethics, media law, commerce and the state to elucidate. Normatively, whether identified, anonymous or confidential sources or whistle-blowers, sources can provide journalists with the means to challenge official and elite narratives. This entry details the type and nature of professional relationships between journalists and their personal sources and note the mounting threats to this vital practice. While there are laws to protect journalist’s sources, international organisations note they are at risk of erosion, restriction and compromise - a direct challenge to the universal human rights of freedom of expression and a threat to investigative journalism

Remote detection of invasive alien species

The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail

Do bioclimate variables improve performance of climate envelope models?

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models