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

Modeling viable mammal populations in gap analyses

Gap analysis is an approach to conserving biological diversity that maps species richness and identifies sites that ought to be protected but are not in conservation networks. Gap analyses based on species richness may have high error rates when species models are based solely on species-habitat association, because patches too small to support populations are still considered to be potential habitat. We incorporated information on the home range and dispersal distances of the mammals of Florida to estimate minimum critical areas (MCA) to support minimum viable populations for each mammal species. Incorporating MCA decreases the area occupied by the highest levels of species richness and alters the mapped spacial distribution of potential species richness. For example, in St. Lucie and Okeechobee counties, Florida, the total area occupied by 15 or more species was 30,448 ha under simple mammal-habitat association models, but only 7820 ha under model conditions incorporating MCA. This reflects the fragmented condition of many landscapes, where most patches are too small to support viable populations of larger species. Incorporating minimum area requirements into maps of potential species richness produces more conservative and defensible maps

The spatial distribution of diversity between disparate taxa: Spatial correspondence between mammals and ants across South Florida, USA

Gap Analysis takes a proactive landscape-level approach to conserving native species by identifying nodes of high biological diversity. It uses vertebrate species richness as an index of overall biological diversity. However, it remains unknownwhether or not the spatial distribution of vertebrate diversity correspondswith the diversity of other taxa. We tested whether landscape-level diversity patterns corresponded between a vertebrate and an invertebrate taxon, mammals and ants, across the southern half of the Florida peninsula, USA. Composite digital maps with a 30-m spatial resolution were produced for each taxon. Spatial correspondence between the taxa was determined by normalizing and then subtracting the composite maps. There were large areas of spatial correspondence – indicating that richness between mammals and ants was similar over much of southern Florida. However, spatial correspondence occurred where the richness of both taxa was low or moderate, and areas with the highest species richness (highest 20%) for each taxon, the explicit focus of Gap Analyses, corresponded over only 8752 ha. Gap Analysis provides a much needed assessment of landscape-level diversity patterns and proactive reserve design, but it must be explicit that the results are applicable for vertebrate diversity, which does not necessarily correspond with diversity patterns of other taxa. The two taxa investigated differ by orders of magnitude in the scale that they perceive their environment, and it is likely that diversity hotspots vary as the scale of investigation – and the taxa mapped – vary

Assessing state-wide biodiversity in the Florida Gap analysis project

The Florida Gap (FI-Gap) project provides an assessment of the degree to which native animal species and natural communities are or are not represented in existing conservation lands. Those species and communities not adequately represented in areas being managed for native species constitute \u27gaps\u27 in the existing network of conservation lands. The United States Geological Survey Gap Analysis Program is a national effort and so, eventually, all 50 states will have completed it. The objective of FI-Gap was to provide broad geographic information on the status of terrestrial vertebrates, butterflies, skippers and ants and their respective habitats to address the loss of biological diversity. To model the distributions and potential habitat of all terrestrial species of mammals, breeding birds, reptiles, amphibians, butterflies, skippers and ants in Florida, natural land cover was mapped to the level of dominant or co-dominant plant species. Land cover was classified from Landsat Thematic Mapper (TM) satellite imagery and auxiliary data such as the national wetlands inventory (NWI), soils maps, aerial imagery, existing land use/land cover maps, and on-the-ground surveys. Wildlife distribution models were produced by identifying suitable habitat for each species within that species\u27 range. Mammalian models also assessed a minimum critical area required for sustainability of the species\u27 population. Wildlife species richness was summarized against land stewardship ranked by an area\u27s mandates for conservation protection