Earth and Environmental Sciences

Carnivory is found in eight additional plant families besides the well-studied and speciose Droseraceae, Lentibulariaceae, Nepenthaceae, and Sarraceniaceae. These include six species-poor or monogeneric families (Drosophyllaceae, Dioncophyllaceae, Cephalotaceae, Roridulaceae, Byblidaceae); the carnivorous genus Philcoxia in the otherwise noncarnivorous, species-rich Plantaginaceae; and at least one species in each of three monocot genera in which carnivory is not a universal trait: Bromeliaceae (Brocchinia and Catopsis) and Eriocaulaceae (Paepalanthus). We review the current knowledge of these nine genera with a focus on their biology, ecology, and evolution. Although the small carnivorous genera are not diverse in terms of species number, they contain some of the most unique, peculiar, and ecologically novel carnivorous plants. The species include plants reliant upon digestive mutualism with insects; a carnivorous epiphyte; and a part-time carnivore.

Understanding the spatial distribution of human impacts on marine environments is necessary for maintaining healthy ecosystems and supporting 'blue economies'. Realistic assessments of impact must consider the cumulative impacts of multiple, coincident threats and the differing vulnerabilities of ecosystems to these threats. Expert knowledge is often used to assess impact in marine ecosystems because empirical data are lacking; however, this introduces uncertainty into the results. As part of a spatial cumulative impact assessment for Spencer Gulf, South Australia, we asked experts to estimate score ranges (best-case, most-likely and worst-case), which accounted for their uncertainty about the effect of 32 threats on eight ecosystems. Expert scores were combined with data on the spatial pattern and intensity of threats to generate cumulative impact maps based on each of the three scoring scenarios, as well as simulations and maps of uncertainty. We compared our method, which...

Bonamia spp. parasites threaten flat oyster (Ostrea spp.) farming worldwide. Understanding test performance is important for designing surveillance and interpreting diagnostic results. Following a pilot survey which found low Bonamia sp. intensity in farmed Ostrea angasi, we tested further oysters (n = 100-150) from each of three farms for Bonamia sp. using heart smear, histology and qPCR. We used a Bayesian Latent Class Model to assess diagnostic sensitivity (DSe) and specificity (DSp) of these tests individually or in combination, and to assess prevalence. Histology was the best individual test (DSe 0.76, DSp 0.93) compared to quantitative polymerase chain reaction (qPCR) (DSe 0.69, DSp 0.93) and heart smear (DSe 0.61, DSp 0.60). Histology combined with qPCR and defining a positive from either test as an infected case maximized test performance (DSe 0.91, DSp 0.88). Prevalence was higher at two farms in a high-density oyster growing region than at a farm cultivating oysters at low...

This file set includes:<br><b>Six raster datasets of the processed cumulative impact maps</b> produced for the Spencer Gulf cumulative impact assessment. There are three rasters for the benthic ecosystems and three for the pelagic ecosystem, based on three scoring scenarios (best-case, worst-case and most-likely). The scores came from expert elicitation. For each of the six rasters there is an associated projection file with the same name.<br><b>Six tiff files</b> of the cumulative impact layers (illustrating what they look like when plotted) <br><b>A metadata text file</b> with details of the data layers and the projection - as well as sources of further information.<br><br>

This file set includes:<br><b>32 raster spatial data layers </b>(scaled to be between 0 and 1) for 32 human activities that were considered as potential threats to marine ecosystems in a cumulative impact assessment of Spencer Gulf in South Australia. The treats are mostly human activities (such as fishing and coastal development), but also include climate change. Each raster data layer has an associated projection file with the same filename. <br><b>32 tiff images</b> showing plots of each threat mapped across the Spencer Gulf study area (illustrating what the data look like when plotted).<br><br><b>A metadata excel file </b>with details about each threat layer, including data sources and native resolution, as well as details on abbreviations used.<br><b>A metadata (read me) text file</b> with information about the spatial data files and the projections - as well as sources of further information.<br>