Assessing the Conservation Status and Threats to Priority Plants: a threat assessment approach and case study in South-East Queensland, Australia

bstract: During the Regional Forest Agreement (RFA) process in south-east Queensland, the conservation status of, and threats to, priority vascular plant taxa in the region was assessed. Characteristics of biology, demography and distribution were used to assess the species' intrinsic risk of extinction. In contrast, the threats to the taxa (their extrinsic risk of extinction) were assessed using a decision-support protocol for setting conservation targets for taxa lacking population viability analyses and habitat modelling data. Disturbance processes known or suspected to be adversely affecting the taxa were evaluated for their intensity, extent and time-scale. Expert opinion was used to provide much of the data and to assess the recommended protection areas. Five categories of intrinsic risk of extinction were recognised for the 105 priority taxa: critically endangered (43 taxa); endangered (29); vulnerable (21); rare (10); and presumed extinct (2). Only 6 of the 103 extant taxa were found to be adequately reserved and the majority were considered inadequately protected to survive the current regimes of threatening processes affecting them. Data were insufficient to calculate a protection target for one extant taxon. Over half of the taxa require all populations to be conserved as well as active management to alleviate threatening processes. The most common threats to particular taxa were competition from weeds or native species, inappropriate fire regimes, agricultural clearing, forestry, grazing by native or feral species, drought, urban development, illegal collection of plants, and altered hydrology. Apart from drought and competition from native species, these disturbances are largely influenced or initiated by human actions. Therefore, as well as increased protection of most of the taxa, active management interventions are necessary to reduce the effects of threatening processes and to enable the persistence of the taxa.

Evaluation and comparison of mammalian subcellular localization prediction methods

Background: Determination of the subcellular location of a protein is essential to understanding its biochemical function. This information can provide insight into the function of hypothetical or novel proteins. These data are difficult to obtain experimentally but have become especially important since many whole genome sequencing projects have been finished and many resulting protein sequences are still lacking detailed functional information. In order to address this paucity of data, many computational prediction methods have been developed. However, these methods have varying levels of accuracy and perform differently based on the sequences that are presented to the underlying algorithm. It is therefore useful to compare these methods and monitor their performance. Results: In order to perform a comprehensive survey of prediction methods, we selected only methods that accepted large batches of protein sequences, were publicly available, and were able to predict localization to at least nine of the major subcellular locations (nucleus, cytosol, mitochondrion, extracellular region, plasma membrane, Golgi apparatus, endoplasmic reticulum (ER), peroxisome, and lysosome). The selected methods were CELLO, MultiLoc, Proteome Analyst, pTarget and WoLF PSORT. These methods were evaluated using 3763 mouse proteins from SwissProt that represent the source of the training sets used in development of the individual methods. In addition, an independent evaluation set of 2145 mouse proteins from LOCATE with a bias towards the subcellular localization underrepresented in SwissProt was used. The sensitivity and specificity were calculated for each method and compared to a theoretical value based on what might be observed by random chance. Conclusion: No individual method had a sufficient level of sensitivity across both evaluation sets that would enable reliable application to hypothetical proteins. All methods showed lower performance on the LOCATE dataset and variable performance on individual subcellular localizations was observed. Proteins localized to the secretory pathway were the most difficult to predict, while nuclear and extracellular proteins were predicted with the highest sensitivity.