927 resultados para Threat


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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.

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Various neuroimaging investigations have revealed that perception of emotional pictures is associated with greater visual cortex activity than their neutral counterparts. It has further been proposed that threat-related information is rapidly processed, suggesting that the modulation of visual cortex activity should occur at an early stage. Additional studies have demonstrated that oscillatory activity in the gamma band range (40-100 Hz) is associated with threat processing. Magnetoencephalography (MEG) was used to investigate such activity during perception of task-irrelevant, threat-related versus neutral facial expressions. Our results demonstrated a bilateral reduction in gamma band activity for expressions of threat, specifically anger, compared with neutral faces in extrastriate visual cortex (BA 18) within 50-250 ms of stimulus onset. These results suggest that gamma activity in visual cortex may play a role in affective modulation of visual processing, in particular with the perception of threat cues.

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Neurocognitive models propose a specialized neural system for processing threat-related information, in which the amygdala plays a key role in the analysis of threat cues. fMRI research indicates that the amygdala is sensitive to coarse visual threat relevant information—for example, low spatial frequency (LSF) fearful faces. However, fMRI cannot determine the temporal or spectral characteristics of neural responses. Consequently, we used magnetoencephalography to explore spatiotemporal patterns of activity in the amygdala and cortical regions with blurry (LSF) and normal angry, fearful, and neutral faces. Results demonstrated differences in amygdala activity between LSF threat-related and LSF neutral faces (50-250 msec after face onset). These differences were evident in the theta range (4-8 Hz) and were accompanied by power changes within visual and frontal regions. Our results support the view that the amygdala is involved in the early processing of coarse threat related information and that theta is important in integrating activity within emotion-processing networks.

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