Experience Does Not Equal Expertise in Recognizing Infrequent Incoming Gunfire: Neural Markers for Experience and Task Expertise at Peak Behavioral Performance

For a soldier, decisions to use force can happen rapidly and sometimes lead to undesired consequences. In many of these situations, there is a rapid assessment by the shooter that recognizes a threat and responds to it with return fire. But the neural processes underlying these rapid decisions are largely unknown, especially amongst those with extensive weapons experience and expertise. In this paper, we investigate differences in weapons experts and non-experts during an incoming gunfire detection task. Specifically, we analyzed the electroencephalography (EEG) of eleven expert marksmen/soldiers and eleven non-experts while they listened to an audio scene consisting of a sequence of incoming and non-incoming gunfire events. Subjects were tasked with identifying each event as quickly as possible and committing their choice via a motor response. Contrary to our hypothesis, experts did not have significantly better behavioral performance or faster response time than novices. Rather, novices indicated trends of better behavioral performance than experts. These group differences were more dramatic in the EEG correlates of incoming gunfire detection. Using machine learning, we found condition-discriminating EEG activity among novices showing greater magnitude and covering longer periods than those found in experts. We also compared group-level source reconstruction on the maximum discriminating neural correlates and found that each group uses different neural structures to perform the task. From condition-discriminating EEG and source localization, we found that experts perceive more categorical overlap between incoming and non-incoming gunfire. Consequently, the experts did not perform as well behaviorally as the novices. We explain these unexpected group differences as a consequence of experience with gunfire not being equivalent to expertise in recognizing incoming gunfire.     

Testing the Role of Climate Change in Species Decline: Is the Eastern Quoll a Victim of a Change in the Weather?

To conserve a declining species we first need to diagnose the causes of decline. This is one of the most challenging tasks faced by conservation practitioners. In this study, we used temporally explicit species distribution models (SDMs) to test whether shifting weather can explain the recent decline of a marsupial carnivore, the eastern quoll (Dasyurus viverrinus). We developed an SDM using weather variables matched to occurrence records of the eastern quoll over the last 60 years, and used the model to reconstruct variation through time in the distribution of climatically suitable range for the species. The weather model produced a meaningful prediction of the known distribution of the species. Abundance of quolls, indexed by transect counts, was positively related to the modelled area of suitable habitat between 1990 and 2004. In particular, a sharp decline in abundance from 2001 to 2003 coincided with a sustained period of unsuitable weather over much of the species’ distribution. Since 2004, abundance has not recovered despite a return to suitable weather conditions, and abundance and area of suitable habitat have been uncorrelated. We suggest that fluctuations in weather account for the species’ recent decline, but other unrelated factors have suppressed recovery.     

FBXO44 promotes DNA replication-coupled repetitive element silencing in cancer cells

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Nice weather for bettongs: using weather events,not climate means,in species distribution models

Current applications of species distribution models (SDM) are typically static, in that they are based on correlations between where a species has been observed (ignoring the date of the observation) and environmental features, such as long‐term climate means, that are assumed to be constant for each site. Because of this SDMs do not account for temporal variation in the distribution of suitable habitat across the range of a species. Here, we demonstrate the temporal variability in the potential geographic distributions of an endangered marsupial, the northern bettong Bettongia tropica as a case study. Models of the species distribution using temporally matched observations of the species with weather data (including extreme weather events) at the time of species observations, were better able to define habitat suitability, identify range edges and uncover competitive interactions than models based on static long‐term climate means. Droughts and variable temperature are implicated in low densities and local extinctions of northern bettong populations close to range edges. Further, we show how variable weather can influence the results of competition with the common rufous bettong Aepyprymnus rufescens. Because traditional SDMs do not account for temporal variability of suitable habitat, static SDMs may underestimate the impacts of climate change particularly as the incidence of extreme weather events is likely to rise.     

Monitoring an invasive perennial at the landscape scale with remote sensing

Summary   Worldwide, invasive weeds threaten agricultural, natural and urban ecosystems. In Australia's agricultural and grazing regions, invasive species often establish across extensive areas where weed management is hampered by an inability to detect the location and timing of an outbreak. In these vast landscapes, an effective detection and monitoring system is required to delineate the extent of the invasion and identify spatial and temporal factors associated with weed establishment and thickening. In this study, we utilize a time series of remote sensing imagery to detect the spatial and temporal patterns of Prickly Acacia ( Acacia nilotica ) invasion in the Mitchell grass plains of North Queensland. We develop a spectral index from Landsat images which is applied to images from 1989 to 2004, in combination with a classification mask, to identify locations and monitor changes in Prickly Acacia density across 29 000 km² of Mitchell grass plains. The approach identified spectral and temporal signatures consistent with Prickly Acacia infestation on 1.9% of this landscape. Field checking of results confirmed presence of the weed in previously unrecorded locations. The approach may be used to evaluate future spread, or outcomes of management strategies for Prickly Acacia in this landscape and could be employed to detect and monitor invasions in other extensive landscapes.