South Texas Wildlife Activity Across a Fragmented Landscape and Road Mitigation Corridor

Wildlife crossing structures (WCS) and roadside fencing are commonly installed to mitigate habitat fragmentation, wildlife road mortalities, and other negative effects that roads can have on the surrounding landscape. Eight such WCS were constructed below Farm-to-Market (FM)106 in Cameron County, Texas, across a 16 km corridor transecting the Laguna Atascosa National Wildlife Refuge. These WCS, paired with adjacent roadside fencing, were intended to prevent road mortalities of the endangered ocelot (Leopardus pardalis) and to mitigate the barrier effect of FM106 on this and other meso-mammal species. This study will analyze camera trap data from roadside and habitat reference sites to model target species activity throughout the study corridor and identify changes in broader community composition associated with the road and its mitigation structures. This analysis will allow for more accurate estimates of mitigation structure performance while controlling for the influence of land cover characteristics on target species detections

Contour Integration Across Polarities and Spatial Gaps: From Local Contrast Filtering to Global Grouping

This article introduces an experimental paradigm to selectively probe the multiple levels of visual processing that influence the formation of object contours, perceptual boundaries, and illusory contours. The experiments test the assumption that, to integrate contour information across space and contrast sign, a spatially short-range filtering process that is sensitive to contrast polarity inputs to a spatially long-range grouping process that pools signals from opposite contrast polarities. The stimuli consisted of thin subthreshold lines, flashed upon gaps between collinear inducers which potentially enable the formation of illusory contours. The subthreshold lines were composed of one or more segments with opposite contrast polarities. The polarity nearest to the inducers was varied to differentially excite the short-range filtering process. The experimental results are consistent with neurophysiological evidence for cortical mechanisms of contour processing and with the Boundary Contour System model, which identifies the short-range filtering process with cortical simple cells, and the long-range grouping process with cortical bipole cells.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657); Centre National de la Recherche Scientifique (France) URA (1939

Ecological Separation of Mallards (Anas Platyrhynchos) and American Black Ducks (Anas Rubripes) In the Adirondack Park of New York State

The American black duck is a large–bodied native dabbling duck in the northeast United States and Canada which has declined \u3e 50% to ~ 500,000 breeding pairs since the 1950s. Concurrently mallards have replaced black ducks in Atlantic flyway breeding habitats. I used Bayesian statistical modeling to test for differences in mallard and black duck occupancy and productivity between and within beaver–modified wetlands and lakes in the Adirondack Park of New York. Mallard occupancy was ≥ 6.7% greater than black duck in all habitats surveyed. I further propose that mallards may outproduce black ducks in years where wetlands experience negative environmental effects such as drought or absence of beaver. I also compared the utility of drones to ground observers to survey black ducks, and discovered drones detect black ducks and other secretive waterfowl more reliably. However, when considering all ducks present, overall detection probability was similar between methods

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version