Geology topics

Paul R. Marban

Publications and source records attributed to Paul R. Marban.

3 recordsLinked to original sources

USGS2020

Assessing nest attentiveness of Common Terns via video cameras and temperature loggers

While nest attentiveness plays a critical role in the reproductive success of avian species, little nest attentiveness data with high temporal resolution is available for many species. However, improvements in both video monitoring and temperature logging devices present an opportunity to improve our understanding of this aspect of avian behavior. To investigate nest attentiveness behaviors and evaluate the ability of these technologies to record the needed data we monitored 13 nests across two Common Tern (Sterna hirundo) breeding colonies with a paired video camera - temperature logger approach, while monitoring 63 additional nests with temperature loggers alone. We examined data respective to four times of day: Morning (civil dawn-11:59), Peak (12:00-16:00), Cooling (16:01-civil dusk), and Night (civil dusk-civil dawn). Our results show that while successful nests had mostly short duration off-bouts and maintained consistent nest attentiveness throughout the day, failed nests had dramatic reductions in nest attentiveness during the Cooling and Night periods (p < 0.05) with one colony experiencing repeated nocturnal abandonment due to predation pressure from a Great Horned Owl (Bubo virginianus). Incubation appeared to ameliorate ambient temperatures during Night, as nests were significantly warmer during Night when birds were on versus off the nest (p < 0.05). Meanwhile, off bouts during the Peak period occurred during higher ambient temperatures, perhaps due to adults leaving the nest during the hottest periods to perform belly soaking. Unfortunately, temperature logger data alone had limited ability to predict nest attentiveness status, with results highly dependent on time of day and bout duration. While our methods did not affect hatching success (p > 0.05) video-monitored nests did have significantly lower clutch sizes (p < 0.05). Despite the high-quality data recorded in this study, the logistical and potential biological complications reported suggest that careful planning is needed before these devices can be utilized.

Avian Research↗

USGS2020

Using thermal infrared cameras to detect avian chicks at various distances and vegetative coverages

Population monitoring of nesting waterbirds often involves frequent entries into the colony, but alternative methods such as local remotely sensed thermal imaging may help reduce disturbance while providing a cost-effective way to survey breeding populations. Such an approach can have high initial costs, however, which may have reduced the number of studies investigating functionality of paired thermal infrared camera and small unmanned aerial systems. Here, we take the first step of exploring the ability of two thermal infrared cameras to detect an avian chick under varying vegetative cover and distances, preceding field-mounting applications on a small unmanned aerial system. We created seven “bioboxes” to simulate a range of natural vegetation types and densities for a globally important colonial ground-nesting waterbird species, the common tern Sterna hirundo . We placed a juvenile chicken Gallus gallus (surrogate for the locally endangered common tern) in each box, and we tested two market-accessible infrared cameras (produced by FLIR Systems and Infrared Cameras, Inc.) at five elevations using a stationary boom (maximum height = 12 m). We applied computer-based digital thresholding to collected images, identifying pixels meeting one of seven threshold values. The chick was visible from at least one threshold value in 19 and 31 of 35 processed by the FLIR Systems and Infrared Cameras, respectively. Percentage of the chick identified across thresholds was generally highest at lower threshold values and elevations and decreased as elevation and threshold increased; however, the relative importance of each variable changed dramatically across bioboxes and camera types. Ability to detect a chick from processed images generally decreased with increasing elevation, and although we made no quantitative comparisons among boxes, detectability appeared greatest in images from both cameras when little or no vegetation was present. Interestingly, no single threshold value was best for all bioboxes. We observed notable differences between cameras including visual resolution of detected temperature differentials and image processing speed. Results of this controlled study show promise for the use of thermal infrared systems for detecting cryptic species in vegetation. Future research should work to combine thermal infrared and visual sensors with small unmanned aerial systems to test applicability in a mobile field application.

Journal of Fish and Wildlife Management↗

USGS2019

Assessing beach and island habitat loss in the Chesapeake Bay and Delmarva coastal bay region, USA, through processing of Landsat TM and OLI imagery: A case study

Beaches and islands provide economic value to humans and critical habitat for breeding and foraging wildlife. These ecosystems, however, are being severely impacted by global climate change and sea level rise through increased erosion and frequency of inundation. The case study presented here aimed to document island loss in the Chesapeake Bay and Delmarva coastal bay region of the United States using image processing techniques within a GIS from 1986 to 2016. Satellite imagery from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) sensors were processed within ArcMap 10.5 to determine spatial and temporal trends in island and beach habitat. Calculation of unweighted Cohen’s Kappa showed that classified scenes were, on average, within the range of moderate agreement between the classified Landsat scenes and the validation imagery within Google Earth (0.539). Recommendations regarding existing beach habitat management and future supplementation were created based on these results. From 1986 to 2016, island area declined by over 1,200 hectares (ha) with agriculture/open field (all open vegetated spaces) declining by nearly 82% and beach, surprisingly, increasing nearly 2%. This study was the first to document Chesapeake Bay region-wide island loss beyond the mid-2000s. The accuracy of this study was limited slightly by the 30 m spatial resolution of the imagery used. This technique may be best suited for documenting trends on large islands and along the mainland coastline.

Maryland, Virginia↗