Geology topics

Barry C. Wilson

Publications and source records attributed to Barry C. Wilson.

5 recordsLinked to original sources

USGS2025

Decision support tools for brown pelican management in the northern Gulf of America (Gulf of Mexico)

Management plans with clear priorities can help to achieve brown pelican Pelecanus occidentalis conservation objectives in the northern Gulf of America (Gulf of Mexico). Efforts to establish clear priorities can be hindered by information gaps, especially those related to the uncertainty associated with changing conditions that influence brown pelican populations. We addressed these gaps by creating a model that uses island-specific conditions (e.g., geomorphology; predator-related conditions; brown pelican terrestrial nesting, roosting, and loafing habitats) to predict the nest count as a proxy for breeding pairs on the island. We used the model and 2000–2015 brown pelican nest count data to estimate if breeding pair targets that we identified or estimated for 10 U.S Fish and Wildlife Service Gulf Coast Biological Planning Units were met while accounting for uncertainty. Our results indicate that breeding pair targets were met in 7 of the 10 units by existing conditions. Our confidence in judging nest deficits tended to decrease from west to east because the model over-predicted total nests in the east Gulf Coast. Using an island from our data, we show how the model could be used to quantify the uncertainty of nest count outcomes under simulated changes in island conditions. The model indicated that the island's existing conditions most probably result in nests (probability = 0.51) and that increasing the area of nesting habitat (shrubs) could increase the probability of nests from 49% to 70%. Increasing shrub habitat in the model also increased nest count uncertainty by 60%, but this was due to a greater probability of larger nest counts. Our model suggests that nest count uncertainty could be reduced by improving data on island size, shrub area, and predator presence, depending on the unit and how isolated the island is from the mainland. These tools could help managers understand and incorporate the uncertainty associated with creating island conditions that are intended to help achieve brown pelican conservation objectives.

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2022

Structured decision making to rank North American Wetland Conservation Act proposals within joint venture regions

The North American Wetlands Conservation Act (16 U.S.C. 4401-4412) provided funding and administration for wetland management and conservation projects. The North American Wetland Conservation Fund, enabled in 1989 with the Act, provides financial resources. Resource allocation decisions are based, in part, on regional experts, particularly migratory bird Joint Ventures (JVs) (i.e., partnerships for cooperative planning and coordinated management of the continent’s waterfowl populations and habitats). The JVs evaluate funding proposals submitted with their respective regions each year and make funding recommendations to decision makers. Proposal evaluation procedures differ among JVs, however, it could be helpful to consider a transparent, repeatable, and data-driven framework for prioritization within regions. We used structured decision making and linear additive value models for ranking proposals within JV regions. We used two JVs as case studies and constructed two different value models using JV-specific objectives and weights. The framework was developed through a collaborative process with JV staff and stakeholders. Models were written in Microsoft Excel. To test these models, we used six NAWCA proposals submitted to the Upper Mississippi / Great Lakes Joint Venture in 2016 and seven proposals submitted to the Gulf Coast Joint Venture in 2017. We compared proposal ranks assigned by the value model to ranks assigned by each JV’s management board. Ranks assigned by the value model differed from ranks assigned by the board for the Upper Mississippi / Great Lakes Joint Venture, but not for the Gulf Coast Joint Venture. However, ranks from the value model could change markedly with different objective weights and value functions. The weighted linear value model was beneficial for ranking NAWCA proposals because it allows JVs to treat the ranking as a multiple objective problem and tailor the ranking to their specific regional concerns. We believe a structured decision making approach could be adapted by JV staff to facilitate a systematic and transparent process for proposal ranking by their management boards.

Journal of Fish and Wildlife Management↗

USGS2015

Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010

Coastal zone managers and researchers often require detailed information regarding emergent marsh vegetation types (that is, fresh, intermediate, brackish, and saline) for modeling habitat capacities and needs of marsh dependent taxa (such as waterfowl and alligator). Detailed information on the extent and distribution of emergent marsh vegetation types throughout the northern Gulf of Mexico coast has been historically unavailable. In response, the U.S. Geological Survey, in collaboration with the Gulf Coast Joint Venture, the University of Louisiana at Lafayette, Ducks Unlimited, Inc., and the Texas A&M University-Kingsville, produced a classification of emergent marsh vegetation types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama. This study incorporates about 9,800 ground reference locations collected via helicopter surveys in coastal wetland areas. Decision-tree analyses were used to classify emergent marsh vegetation types by using ground reference data from helicopter vegetation surveys and independent variables such as multitemporal satellite-based multispectral imagery from 2009 to 2011, bare-earth digital elevation models based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables. Image objects were created from 2010 National Agriculture Imagery Program color-infrared aerial photography. The final classification is a 10-meter raster dataset that was produced by using a majority filter to classify image objects according to the marsh vegetation type covering the majority of each image object. The classification is dated 2010 because the year is both the midpoint of the classified multitemporal satellite-based imagery (2009–11) and the date of the high-resolution airborne imagery that was used to develop image objects. The seamless classification produced through this work can be used to help develop and refine conservation efforts for priority natural resources.

Alabama, Louisiana, Mississippi, Texas↗

USGS2014

Delineation of marsh types of the Texas coast from Corpus Christi Bay to the Sabine River in 2010

Coastal zone managers and researchers often require detailed information regarding emergent marsh vegetation types for modeling habitat capacities and needs of marsh-reliant wildlife (such as waterfowl and alligator). Detailed information on the extent and distribution of marsh vegetation zones throughout the Texas coast has been historically unavailable. In response, the U.S. Geological Survey, in cooperation and collaboration with the U.S. Fish and Wildlife Service via the Gulf Coast Joint Venture, Texas A&M University-Kingsville, the University of Louisiana-Lafayette, and Ducks Unlimited, Inc., has produced a classification of marsh vegetation types along the middle and upper Texas coast from Corpus Christi Bay to the Sabine River. This study incorporates approximately 1,000 ground reference locations collected via helicopter surveys in coastal marsh areas and about 2,000 supplemental locations from fresh marsh, water, and “other” (that is, nonmarsh) areas. About two-thirds of these data were used for training, and about one-third were used for assessing accuracy. Decision-tree analyses using Rulequest See5 were used to classify emergent marsh vegetation types by using these data, multitemporal satellite-based multispectral imagery from 2009 to 2011, a bare-earth digital elevation model (DEM) based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables believed to be important for delineating the extent and distribution of marsh vegetation communities. Image objects were generated from segmentation of high-resolution airborne imagery acquired in 2010 and were used to refine the classification. The classification is dated 2010 because the year is both the midpoint of the multitemporal satellite-based imagery (2009–11) classified and the date of the high-resolution airborne imagery that was used to develop image objects. Overall accuracy corrected for bias (accuracy estimate incorporates true marginal proportions) was 91 percent (95 percent confidence interval [CI]: 89.2–92.8), with a kappa statistic of 0.79 (95 percent CI: 0.77–0.81). The classification performed best for saline marsh (user’s accuracy 81.5 percent; producer’s accuracy corrected for bias 62.9 percent) but showed a lesser ability to discriminate intermediate marsh (user’s accuracy 47.7 percent; producer’s accuracy corrected for bias 49.5 percent). Because of confusion in intermediate and brackish marsh classes, an alternative classification containing only three marsh types was created in which intermediate and brackish marshes were combined into a single class. Image objects were reattributed by using this alternative three-marsh-type classification. Overall accuracy, corrected for bias, of this more general classification was 92.4 percent (95 percent CI: 90.7–94.2), and the kappa statistic was 0.83 (95 percent CI: 0.81–0.85). Mean user’s accuracy for marshes within the four-marsh-type and three-marsh-type classifications was 65.4 percent and 75.6 percent, respectively, whereas mean producer’s accuracy was 56.7 percent and 65.1 percent, respectively. This study provides a more objective and repeatable method for classifying marsh types of the middle and upper Texas coast at an extent and greater level of detail than previously available for the study area. The seamless classification produced through this work is now available to help State agencies (such as the Texas Parks and Wildlife Department) and landscape-scale conservation partnerships (such as the Gulf Coast Prairie Landscape Conservation Cooperative and the Gulf Coast Joint Venture) to develop and (or) refine conservation plans targeting priority natural resources. Moreover, these data may improve projections of landscape change and serve as a baseline for monitoring future changes resulting from chronic and episodic stressors.

Texas↗

USGS2011

Potential use of weather radar to study movements of wintering waterfowl

To protect and restore wintering waterfowl habitat, managers require knowledge of routine wintering waterfowl movements and habitat use. During preliminary screening of Doppler weather radar data we observed biological movements consistent with routine foraging flights of wintering waterfowl known to occur near Lacassine National Wildlife Refuge (NWR), Louisiana. During the winters of 2004–2005 and 2005–2006, we conducted field surveys to identify the source of the radar echoes emanating from Lacassine NWR. We compared field data to weather radar reflectivity data. Spatial and temporal patterns consistent with foraging flight movements appeared in weather radar data on all dates of field surveys. Dabbling ducks were the dominant taxa flying within the radar beam during the foraging flight period. Using linear regression, we found a positive log-linear relationship between average radar reflectivity ( Z ) and number of birds detected over the study area ( P < 0.001, r 2 = 0.62, n = 40). Ground observations and the statistically significant relationship between radar data and field data confirm that Doppler weather radar recorded the foraging flights of dabbling ducks. Weather radars may be effective tools for wintering waterfowl management because they provide broad-scale views of both diurnal and nocturnal movements. In addition, an extensive data archive enables the study of wintering waterfowl response to habitat loss, agricultural practices, wetland restoration, and other research questions that require multiple years of data.

Louisiana↗