Date of Award

8-2019

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Forestry and Environmental Conservation

Committee Member

Kyle Barrett, Committee Chair

Committee Member

Richard M Kaminski

Committee Member

Beth E Ross

Committee Member

Patrick D Gerard

Abstract

Aerial surveys are effective and cost-efficient for quantifying population size and habitat use of waterfowl and other waterbirds across vast and especially inaccessible landscapes. Surveys and associated data are critically important to understand population dynamics and to guide habitat management, land acquisition, and conservation decision-making. Due to cessation of the Midwinter Waterfowl Survey in 2016 and need for reliable surveys to monitor wintering waterbird populations in South Carolina, I evaluated fixed-wing, 250-m wide aerial strip-transect surveys during fall–winter 2016–2019. My objectives were to design efficient and affordable aerial survey methodologies to estimate waterbird abundance, distribution, and habitat use in South Carolina. To my knowledge, South Carolina currently is the only state in the Atlantic Flyway conducting probability based aerial surveys inland of the Atlantic Ocean. I revised survey strata following 2016–2017 surveys to reduce variation and increase survey efficiency. Overall, I reduced surveyed area by 38% but captured and retained 95% of waterfowl and other waterbird detections from 2016–2017 surveys. I used design-based analyses to estimate population indices (Î; abundance not corrected for detection bias) of dabbling ducks, diving ducks, total ducks, geese and swans, coots and gallinules, pelagic and piscivorous waterbirds, wading birds, and raptors. I desired an a priori goal of precision at coefficient of variation (CV) ≤ 15–20%. My January 2018–2019 estimates for total ducks (74,504 ≤ Î ≤ 102,421) were similar to estimates reported for Midwinter Waterfowl Surveys 2012–2015. However, I did not achieve desired precision for waterbirds during most surveys. Therefore, I estimated a theoretical survey effort to achieve CV = 20%. Increasing survey effort three-fold (i.e., ~66 flight hours = 7.5 days) theoretically would provide desired precision across all waterbird taxa. Furthermore, I suggest additional survey stratification of high density waterbird areas, optimal allocation of transects, geographically strategic increases in survey effort, and simulations to evaluate proposed variance-reduction methods. Moreover, I developed and advocate an adaptive monitoring framework to elicit and evaluate goals, improve precision, and optimize survey efficiencies of future waterbird surveys in South Carolina.

I acknowledge population indices are inherently biased because they do not account for imperfect detection. Thus, we implemented two tandem-team simultaneous aerial observers in January and February 2018 and I analyzed data as replicated counts using N-mixture models to estimate detection probability, abundance, and species-habitat relationships for dabbling ducks, diving ducks, pelagic waterbirds, and wading birds. Model-based inference generally was comparable and more precise for dabbling and diving ducks (11% ≤ CV ≤ 27%) compared to single-observer, design-based estimation (17% ≤ CV ≤ 36%). However, both front- and rear-seat observers exhibited low detection probabilities across all taxa (p ≤ 0.35) and detection probability varied among habitats (i.e., open water, intermediate emergent marsh, and forest/scrub-shrub wetlands). Thus, I suggest using methods such as double or repeated sampling to minimize detection bias during aerial surveys for wintering waterfowl. Additionally, habitats influencing waterbird abundance were temporally dynamic between January and February 2018. Managed and non-managed historic rice fields influenced dabbling and diving duck abundances positively in January 2018 but not in February 2018. Complete drawdowns of managed impoundments following waterfowl hunting in late January may have influenced duck-habitat relationships in February 2018. N-mixture models provided benefit of estimating abundance, detection probability, and habitat associations in one consolidated analysis. Our results suggest these, and other hierarchical analytical approaches are promising, and similar methods could be adopted to improve monitoring and estimation of highly aggregated wintering waterbird populations. In summary, aerial surveys and analytical approaches used in my thesis have provided repeatable methodologies and advanced knowledge for monitoring wintering waterfowl and other waterbirds in South Carolina and elsewhere. I recommend continuance of these or integrated aerial and ground surveys in South Carolina.

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