Geology Reports⌕ Search

USGS · 5224322

Capture-recapture analysis for estimating manatee reproductive rates

Abstract

Modeling the life history of the endangered Florida manatee (Trichechus manatus latirostris) is an important step toward understanding its population dynamics and predicting its response to management actions. We developed a multi-state mark-resighting model for data collected under Pollock's robust design. This model estimates breeding probability conditional on a female's breeding state in the previous year; assumes sighting probability depends on breeding state; and corrects for misclassification of a cow with first-year calf, by estimating conditional sighting probability for the calf. The model is also appropriate for estimating survival and unconditional breeding probabilities when the study area is closed to temporary emigration across years. We applied this model to photo-identification data for the Northwest and Atlantic Coast populations of manatees, for years 1982?2000. With rare exceptions, manatees do not reproduce in two consecutive years. For those without a first-year calf in the previous year, the best-fitting model included constant probabilities of producing a calf for the Northwest (0.43, SE = 0.057) and Atlantic (0.38, SE = 0.045) populations. The approach we present to adjust for misclassification of breeding state could be applicable to a large number of marine mammal populations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

W. L. Kendall, C.A. Langtimm, C.A. Beck, M.C. Runge. 2006-08-26. Capture-recapture analysis for estimating manatee reproductive rates. https://doi.org/10.1111/j.1748-7692.2004.tb01170.x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

Detecting polar bears on land using satellite imagery and deep learning

Summer land use is increasing for polar bears in the Beaufort Sea, which may lead to increases in human-bear interactions. Remote methods may enable noninvasive approaches to monitor polar bear abundance and distribution on land. We examined the potential of using very high-resolution optical satellite imagery to detect polar bears on land from images collected at three sites on the Beaufort Sea coast of Alaska. Additionally, we evaluated the use of deep learning models to automate detection of polar bears in these images and the degree to which detection of polar bears differed among three very high-resolution optical satellite missions. Pléiades Neo images exhibited 90% average precision in validation analyses using a faster region-based convolutional neural network model, which was 55% and 11% greater than the average precision using WorldView-2 or -3 images, respectively. Applying the Pléiades Neo training set to new imagery exhibited lower precision (35%), but high recall (100%), which indicates that deep learning could be used to expedite the visual detection of polar bears in imagery. Very high-resolution optical satellite imagery provides a remote method for monitoring polar bears on land where they occur in high densities, but satellite mission, off-nadir angle, pansharpening methods, and image resolution are important considerations.

Alaska↗

Quantifying southern sea otter (Enhydra lutris nereis) reactions to a quadcopter drone in central California

Drones are useful for wildlife research and management, but they can cause disturbance and harassment to wildlife. Sea otters ( Enhydra lutris ) are candidates for drone-based observation and monitoring but are vulnerable to disturbance. No studies have evaluated drone effects on sea otter behavior, but based on prior disturbance studies, we hypothesized: (1) sea otters would exhibit behaviors indicating higher reactivity in the presence of drones than when drones were absent and (2) drone disturbance to sea otters would be greater when drones were closer. At two sites in Monterey Bay, CA, we conducted 37 observational sessions, recording behavior codes for focal sea otters during a baseline (no drone) period and three consecutive drone flights. Data were analyzed using ANOVA and ordinal logistic regression models. At both locations, focal sea otters had higher behavior codes during drone trials compared to baseline, and behavior codes increased with descending drone altitude. Pup presence, group size, flight trial number, and gull presence were significant covariables. We calculated multipliers to predict drone-mediated behavioral responses at a range of drone altitudes. Our findings can inform best practices for a variety of uses of drones around sea otters, including population monitoring, oil spill response, and drone photography/videography.

California↗