Search USGS⌕ Search
Home/Archive/USGS

USGS · 5224321

A stage-based model of manatee population dynamics

M.C. Runge·C.A. Langtimm·W. L. Kendall
View original source ↗Export metadata ↓

Abstract

A stage-structured population model for the Florida manatee (Trichechus manatus latirostris) was developed that explicitly incorporates uncertainty in parameter estimates. The growth rates calculated with this model reflect the status of the regional populations over the most recent 10-yr period. The Northwest and Upper St. Johns River regions have growth rates (8) of 1.037 (95% interval, 1.016?1.056) and 1.062 (1.037?1.081), respectively. The Southwest region has a growth rate of 0.989 (0.946?1.024), suggesting this population has been declining at about 1.1% per year. The estimated growth rate in the Atlantic region is 1.010 (0.988?1.029), but there is some uncertainty about whether adult survival rates have been constant over the last 10 yr; using the mean survival rates from the most recent 5-yr period, the estimated growth rate in this region is 0.970 (0.938?0.998). Elasticity analysis indicates that the most effective management actions should seek to increase adult survival rates. Decomposition of the uncertainty in the growth rates indicates that uncertainty about population status can best be reduced through increased monitoring of adult survival rate.

Explore related subjects

Marine Mammal ScienceArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M.C. Runge, C.A. Langtimm, W. L. Kendall. 2006-08-26. A stage-based model of manatee population dynamics. https://doi.org/10.1111/j.1748-7692.2004.tb01167.x

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

KEEP EXPLORING

Related USGS reports

USGS2026

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↗
USGS2026

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↗