Geology Reports⌕ Search

USGS · 70162154

Manatees in the Gulf of Mexico

Abstract

The endangered Florida manatee (Trichechus manatus latirostris) inhabits rivers and estuaries along both coasts of Florida and, to a lesser extent, adjacent states (Figure 1). Since 1990, documented sightings of manatees outside of Florida have been increasing. This increase in sightings probably represents northward shifts in manatee distribution made possible by man-made sources of warm water (i.e., industrial effluents), as well as a decade of relatively warm winters. The most likely source of emigrants on the Gulf coast is the population of manatees that overwinter in the headwaters of the Crystal and Homosassa Rivers, Citrus County, FL. This group of manatees has undergone a steady increase in numbers, (approximately 7% per year from 1977-1991; Eberhardt and O’Shea 1995). Some emigrants may also come from the Tampa-Ft. Myers region, where human impacts on habitat are greater. Manatees are intelligent, long-lived mammals that appear to adapt readily to new environments and situations. However, manatees have relatively low metabolic rates, and cold winter temperatures restrict their northern distribution.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Robert K. Bonde, Lynn W. Lefebvre. 2001. Manatees in the Gulf of Mexico. https://pubs.usgs.gov/publication/70162154

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

KEEP EXPLORING

Related USGS reports

Improving building footprint extraction using NAIP and 3DEP lidar derived features with deep learning

Accurate building footprint extraction is critical for applications ranging from population estimation to disaster management. Although optical imagery provides detailed spectral information, it often struggles with shadows, occlusions, and background clutter in dense urban environments. Lidar data, by contrast, offer precise elevation and structural attributes but face challenges such as variable point density and noise. This study integrates multispectral imagery from the U.S. Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) with lidar-derived feature height and intensity from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) to improve footprint extraction using a U-Net–based deep learning model. A six-band input stack (RGB, near-infrared, height, intensity) was developed, normalized, and tiled for training and evaluation against Microsoft Global Building Footprints (GBF). Results from the Houston, TX test site show that the six-band model achieved a precision of 0.86, recall of 0.88, F1 score of 0.87, and Intersection-over-Union (IoU) of 0.76, consistently outperforming four-band baselines by reducing false positives while maintaining sensitivity. Predictions on withheld Houston tiles confirmed strong within-region generalization, yielded a precision of 0.78, recall of 0.81, F1 score of 0.79, and IoU of 0.66. Qualitative analysis further revealed limitations stemming from both training label quality and vegetation–building confusion. These findings demonstrate the complementary value of integrating spectral and structural information for robust building footprint extraction and how domain adaptation strategies can be used to enhance cross-regional transferability.

Texas↗

Time-to-depth conversion of seismic-reflection data from eastern Lake Superior and implications for the eastern arm of the Midcontinent Rift

Seismic-reflection data were acquired in the mid 1980s along several lines across eastern Lake Superior by industry and the Great Lakes International Multidisciplinary Program on Crustal Evolution (GLIMPCE) (Fig. 1). The lines form part of a larger network of crossing lines over the entire lake, which can be used to develop three-dimensional geologic models of the Mesoproterozoic Midcontinent Rift that lies below. To better interpret these lines, we developed velocity models to convert seismic reflections versus two-way travel time (TWTT) to reflections versus depth. In addition, the velocity models themselves provide insights into the structure of the Midcontinent Rift by recognizing common velocity ranges for certain rock types (Grauch, 2023).

eastern Lake Superior↗

Revisiting the utility of regional-scale, high-quality geophysical data in mineral exploration - A case study featuring the Mammoth Magnetic Anomaly, Pinal County, Arizona

Regional aeromagnetic surveys passively measure the total magnetic intensity (TMI) and are a foundational tool used in mineral exploration (Airo, 2015). With the increased global demand and the number of critical mineral resources required for manufacturing high-tech devices, developing high-quality, regional-scale geophysical surveys could aid critical mineral exploration efforts and geologic mapping. In 2019, the U. S. Geological Survey launched the Earth Mapping Resources Initiative (Earth MRI) to modernize the geologic and geophysical mapping of regions that have the potential to contain critical mineral resources within the United States. In support of planning Earth MRI geophysical surveys, Drenth and Grauch (2019) defined five aeromagnetic data quality rankings (rank 1 through rank 5) applying them to the airborne geophysical survey inventory of the United States (Johnson et al., 2021). Rank 1 aeromagnetic surveys are of the highest quality, meeting modern standards and allowing best practices for qualitative and quantitative interpretation; whereas rank 5 aeromagnetic surveys are of the lowest quality, being useful only for qualitative interpretation of broad features. Through the Earth MRI effort, 48 high-quality, regional-scale rank 1 and 2 airborne magnetic and radiometric geophysical surveys have been planned, collected, or publicly release through May 2025 (U. S. Geological Survey, 2025). Here, a portion of a rank 1 Earth MRI aeromagnetic survey in southeast Arizona is presented and compared to a legacy rank 5 aeromagnetic survey over the Mammoth Magnetic Anomaly (MMA), demonstrating how modern, high-quality aeromagnetic data improves our view of crustal geology, aiding mineral exploration.

Arizona↗