Geology topics

Connor F. White

Publications and source records attributed to Connor F. White.

4 recordsLinked to original sources

USGS2026

Fish locomotor dynamics quantified using multiple inertial measurement units

Animals exhibit a diversity of locomotor behaviours that vary with body shape, ontogeny, motivation and environment. Documenting these behaviours often relies on recording videos of animals in controlled conditions displaying stereotyped behaviours. By contrast, animal-borne motion sensing data loggers can document diverse behaviours of animals in their natural environment. Traditionally, one data logger is used to infer body dynamics; however, a single location presents an incomplete picture of body movement. Here, we deploy multiple independent inertial measurement units on individual fish to study within-body dynamics and assess the accuracy of this approach compared to traditional video methods. Using a combination of robotic and animal experiments, we accurately measure the timing, direction and magnitude of movement of multiple locations along the body and can recreate accurate three-dimensional midline kinematics without video data. Furthermore, we show that with as few as two data loggers, parameters such as propulsive wavelength can be calculated, even during high-intensity behaviours. Using multiple data loggers has the potential to provide unbiased spatio-temporal estimates of fish kinematics, allowing kinematic studies to be extended to new species, new habitats and a diversity of natural fish locomotor behaviours beyond those traditionally studied in laboratory settings.

Royal Society Open Science↗

USGS2025

Biologging to identify nesting and non-nesting emergences for four species of imperiled sea turtles

Quantifying sea turtle nesting behavior is essential for recovery planning and evaluating management actions. Traditional monitoring approaches, based on nest counts from beach surveys, can misclassify non-nesting emergences, obscure true fecundity, and underestimate clutch frequency, metrics that directly influence population models and regulatory decisions. Here, we demonstrate that high-resolution acceleration data loggers (ADLs) can reliably discriminate nesting from non-nesting emergences across four imperiled species of sea turtles at sites in the Gulf of America, southeast USA, and Caribbean. From 60 recovered ADL deployments on green ( Chelonia mydas ; N = 10), hawksbill ( Eretmochelys imbricata ; N = 7), Kemp’s ridley ( Lepidochelys kempii ; N = 21), and loggerhead sea turtles ( Caretta caretta ; N = 22) lasting on average 17.5 ± 8.7 days (range 2–43 days), we identified 54 nesting events and 76 non-nesting emergences, with >97% accuracy when compared to direct observations. These data provide the first observer-validated, species-specific behavioral signatures of nesting phases and reveal correlations between egg-laying duration and clutch size. All non-nesting emergences occurred within 72 hours of subsequent nesting, allowing managers to anticipate nest deposition windows. By refining inter-nesting intervals and fecundity estimates, ADLs offer a practical path to reduce error in clutch frequency estimates. The integration of ADL-derived algorithms with satellite-transmitting tags would enable the remote, real-time monitoring of nesting activity, creating a system for the remote monitoring of inter-nesting intervals and nest fecundity that are crucial to quantify the impacts of climate change and other threats to sea turtle nesting habitat.

Alabama, Florida, North Carolina, Texas↗

USGS2021

Accelerometry to study fine-scale activity of invasive Burmese pythons (Python bivittatus) in the wild

The establishment of Burmese pythons ( Python bivittatus) in Everglades National Park, Florida, USA, has been connected to a > 90% decline in the mesomammal population in the park and is a major threat to native reptile and bird populations. Efforts to control this population are underway, but are hampered by a lack of information about fine-scale activity cycles and ecology of these cryptic animals in the wild. We aimed to establish a technique for monitoring the activity of Burmese pythons in the wild using acceleration data loggers (ADLs), while attempting to identify any behavioral patterns that could be used to help manage this invasive species in the Greater Everglades and South Florida.

Florida↗

USGS2016

Trading shallow safety for deep sleep: Juvenile green turtles select deeper resting sites as they grow

To better protect endangered green sea turtles Chelonia mydas , a more thorough understanding of the behaviors of each life stage is needed. Although dive profile analyses obtained using time-depth loggers have provided some insights into habitat use, recent work has shown that more fine-scale monitoring of body movements is needed to elucidate physical activity patterns. We monitored 11 juvenile green sea turtles with tri-axial acceleration data loggers in their foraging grounds in Dry Tortugas National Park, Florida, USA, for periods ranging from 43 to 118 h (mean ± SD: 72.8 ± 27.3 h). Approximately half of the individuals (n = 5) remained in shallow (overall mean depth less than 2 m) water throughout the experiment, whereas the remaining individuals (n = 6) made excursions to deeper (4 to 27 m) waters, often at night. Despite these differences in depth use, acceleration data revealed a consistent pattern of diurnal activity and nocturnal resting in most individuals. Nocturnal depth differences thus do not appear to represent differences in behavior, but rather different strategies to achieve the same behavior: rest. We calculated overall dynamic body acceleration (ODBA) to assess the relative energetic cost of each behavioral strategy in an attempt to explain the differences between them. Animals in deeper water experienced longer resting dives, more time resting per hour, and lower mean hourly ODBA. These results suggest that resting in deeper water provides energetic benefits that outweigh the costs of transiting to deep water and a potential increased risk of predation.

Florida↗