Geology topics

Jonathan C. Hall

Publications and source records attributed to Jonathan C. Hall.

4 recordsLinked to original sources

USGS2023

Supervised versus unsupervised approaches to classification of accelerometry data

Sophisticated animal-borne sensor systems are increasingly providing novel insight into how animals behave and move. Despite their widespread use in ecology, the diversity and expanding quality and quantity of data they produce have created a need for robust analytical methods for biological interpretation. Machine learning tools are often used to meet this need. However, their relative effectiveness is not well known and, in the case of unsupervised tools, given that they do not use validation data, their accuracy can be difficult to assess. We evaluated the effectiveness of supervised ( n = 6), semi-supervised ( n = 1), and unsupervised ( n = 2) approaches to analyzing accelerometry data collected from critically endangered California condors ( Gymnogyps californianus ). Unsupervised K-means and EM (expectation–maximization) clustering approaches performed poorly, with adequate classification accuracies of <0.8 but very low values for kappa statistics (range: −0.02 to 0.06). The semi-supervised nearest mean classifier was moderately effective at classification, with an overall classification accuracy of 0.61 but effective classification only of two of the four behavioral classes. Supervised random forest (RF) and k-nearest neighbor (kNN) machine learning models were most effective at classification across all behavior types, with overall accuracies >0.81. Kappa statistics were also highest for RF and kNN, in most cases substantially greater than for other modeling approaches. Unsupervised modeling, which is commonly used for the classification of a priori-defined behaviors in telemetry data, can provide useful information but likely is instead better suited to post hoc definition of generalized behavioral states. This work also shows the potential for substantial variation in classification accuracy among different machine learning approaches and among different metrics of accuracy. As such, when analyzing biotelemetry data, best practices appear to call for the evaluation of several machine learning techniques and several measures of accuracy for each dataset under consideration.

Ecology and Evolution↗

USGS2021

Seasonal and age-related variation in daily travel distances of California Condors

Despite a dramatic recovery from the brink of extinction, California Condors ( Gymnogyps californianus ) still face significant anthropogenic threats. Although condor movement patterns across large temporal scales are understood, less is known about their movements on a fine temporal scale. We used a trajectory-based analysis of GPS telemetry data gathered from condors during 2013 to 2018 to investigate the relationship between the distances condors travel in a day, demographic characteristics (e.g., age and sex), and time of year. Most (>71.4%) daily travel distances by condors were <100 km, and, on average, condors traveled 70.1 ± 60.9 km/d ( x̄ ± SD). On two occasions one condor traveled >400 km in a single day (477 km one day and 415 km the following day). The tendency for condors to travel long distances increased with age, and condors traveled longer distances during the summer and when nesting. Traveling such long distances likely exposes birds to threats across a greater variety of landscapes than would be expected for birds that moved shorter distances. Given anticipated condor range expansion and population increase, this work highlights the importance of coordinating condor conservation across the broad spatial scales at which they move.

California↗

USGS2019

Characteristics of feeding sites of California Condors (Gymnogyps californianus) in the human-dominated landscape of Southern California

Wildlife conservation is often improved by understanding the movement ecology of species and adapting management strategies to dynamic conditions associated with movement. Despite a remarkable recovery over the past 30 year, the establishment of self-sustaining populations of California Condors (Gymnogyps californianus) has been challenging in the human-dominated landscapes of southern California. Among these challenges are those imposed by condor ground-foraging behavior that exposes them to environmental contamination. These include lead poisoning from the ingestion of spent ammunition and micro-trash ingestion and, during takeoff and landing, collisions with human structures. We tracked 28 California Condors for 24 months with patagially mounted GPS telemetry units to investigate the characteristics of ground sites condors visited and to identify spatiotemporal trends that might aid in conservation of this critically endangered species. Ground sites occurred on a wide variety of land cover types, primarily on steep slopes, and those more frequently used were associated with open cover. Condors concentrated their visits to ground sites around a 3 h period near midday, and usage increased from winter to late summer. Our study is the first to use remotely sensed telemetry data to describe fine-scale ecological correlates of condor ground-foraging ecology and therefore has important relevance for ongoing conservation and management strategies for this species. The descriptions of ground sites we provide can be used to target conservation or management actions.

California↗

USGS2018

Improving estimation of flight altitude in wildlife telemetry studies

Altitude measurements from wildlife tracking devices, combined with elevation data, are commonly used to estimate the flight altitude of volant animals. However, these data often include measurement error. Understanding this error may improve estimation of flight altitude and benefit applied ecology. There are a number of different approaches that have been used to address this measurement error. These include filtering based on GPS data, filtering based on behaviour of the study species, and use of state-space models to correct measurement error. The effectiveness of these approaches is highly variable. Recent studies have based inference of flight altitude on misunderstandings about avian natural history and technical or analytical tools. In this Commentary, we discuss these misunderstandings and suggest alternative strategies both to resolve some of these issues and to improve estimation of flight altitude. These strategies also can be applied to other measures derived from telemetry data. Synthesis and applications. Our Commentary is intended to clarify and improve upon some of the assumptions made when estimating flight altitude and, more broadly, when using GPS telemetry data. We also suggest best practices for identifying flight behaviour, addressing GPS error, and using flight altitudes to estimate collision risk with anthropogenic structures. Addressing the issues we describe would help improve estimates of flight altitude and advance understanding of the treatment of error in wildlife telemetry studies.

Journal of Applied Ecology↗