J. A. Royle

Towards estimating marine wildlife abundance using aerial surveys and deep learning with hierarchical classifications subject to error

This paper develops a hierarchical model to estimate marine wildlife abundance using aerial surveys and deep learning, accounting for classification errors and ecological factors.

Estimating occupancy dynamics and encounter rates with species misclassification: a semi-supervised individual-level approach

The paper develops a semi-supervised occupancy model that accounts for species misclassification and non-detection in biodiversity monitoring, showing improved classification performance for abundant species.

Movement-assisted localization from acoustic telemetry data

This paper introduces a localization method for acoustic telemetry data that integrates movement, signal rate, and detection probability to improve accuracy and precision of animal position estimates.

Spatial Capture-Recapture for Categorically Marked Populations with An Application to Genetic Capture-Recapture

The paper introduces a categorical spatial partial identity model (SPIM) to improve density estimation in populations with uncertain individual identity, showing it performs better than existing methods in high uncertainty scenarios.

Estimating spatially and temporally complex range dynamics when detection is imperfect

This paper develops a dynamic occupancy model to estimate complex spatial and temporal range dynamics of species with imperfect detection.

Modeling sound attenuation in heterogeneous environments for improved bioacoustic sampling of wildlife populations

This paper develops a non-Euclidean sound attenuation model for bioacoustic data in heterogeneous environments, enabling more accurate signal localization and detection range estimation.

Unifying Population and Landscape Ecology with Spatial Capture-recapture

This paper reviews spatial capture-recapture methods to integrate ecological hypotheses with individual encounter data for studying population ecology.

Spatial capture-recapture with partial identity: an application to camera traps

The paper introduces SPIM, a method to improve density analysis by incorporating partial identity individuals from camera traps, showing improved inference in ocelot and bobcat datasets.