Andy Royle

Eastern box turtle detection study field data collected at Patuxent Refuge, Laurel MD

A total of 264 unique turtle shells were observed in the dataset, based on the unique turtle IDs. Dataset Overview: This dataset contain information about various turtle sightings, with each entry detailing the image file and associated metadata for each turtle sighting. The dataset includes the following fields: index: An identifier for each row or entry in the dataset (appears to be auto-generated). photoname: The filename of the photo taken of the turtle, including the date and description of the image (e.g., carapace photos). turtle_ID: A unique identifier for each turtle, possibly denoting a tag or observation number. year: The year when the photo was taken. month: The month when the photo was taken. day: The...

Categories: Data; Tags: Detectability, Detection models, Detection probability, Detection strategies, Eastern box turtle conservation, All tags... Ecology, North America, Observer-based detection studies, Population studies, Probability modeling, Shell scattering, Species monitoring techniques, Turtle detection field experiments, Turtle population surveys, Turtle shell visibility, USGS Science Data Catalog (SDC), Wildlife management based on detectability, biota, eastern box turtle, Fewer tags

Estimating species response to environmental change is a key challenge for ecologists and a core mission of the USGS. Effective forecasting of species response requires models that are detailed enough to capture critical processes and at the same time general enough to allow broad application. This tradeoff is difficult to reconcile with most existing methods. We propose to extend and combine existing models that operate at different scales and with different levels of data complexity into a modeling framework that will allow robust estimation of population response to environmental change across a species’ range. This integrated modeling is now possible with the increasing development and application of population...

Categories: Project; Tags: Active, All Working Groups, Bayesian, Climate, Ecosystems, All tags... Land Resources, Species, environmental change, Fewer tags

Researchers from North Carolina State University and the USGS integrated models of urbanization and vegetation dynamics with the regional climate models to predict vegetation dynamics and assess how landscape change could impact priority species, including North American land birds. This integrated ensemble of models can be used to predict locations where responses to climate change are most likely to occur, expressing results in terms of species persistence to help resource managers understand the long-term sustainability of bird populations.

Categories: Project; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: 2011, Birds, Birds, CASC, Climate Change, All tags... Completed, LCC, Projects by Region, SERAP, Science Tools for Managers, Science Tools for Managers, South Atlantic LCC, South Atlantic Landscape Conservation Cooperative, South Atlantic Landscape Conservation Cooperative, Southeast, Southeast CASC, Wildlife and Plants, Wildlife and Plants, Fewer tags

Capture-recapture meets big data: integrating statistical classification with ecological models of species abundance and occurrence

Advances in new technologies such as remote cameras, noninvasive genetics and bioacoustics provide massive quantities of electronic data. Much work has been done on automated (“machine learning”) methods of classification which produce “sample class designations” (e.g., identification of species or individuals) that are regarded as observed data in ecological models. However, these “data” are actually derived quantities (or synthetic data) and subject to various important sources of bias and error. If the derived quantities are used to make ecological determinations without consideration of these biases, those inferences which inform monitoring, conservation, and management will be flawed. We propose to develop...

Categories: Project; Tags: Active, All Working Groups, Classification, Core Science Systems, Ecological Modelling, All tags... Ecological Modelling, Ecosystems, Ecosystems, Machine learning, ecosystem ecology, synthesis, Fewer tags

Modeling structured population dynamics using data from unmarked individuals

The study of population dynamics requires unbiased, precise estimates of abundance and vital rates that account for the demographic structure inherent in all wildlife and plant populations. Traditionally, these estimates have only been available through approaches that rely on intensive mark-recapture data. We extended recently developed N-mixture models to demonstrate how demographic parameters and abundance can be estimated for structured populations using only stage-structured count data. Our modeling framework can be used to make reliable inferences on abundance as well as recruitment, immigration, stage-specific survival, and detection rates during sampling. We present a range of simulations to illustrate the...

Categories: Data, Publication; Types: Citation