Mark-Recapture Science
Mark-Recapture Science
Filter Total Items: 6
Northeast Amphibian Research and Monitoring Initiative
The U.S. Geological Survey’s Eastern Ecological Science Center is home to the Northeast Amphibian Research and Monitoring Initiative (NEARMI), one of 7 ARMI regions across the United States. NEARMI works on public lands in thirteen states from Maine to Virginia, including many National Parks and National Wildlife Refuges.
Red Knot Migration and Population Ecology
Eastern Ecological Science Center biologists are studying migration and population ecology of the rufa red knot, a bird species that is dependent on horseshoe crab eggs to complete its trans-hemispheric migration. The birds' spring migration is timed with spawning of horseshoe crabs because the eggs are the perfect food for a migrating red knot.
Quantitative Turtle Analysis Project: Machine learning with turtles
The Quantitative Turtle Analysis Project (QTAP) was created in 2019 with the goal of investigating how machine learning can be used to study wildlife populations using capture-recapture methods. QTAP has specifically been researching how digital images of the eastern box turtle (Terrapene carolina carolina) can be used by automated programs to recognize unique individual turtles, in place of a...
Hierarchical Models of Animal Abundance and Occurrence
The Challenge: Research goals of this project are to develop models, statistical methods, sampling strategies and tools for inference about animal population status from survey data. Survey data are always subject to a number of observation processes that induce bias and error. In particular, inferences are based on spatial sampling – we can only ever sample a subset of locations where species...
Spatial Capture-Recapture Models to Estimate Abundance and Density of Animal Populations
The Challenge: For decades, capture-recapture methods have been the cornerstone of ecological statistics as applied to population biology. While capture-recapture has become the standard sampling and analytical framework for the study of population processes (Williams, Nichols & Conroy 2002) it has advanced independent of and remained unconnected to the spatial structure of the population or the...
Development of Statistical Methods for Biological Applications
The Challenge: Wildlife science and management are guided by data, and it is unquestionably the case that the greatest success occurs when good data are analyzed by good statistical methods.