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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...
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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
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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
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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...
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Wildlife populations consist of individuals that contribute disproportionately to growth and viability. Understanding a population's spatial and temporal dynamics requires estimates of abundance and demographic rates that account for this heterogeneity. Estimating these quantities can be difficult, requiring years of intensive data collection. Often, this is accomplished through the capture and recapture of individual animals, which is generally only feasible at a limited number of locations. In contrast, N-mixture models allow for the estimation of abundance, and spatial variation in abundance, from count data alone. We extend recently developed multistate, open population N-mixture models, which can additionally...
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