Clinton Leach, PhD
Assistant Unit Leader - Nebraska Cooperative Fish and Wildlife Research Unit
Science and Products
Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models
Sea otters are apex predators that can exert considerable influence over the nearshore communities they occupy. Since facing near extinction in the early 1900s, sea otters are making a remarkable recovery in Southeast Alaska, particularly in Glacier Bay, the largest protected tidewater glacier fjord in the world. The expansion of sea otters across Glacier Bay offers both a challenge to...
Authors
Clinton B. Leach, Benjamin P. Weitzman, James L. Bodkin, Daniel Esler, George G. Esslinger, Kimberly A. Kloecker, Daniel Monson, Jamie N. Womble, Mevin B. Hooten
Recursive Bayesian computation facilitates adaptive optimal design in ecological studies Recursive Bayesian computation facilitates adaptive optimal design in ecological studies
Optimal design procedures provide a framework to leverage the learning generated by ecological models to flexibly and efficiently deploy future monitoring efforts. At the same time, Bayesian hierarchical models have become widespread in ecology and offer a rich set of tools for ecological learning and inference. However, coupling these methods with an optimal design framework can become
Authors
Clinton B. Leach, William J. Perry, Joseph M. Eisaguirre, Jamie N. Womble, Michael R. Bower, Mevin Hooten
Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling
Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti, depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but linking them requires a modeling framework that accounts for the complex non-linear mechanisms...
Authors
Clinton B. Leach, Jennifer A. Hoeting, Kim M. Pepin, Alvaro E. Eiras, Mevin Hooten, Colleen T. Colleen T. Webb
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.
Science and Products
Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models
Sea otters are apex predators that can exert considerable influence over the nearshore communities they occupy. Since facing near extinction in the early 1900s, sea otters are making a remarkable recovery in Southeast Alaska, particularly in Glacier Bay, the largest protected tidewater glacier fjord in the world. The expansion of sea otters across Glacier Bay offers both a challenge to...
Authors
Clinton B. Leach, Benjamin P. Weitzman, James L. Bodkin, Daniel Esler, George G. Esslinger, Kimberly A. Kloecker, Daniel Monson, Jamie N. Womble, Mevin B. Hooten
Recursive Bayesian computation facilitates adaptive optimal design in ecological studies Recursive Bayesian computation facilitates adaptive optimal design in ecological studies
Optimal design procedures provide a framework to leverage the learning generated by ecological models to flexibly and efficiently deploy future monitoring efforts. At the same time, Bayesian hierarchical models have become widespread in ecology and offer a rich set of tools for ecological learning and inference. However, coupling these methods with an optimal design framework can become
Authors
Clinton B. Leach, William J. Perry, Joseph M. Eisaguirre, Jamie N. Womble, Michael R. Bower, Mevin Hooten
Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling
Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti, depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but linking them requires a modeling framework that accounts for the complex non-linear mechanisms...
Authors
Clinton B. Leach, Jennifer A. Hoeting, Kim M. Pepin, Alvaro E. Eiras, Mevin Hooten, Colleen T. Colleen T. Webb
Non-USGS Publications**
**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.