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Quantitative Analysis Group

The Quantitative Analysis Group (Q-Hui) is a team of four experts that advises on design, implementation, analysis, and interpretation of population- and habitat-centered wildlife science. Contact us.

Paul Berkowitz, Kevin Brinck, Rick Camp, and Marcos Gorresen are QAG employees.

They are employees of the Hawaii Cooperative Studies Unit of the University of Hawaii at Hilo, and have a long history of collaboration with PIERC, other federal and state agencies, and NGOs. The Q-Hui can be brought in to assist at any stage of a project - before, during, and after data collection - or for review and comment on theoretical issues such as design of sampling protocols, sampling effort, and analytic strength, as well as applied issues such as GIS analysis and database management.

Examples of Q-Hui expertise

Project Planning

  • Monitoring and experimental design
  • Power analysis
  • Efficient sampling design
  • Spatial and temporal sampling design
  • Geodatabase design
  • Relational database design for research and monitoring
  • Statistical consultation

Project Implementation

  • Point-transect distance sampling bird surveys
  • Quantitative vegetation surveys
  • Occupancy sampling bird and bat surveys
  • Infra-red (IR) videography
  • Automated detection of animal occurrence (e.g., bat echolocation sampling)
  • Population size and distribution trend assessments
  • Relational and geospatial database management

Theoretical Issues

  • Spatial and temporal sampling design
  • Sampling rare or elusive species
  • Adaptive management of monitoring protocols
  • Assess estimator bias and improve sampling techniques

Data Analysis

  • GIS analysis and mapping of results
  • Geospatial habitat occupancy
  • Adjusting sampling metrics for detection probability
  • Habitat selection
  • Automated vegetation mapping
  • Population Viability Analysis (PVA)
  • Time-series and repeated measures analysis
  • Trend analysis
  • Parametric statistics
  • Distribution-free statistics
  • Hierarchal models
  • Bayesian inference (e.g., occupancy, abundance, and trend computations)
  • Model selection and validation

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