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Related Concept Videos

Observational Studies01:11

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One example of...
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
Naturalistic Observations02:30

Naturalistic Observations

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

A hierarchical modeling framework for multiple observer transect surveys.

Paul B Conn1, Jeffrey L Laake, Devin S Johnson

  • 1National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, Seattle, Washington, United States of America. paul.conn@noaa.gov

Plos One
|August 21, 2012
PubMed
Summary
This summary is machine-generated.

Ecologists can now estimate animal populations more accurately using a new hierarchical modeling framework for multiple observer transect surveys. This method improves extrapolation to unsampled areas and incorporates habitat data for better abundance inference.

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Area of Science:

  • Ecology
  • Wildlife Population Monitoring
  • Statistical Modeling

Background:

  • Multiple observer transect surveys are common for animal population census.
  • Existing methods have limitations in extrapolation, information sharing, and accommodating habitat-abundance relationships.

Purpose of the Study:

  • Introduce a novel hierarchical modeling framework for multiple observer line transects.
  • Overcome limitations of existing analysis approaches for transect data.

Main Methods:

  • Utilize a hierarchical model with a complete data representation of the state space.
  • Employ reversible jump Markov chain Monte Carlo (MCMC) for unobserved animals and covariates.
  • Model observer detections using a bivariate normal distribution on the probit scale with distance-dependent correlation.

Main Results:

  • Demonstrate accurate inference of abundance and related parameters using simulated and real data (golf tees).
  • Achieve precise estimation of population-level covariates, such as group size.
  • Showcase the framework's ability to model abundance intensities as a function of habitat covariates.

Conclusions:

  • Recommend hierarchical models for analyzing multiple observer transect data, especially when strict sampling designs are challenging.
  • Highlight the framework's utility for extrapolating to unsampled areas and integrating ecological factors.
  • Introduce the R package 'hierarchicalDS' to support the implementation of these models.