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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure 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 cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Optimal Foraging00:48

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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...
What are Populations and Communities?00:30

What are Populations and Communities?

Populations are groups of individuals of the same species that inhabit a shared environment. Communities include multiple co-existing, interacting populations of different species. Metapopulations span multiple populations of the same species that occupy different areas. Metapopulations interact through immigration and emigration, providing genetic diversity that lends resilience to harsh environments. Population size and density can be estimated using quadrat and mark and recapture...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

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Spotting Cheetahs: Identifying Individuals by Their Footprints
09:47

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Published on: May 1, 2016

Tigers on trails: occupancy modeling for cluster sampling.

J E Hines1, J D Nichols, J A Royle

  • 1United States Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland 20708, USA. jhines@usgs.gov

Ecological Applications : a Publication of the Ecological Society of America
|July 30, 2010
PubMed
Summary
This summary is machine-generated.

New occupancy models account for spatial dependence in sampling, improving tiger occupancy estimates. These models are crucial for accurate conservation monitoring and understanding species distribution.

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Area of Science:

  • Ecology
  • Conservation Biology
  • Statistical Modeling

Background:

  • Occupancy modeling estimates species distribution using detection/non-detection data.
  • Standard models assume random spatial sampling, which is often violated in practice.
  • Non-random sampling designs can introduce spatial dependence, biasing occupancy estimates.

Purpose of the Study:

  • Develop novel occupancy models for data with Markovian spatial dependence.
  • Evaluate the performance of new models compared to standard approaches.
  • Improve the accuracy of occupancy estimation in conservation monitoring.

Main Methods:

  • Developed a Markov model for spatial dependence and a trap response model with Markovian detections.
  • Simulated data under spatial dependence to compare model performance.
  • Applied models to tiger occupancy survey data from India.

Main Results:

  • Standard occupancy models showed substantial bias with non-random sampling.
  • New models significantly reduced bias, with the spatial process model yielding negligible bias.
  • The Markovian spatial dependence model was strongly supported by tiger survey data.

Conclusions:

  • New occupancy models effectively handle spatial dependence in sampling designs.
  • These models enhance the accuracy of species distribution and conservation assessments.
  • The developed models offer valuable tools for ecological and conservation research.