AI Article Synopsis
- The standard line transect methodology assumes that detection probabilities are influenced only by how far objects are from the transect line.
- Heterogeneity in these probabilities can often be reduced through stratification, but this isn't always possible with small sample sizes.
- The authors introduce a new method using conditional likelihood to include multiple factors in estimation, demonstrated with data from dolphin sightings in the eastern tropical Pacific.
Article Abstract
An implicit assumption of standard line transect methodology is that detection probabilities depend solely on the perpendicular distance of detected objects to the transect line. Heterogeneity in detection probabilities is commonly minimized using stratification, but this may be precluded by small sample sizes. We develop a general methodology which allows the effects of multiple covariates to be directly incorporated into the estimation procedure using a conditional likelihood approach. Small sample size properties of estimators are examined via simulations. As an example the method is applied to eastern tropical Pacific dolphin sightings data.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/j.0006-341x.2003.00107.x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!