Models for Estimating Abundance from Repeated Counts of an Open Metapopulation

DOI10.1111/J.1541-0420.2010.01465.XzbMath1217.62164OpenAlexW2157289208WikidataQ42669697 ScholiaQ42669697MaRDI QIDQ112786

D. Dail, L. Madsen, L. B. Madsen, D. Dail

Publication date: 21 July 2010

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2010.01465.x

zbMATH Keywords

breeding bird surveyclosure testavian point countsN-mixture modelspopulation dynamics estimationspatially replicated counts

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to environmental and related topics (62P12) Sampling theory, sample surveys (62D05)

Related Items (10)

Computational aspects of N‐mixture models ⋮ Models for jointly estimating abundances of two unmarked site-associated species subject to imperfect detection ⋮ Computational efficiency and precision for replicated-count and batch-marked hidden population models ⋮ Estimating reproduction and survival of unmarked juveniles using aerial images and marked adults ⋮ Spatially explicit models for inference about density in unmarked or partially marked populations ⋮ Estimating Open Population Site Occupancy from Presence–Absence Data Lacking the Robust Design ⋮ Hidden Markov models for extended batch data ⋮ quickNmix ⋮ Estimating the use of public lands: integrated modeling of open populations with convolution likelihood ecological abundance regression ⋮ Bayesian binomial mixture models for estimating abundance in ecological monitoring studies

Uses Software

• R

Cites Work

• Unnamed Item
• Hierarchical modeling of cluster size in wildlife surveys
• N ‐Mixture Models for Estimating Population Size from Spatially Replicated Counts
• Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests Under Nonstandard Conditions
• A Comparison of n Estimators for the Binomial Distribution
• Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information
• Model Selection: An Integral Part of Inference
• Model Selection and Multimodel Inference

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