Inferences about population dynamics from count data using multistate models: a comparison to capture-recapture...

Area of Science:

• Ecology
• Population Biology
• Wildlife Management

Background:

• Wildlife population dynamics are driven by heterogeneous individual contributions.
• Accurate estimation of abundance and demographic rates is crucial for conservation.
• Traditional capture-recapture methods are intensive and spatially limited.

Purpose of the Study:

• To extend multistate, open population N-mixture models to estimate abundance and demographic rates from count data alone.
• To develop a method for handling unassigned states in N-mixture models.
• To assess the model's performance against capture-recapture data for black-throated blue warblers.

Main Methods:

• Developed an extension of N-mixture models to incorporate unassigned states.
• Utilized state-specific count data to estimate local abundance, recruitment, and survival.
• Applied the model to a 25-year dataset of black-throated blue warblers at Hubbard Brook Experimental Forest.

Main Results:

• The extended N-mixture model accurately estimated population abundance and density-dependent recruitment/immigration.
• Estimates of local carrying capacity and per capita recruitment aligned with existing studies.
• The model showed moderate to severe underestimation of survival probabilities for different sex and age classes.

Conclusions:

• N-mixture models provide a valuable tool for estimating wildlife population parameters from count data, reducing the need for intensive sampling.
• While effective for abundance and recruitment, survival estimates may require integration with intensive data collection methods.
• Integrated population models combining diverse data sources offer the most efficient approach for large-scale demographic estimation.