New features and changes in DENSITY 3.2

Contents of this page:
General
Input
Habitat masks
Output
Closed population estimation
Inverse prediction to estimate Density
Changed defaults for inverse prediction
Open population analysis

Simulation (Power analysis)
Trap builder
Monte Carlo tests for serial correlation
Bugs fixed in Density 3.2
Known bugs and warnings
Minor new features
Discontinued options
Implementation in progress
Acknowledgements

General

Input

Habitat masks

Output

Closed population estimation

Inverse prediction to estimate Density

Changed defaults for inverse prediction in Density 3.2

Open population analysis

Density performs simple multi-session analyses with a variety of models. These include  the closed-form Jolly-Seber methods described by Pollock et al. (1990) (‘Direct’), maximisation of the Cormack-Jolly-Seber likelihood (‘CJS’) or the Pradel (1996) likelihood with γ parameterisation (‘Pradel gamma’), and reduced-parameter versions of the CJS model.

First check Options | Miscellaneous | ‘Enable multi-session analysis’ and then select the desired model under Options | Open population. Note that when Model = ‘Direct’ estimates of survival f and seniority γ are not constrained to lie between 0 and 1. Pradel’s γ and the derived measure of rate of population change (λt) are available both from the Direct method (capture histories are automatically reversed to get γ), from the ‘Pradel gamma’ method, and when a supplementary ‘reversed CJS’ model is used in tandem with CJS to estimate γ.

 Bootstrap confidence intervals may be calculated. See Options | Open population for bootstrap n. The a-level is controlled on the Options | Output page (e.g. a = 0.05 for 95% confidence interval). Bootstrap confidence intervals override the default type in Options | Output.

Simulation (Power analysis)

Density simulates trapping by applying a specified detection model to a population with known parameters. Version 3.2 expands the range of simulation options.

Trap builder

The trap builder creates trap layout files from building blocks with a standard geometry – e.g. trapping grids, webs or lines. New options are:

Monte Carlo tests for serial correlation of capture locations

Serial correlation invalidates the ‘mean recapture distance’ (d-bar) measure of home range. Two tests are implemented.

Bugs fixed in Density 3.2

Known bugs and warnings

Minor new features and changes

Discontinued options

Implementation in progress

Acknowledgements

Many thanks to Deb Wilson, Dave Ramsey, Ed Debevec, and Grant Norbury for bug reports and ideas, and to Dave Fletcher for statistical support.


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