The quantitative study of marked individuals in ecology, evolution ...

EURING 2003 Radolfzell
• Combined Survival-Transition probabilities can be represented as such or decomposed
into transition and survival probabilities (Hestbeck, Nichols et al. 1991).
• Among the transition probabilities with the same state of departure, the one to be
computed as 1 minus the others can be freely picked by the user. User-friendliness is
enhanced by the easiness with which constrained models are built, using a language,
interpreted by a generator of design matrices called GEMACO. This language is alike
those in general statistical software such as SAS or GLIM, i.e., a formula such as t+g
generates a model with additive effects of time and group, thus avoiding tedious and
error-prone matrix manipulations using an editor or a spread-sheet. Examples of various
types of multistate models are developed and presented.
You can download M-Surge freely from ftp.cefe.cnrs-mop.fr/biom/Soft-CR.
03:10 PM - 03:45 PM
DENSITY: software for fitting spatial detection functions to data from passive
sampling
Murray Efford & Deanna Dawson
Rigorous sampling of bird populations to estimate density raises the problem of incomplete
detection (e.g., Pollock et al. 2002). Varying detectability is widely acknowledged,
but variation in its spatial component (how detection declines with distance) is
considered less often. Active methods (double sampling and distance sampling) require
an observer to determine the instantaneous distance between the sampling
point and each animal. Passive methods (mistnets or traps) rely on animals moving to
the detector: instantaneous locations are unknown, and movement is an important
unmeasured component of detectability.
New methods have been developed to fit spatial detection functions to capturerecapture
data from passive detectors. The methods are computer-intensive and depend
on specialised software ('DENSITY'), available for download at
www.landcareresearch.co.nz. DENSITY provides a graphical interface for the analysis
of closed-population capture-recapture data from arrays of passive detectors. Its
simulation capability enables users to perform power analysis of different sampling
designs before going into the field.
We demonstrate the use of DENSITY to estimate bird population density from mist
netting data. Netting was conducted over 1992 to 1996 on a forest-pasture ecotone in
Mexico. In each of 16 netting sessions, six local arrays of 20 nets were run for 2-3
consecutive days. Despite the large number of captures in total, within-session recaptures
were rare for most species. This restricted application of the method to a few
common species (e.g. Sporophila torqueola) and to species aggregates (e.g. 'winter
residents'). Although the use of the method for mist netting data was experimental, it
may lead through simulation in DENSITY to improved design of mist net arrays where
density estimation is a study goal.
The spatially explicit framework of DENSITY opens up new possibilities and we expect
the software to evolve. An exciting prospect is the direct fitting of simple density
surfaces during estimation, given suitable spatial covariates.
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