PIMS

Montana

got these links:

PIMS

Montana

[

link to www.montana.edu]

PIMS

Montana

Know-fate data

[

link to www.google.com]

Then I clicked on this one:

Interesting!!

[

link to www.phidot.org]

In previous chapters, we’ve spent a considerable amount of time modeling situations where the

probability of encountering an individual is less than 1. However, there is at least one situation where

we do not have to model detection probability – known-fate data, so-called because we know the fate

of each marked animal with certainty. In other words, encounter probability is 1.0 (which must be

true if we know the fate of a marked individual with certainty). This situation typically arises when

individuals are radio-marked, although certain kinds of plant data can also be analyzed with the

known fate data type. In such cases, known-fate data are important because they provide a theory for

estimation of survival probability and other parameters (such as emigration). The focus of known fate

models is the estimation of survival probability S, the probability of surviving an interval between

sampling occasions. These are models where it can be assumed that the sampling probabilities are

1. That is, the status (dead or alive) of all tagged animals is known at each sampling occasion. For

this reason, precision is typically quite high, as precise as the binomial distribution allows, even in

cases where sample size is often fairly small. The only disadvantages might be the cost of radios and

possible effects of the radio on the animal or its behavior. The model is a product of simple binomial

likelihoods. Data on egg mortality in nests and studies of sessile organisms, such as mollusks, have

also been modeled as known fate data.