mads.data

<p>These data were generated using DSsim. Two populations were generated inside
a rectangular study region, one of these is called 'CD' (common dolphin) and the 
other is 'WSD' (white-sided dolphin). Density was assumed to be equal across the 
study area and the population sizes for the CD and WSD populations were 3000
and 1500, respectively. Detections of individuals were simulated based on half
normal detection functions with a scale parameter of 0.5 and a truncation 
distance of 1. A systematic parallel line transect design was used. Once both 
sets of data had been generated they were combined and 10
randomly selected to be in the unidentified sightings category.</p>

datasets

Performs distance sampling analyses on a number of species
at once and can account for unidentified sightings, model uncertainty
and covariate uncertainty. Unidentified sightings refer to sightings
which cannot be allocated to a single species but may instead be
allocated to a group of species. The abundance of each unidentified
group is estimated and then prorated to the species estimates. Model
uncertainty should be incorporated when multiple models give equally
good fit to the data but lead to large differences in estimated
density / abundance. Covariate uncertainty should be incorporated
when covariates cannot be measured accurately, for example this is
often the case for group size in marine mammal surveys. Variance
estimation for these methods is via a non parametric bootstrap.
The methods implemented are described in Gerodette T. and Forcada
J. (2005) <doi:10.3354/meps291001> Non-recovery of two spotted and
spinner dolphin populations in the eastern tropical Pacific Ocean.

Laura Marshall

mads

Multi-Analysis Distance Sampling

mads.data function

<p>This is a list of 4 items. The first is dist.data a dataframe with 
distance sampling data including the columns object, transect.ID, distance, 
x, y, true.species, unid, species, observer. The other items are the 
region, sample and observations tables as per the definitions in mrds.</p>

mads.data: Example simulated data used to demonstrate the package functionality

Description

Arguments

Format