Distance - Distance Sampling Detection Function and Abundance Estimation

A simple way of fitting detection functions to distance sampling data for both line and point transects. Adjustment term selection, left and right truncation as well as monotonicity constraints and binning are supported. Abundance and density estimates can also be calculated (via a Horvitz-Thompson-like estimator) if survey area information is provided. See Miller et al. (2019) <doi:10.18637/jss.v089.i01> for more information on methods and <https://examples.distancesampling.org/> for example analyses.

Last updated 16 days ago

8.26 score 9 stars 2 packages 338 scripts 2.0k downloads

mrds - Mark-Recapture Distance Sampling

Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.

Last updated 16 days ago

7.98 score 4 stars 6 packages 78 scripts 1.7k downloads

dsm - Density Surface Modelling of Distance Sampling Data

Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.

Last updated 2 years ago

6.13 score 8 stars 159 scripts 454 downloads

dssd - Distance Sampling Survey Design

Creates survey designs for distance sampling surveys. These designs can be assessed for various effort and coverage statistics. Once the user is satisfied with the design characteristics they can generate a set of transects to use in their distance sampling survey. Many of the designs implemented in this R package were first made available in our 'Distance' for Windows software and are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). Find out more about estimating animal/plant abundance with distance sampling at <http://distancesampling.org/>.

Last updated 1 years ago

5.79 score 1 stars 1 packages 34 scripts 364 downloads

dsims - Distance Sampling Simulations

Performs distance sampling simulations. 'dsims' repeatedly generates instances of a user defined population within a given survey region. It then generates realisations of a survey design and simulates the detection process. The data are then analysed so that the results can be compared for accuracy and precision across all replications. This process allows users to optimise survey designs for their specific set of survey conditions. The effects of uncertainty in population distribution or parameters can be investigated under a number of simulations so that users can be confident that they have achieved a robust survey design before deploying vessels into the field. The distance sampling designs used in this package from 'dssd' are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). General distance sampling methods are detailed in Introduction to Distance Sampling: Estimating Abundance of Biological Populations, Buckland et. al. (2004, ISBN-13: 978-0198509271). Find out more about estimating animal/plant abundance with distance sampling at <http://distancesampling.org/>.

Last updated 11 months ago

4.83 score 1 stars 67 scripts 286 downloads

mads - Multi-Analysis Distance Sampling

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) <10.3354/meps291001> Non-recovery of two spotted and spinner dolphin populations in the eastern tropical Pacific Ocean.

Last updated 1 years ago

2.30 score 2 stars 2 scripts 74 downloads