Projects found


1-5 of 35 shown   (21 visible only to EcologyBox members, 33 visible only to project members).    
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  • AgeNe

    AgeNe calculates effective population size (Ne), effective number of breeders per year (Nb), and Ne/N and Nb/N in populations with overlapping generations


    Chi Do and Robin Waples

    Note:  Version2.0 (September 2013) replaces V1.1 (March 2011) and fixes a bug in calculation of Nb when the user specifies overdispersed variance of same-age, same-sex individuals.

    AgeNe is a stand-alone Fortran95 program for PCs that uses demographic data (age-specific vital rates, as are found in a Leslie matrix) to calculate Ne in age-structured populations.  AgeNe combines the best features of Felsenstein's (1971) and Hill's (1972, 1979) methods.  Like Felsenstein’s, the new method is based on age-specific survival and fertility rates and therefore can be directly applied to any species for which life-table data are available. Like Hill, we relax the restrictive assumptions in Felsenstein’s model regarding variance in reproductive success, which allows more general application.  The basic principle underlying the new method is that age structure stratifies a population into winners and losers in the game of life: individuals that live longer have more opportunities to reproduce and therefore have a higher mean lifetime reproductive success.  Grouping individuals by age at death provides a simple means of calculating lifetime variance in reproductive success of a newborn cohort.

    Some features of AgeNe:

    • - It uses demographic information of the type found in a life table or Leslie matrix.
    • - It can accommodate two sexes with unequal primary sex ratio and/or different vital rates.
    • - It can accommodate sex-specific and/or age-specific departures from Poisson variance in reproductive success.
    • - It can calculate Ne and Ne/N based on various ways of defining N.
    • - It can calculate the effective number of breeders each year (Nb), which represent the effective number of parents that produce a single cohort.
    • - It can accommodate a haploid life history.
    • - It can calculate Ne in species that change sex during their lifetime.

    Please familiarize yourself with AgeNe's model and assumptions.  For more information please consult the AgeNe ReadMe document and Waples et al. (2011); see details below.

    AgeNe reads user-generated text input files and allows batch processing.

    AgeNe runs in a DOS window and has a simple command-line interface.  It is not necessary to install AgeNe; simply double-click AgeNe.exe and you will be prompted for the names of an input file and output file.  

    AgeNe is compiled for use on Windows and DOS operating systems; at present it is not designed for use on Mac, Linux or other operating systems.


    [Version Info]

    Version 2.0 released September 2013, replaces Version 1.1 (March 2011)

    [Other Links]

    For more information:

    Waples, R.S., C. Do, and J. Chopelet.  2011.  Calculating Ne and Ne/N in age-structured populations:  a hybrid Felsenstein-Hill approach.  Ecology 92:1513-1522.

    Related Articles:

    Waples, R. S.  2010.  Spatial-temporal stratifications in natural populations and how they affect understanding and estimation of effective population size.  Molecular Ecology Resources 10: 785–796.

    Felsenstein, J. 1971. Inbreeding and variance effective numbers in populations with overlapping generations. Genetics 68:581–597.

    Hill, W.G. 1972.  Effective size of population with overlapping generations. Theoretical Population Biology 3:278–289.

    Hill, W. G. 1979 A note on effective population size with overlapping generations. Genetics 92:317–322.



    For an application, see:

    Waples, R. S., G. Luikart, J. R. Faulkner, D. A. Tallmon.  2013.  Simple life history traits explain key effective population size ratios across diverse taxa.  Proc. Royal Society London, Ser. B. 280: 20131339, published 7 August 2013.



  • Animate acoustic tag data in Excel

    by brice.semmens, last updated 4/24/09, sharing set to public
    This program (VBA implemented in Excel) animates the path of a tagged animal in a VR2 hydrohpone network. It DOES NOT animate real time sped up. Instead, it animates movement based on relocation events. Thus, whenever a tag is heard at a new hydrophone, the map updates, and provides a summary of how many times and over how many days the animal/tag was heard at that hydrophone. This is a good strategy when you are interested in highlighting migrations as opposed to daily movements, for instance.
  • BaMM

    by gholtgrieve, last updated 8/6/13, sharing set to public
    BaMM (for Bayesian Metabolic Model) is a Bayesian statistical model of oxygen dynamics which accounts for the dominant physical and biological processes that control dissolved oxygen in aquatic ecosystems. Using this model is it possible to estimate ecosystem metabolic rates (gross primary production, community respiration, gas exchange) from diel data of oxygen concentration and, if available oxygen-18 isotopes.

    Please see the instructions document for a description on how to work with BaMM.
  • Bayes Factors (R)

    by eric.ward, last updated 6/5/07, sharing set to public
    These routines allow you to take a matrix of MCMC samples and calculate the Bayes factor based on the harmonic mean algorithm proposed by Gelfand and Dey (1994). Caution: Bayes factors tend to be numerically unstable!
  • Bayesian SI mixing models

    by eric.ward, last updated 11/20/09, sharing set to public
    This project is a variation of stable isotope mixing models. Previous approaches have fixed source parameters (mean, variance) at their MLE estimates, and proceeded to do a Bayesian analysis of the mixture of consumer diets. This assumption is fine when sample sizes are large, but often in ecology, they are small. Incorporating this uncertainty adds additional parameters, and slightly increases the total variance of mixture estimates, but has the advantage of improving mixing and reducing bias. Often, using the traditional approach may lead to multi-modal estimates of the mixture; we demonstrate that the fully Bayesian mixing model avoids this problem. A final benefit is that it allows prior information about sources to be included. The code folder contains the code to replicate the comparison done with simulated datasets.


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