New Tools for Developing and Analyzing Population Models with RAMAS
Recent developments make it easier to use RAMAS Metapop and RAMAS GIS to develop and
analyze population models.
Using mark-recapture data
Mark-recapture data are collected by marking individuals (e.g., using bands or tags) at their first
capture and recording their subsequent recaptures. This type of data is immensely valuable for
estimating parameters of population models, including survival rates and fecundities. Many
methods have been developed for analyzing such data, but most of them are either incomplete
(i.e., they do not allow a full population model) or are too complex.
A new method, implemented as an R script, allows building fully-specified population models for
RAMAS, based only on mark-recapture data (Ryu et al. 2016). It creates a fully specified RAMAS
model file, which includes stage structure, standard deviations, and density dependence functions.
Its main features include
estimating true survival based on apparent survival estimates and population trends;
fecundity as an unbiased estimate of juvenile:adult ratio, by using the relative capture probabilities of juveniles and adults;
estimating density dependence in survival and fecundity;
estimating natural temporal variability in survival and fecundity (excluding sampling variability);
creating ready-to- run RAMAS input files;
incorporating uncertainties and preparing the files necessary for a global sensitivity analysis (see below).
The new method, including the R script, data for case studies and sample results, is freely available
Global sensitivity analysis
The sensitivity analysis module of RAMAS GIS (see Chapter 13 of the manual) allows analyzing
sensitivity to one parameter at a time. A more comprehensive method, called global sensitivity
analysis, considers all parameters simultaneously.
A new method, implemented as an R package, allows global sensitivity analyses using RAMAS
(Aiello-Lammens and Akçakaya 2016). The R package, including sample data and a tutorial, is
freely available at https://github.com/mlammens/demgsa.
Aiello-Lammens, M.A. and H.R. Akçakaya. 2016. Using global sensitivity analysis of demographic models for ecological impact assessment. Conservation Biology (in press). DOI: 10.1111/cobi.12726
Ryu, H.Y., K.T. Shoemaker, É. Kneip, A.M. Pidgeon, P.J. Heglund, B.L. Bateman, W.E. Thogmartin, and H.R. Akçakaya. 2016. Developing population models with data from marked individuals. Biological Conservation 197:190–199.