Recent developments make it easier to use RAMAS Metapop and RAMAS GIS to develop and

analyze population models.

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Using mark-recapture data

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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

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1. estimating true survival based on apparent survival estimates and population trends;

2. fecundity as an unbiased estimate of juvenile:adult ratio, by using the relative capture probabilities of juveniles and adults;

3. estimating density dependence in survival and fecundity;

4. estimating natural temporal variability in survival and fecundity (excluding sampling variability);

5. creating ready-to- run RAMAS input files;

6. incorporating uncertainties and preparing the files necessary for a global sensitivity analysis (see below).

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The new method, including the R script, data for case studies and sample results, is freely available

at https://github.com/Akcakaya/MAPS-to-Models.

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Global sensitivity analysis

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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.