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| RAMAS Stage | RAMAS Ecotoxicology |

RAMAS Stage
Generalized Stage Modeling for Population Dynamics

RAMAS Stage

        RAMAS Stage lets a user build, run and analyze discrete-time models for species with virtually any life history. It is useful for modeling species with complex life histories or other biologies in which stage membership (rather than age) determines the demographic characteristics of an individual.

        RAMAS Stage comes with templates for species from many taxa (such as mammals, insects, fish, birds and plants) which are easy to customize. RAMAS Stage allows a model to include environmental factors which vary through time, and it yields summaries of the expected abundance of stages and the uncertainty of those expectations. It also estimates the risks that an abundance will fall below, or grow above, any threshold.

General life histories. Simple models can be based on developmental stage. These include widely used transition matrix models such as Leslie or Lefkovitch formulations. More complicated models can be constructed to represent more complex life histories that may include multidimensional structure (for example age and size simultaneously) and linear or nonlinear transitions.

Environmental fluctuations.   Models may include information on environmental factors that influence the population such as temperature or rainfall. These are specified in terms of their means, variances, and temporal autocorrelations. Specifying environmental fluctuation allows the user to introduce stochasticity into models.

Risk assessment. RAMAS Stage estimates the chance that a population will go extinct or suffer a decline. It also estimates the chance that the population will grow to some level. It can also compute the risks for any function of the stage abundances.

        RAMAS Stage is a novel implementation of a Lefkovitch matrix. It generalizes RAMAS Age which was based on the traditional Leslie matrix. In a Lefkovitch matrix, any of the transition elements may have non-zero values. This is necessary to represent phenomena observed in some species that cause individuals to skip stages, revert to previous stages, or produce offspring of different status. RAMAS Stage allows you to substitute a function for any of these coefficients. These functions can take constants, the abundances of other stages, or the values of environmental factors as arguments.

        RAMAS Stage supports generalized models of life histories based on stages (such as seeds, seedling, ..., canopy trees), simple or complex transitions (skipping, regression), single- or multidimensional structure (such as age and size), linear or nonlinear transitions, pulse or event-structured dynamics. Its interface features a wide variety of example models for several taxa, a graphical model editor, a matrix editor, and an equation list editor.

        The outputs produced by RAMAS Stage include average population size over time, interval and terminal quasi-extinction risk, percent decline and quasi-explosion probability, and time to cross predefined threshold levels. The program also computes the asymptotic growth rate (dominant eigenvalue), the stable distribution, reproductive values, average stage residence times, sensitivities and elasticities.

        RAMAS Stage was originally developed for the United States electric power industry under the sponsorship of the Electric Power Research Institute.

Requirements:   IBM PC compatible; DOS 3+; 380K RAM; VGA, EGA, MCGA, CGA, or Hercules video. A math coprocessor is recommended.

Cost: See Software Price List and Ordering Information.


 
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RAMAS Ecotoxicology and RAMAS Ecosystem
Population and Community-level Risk Assessment Software


RAMAS Ecotoxicology

        RAMAS Ecotoxicology and RAMAS Ecosystem carry out ecological risk assessments for systems of two kinds:

  • structured single populations
  • food chains

  Link bioassay results to population and community dynamics with Windows software…

Food chain/web models (RAMAS Ecosystem)


  • Build chain or web using boxes and arrows
  • Model toxicant kinetics or set bioaccumulation factors
  • Select dose-response and predator-prey functions
  • Simulate dynamics and estimate risk or adverse events

 

Structured population models (RAMAS Ecotoxicology)

  • Specify survival and fecundity for each age/stage class
  • Add density dependence for selected ages/stages
  • Select dose-response models for survival and fecundity
  • Estimate population-level parameters such as growth rate and extinction risk
 

        In each case, a model of population dynamics and toxicant kinetics is constructed using a simple Windows interface, and linked to bioassay data. Parameters can be specified as scalars, intervals or distributions, to take account of environmental variability and ignorance. Monte Carlo simulations are then used to predict future population trajectories, and calculate the risk of adverse events such as extinctions or algal blooms.RAMAS Ecotoxicology and RAMAS Ecosystem are practical tools that highlight the importance of including ecological interactions in risk assessments.

Population-level Ecotoxicological Risk Assessment (RAMAS Ecotoxicology)

        RAMAS Ecotoxicology is used to make population-level ecological risk assessments for environmental contaminants. It imports data from standard laboratory bioassays, incorporates these data into the parameters of a population model, and performs a risk assessment by analyzing population-level differences between control and impacted samples.

        Bioassays for assessing the impact of toxins on natural systems are usually expressed in terms of individual-level assessment endpoints such as growth, survivorship and fecundity. RAMAS Ecotoxicology translates such results into a forecast of their likely consequences at the level of the entire population. For instance, if there is an increase in mortality rate due to a contaminant, the meaning of this effect can only be determined by projecting the consequence in terms of the total population’s future abundance and vitality. It is generally important to do this projection to the poulation level because impacts at the organismal level cannot be easily extrapolated to predict their population-level consequences. For instance, minor and inconspicuous impacts on individuals can sometimes cascade through population dynamics into significant effects at the level of the population. Conversely, seemingly major impacts on individuals may translate into only minor population-level consequences once the normal population feedbacks have been taken into account. Moreover, contradictory findings are possible at the level of the individual (e.g., decreased survival but increased fecundity) that must be resolved.

        RAMAS Ecotoxicology uses stage-structured single-population models and food chain models to make the necessary projections. The software checks the validity of the input and model structure specified by the user. It uses a sophisticated second-order Monte Carlo engine to project both natural temporal variability and measurement error, and expresses its results in risk-analytic outputs such as the risk of the population’s declining to a given level.

        RAMAS Ecotoxicology was developed by Applied Biomathematics with support from the Electric Power Research Institute.

 

Ecosystem-level Ecotoxicological Risk Assessment

Manage variability and uncertainty, express results as ecological risks.

Features include:

  • Specify parameters as scalar numbers, intervals (e.g. [10,15] mg per liter) or distributions (e.g. [10,1]mg per liter) . Automatic unit conversions and checking for dimensional consistency
  • Dose-response model: Weibull, probit, logit
  • Predator-prey interactions: Lotka-Volterra, Holling type II. Ratio-dependent
  • Density dependence: ceiling, logistic, Ricker, Beverton-Holt
  • Monte Carlo treatment of measurement error and evironmental variation
  • Summarize results as biomass/abundance projections and risk statistics
  • Display graphs and tables, save or paste into other applications
  • Comprehensive online help

        RAMAS Ecosystem was developed by Applied Biomathematics with support from the Electric Power Research Institute.

 
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