RAMAS Ecotoxicology and RAMAS Ecosystem

Population and Community-level Risk Assessment Software

funded by Electric Power Research Institute

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

• structured single populations
• food chains

        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 extinction's or algal blooms. RAMAS Ecotoxicology and RAMAS Ecosystem are practical tools that highlights the importance of including ecological interactions in risk assessments.

RAMAS Ecotoxicology and RAMAS Ecosystem

Population-level Ecotoxicological Risk Assessment

        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 population 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 and RAMAS Ecosystem use 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.

• 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

• 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

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 9e.g. (10,1) mg per liter) . Automatic unit conversions and checking for dimensional consistency
• Dose-response model: Weibul, probit, logit
• Predator-pray interactions: Lotka-Volterra, Holling type II. Ratio-dependent
• Density dependence: ceiling, logistic, Ricker, Beverton-Holt
• Monte Carlo treatment of measurement error and environmental variation
• Summarize results as biomass/abundance projections and risk statistics
• Display graphs and tables, save or paste into other applications
• Comprehensive online Help

Minimum system requirements: IBM PC-compatible, 486 with VGA monitor, Windows 3.1 (math coprocessor and 8Mb RAM recommended).

        Developed with support from the Electric Power Research Institute. RAMAS is registered trademark of Applied Biomathematics. Other companies and product mentioned herein are trademarks or registered trademarks of their respective trademark owners.