STATISTICAL ISSUES IN ECOLOGICAL RISK ASSESSMENT

Biplob Das

Psyc 802

University of Regina

Student # 200228135

Fall 2008

STATISTICAL ISSUES IN ECOLOGICAL RISK ASSESSMENT

ABSTRACT

This paper discusses some statistical issues arising in the ecological data analysis based on biogeochemical measurements in ecosystems. A brief overview on statistical issues in ecological data analysis has been described in the first part of this discussion. In the later part replication and effect size issues are discussed. Replication of large-scale experiments is desirable, but the numbers of replicates needed are not known. Costs and feasibility of ecosystem experiments depend critically on the numbers of replicates needed because of the high cost per replicate and the scarcity of experimental ecosystems. A partial solution to determine statistical significance of non-replicated ecological variables was tested on two lake data by applying randomization technique. Statistical power plays a critical role in strengthening the interpretation of an experiment or analysis. For effect size issue, the analyses on Canada North Environmental Services (2003) data showed that statistical power is too low to detect any potential cumulative effects due to low sample sizes.

1

INTRODUCTION

Ecological risk assessment is defined as the process of assigning magnitudes and probabilities to the adverse effects of human activities or natural catastrophes (Suter1993). The design and analysis of the vast majority of scientific research has traditionally focused on minimization of false positives (i.e., false positive; or Type I errors) and at the same time maximize power to reject the null hypothesis (i.e., there is an effect). It is rare for an ecological risk assessment to be undertaken to disprove the null hypothesis, since neither industry nor government generally wants to prove that significant risk exists (Holdway 1997). Rather, the norm for ecological risk assessment is to attempt to accept the null hypothesis; that is, there is no risk. Thus, risk assessments are generally fraught with Type II errors (accepting as true a false null hypothesis) while spending the majority of their efforts in minimizing Type I errors. This problem is a very serious statistical one, even when only working with single species toxicity testing, much less when involving the enormous difficulties of measuring and predicting the behaviour of complex ecosystems (Holdway 1997). Thus, to maximize the probability of protecting the environment, it is more appropriate to guard against false negatives (Type II errors) than false positives (Type I errors). This can be done by establishing levels of statistical significance to take into account Type II errors and improving the statistical power of test designs to detect existing effects so that both Type I and Type II error rates are minimized can greatly strengthen the risk assessment (Power and Adams 1993). The process is used to identify and evaluate hazards using measurement, testing and mathematical or statistical models to quantify the relationship between the initiating event and the effect. By expressing results as probabilities, risk assessment acknowledges the inherent uncertainty in predicting future environmental situations, thereby making the assessment more credible. A brief overview on statistical issues in ecological data analysis has been described in the first part of this discussion. The later part is concentrated on randomization and effect size issues with case studies.

Biplob Das

Psyc 802

University of Regina

Student # 200228135

Fall 2008

STATISTICAL ISSUES IN ECOLOGICAL RISK ASSESSMENT

ABSTRACT

This paper discusses some statistical issues arising in the ecological data analysis based on biogeochemical measurements in ecosystems. A brief overview on statistical issues in ecological data analysis has been described in the first part of this discussion. In the later part replication and effect size issues are discussed. Replication of large-scale experiments is desirable, but the numbers of replicates needed are not known. Costs and feasibility of ecosystem experiments depend critically on the numbers of replicates needed because of the high cost per replicate and the scarcity of experimental ecosystems. A partial solution to determine statistical significance of non-replicated ecological variables was tested on two lake data by applying randomization technique. Statistical power plays a critical role in strengthening the interpretation of an experiment or analysis. For effect size issue, the analyses on Canada North Environmental Services (2003) data showed that statistical power is too low to detect any potential cumulative effects due to low sample sizes.

1

INTRODUCTION

Ecological risk assessment is defined as the process of assigning magnitudes and probabilities to the adverse effects of human activities or natural catastrophes (Suter1993). The design and analysis of the vast majority of scientific research has traditionally focused on minimization of false positives (i.e., false positive; or Type I errors) and at the same time maximize power to reject the null hypothesis (i.e., there is an effect). It is rare for an ecological risk assessment to be undertaken to disprove the null hypothesis, since neither industry nor government generally wants to prove that significant risk exists (Holdway 1997). Rather, the norm for ecological risk assessment is to attempt to accept the null hypothesis; that is, there is no risk. Thus, risk assessments are generally fraught with Type II errors (accepting as true a false null hypothesis) while spending the majority of their efforts in minimizing Type I errors. This problem is a very serious statistical one, even when only working with single species toxicity testing, much less when involving the enormous difficulties of measuring and predicting the behaviour of complex ecosystems (Holdway 1997). Thus, to maximize the probability of protecting the environment, it is more appropriate to guard against false negatives (Type II errors) than false positives (Type I errors). This can be done by establishing levels of statistical significance to take into account Type II errors and improving the statistical power of test designs to detect existing effects so that both Type I and Type II error rates are minimized can greatly strengthen the risk assessment (Power and Adams 1993). The process is used to identify and evaluate hazards using measurement, testing and mathematical or statistical models to quantify the relationship between the initiating event and the effect. By expressing results as probabilities, risk assessment acknowledges the inherent uncertainty in predicting future environmental situations, thereby making the assessment more credible. A brief overview on statistical issues in ecological data analysis has been described in the first part of this discussion. The later part is concentrated on randomization and effect size issues with case studies.