Predicting acidification effects on fish populations, using laboratory data and field information

Abstract

We developed a framework of linked models that uses lake levels of pH, Al, and Ca to predict the occurrence of fish species on a regional scale. As a first step, statistical models are fitted to laboratory data on brook trout (Salvelinus fontinalis). The resulting functions predict mortality and reduction in fecundity resulting from chemical conditions (pH, Al, and Ca). The second step applies a life-cycle model to results from the first step to estimate the reproductive potential expected for fish living in lakes with specified chemical conditions relative to that expected for fish living under nonstressful chemical conditions. Use of the framework requires field data on the presence or absence of brook trout in lakes having measured pH, Al, and Ca. The chemical conditions for each lake are processed through the first two steps to estimate the relative reproductive potential for brook trout in that lake. After, obtaining estimates for all lakes, a logistic regression model is fitted, with probability of occurrence as the dependent variable and relative reproductive potential as the independent variable. The resulting function, representing the calibrated framework, relates the probability of brook trout occurrence in high-elevation Adirondack (New York) lakes to relative (i.e., reduced) reproductive potential attributable to chemical stress. This function, when tested against additional data, can be used to predict changes in brook trout population status in relation to changed chemical conditions, stocking, or altered fishing pressure.