Waging War Against the Zebra Mussel 405 Graph 2. Comparing High and Low Density Popul 7194 196 l9771971/1I987F 1991/1/o 。eaon"t。 ation2Location3 ocation目 Figure 2. Comparison of high-and low-density populations. Another weakness of our model is that it relies on chemical and data from only one lake. By slightly varying the values of the coefficients observing whether the altered model more accurately predicts the density of the zebra mussels in the newly incorporated lakes, a better model can be achieved Information from other lakes could also be used to refine the value chosen for the division between low and high densities. Other factors, such as total ion concentration. could also be included in the model if the factor were shown in a variety of lakes to correspond to population densities We are not able to predict, using our model, how fast a population of ze bra mussels will spread from one site to another within a lake. However, by qualitatively examining the data from Lake A, it appears to take only a few years for the population to spread from one area to another as long as the new site is suitable for zebra mussels. For example, in site 5 in 1994 and 1995 there were no zebra mussels collected, but from 1996 to 1998, the population rapidly increased to a high density. Since zebra mussels can very quickly reach high density populations in a supportive environment, it seems that knowing whether a given site is a suitable habitat is a more useful piece of information than the rate at which the population grows Using Model for Lake a to Predict for Lake b and lake c Using the equations from our models, we can average pH, calcium concen- tration, total phosphorus concentration, and total nitrogen concentration forGraph 2. Comparing High and Low Density Popula 0 500000 1000000 1500000 2000000 2500000 3000000 3500000 7/1/94 1/1/95 7/1/95 1/1/96 7/1/96 1/1/97 7/1/97 1/1/98 7/1/98 1/1/99 7/1/99 1/1/00 7/1/00 Date juveniles/m^2 Location 1 Location 2 Location 3 Location 5 Waging War Against the Zebra Mussel 405 Figure 2. Comparison of high- and low-density populations. Another weakness of our model is that it relies on chemical and population data from only one lake. By slightly varying the values of the coefficients and observing whether the altered model more accurately predicts the density of the zebra mussels in the newly incorporated lakes, a better model can be achieved. Information from other lakes could also be used to refine the value chosen for the division between low and high densities. Other factors, such as total ion concentration, could also be included in the model if the factor were shown in a variety of lakes to correspond to population densities. We are not able to predict, using our model, how fast a population of ze￾bra mussels will spread from one site to another within a lake. However, by qualitatively examining the data from Lake A, it appears to take only a few years for the population to spread from one area to another as long as the new site is suitable for zebra mussels. For example, in site 5 in 1994 and 1995, there were no zebra mussels collected, but from 1996 to 1998, the population rapidly increased to a high density. Since zebra mussels can very quickly reach high density populations in a supportive environment, it seems that knowing whether a given site is a suitable habitat is a more useful piece of information than the rate at which the population grows. Using Model for Lake A to Predict for Lake B and Lake C Using the equations from our models, we can average pH, calcium concen￾tration, total phosphorus concentration, and total nitrogen concentration for