I have run a glm to look at the effects of several environemental variables (x variables) on the numbers of bacteria in a soil sample(y variable)

sludgemodel <- glm(cfu.g ~ days + pH + temperature, family = gaussian (link=identity), na.action = na.exclude, data = sludgeqpcr)

I then plotted the model to check the model assumptions

plot(sludgemodel)

The plots all look okay except the residuals vs fitted plot which does not have a random distribution but more of a curved distribution.

Should I transform the data or use a different error/link function? I am very new to R and statistics so advice would be appreciated.

I have run a glm to look at the effects of several environmental variables (x variables) on the numbers of bacteria in a soil sample  (y variable):

sludgemodel <- glm(cfu.g ~ days + pH + temperature, family = gaussian (link=identity),
                   na.action = na.exclude, data = sludgeqpcr)

I then plotted the model to check the model assumptions:

plot(sludgemodel)

The plots all look okay except the residuals vs fitted plot which does not have a random distribution but more of a curved distribution.

Should I transform the data or use a different error/link function? I am very new to R and statistics so advice would be appreciated.