There is nothing to validate. We can stop at this point and say that there is not enough data for validation. However we can make another try and separate the data in a different way. In our case, population numbers in year t depends on population numbers in tear t-2. Thus, we can estimate the regression using uneven years only and then test it using even years.
Effect is highly significant.
Now we test this equation using data for even years. This means that the equation derived from uneven years did not help to predict population dynamics in even years. The idea of crossvalidation is to exclude one observation at a time when estimating regression coefficients, and then use these coefficients to predict the excluded data point. This procedure is repeated for all data points, and then, estimated values can be compared with real values. However, I don't know any methods for testing statistical significance with crossvalidation.