We review a model we developed in 2007 to predict the invasion potential of an introduced population, based on the relationship between juvenile growth (mean total length at age 2) and mean age-at-maturity, and test the model with data from 75 non-native populations of pumpkinseed Lepomis gibbosus in nine countries of Europe and western Asia. The database used to construct the model was derived from a mix of primary and secondary data collected between 1981 and 2016. Based on number of specimens collected or catch-per-unit-effort data, and/or comments by the original authors, populations were classified a priori as “invasive” or “non-invasive.” The plot of the proposed model placed populations in three invasiveness categories (non-invasive, transitional, invasive). Of those predicted by the model to be invasive, only 57% were correct with regard to their a priori classification, a result that was not statistically significant. For populations predicted by the model to be non-invasive, 70% were correct with regard to their a priori classification when “transitional” were grouped with non-invasive (P = 0.0024), and 64% were correct when “transitional” were excluded from the test (P = 0.12). Applications of the model to two other non-native freshwater fishes (black bullhead Ameiurus melas and crucian carp Carassius carassius) are also discussed. The lack of stronger statistical support for the model may have been the result of using life-history traits from the populations after they were introduced, as the source populations were unknown, as well as to shortcomings in the dataset that could affect a priori and modeled classifications. We conclude that such life-history models can be useful for predicting invasiveness status in non-native freshwater fishes, though the basis of the predictions, statistical or heuristic, will depend on the size and quality of the underlying life-history database.
Life-history traits and potential invasiveness of introduced pumpkinseed Lepomis gibbosus populations in northwestern Europe
