A Combined Multiple-Regression and Bioenergetics Model for Simulating Fish Growth in Length and Condition

Abstract

We determined the relationships between the length relative growth rate (LRGR) and weight relative growth rate (WRGR) in white crappies Pomoxis annularis of various sizes (35–399 g) and condition factors (relative weight [W_r ] range, 73–109) grown at varying rates via the manipulation of ration in eight 60-d laboratory experiments. The WRGR alone explained 63% of the variation in LRGR. However, the slope (β₁) of the LRGR–WRGR relationship varied with white crappie W_r and body weight; β₁ increased exponentially with W_r and declined as a power function of weight. A multiple-regression (MR) model that predicted LRGR from WRGR, W_r , and fish weight was constructed (R ² = 0.77; P < 0.0001) and combined with an existing white crappie bioenergetics model (BEM) that was originally capable of predicting growth in terms of weight only. Output from the BEM provided input to the MR model in each modeling time step. Independent evaluations showed that the combined BEM–MR model accurately predicted the observed trajectories of white crappie growth in length (±2.1%) and W_r (±4.1 units) from daily predictions of fish weight and length over 60-d periods. The combined BEM–MR model was initiated with a fish's day-1 observed weight, length, and W_r values and was run continuously (without resetting values) for full-experiment durations by use of previous-day outputs as inputs for next-day predictions. The demonstrated capacity to adapt an existing BEM to accurately simulate a fish species' growth in length and change in condition, in addition to growth in weight, is expected to substantially expand the scope of application of BEMs in fisheries management and aquatic ecology.