Do habitat models accurately predict the depth distribution of pelagic fishes?
Habitat models are used to correct estimates of fish abundance derived from pelagic longline fishing gear. They combine information on hook depth with the species’ preferences for ambient environmental conditions to adjust the gear's catchability. We compare depth distributions of bigeye tuna (Thunnus obesus) catch predicted by a habitat model with distributions derived from data collected by observers on longliners in the tropical Pacific Ocean. Our analyses show that the habitat model does not accurately predict the depth distribution of bigeye tuna; its predictions are worse than those from models that assume no effect of depth on catches. Statistical models provided superior fits to the observed depth distribution. The poor performance of the habitat model is probably due to (1) problems in estimating hook depth, (2) fine-scale variations in environmental conditions, (3) incomplete knowledge of habitat preferences and (4) differences between the distribution of bigeye tuna and their vulnerability to longline gear.