Combining telemetry and fisheries tagging models to estimate fishing and natural mortality rates

The traditional multiple-year tag return method is a fundamental approach to estimating fishing and natural mortality rates in fisheries. It can provide reliable and precise estimation of the parameters when the tag reporting rate, lambda, is known. However, it is often difficult to estimate lambda accurately. An additional disadvantage is that this method has to estimate natural mortality indirectly because natural deaths are not observable. Recently, fisheries biologists have begun to implement telemetry methods to estimate fishing and natural mortality. The advantage of a telemetry method is that it contains direct information about natural mortality and survival. A shortcoming is that there is no direct information on fishing deaths. In this paper, we combine the two methods, using direct information about both fishing and natural mortality to obtain more precise and effectively unbiased parameter estimates, including reporting rate estimates for the regular tags. Using simulation when the telemetry tag relocation probability equals 1, we found that the relative standard error (RSE) of natural mortality estimates in the combination method is much improved relative to those of the tag return and telemetry methods. Annual fishing mortality estimates can also be estimated precisely. As expected, as the relocation probability of telemetry tags decreases the RSE of natural mortality and the other parameter estimates increases, but only slightly. Studies clearly should have relocation probabilities that are as high as possible. The tag reporting rate estimates are typically precisely estimated in the combined model. This is very important, as the tag reporting rate is not easy to estimate with other methods.