Wei-Chuan Chiang

DIEL OSCILLATIONS IN SAILFISH VERTICAL MOVEMENT BEHAVIOR IN THE EAST CHINA SEA

A sailfish (Istiophorus platypterus) was tagged with a pop-up satellite archival tag off the eastern coast of Taiwan and moved in a northerly direction to the East China Sea, where the tag popped-up after 160 days. The total linear displacement was 550 km from deployment to pop-up location and all movements were confined to the East China Sea. After the primarily southward movement during first two months at-liberty, the sailfish changed course after September and began to swim in a northerly direction paralleling the Kuroshio Current. During these horizontal movements, the tagged animal exhibited diel oscillations in its vertical diving behavior. On 22 days of the entire 160 days-at-liberty, the sailfish dove to depths deeper than 100 m. The sailfish spent ＞85% of its time in the upper uniformly mixed layer above ~50 m, but made more extensive vertical movements during the daytime ( x = 32.2 m ± 34.5 SD) than nighttime ( x = 9.5 m ± 16.7 SD). Depths and ambient water temperatures visited ranged from 0 to 153 m and 29.7°C to 17.8°C, respectively. The depth distribution appeared to be limited by ~6°C to 8°C changes in water temperature (Delta T) relative to sea surface temperature.

Incorporating uncertainty into the estimation of biological reference points for a spiny lobster (Panulirus penicillatus) fishery

Abstract The status of a fishery is often defined as the probability of fishing mortality rate exceeding a perilous level for long‐term sustainability. Lobster stock assessments are often subject to large uncertainty in input data and high levels of natural variability in lobster life history processes, which calls for incorporating uncertainty associated with both indicator and management reference points in an evaluation of biological risk of overfishing. Using a Monte Carlo simulation approach, we evaluated the impacts of uncertainty in modelling on the determination of the status of the Taitung spiny lobster (Panulirus penicillatus) fishery (Taiwan), which has not been quantitatively determined despite its commercial importance. The commonly used biological reference points derived from the per recruit model (F 0.1 the fishing mortality rate where the slope of the curve of yield‐per‐recruit model is 10% of the maximum slope and F 4Q%, the fishing mortality rate that reduces the expected egg production for a cohort of female lobsters to 40% of that produced in the absence of a fishery of the egg‐per‐recruit model) were influenced by uncertainties associated with lobster life history and fishery parameters. A large uncertainty in the current fishing mortality rate (F cnr) and estimates of biological reference points (F BRPs) increased the uncertainty in determining the risk of overexploitation throughout the confidence levels of the stochastic decision‐making framework. This simulation study suggests that the target reference point of F 40% is less sensitive to the input parameters’ uncertainty than F 0.1 We suggest a further evaluation of other F‐based references points and development of biomass‐based reference points before final selection and implementation for the management of the Taitung lobster fishery.

Errors in Coefficient or Expected Value? Effects of Different Methods on Simulated Values for a Linear Model and the Nonlinear Von Bertalanffy Growth Model

Monte Carlo simulation is widely applied to incorporate the uncertainties in fisheries assessment models. However, even modeling the same kind of uncertainty, different practices often occur among studies, which may lead to erroneous results. We demonstrate how simulation results differed among methods of incorporating uncertainty into simulation in different ways: adding random errors to model either coefficients or expected values. Using life history parameter data from sailfish (Istiophorus platypterus), natural mortality from Paulys empirical equation, and lengths-at-age from the von Bertalanffy growth model (VBGM) were simulated using different methods. Different simulation methods did not affect the averages of simulated values from Paulys empirical equation and had only slight effects on the simulated lengths-at-age from the VBGM. For both linear Paulys equation and nonlinear VBGM, the variances of the simulated values from the errors-in-coefficients methods were under-estimated, being approximately 1 to 7% or 40 to 95% of those from errors-in-expected-values methods, depending on whether the correlation among coefficients was included or not. Therefore, adding random errors with either an additive or multiplicative error structure to the expected values is preferred over the errors-incoefficient methods for fully representing uncertainty in the data.