Focusing on the front end: A framework for incorporating uncertainty in biological parameters in model ensembles of integrated stock assessments

Uncertainty in population status estimates from stock assessments is important for providing a comprehensive picture of current knowledge stock. The use model ensembles to encapsulate uncertainty has become increasingly prevalent. biological parameters that are often fixed assessment models can be quantified presentation management advice through ensembles. An ensemble created by randomly drawing values the likely parameter space using Monte-Carlo/bootstrap (MCB ensemble) or at either high, medium, low value encapsulates variability and applied full factorial grid across (factorial ensemble). We calculated MCB various sizes 243 Southwest Pacific swordfish (Xiphias gladius) compared reference points which included only, estimation uncertainty, both uncertainties weighted sampling importance resampling. Median were significantly different between two types with having larger estimate than ensemble. Stock characterize these more efficiently approach. A approach appropriate comparing structure assumptions functional forms relationships used combination Incorporation when because including only lead biased precision points. Further work needed regarding weighting incorporate data sources have likelihood weightings.