The default settings for openMSE allows for conditioning operating models (OMs) for stocks if no data were available. Ranges of biological and selectivity parameters are specified to incorporate uncertainty in the OM. For historical reconstruction of the stock, historical effort trajectories can be sketched, which, along with a fixed assumption on current depletion, can be used to calculate the implied historical fishing mortality and catches.
In more data-rich situations, biological studies can be used to inform life history parameters such as growth and maturity, while other parameters such as historical depletion, selectivity, and fishing mortality have typically been informed by an assessment model. In such situations, operating models may be generated from fitting assessment models. In between the data-rich and the no-data situations are the so-called data-limited or data-moderate settings, with series of data but the potential lack of an accepted stock assessment.
The Rapid Conditioning Model (
RCM) in the
SAMtool package is designed to help condition OMs for data-limited and data-rich situations. From a fitted model, historical depletion and F could be informed via a more objective method, as opposed to intuition or a simple guess for these key parameters. The RCM is sufficiently flexible to be parameterized as full stock assessment model with various data weighting schemes and some time-varying dynamics explored, although that is not the intent of the software to used for stock assessment.
RCM is intended to guide exploration of a set of operating models (conditioned on data) that encapsulates a range of views on the productivity and historical exploitation. In this context, we don’t look at point estimates, but rather try to reduce the range of plausible parameters. Walters et al. (2006) used the term “stock reduction analysis” to describe an approach with age-structured models to address a broader question: what combinations of historical fishing mortality and recruitment could have generated the observed data?
The stock reduction analysis (SRA) paradigm can be highly valuable for conditioning operating models in the absence of stock assessments. In any model, point estimates of depletion and unfished recruitment may not be credible if there is high uncertainty of input values to the model such as natural mortality and recruitment compensation (e.g., parameterized as steepness). We would then exclude parameters that would otherwise generate implausible scenarios in the historical reconstruction of the stock. Consider two extreme scenarios. If the productivity or unfished stock size is too low, then the modeled population would crash while trying to explain the observed catches over time. On the other hand, if the productivity or unfished stock size is too high, then the observed catches are tiny in relation to the population, and implies still unfished conditions despite the observed fishing history. Finding a suitable range of parameters is akin to “threading the needle” in order to avoid these two extreme scenarios.
For some life history parameters such as growth, we may be more certain or we prefer to incorporate uncertainty in other parameters. Suppose that we are unsure about how to specify certain life history parameters, e.g. steepness and natural mortality. With some data, we can try to fit an age-structured model that estimates historical depletion, stock size, recruitment, and fishing mortality that are consistent with the specified parameter values.
RCM will be the main function for scoping historical scenarios for an operating model
OM. From an operating model and a list of data inputs, the RCM returns an object with an updated OM and predicted outputs from the SRA. All model configurations for the SRA will also be specified through arguments passed through