Leveraging ecological indicators to improve short term forecasts of fish recruitment

• Published in: Fish and fisheries (Oxford, England) England), 2024-11, Vol.25 (6), p.895-909
• Main Authors: Ward, Eric J., Hunsicker, Mary E., Marshall, Kristin N., Oken, Kiva L., Semmens, Brice X., Field, John C., Haltuch, Melissa A., Johnson, Kelli F., Taylor, Ian G., Thompson, Andrew R., Tolimieri, Nick
• Format: Article
• Language: English
• Subjects: Animal population; Assessments; CalCOFI; Data base management systems; Ecosystem management; Ecosystems; Environmental management; Environmental monitoring; environmental variables; Error analysis; Fish; Fish populations; Fish stocks; Fisheries; Fisheries management; Fishery management; Forecasting; Indicators; Information management; Juveniles; Mathematical models; Modelling; Ocean models; Parametric methods; Qualitative analysis; Recruitment; Recruitment (fisheries); ROMS; Species; stock assessment
• ISSN: 1467-2960; 1467-2979
• DOI: 10.1111/faf.12850

Forecasting the recruitment of fish populations with skill has been a challenge in fisheries for over a century. Previous large‐scale meta‐analyses have suggested linkages between environmental or ecosystem drivers and recruitment; however, applying this information in a management setting remains underutilized. Here, we use a well‐studied database of groundfish assessments from the West Coast of the USA to ask whether environmental variables or ecosystem indicators derived from long‐term monitoring datasets offer an improvement in our ability to skilfully forecast fish recruitment. A secondary question is which types of modelling approaches (ranging from linear models to non‐parametric methods) yield the best forecast skill. Third, we examine whether simultaneous forecasting of multiple species offers an advantage over generating species‐specific forecasts. We find that for approximately one third of the 29 assessed stocks, ecosystem indicators from juvenile surveys yields the highest out of sample predictive skill compared to other covariates (including environmental variables from Regional Ocean Modeling System output) or null models. Across modelling approaches, our results suggest that simpler linear modelling approaches do as well or better than more complicated approaches (reducing out of sample Root Mean Square Error by ~40% compared to null models), and that there appears to be little benefit to performing multispecies forecasts instead of single‐species forecasts. Our results provide a general framework for generating recruitment forecasts in other species and ecosystems, as well as a benchmark for future analyses to evaluate skill. The most promising applications are likely for species that are short lived, have relatively high recruitment variability, and moderate amounts of age or length data. Forecasts using our approach may be useful in identifying covariates or mechanisms to include in operational assessments but also provide qualitative advice to managers implementing ecosystem based fisheries management.