Modeling Slot Limits and MPA’s - California Spiny Lobster

Overview

I’m building a stage-structured population model for California spiny lobster (Panulirus interruptus) to test how slot-limit harvest rules interact with MPA coverage and spatial structure. The emphasis is on conserving large spawners while maintaining viable yields across commercial and recreational sectors. The framework borrows from two-sex/spatial lobster matrices (growth via VBGF, size-based fecundity, exponential natural mortality) and implements stage-selective fishing applied after projection and only outside MPAs, so harvest is computed as the difference between raw and fished projections.1

Quick Facts

  • Role: Research Intern, Stier Lab (UCSB)
  • When: Sep 2024 – present
  • Skills: R (matrix models, tidyverse), stage-structured dynamics, scenario sweeps, sensitivity analysis, figure design
  • Links: Code (selected snippets): https://github.com/jadenorli

Background and Questions

  • How do slot widths (min–max carapace) and MPA coverage jointly shape biomass, size structure, and reproductive output?
  • What are the trade-offs for yield and stability under differing slot rules and fishing pressure?
  • How sensitive are outcomes to growth, fecundity, and mortality assumptions?

Methods

  • Model: female-first stage matrix with size-based growth (VBGF), fecundity from size–weight, natural mortality (exponential), and scenario-applied fishing survival.
  • Management scenarios: grid over slot width × MPA coverage ± effort patterns; optional external recruitment.
  • Time step: quarterly (dt = 0.25) to capture seasonal reproduction, larval duration, and fishing activity.
  • Sensitivity: global variance-based (Sobol-Salt) on key biological parameters; stability and performance metrics.
  • Outputs: trajectories of abundance/biomass, size distribution, reproductive output, and harvest.

Results (preview)

  • Scenario grid & baseline calibration are in progress; early checks focus on stable size distributions and realistic biomass ranges.
  • Sensitivity screens identify high-leverage parameters to prioritize for data collection and uncertainty reduction.
    (I’ll add figures and summary tables as the analysis solidifies.)

Acknowledgements

Stier Lab (UCSB) and collaborators.

Footnotes

  1. Modeling approach inspired by published two-sex/stage-structured lobster frameworks (e.g., Gnanalingam et al., 2020, Panulirus argus) and adapted to P. interruptus and Southern California management questions.↩︎