Add MSY success story: Pacific Coast lingcod recovery

Insert a new section before "Randomness, risk and collapse" so the
lecture shows MSY-based management working before discussing its
failures. Uses RAM Legacy Stock Assessment Database series for U.S.
Pacific Coast lingcod (B/B_MSY and F/F_MSY, 1928-2009): sustained
overfishing drives biomass to ~0.27 B_MSY, then an MSY-anchored
rebuilding plan (overfished 1999, rebuilt 2005) pulls F below F_MSY and
biomass recovers and overshoots -- the model's negative feedback in real
data. Includes honest caveats that tee up the randomness section.

Vendor the small series as datasets/lingcod_msy_recovery.csv (read
locally, no build-time download); add pandas import and the ricard2012
bib entry.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Author: jstac

lectures/_static/quant-econ.bib

@@ -2957,3 +2957,14 @@ @book{clark1990
   year={1990},
   publisher={Wiley}
 }
+
+@article{ricard2012,
+  title={Evaluating the knowledge base and status of commercially exploited marine species with the {RAM} Legacy Stock Assessment Database},
+  author={Ricard, Daniel and Minto, Coilin and Jensen, Olaf P. and Baum, Julia K.},
+  journal={Fish and Fisheries},
+  volume={13},
+  number={4},
+  pages={380--398},
+  year={2012},
+  publisher={Wiley}
+}

lectures/datasets/lingcod_msy_recovery.csv

@@ -0,0 +1,83 @@
+year,B_over_Bmsy,F_over_Fmsy
+1928,3.859289617,0.12815534
+1929,3.842213115,0.177669903
+1930,3.825136612,0.198058252
+1931,3.790983607,0.19223301
+1932,3.756830601,0.142718447
+1933,3.756830601,0.224271845
+1934,3.705601093,0.14368932
+1935,3.705601093,0.170873786
+1936,3.671448087,0.133009709
+1937,3.671448087,0.173786408
+1938,3.654371585,0.146601942
+1939,3.637295082,0.127184466
+1940,3.637295082,0.145631068
+1941,3.637295082,0.117475728
+1942,3.637295082,0.067961165
+1943,3.654371585,0.131067961
+1944,3.654371585,0.132038835
+1945,3.654371585,0.130097087
+1946,3.637295082,0.216504854
+1947,3.603142077,0.430097087
+1948,3.50068306,0.462135922
+1949,3.398224044,0.413592233
+1950,3.31284153,0.45631068
+1951,3.227459016,0.453398058
+1952,3.142076503,0.350485437
+1953,3.090846995,0.246601942
+1954,3.073770492,0.290291262
+1955,3.056693989,0.308737864
+1956,3.039617486,0.355339806
+1957,3.005464481,0.526213592
+1958,2.920081967,0.539805825
+1959,2.851775956,0.466990291
+1960,2.800546448,0.417475728
+1961,2.74931694,0.449514563
+1962,2.698087432,0.386407767
+1963,2.663934426,0.391262136
+1964,2.629781421,0.340776699
+1965,2.612704918,0.413592233
+1966,2.578551913,0.518446602
+1967,2.527322404,0.57961165
+1968,2.476092896,0.612621359
+1969,2.407786885,0.575728155
+1970,2.339480874,0.850485437
+1971,2.237021858,1.077669903
+1972,2.117486339,1.563106796
+1973,1.929644809,1.766990291
+1974,1.741803279,1.951456311
+1975,1.572745902,1.951456311
+1976,1.449795082,2.077669903
+1977,1.321721311,1.485436893
+1978,1.270491803,1.941747573
+1979,1.166325137,2.718446602
+1980,1.00068306,3.378640777
+1981,0.840163934,3.378640777
+1982,0.712090164,3.504854369
+1983,0.594262295,2.67961165
+1984,0.544740437,2.805825243
+1985,0.488387978,3.951456311
+1986,0.403005464,3.854368932
+1987,0.37568306,4.019417476
+1988,0.37397541,4.310679612
+1989,0.358606557,4.834951456
+1990,0.3125,4.553398058
+1991,0.285177596,4.32038835
+1992,0.268101093,4.174757282
+1993,0.256147541,3.485436893
+1994,0.266393443,2.27184466
+1995,0.315915301,2.019417476
+1996,0.365437158,2.038834951
+1997,0.396174863,1.815533981
+1998,0.425204918,1.300970874
+1999,0.479849727,1.398058252
+2000,0.539617486,0.63592233
+2001,0.650614754,0.49223301
+2002,0.800887978,1.029126214
+2003,0.988729508,1.291262136
+2004,1.243169399,0.195145631
+2005,1.656420765,0.261165049
+2006,2.083333333,0.249514563
+2007,2.510245902,0.144660194
+2008,2.885928962,0.093203883
+2009,3.193306011,

lectures/msy_fishery.md

@@ -49,6 +49,7 @@ Let's start with some standard imports.
 
 ```{code-cell} ipython3
 import numpy as np
+import pandas as pd
 import matplotlib.pyplot as plt
 ```
 
@@ -556,6 +557,118 @@ Over-fishing is then the exact analogue of over-saving (dynamic inefficiency):
 more of the control, less of the flow.
 ```
 
+## A success story: Pacific Coast lingcod
+
+Before turning to the risks, it is worth seeing the MSY framework succeed.
+
+A clean example is the lingcod (*Ophiodon elongatus*) fishery off the U.S.
+Pacific Coast.
+
+Lingcod is managed using MSY-based reference points: a target biomass and a
+target fishing pressure that together define the maximum sustainable yield.
+
+To follow the fishery we use two dimensionless ratios.
+
+The first is $B / B_{MSY}$, the stock biomass relative to the biomass $B_{MSY}$
+that supports the MSY.
+
+(In our model this reference stock is $x^* = K/2$.)
+
+The second is $F / F_{MSY}$, fishing pressure relative to the pressure
+$F_{MSY}$ that achieves the MSY.
+
+(In our model this is the MSY effort $e_{MSY}$.)
+
+Each ratio is measured against its own MSY reference point, so the value $1$ is
+simultaneously the target and the limit.
+
+The data come from the RAM Legacy Stock Assessment Database {cite:t}`ricard2012`.
+
+```{code-cell} ipython3
+---
+mystnb:
+  figure:
+    caption: Pacific Coast lingcod recovery
+    name: fig:lingcod
+---
+lingcod = pd.read_csv('datasets/lingcod_msy_recovery.csv')
+
+fig, ax = plt.subplots()
+
+ax.axhline(1.0, color='black', lw=1, ls='--')
+ax.text(1930, 1.05, r'MSY reference ($=1$)', fontsize=9, va='bottom')
+
+ymax = max(lingcod['B_over_Bmsy'].max(), lingcod['F_over_Fmsy'].max()) * 1.06
+
+# shade the years when fishing pressure exceeded the MSY level
+over = lingcod['F_over_Fmsy'] > 1
+ax.fill_between(lingcod['year'], 0, ymax, where=over,
+                color='C3', alpha=0.08)
+
+ax.plot(lingcod['year'], lingcod['B_over_Bmsy'], color='C0', lw=2,
+        label=r'$B / B_{MSY}$  (biomass)')
+ax.plot(lingcod['year'], lingcod['F_over_Fmsy'], color='C3', lw=2,
+        label=r'$F / F_{MSY}$  (fishing pressure)')
+
+ax.axvline(1999, color='grey', lw=0.8, ls=':')
+ax.axvline(2005, color='grey', lw=0.8, ls=':')
+ax.text(2000, ymax * 0.97, 'overfished (1999)', ha='left', va='top',
+        fontsize=8.5, color='dimgrey')
+ax.text(2000, ymax * 0.88, 'rebuilt (2005)', ha='left', va='top',
+        fontsize=8.5, color='dimgrey')
+
+ax.set_xlabel('year')
+ax.set_ylabel('ratio to MSY reference point')
+ax.set_xlim(lingcod['year'].min(), lingcod['year'].max())
+ax.set_ylim(0, ymax)
+ax.legend(loc='upper center', frameon=False)
+plt.tight_layout()
+plt.show()
+```
+
+The story unfolds in three acts.
+
+Through the 1960s the stock is lightly exploited: $F$ sits well below $F_{MSY}$
+and biomass drifts down slowly from a high level.
+
+From the early 1970s fishing pressure climbs above $F_{MSY}$ and stays there for
+nearly three decades.
+
+Biomass falls steadily, reaching a trough of about $0.27\,B_{MSY}$ in 1993 ---
+close to collapse.
+
+In 1999 the stock was formally declared *overfished*, and a rebuilding plan cut
+catches sharply.
+
+Fishing pressure dropped well below $F_{MSY}$, and biomass climbed back through
+$B_{MSY}$ by 2004.
+
+The stock was declared fully *rebuilt* in 2005, ahead of schedule, and then
+overshot its target.
+
+This is exactly the negative feedback built into the model.
+
+Push fishing above the MSY level and the stock falls; pull it below and the
+stock recovers toward $B_{MSY}$.
+
+Used as a steering target, the MSY reference point did its job.
+
+A few caveats keep the story honest --- and set up the next section.
+
+First, attribution is never clean: a strong 1999 year-class also helped, so the
+cut in fishing was necessary but recruitment luck set the speed of recovery.
+
+Second, $B_{MSY}$ and $F_{MSY}$ are *estimated* quantities, revised at each
+assessment; the famous MSY failures are often failures of estimating the
+reference point rather than of the control rule itself.
+
+Third, the recovery rode partly on coast-wide measures aimed at the whole
+groundfish community, a reminder that single-species MSY targets sit inside a
+multispecies system.
+
+The next section takes these caveats seriously, and asks what randomness does to
+MSY-based management.
+
 ## Randomness, risk and collapse
 
 As mentioned in the introduction to this lecture, MSY-based policies have not