In iantaylor-NOAA/Lingcod_2021: Estimate the Status of Lingcod off the US West Coast in 2021

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.code-bg-white .remark-code, .code-bg-white .remark-code * {
 background-color:white!important;
}

.bg-text[

Lingcod data

Catches, indices, length compositions, conditional age-at-length compositions, ageing error, mean body weight, discard rates

---

Lingcod STAT

July 12, 2021
]

---

class: split-20 name: data-north

.column.bg-noaablue[

.white[Northern data]

] .column.bg-white[

sa4ss::add_figure(
  width = 80,
  file.path("..", "docs", "North", "data_plot.png"),
  caption = ""
)

]

class: split-20 name: data-south

.column.bg-noaablue[

.white[Southern data]

] .column.bg-white[

sa4ss::add_figure(
  width = 80,
  file.path("..", "docs", "South", "data_plot.png"),
  caption = ""
)

]

---

class: top

2017 STAR panel recommendations

--

1. Cross-validate age-readings among labs and year

2. Acquire information from Canadian and Mexican authorities

3. Investigate stock structure

4. Concern for ages of unsexed fish being assigned equally to the sexes without regard for length

5. Perform a spatially-explicit stock assessment model

6. Fixed length at age 14 in North model

7. Estimate other key parameters, namely $M$ and $h$

8. Estimate area of habitat per area

---

class: top name: star-wresponse

2017 STAR panel recommendations

1. Cross-validate age-readings among labs and year: .noaablue[similar across labs and years]

2. Acquire information from Canadian and Mexican authorities: .noaablue[contacted and responses in document]

3. Investigate stock structure: .noaablue[Split data at 40°10'N instead of 42°00'N; revisited every data source; added a northern California recreational fleet]

4. Concern for ages of unsexed fish being assigned equally to the sexes without regard for length: .noaablue[utilized sex-specific conditional age-at-length data when available]

5. Perform a spatially-explicit stock assessment model

6. Fixed length at age 14 in North model

7. Estimate other key parameters, namely $M$ and $h$

8. Estimate area of habitat per area

??? Kept OR and CA recreational data as separate fleets because of differences in management Trawl logbook data was not re-analyzed just re-stratified, a re-analysis was not necessary because updates to Oregon information did not pertain to the time period under consideration

---

class: split-20

.column.bg-noaablue[

.white[2021 landings]

.white[ * North

• South ] ]

.column.bg-white[

sa4ss::add_figure(
  width = 75,
  file.path("..", "docs", "North", "catch2 landings stacked.png"),
  caption = ""
)
sa4ss::add_figure(
  width = 75,
  file.path("..", "docs", "South", "catch2 landings stacked.png"),
  caption = ""
)

] ??? Large percentage of landings in the southern area from the recreational fleet in comparison to a much smaller percentage in the north.

---

class: split-20

.column.bg-noaablue[

.white[Indices]

.white[No surveys in 2020 because of Covid-19]

.white[ * North

• South ]

]

.column.bg-white[

sa4ss::add_figure(
  width = 80,
  file.path("..", "docs", "North", "index_fits_all_fleets.png"),
  caption = ""
)
sa4ss::add_figure(
  width = 70,
  file.path("..", "docs", "South", "index_fits_all_fleets.png"),
  caption = ""
)

] ??? No surveys Limited biological sampling Difficulties getting age structures prepped and read because of issues with social distancing and lab availability to do - insert figure

---

Sex-specific age- and length-composition data

.large[- Three sets of length compositions per fleet: female, male, and unsexed]

.large[- Two sets of conditional age-at-length data per fleet: female and male]

---

ggplot2::ggplot(bio.WCGBTS %>% dplyr::filter(!is.na(Sex)),
  ggplot2::aes(
    x = Length_cm,
    y = Year,
    group = interaction(Year,factor(!is.na(Age))),
    fill = factor(!is.na(Age))
    )
  ) +
  ggridges::geom_density_ridges2(scale = 5, alpha = 0.7) +
  ggplot2::facet_grid(Sex ~ ifelse(Latitude_dd <= 40.1667, "South", "North")) +
  ggplot2::theme_bw() +
  ggplot2::guides(fill = ggplot2::guide_legend(title = "Aged")) +
  ggplot2::theme(
    text = ggplot2::element_text(size=20),
    strip.background = ggplot2::element_rect(colour = "black", fill = "white"),
    legend.position = "top"
  ) +
  ggplot2::xlab("Length (cm) of West Coast Groundfish Bottom Trawl Survey") +
  ggplot2::ylab("Year") +
  ggplot2::scale_fill_manual(values = c("gray", "blue"))

---

ggplot2::ggplot(bio.Triennial[[2]] %>% dplyr::filter(!is.na(Sex)),
  ggplot2::aes(
    x = Length_cm,
    y = Year,
    group = interaction(Year,factor(!is.na(Age))),
    fill = factor(!is.na(Age))
    )
  ) +
  ggridges::geom_density_ridges2(scale = 5, alpha = 0.7) +
  ggplot2::facet_grid(Sex ~ ifelse(Latitude_dd <= 40.1667, "South", "North")) +
  ggplot2::theme_bw() +
  ggplot2::guides(fill = ggplot2::guide_legend(title = "Aged")) +
  ggplot2::theme(
    text = ggplot2::element_text(size=20),
    strip.background = ggplot2::element_rect(colour = "black", fill = "white"),
    legend.position = "top"
  ) +
  ggplot2::xlab("Length (cm) of Triennial Survey") +
  ggplot2::ylab("Year") +
  ggplot2::scale_fill_manual(values = c("gray", "blue"))

??? Triennial does not cover the entire Southern area Triennial changed spatial coverage mid-stream, which is not accounted for within the composition data

---

.pull-right[

ggplot2::ggplot(bio.HKLage.Lam %>% dplyr::filter(!is.na(sex)),
  ggplot2::aes(
    x = length_cm,
    y = year,
    group = interaction(year,factor(!is.na(age_years))),
    fill = factor(!is.na(age_years))
    )
  ) +
  ggridges::geom_density_ridges2(scale = 5, alpha = 0.7) +
  ggplot2::facet_grid(sex ~ .) +
  ggplot2::theme_bw() +
  ggplot2::guides(fill = ggplot2::guide_legend(title = "Aged")) +
  ggplot2::theme(
    text = ggplot2::element_text(size=40),
    strip.background = ggplot2::element_rect(colour = "black", fill = "white"),
    legend.position = "top"
  ) +
  ggplot2::xlab("Length (cm) of Hook & Line Survey") +
  ggplot2::ylab("Year") +
  ggplot2::scale_fill_manual(values = c("blue"))

]

---

.center[ ]

---

.center[ ]

---

Maximum age

| Female | Male | Method | Source | ------ | ---- | -------- | ------ | 21 | 21 | Max seen | Haltuch et al. 2017 | 18 | 13 | 99th % | Taylor et al. 2021 | 18 | 13 | 99th % | Johnson et al. 2021 | 20 | 14 | Max seen | DFO Canada | 36 | 36 | Max seen | Alaska

---

name: ageing-error

Ageing error

• Similar among age readers

• Informed using ages from fin-rays only

--

• Standard deviation of ageing error

• 0.24 years at age one,

• 0.56 years at age five,

• 1.10 years at age ten, and

• 1.65 years at age fifteen

--

• Future research

• International comparison of ages from fin rays versus otoliths

  • Alaska == otoliths; elsewhere == fin-ray

  • Old fish are 5+ years older with otoliths compared to fins

  • Are Alaskan lingcod actually older?

• Simulation tools

---

class: top name: mean-weight-at-age

Mean weight-at-age WCGBT Survey

ggplot2::ggplot(
  data = bio.WCGBTS %>%
    dplyr::mutate(area = ifelse(Latitude_dd < 40.167, "South", "North")) %>%
    dplyr::filter(!is.na(Age) & !is.na(Weight), Age > 0, Age < 7, Sex != "U") %>%
    dplyr::group_by(Year, Age, Sex, area, Pass) %>%
    dplyr::summarize(mnwgt = mean(Weight), .groups = "keep") %>%
    dplyr::ungroup(),
  ggplot2::aes(Year, mnwgt)
) +
ggplot2::geom_smooth(ggplot2::aes(group = Age, col = factor(Age))) +
ggplot2::facet_grid(Sex ~ area + Pass) +
ggplot2::theme_bw() +
ggplot2::labs(y = "Mean weight (kg)", col = "Age (year)") +
ggplot2::guides(col = ggplot2::guide_legend(nrow = 1)) +
ggplot2::theme(
  text = ggplot2::element_text(size=18),
  strip.background = ggplot2::element_rect(colour = "black", fill = "white"),
  legend.position = c(0.18, 0.95),
  legend.background = element_rect(fill = alpha("white", 0.1)),
)

---

class: top name: star-wresponse-repeat

2017 STAR panel recommendations

1. Cross-validate age-readings among labs and year: .noaablue[similar across labs and years]

2. Acquire information from Canadian and Mexican authorities: .noaablue[contacted and responses in document]

3. Investigate stock structure: .noaablue[Split data at 40°10'N instead of 42°00'N; revisited every data source; added a northern California recreational fleet]

4. Concern for ages of unsexed fish being assigned equally to the sexes without regard for length: .noaablue[utilized sex-specific conditional age-at-length data when available]

5. Perform a spatially-explicit stock assessment model

6. Fixed length at age 14 in North model

7. Estimate other key parameters, namely $M$ and $h$

8. Estimate area of habitat per area

iantaylor-NOAA/Lingcod_2021 documentation built on Oct. 30, 2024, 6:42 p.m.