---
title: "Targeting in a longline tuna fishery: a step-by-step guide"
author: "Henning Winker<br><span style=\"font-size: 0.8em;\">FAO Fisheries and Aquaculture Division, Assessment and Management Team (NFIFM)<br><a href=\"mailto:henning.winker@fao.org\">henning.winker@fao.org</a></span>"
output:
  rmarkdown::html_document:
    toc: true
    toc_float: true
    number_sections: true
    theme: readable
vignette: >
  %\VignetteIndexEntry{Targeting in a longline tuna fishery}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE,
                      fig.width = 9, fig.height = 5, dpi = 110)
```

This guide uses a single iteration of the tuna operating model to compare the
three practical approaches to fisher targeting in CPUE standardisation
(Hoyle et al. 2014):

* **Cluster**: discrete fishing tactics from clustering the catch composition;
* **DPC**: continuous targeting from the principal components of the catch
  composition (Winker et al. 2013, 2014);
* **HBF**: the number of hooks between floats as a recorded gear covariate.

We use two scenarios:

1. **Fixed**: species depth varies in space with the thermocline, but the
   pattern is the same every year. This is the main case.
2. **Moving**: the thermocline also moves between years (ENSO-like), so the
   depth of the fish, and with it the catchability of a given HBF, changes
   from year to year.

Everything else, including the seed, is identical in the two scenarios.

```{r packages}
library(cpueTargeting)
library(ggplot2)

# Optional extra steps (spatial resolution, year interactions) take longer;
# set to TRUE to run them.
run_extras <- FALSE
# Replicates over many seeds (e.g. overnight); set to TRUE to run them.
run_sims <- FALSE
```

# The operating model in brief

The operating model extends the spatial multispecies simulator of Thorson
et al. (2017, Appendix A), itself a generalisation of the simulator of Ono et
al. (2015), which is implemented for the bathymetric Run 22 scenario in
`simulate_bathymetric()`. The tuna extension adds a pelagic horizontal
habitat, species vertical distributions, longline hook depth by HBF and
vertical targeting. A full description of both models is in the [background
document](https://henning-winker.github.io/cpueTargeting/appendix.html).


In brief:

* Four species in a conceptual Southern Hemisphere ocean (north = warm):
  yellowfin (YFT) and bigeye (BET) prefer warm water, southern bluefin (BFT)
  and albacore (ALB) cooler water.
* Each species has a normal depth profile. Within each pair one species is
  shallow (YFT, ALB) and one deep (BET, BFT). A thermocline anomaly moves all
  profiles up (shallow thermocline) or down (deep thermocline).
* Each HBF level has a typical hook depth (roughly as in Bigelow et al. 2002,
  Fig. 2). Each set deviates from it by a lognormal error (line angle,
  currents, ...), so HBF is an imperfect proxy for where the hooks are.
* The catchability of a set for a species is the vessel's catchability times
  the overlap between hook and fish depth distributions.
* Prices favour ALB in years 1-10 (shallow, cool water), BET in years 11-20
  (deep, warm water) and both BET and BFT in years 21-30 (deep water, warm
  north and cool south), loosely following the historical shift from
  albacore to bigeye targeting. The first switch changes both location and
  depth; in the last decade the fleet splits between two deep targets in
  different areas.
  Skippers choose where to fish (spatial targeting) and which HBF to use
  (vertical targeting) from the expected catch value.
* Effort increases towards 800 sets per year and is cut by 25% in years
  21-30 (~17,000 sets in total).
* True trends differ between species. `B_start` sets where each stock
  starts (YFT 0.6, BET 0.7, BFT 0.2, ALB 0.9 of carrying capacity) and `q`
  how hard it is fished. ALB is fished down to about half while targeted and
  then levels off; BET declines steadily to about 30%; BFT recovers from 20%
  to almost 50% and declines again once it is targeted; YFT dips and then
  recovers once effort is cut. Use `effort`, `q`, `B_start` and `r` to change
  these trends.

Where the hooks fish and where the fish are. Each panel is one HBF level:
the grey bars show the expected depth distribution of the hooks, the
coloured areas the depth distribution of each species. Where a species'
area overlaps the grey bars, a set with that HBF can catch it; the numbers
give the resulting vertical availability, which multiplies catchability in
the operating model. Shallow gear (HBF 5) catches mostly YFT and ALB; deep
gear (HBF 20) catches relatively more BET and BFT.

```{r profiles, fig.height = 4.5}
plot_tuna_vertical_distributions()
```

# How to control the scenario

All settings live in one list, `tuna_settings()`. Print it to see the
defaults, change elements with `modifyList()` (or `$`), and pass the list to
`simulate_tuna()`. Settings you leave out keep their default values.

```{r settings-overview, eval = FALSE}
s <- tuna_settings()
str(s)                                        # all settings and defaults
s2 <- modifyList(s, list(effort = 500, set_cv = 0.4))
sim <- simulate_tuna(s2, seed = 123)
```

The settings come in five groups. Species-specific settings are vectors in
the order of `species`: YFT, BET, BFT, ALB.

## Targeting through prices

Targeting changes are driven by species prices: each year skippers send
effort to the cells, and choose the HBF, with the highest expected catch
value (price x expected catch). `price` is a matrix with one row per year
and one column per species. The default has three 10-year blocks:

| Years | Most valuable | Where it lives | Fleet goes to |
|---|---|---|---|
| 1-10 | ALB | shallow, cool south | shallow gear, south |
| 11-20 | BET | deep, warm north | deep gear, north |
| 21-30 | BET and BFT | deep, warm north and cool south | deep gear, north and south |

This loosely follows the historical shift from albacore (canning) to bigeye
(sashimi) targeting in the Japanese longline fleet (Hoyle and Okamoto 2013),
followed by increasing interest in southern bluefin as it recovers. The
first switch changes both where and how deep the fleet fishes. In the last
decade both targets are deep, so the fleet splits by area rather than by
depth, which helps to separate spatial from vertical targeting.

```{r prices}
tuna_settings()$price[c(1, 11, 21), ]   # one row from each block
```

To define your own sequence, build a new matrix, e.g. a single switch from
ALB to BET:

```{r prices-custom, eval = FALSE}
s <- tuna_settings()
s$price <- matrix(c(rep(c(1.0, 0.5, 0.5, 2.0), 15),   # years 1-15: ALB
                    rep(c(1.0, 2.0, 1.0, 0.5), 15)),   # years 16-30: BET
                  ncol = 4, byrow = TRUE)              # columns: YFT, BET, BFT, ALB
```

Constant prices (the same row every year) switch off targeting changes,
which is a useful control scenario.

**Presets.** The default has strong targeting: the target species is worth
four times as much as the others and skippers respond almost
deterministically, so nominal CPUE is biased by about 15-20%, enough to
separate the standardisation methods in a single run. Two alternatives:

* `tuna_settings("mild")`: the same stocks and fishery but moderate
  targeting (prices differ by a factor of two, skippers respond less
  strictly). Nominal CPUE is only slightly biased and the methods are hard
  to tell apart without replicates: when targeting is weak, the choice of
  method matters less than noise.
* `tuna_settings("stress_test")`: strong targeting of lightly fished,
  unfished stocks (`q = 0.2`, `B_start = 1`), the settings of the first
  runs. True trends are nearly flat, so nominal CPUE changes mainly through
  targeting.

```{r presets, eval = FALSE}
base <- tuna_settings("mild")   # instead of tuna_settings() in Step 1
```

How strongly skippers respond:

| Setting | Effect | Default |
|---|---|---|
| `preference` | how strongly effort concentrates on high-value cells (spatial targeting); 0 = effort spread evenly | 1.5 (mild: 1.25) |
| `HBF_choice_temp` | how strictly skippers pick the best HBF (vertical targeting); small = strict, large = nearly random | 0.15 (mild: 0.2) |

## Species habitat

| Setting | Meaning | Default (YFT, BET, BFT, ALB) |
|---|---|---|
| `sst_opt`, `sst_tol` | preferred SST and tolerance (deg C): horizontal distribution | 25, 24, 20, 21; 3, 3.5, 3, 3.5 |
| `depth_mu`, `depth_sd` | mean and SD of the depth profile (m): vertical distribution | 90, 260, 240, 110; 60, 50, 55, 60 |
| `B0`, `r` | carrying capacity (t) and intrinsic growth rate | 400, 300, 150, 350 thousand t; 0.30, 0.18, 0.25, 0.25 |
| `B_start` | initial biomass as a fraction of carrying capacity | 0.6, 0.7, 0.2, 0.9 |
| `dispersal` | mean movement distance per year (cells) | 1.5 |

Closer `sst_opt` values or larger `sst_tol` mix the species more in space;
closer `depth_mu` or larger `depth_sd` mix them more in depth, which weakens
the contrast between shallow and deep gear. Check the result with
`plot_tuna_vertical_distributions(s)` and, after simulating,
`plot_availability(sim)`.

## Longline gear

| Setting | Meaning | Default |
|---|---|---|
| `HBF_levels` | HBF values the fleet can use | 5, 10, 15, 20 |
| `HBF_depth` | median hook depth for each HBF level (m) | 110, 160, 205, 250 |
| `set_cv` | set-level error in hook depth: how well HBF predicts where the hooks are (0 = perfectly) | 0.2 |
| `hook_cv` | spread of hooks within a set | 0.35 |

`set_cv` is the main "error in variable" knob for the HBF covariate.

## Environment

| Setting | Meaning | Default |
|---|---|---|
| `sst_mean`, `sst_gradient`, `sst_noise` | SST field: mean, south-north increase, random variation (deg C) | 23, 10, 1 |
| `thermocline_sd` | fixed spatial pattern of the thermocline anomaly (m) | 30 |
| `thermocline_annual_sd` | basin-wide annual shift (m); ~10-15 is realistic | 0 |
| `thermocline_annual_field_sd` | new spatial pattern each year (m); ~5-10 | 0 |

## Fleet and data

| Setting | Meaning | Default |
|---|---|---|
| `n_years` | number of years (the `price` matrix needs at least as many rows) | 30 |
| `effort` | sets per year: one value per year, or a single value for a logistic increase towards it | logistic increase towards 800, cut by 25% in years 21-30 |
| `n_vessels`, `q`, `vessel_cv` | number of vessels, fishing catchability (how hard stocks are fished), between-vessel CV | 30, 3, 0.2 |
| `q_index` | catchability at which CPUE is recorded: sets the zeros and noise of the data independently of `q` (set it equal to `q` to couple them) | 0.2 |
| `tweedie_power`, `tweedie_phi` | set-level noise in catchability | 1.2, 0.1 |
| `obs_res` | positions in `Data` are reported as centres of `obs_res` x `obs_res` blocks | 5 |

# Step 1: define the scenarios

The thermocline anomaly in cell *i* and year *t* is
`fixed[i] + shift[t] + annual[i, t]`:

| Setting | Meaning | Fixed | Moving |
|---|---|---|---|
| `thermocline_sd` | SD of the fixed spatial pattern (m) | 30 | 30 |
| `thermocline_annual_sd` | SD of the basin-wide annual shift (m, AR(1)) | 0 | 15 |
| `thermocline_annual_field_sd` | SD of a new spatial pattern each year (m) | 0 | 10 |

The annual values give typical year-to-year anomalies of about 20-30 m and
occasionally 50 m, a realistic range for interannual changes in thermocline
depth over most of the tropical Pacific.

```{r scenarios}
base <- tuna_settings()      # effort: up to ~700 sets per year, cut by 25% after year 20

scenarios <- list(
  Fixed  = modifyList(base, list(thermocline_sd = 30)),
  Moving = modifyList(base, list(thermocline_sd = 30,
                                 thermocline_annual_sd = 15,
                                 thermocline_annual_field_sd = 10))
)
```

# Step 2: simulate

With the same seed, both runs share the SST field, the fixed thermocline
pattern and the vessels.

```{r simulate}
seed <- 123
sims <- lapply(scenarios, simulate_tuna, seed = seed)
```

# Step 3: the environment

SST (warm north, cool south) drives the horizontal distribution; the fixed
thermocline pattern shifts the species' depth:

```{r environment, fig.height = 4}
sims$Fixed$Plots$Env
sims$Fixed$Plots$Thermocline
```

The resulting species densities:

```{r densities, fig.height = 6}
sims$Fixed$Plots$SpDens
```

The same distributions in three dimensions. The latitude x depth section
averages over x: YFT and BET sit in the warm north, BFT and ALB in the cool
south, and within each pair one species is shallow and one deep. The layer
maps show the density in each depth layer, i.e. what a set fishing at that
depth would encounter.

```{r sections, fig.height = 3.5}
sec <- plot_sections(sims$Fixed)
sec$section
```

```{r layers, fig.height = 7}
sec$layers
```

In the Moving scenario the basin-wide thermocline goes up and down between
years, and the spatial pattern changes on top of it:

```{r thermocline-series, fig.height = 3.5}
plot_thermocline(sims$Moving)$series
```

```{r thermocline-maps, fig.height = 5.5}
plot_thermocline(sims$Moving)$maps
```

# Step 4: why the thermocline matters for HBF

When the thermocline is shallow (orange), the deep species move up towards
the hooks of shallow sets; when it is deep (blue), they move below the reach
of all but the deepest sets. The same HBF therefore catches a different mix
of species depending on the thermocline.

```{r thermocline-effect, fig.height = 4}
plot_thermocline_effect(shifts = c(-30, 0, 30))
```

# Step 5: how the fishery responds

## Gear use

The fleet sets deeper gear once BET is valuable (from year 11):

```{r hbf-use, fig.height = 3.5}
sims$Fixed$Plots$HBF + ggtitle("Fixed: proportion of sets by HBF")
```

## Vertical availability by HBF

The average overlap between hooks and fish for sets with each HBF. In the
Fixed scenario the lines are fairly flat, so an HBF factor is a reasonable
proxy for vertical availability. In the Moving scenario the catchability of a
given HBF changes from year to year with the thermocline.

```{r availability, fig.height = 3.2}
for (sc in names(sims)) print(plot_availability(sims[[sc]]) + ggtitle(sc))
```

## Nominal CPUE versus true biomass

```{r nominal, fig.height = 4.5}
for (sc in names(sims)) {
  print(sims[[sc]]$Plots$CPUE + ggtitle(paste(sc, "- nominal (points) vs true (blue)")))
}
```

# Step 6: standardise

Each model is `year` plus a targeting term; `+S` adds a spatial smoother
`s(Xs, Ys)` on the reported 5 x 5 blocks.

| Model | Targeting term | Treats targeting as | Uses |
|---|---|---|---|
| Base | none | ignored | catch per set |
| Base+S | none, + space | ignored | catch per set + position |
| Cluster+S | k-means clusters of catch composition (4 = PCs + 1) | discrete | all species' catches |
| DPC+S | `s(PC1) + s(PC2) + s(PC3)` of catch composition | continuous | all species' catches |
| HBF+S | HBF factor | gear covariate | HBF from logbooks |

One addition: **DPC+S (2 PCs)** uses only the first two principal
components, as in the 2023 CPUE guidelines. The default (`n_pc = 3`) uses
three, which with four species is all the information in the catch
composition.

Positions are reported only as 5 x 5 blocks. This is deliberate: logbooks
rarely report positions at the resolution at which fish and fleets interact,
and a longline set may span a front. The spatial term therefore cannot
resolve all spatial targeting, which is the realistic, data-limited case.

`engine = "bam"` uses `mgcv::bam(discrete = TRUE)` for speed; use
`engine = "gam"` to reproduce the guideline approach exactly.

```{r standardise}
models <- c("Base", "Base+S", "Cluster+S", "DPC+S", "HBF+S")

fits_main <- lapply(sims, standardize_cpue, models = models,
                    spatial_res = 5, n_pc = 3, engine = "bam")

fits_dpc2 <- lapply(sims, standardize_cpue, models = "DPC+S",
                    spatial_res = 5, n_pc = 2, engine = "bam", label = " (2 PCs)")

fits <- Map(bind_comparisons, fits_main, fits_dpc2)
fits$Fixed$formulas
```

The combinations `Cluster_HBF+S` and `DPC_HBF+S` can be added to `models` in
the same way.

# Step 7: compare the indices with the truth

## Indices

```{r indices, fig.height = 6}
plot_indices(fits, models = c("Base", "Base+S", "Cluster+S",
                              "DPC+S (2 PCs)", "DPC+S", "HBF+S"))
```

Annual indices are noisy. To compare the trends, show only loess smoothers
(`type = "smooth"`), or the annual values as points with the smoother
(`type = "points+smooth"`); `span` sets the smoothness:

```{r indices-smooth, fig.height = 6}
plot_indices(fits, models = c("Base", "Cluster+S", "DPC+S", "HBF+S"),
             type = "smooth", span = 0.5)
```

## Accuracy

Root mean squared error of the log index relative to the true biomass trend:

```{r diagnostics, fig.height = 5}
plot_diagnostics(fits)
```

```{r rmse-table}
rmse <- do.call(rbind, lapply(names(fits), function(sc) {
  data.frame(scenario = sc, fits[[sc]]$diagnostics)
}))
rmse_wide <- reshape(rmse, idvar = c("scenario", "species"), timevar = "model",
                     direction = "wide")
names(rmse_wide) <- sub("rmse_log.", "", names(rmse_wide), fixed = TRUE)
knitr::kable(rmse_wide, digits = 3, row.names = FALSE)
```

## What to look for

* **Spatial targeting.** Base (no space) is biased for species whose
  targeting is mainly spatial, e.g. BFT when the fleet moves into cool water.
  Base+S removes most of that bias.
* **Vertical targeting.** The shallow species (YFT, ALB) drop in nominal CPUE
  at year 11, and BET rises, when the fleet switches to deeper gear. Space alone cannot
  remove this; DPC and HBF can.
* **Cluster versus DPC.** Targeting in this simulator is continuous (a graded
  choice among locations and HBF levels), so a few discrete clusters (by
  default the number of PCs plus one, i.e. 4) capture only part of it. Clusters are recommended only when discrete strategies exist
  (Hoyle et al. 2014).
* **Moving thermocline.** An HBF factor assumes the same catchability for an
  HBF level in every year, so it cannot follow the annual changes, and may do
  worse than Base+S because skippers choose HBF partly in response to the
  thermocline. DPC uses the realised catch mix of each set, which reflects
  where the hooks actually fished relative to the fish that year, so it can
  partly correct for it.
* **Number of PCs.** With two PCs, DPC misses part of the targeting signal
  for some species (e.g. BFT); the third PC adds it. With four species, three
  PCs use all the information in the catch composition.
* **Limits of DPC.** The catch composition only shows *relative*
  catchability. If a shift changes the availability of several species in
  the same proportion, the mix does not change and DPC cannot see it. And the
  more PCs are used, the more risk that real changes in relative abundance
  are absorbed by the "targeting" terms (Winker et al. 2014 select the number
  of PCs with a scree test for this reason).

A single iteration is only an illustration; repeat over seeds before drawing
conclusions.

# Step 8: what do the principal components capture?

The DPC models use the PC scores of each set as predictors. To see what they
represent, `plot_pcs()` shows the species loadings, the scores in space,
against the realised hook depth of the set (latent truth from the simulator,
not used in any model) and over time.

```{r pcs-fixed}
pcs_fixed <- plot_pcs(fits$Fixed, pcs = c("PC1", "PC2", "PC3"))
```

What the PCs mean in terms of species:

```{r pcs-loadings, fig.height = 3.5}
pcs_fixed$loadings
```

Where each tactic is used. Compare with the SST field: warm-water (YFT, BET)
versus cool-water (BFT, ALB) catches show up as a north-south pattern.

```{r pcs-space, fig.height = 4}
pcs_fixed$space
```

How the scores change with the depth at which the hooks actually fished, for
each HBF. A PC that changes with hook depth *within* an HBF level carries
information about the realised fishing depth that the HBF factor does not.

```{r pcs-depth, fig.height = 4.5}
pcs_fixed$depth
```

How the average tactic changes over time, following the price blocks:

```{r pcs-time, fig.height = 3.5}
pcs_fixed$time
```

The same plots for the Moving scenario; in particular, the scores over time
now also follow the thermocline:

```{r pcs-moving, fig.height = 3.5}
pcs_moving <- plot_pcs(fits$Moving, pcs = c("PC1", "PC2", "PC3"))
pcs_moving$depth
pcs_moving$time
```

# Replicates: is the ranking robust?

One iteration is only an illustration. `run_replicates()` repeats the
simulation and standardisation for many seeds. Results are saved after every
fit and finished seeds are skipped, so the run can be interrupted and resumed
(e.g. overnight). Set `run_sims <- TRUE` to run it.

```{r replicates, eval = run_sims}
reps <- run_replicates(
  scenarios, seeds = 1:20,
  models = c("Base", "Base+S", "Cluster+S", "DPC+S", "HBF+S"),
  extra = list(list(models = "DPC+S", n_pc = 2, label = " (2 PCs)", engine = "bam")),
  spatial_res = 5, n_pc = 3, engine = "bam",
  file = "replicates_fixed_moving.rds")
```

```{r replicates-plot, eval = run_sims, fig.height = 6}
plot_replicates(reps)
```

Afterwards, the results can be loaded and plotted without re-running:

```{r replicates-load, eval = FALSE}
reps <- readRDS("replicates_fixed_moving.rds")
plot_replicates(reps)
```

# Optional: with or without space, and at what resolution

Set `run_extras <- TRUE` to run this step. Every model is fitted with and
without the smoother at three reporting resolutions: 1 x 1 (exact cell),
5 x 5 and 10 x 10 cells.

```{r spatial-resolution, eval = run_extras}
targeting <- c("Base", "Cluster", "DPC", "HBF")
resolutions <- c("1x1" = 1, "5x5" = 5, "10x10" = 10)
fits_res <- lapply(resolutions, function(res) {
  standardize_cpue(sims$Fixed, models = c(targeting, paste0(targeting, "+S")),
                           spatial_res = res, spatial_k = if (res == 1) 30 else NULL,
                           engine = "bam")
})
plot_diagnostics(fits_res[["5x5"]])
plot_diagnostics(fits_res, models = paste0(targeting, "+S"))
```

* Space matters most for species whose targeting is mainly spatial. It cannot
  replace the targeting term for gear depth within a pair (YFT vs BET, ALB vs
  BFT).
* At 1 x 1 the smoother needs a larger basis (`spatial_k`) and may start to
  absorb part of the targeting signal.

# Optional: interactions with year

When the effect of gear changes over time, one idea is to let the HBF effect
vary with time. Two versions can be added through `add_models`:

* `HBF_period`: a separate HBF effect in each 10-year price block. It cannot
  follow year-to-year thermocline changes.
* `HBF_year_re`: a random HBF x year interaction, which gives each year its
  own (shrunk) HBF effects.

```{r interactions, eval = run_extras}
extra <- c(
  HBF_period  = "year + s(Xs, Ys, k = 8) + HBF_period",
  HBF_year_re = "year + s(Xs, Ys, k = 8) + HBF + s(year, HBF, bs = 're')"
)
fits_int <- lapply(sims, standardize_cpue,
                   models = c("HBF+S", "DPC+S"), add_models = extra, engine = "bam")
plot_diagnostics(fits_int)
plot_indices(fits_int, models = c("HBF+S", "DPC+S", "HBF_period", "HBF_year_re"))
```

Points to keep in mind:

* With a year x HBF interaction the "year effect" is no longer unique: the
  index refers to the reference HBF (the most common level), and a different
  reference gives a different trend.
* A more defensible alternative is to average the predictions over the HBF
  levels (or over the fished cells and gear configurations) in each year,
  giving a marginal index.
* The interaction can absorb part of the abundance signal of species that are
  caught mainly with one gear configuration. Compare the year effects with
  the truth, not only the fit statistics.

# A note on habitat-based standardisation

Habitat-based standardisation (HBS; Bigelow et al. 2002) estimates nothing
about the gear. It weights each set's effort by the predicted overlap between
the hook depths of its HBF and the species' depth distribution from a habitat
model. It needs a validated habitat model and ocean data, so it is a
specialised approach and not part of the comparison here. It can be fitted
with `models = "HBS+S"`; in this simulator its habitat model knows the
species' depth preferences and the average thermocline, but not the annual
anomalies.

# References

Bigelow, K.A., Hampton, J. and Miyabe, N. (2002) Application of a habitat-based
model to estimate effective longline fishing effort and relative abundance of
Pacific bigeye tuna (*Thunnus obesus*). Fisheries Oceanography 11, 143-155.

Hoyle, S.D., Langley, A.D. and Campbell, R.A. (2014) Recommended approaches for
standardizing CPUE data from pelagic fisheries. WCPFC-SC10-2014/SA-IP-10.

Ono, K., Punt, A.E. and Hilborn, R. (2015) Think outside the grids: an
objective approach to define spatial strata for catch and effort analysis.
Fisheries Research 170, 89-101.

Thorson, J.T., Fonner, R., Haltuch, M.A., Ono, K. and Winker, H. (2017)
Accounting for spatiotemporal variation and fisher targeting when estimating
abundance from multispecies fishery data. Canadian Journal of Fisheries and
Aquatic Sciences 74, 1794-1807.

Hoyle, S.D. and Okamoto, H. (2013) Target changes in the tropical WCPO Japanese
longline fishery, and their effects on species composition.
WCPFC-SC9-2013/SA-IP-04.

Winker, H., Kerwath, S.E. and Attwood, C.G. (2013) Comparison of two approaches
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