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Synchrony and Stability of the Catch

Source: R/catch_synchrony.R
catch_synchrony.Rd

This function measures how synchronously the catches of the species (or countries) of the data fluctuate, and how much their asynchrony stabilises the total catch: the synchrony index of Loreau and de Mazancourt (2008), the portfolio effect used by Ong et al. (2021) for FAO catch series, the decomposition of the variability of the total catch of Thibaut and Connolly (2013), and the pairwise correlations of the series.

Usage

catch_synchrony(
  area_data,
  by = c("species", "country"),
  detrend = c("linear", "none"),
  window = 20,
  top_n = 20,
  min_median = 10,
  min_nonzero = 0.75,
  plot = TRUE,
  table = TRUE,
  lang = NULL,
  pptx = getOption("faofishingreport.pptx", FALSE),
  plot_show = fao_setting("plot_show"),
  label = NULL
)

Arguments

area_data

FAO catch data: fishing_area_data()$area_data, or any subset of load_fao_data() (e.g. one country, or several fishing areas).

by

What a series is: "species" (default) or "country".

detrend

"linear" (default) to remove a linear trend from each series, or "none".

window

The length in years of the moving windows. Default is 20.

top_n

The number of series with the largest catch shown in the charts. Default is 20.

min_median, min_nonzero

Series need a median catch above min_median tonnes a year and catch in more than min_nonzero of the years. Defaults are 10 and 0.75 (Ong et al. 2021).

plot

A logical value. If TRUE (default), charts are saved in figures/png/summary/.

table

A logical value. If TRUE (default), tables are saved in tables/.

lang

The language of the chart text and names: "en" (English), "zh" (Chinese) or "both" (Chinese and English). Default is NULL, which uses the language set with fao_settings() ("en" unless changed).

pptx

A logical value. If TRUE, charts are also saved as editable PowerPoint files in figures/pptx/summary/. Default is FALSE, which can be changed for all functions with fao_settings(pptx = TRUE).

plot_show

A logical value. If TRUE, each chart is also drawn in the plot window (e.g. the Plots pane of RStudio) as it is saved, to look at it at once. Default is FALSE, which can be changed for all functions with fao_settings(plot_show = TRUE). The charts are saved as files (plot = TRUE) either way.

label

The name used in the output file names. Default is the fishing area (or the country) of the data.

Value

Invisibly, a list with summary (synchrony, CVs and portfolio effect), series (mean, standard deviation and CV of each series, the share of its standard deviation in the sum of the standard deviations, sd_share_pct, and its contribution to the variance of the total catch, variance_contribution_pct: its covariance with the total divided by the variance of the total, negative for a series that damps the total), windows (the measures in each moving window) and correlation (pairwise correlations of the series).

Details

Series are chosen as by Ong et al. (2021): the median catch is above min_median (10) tonnes a year and more than min_nonzero (3/4) of the years have catch; with by = "species", only items identified to species are used (Ong et al. left out miscellaneous and "nei" items). Each series is detrended with a linear regression on year (as by Ong et al.), since trends make series look synchronous (Lepš et al. 2019); use detrend = "none" to keep them. From the standard deviations \(\sigma_i\) of the series, the standard deviation \(\sigma_T\) of the total catch and its mean \(m_T\):

  • synchrony \(\phi = \sigma_T^2 / (\sum_i \sigma_i)^2\), from 0 (fluctuations fully compensate each other) to 1 (perfectly synchronous) (Loreau and de Mazancourt 2008, eq. 9);

  • the coefficient of variation (CV) of the total catch equals \(\sqrt{\phi}\) times the weighted mean CV of the series, \(\sum_i \sigma_i / m_T\) (Thibaut and Connolly 2013, eqs. 2-4): the lower the synchrony, the more stable the total;

  • portfolio effect = (mean CV of the series - CV of the total) / CV of the total x 100 (Ong et al. 2021, after Carlson and Satterthwaite 2011): how much more variable the single series are than their total, in percent.

The same measures are computed in moving windows of window years (linear trend removed in each window, a choice of this package) to show how synchrony changed over time. Ong et al. (2021) measured synchrony with wavelet coherence, which separates time scales; this function uses the simpler correlation-based measures above.

References

Carlson, S.M. and Satterthwaite, W.H. (2011). Weakened portfolio effect in a collapsed salmon population complex. Canadian Journal of Fisheries and Aquatic Sciences 68: 1579-1589. doi:10.1139/f2011-084

Lepš, J., Götzenberger, L., Valencia, E. and de Bello, F. (2019). Accounting for long-term directional trends on year-to-year synchrony in species fluctuations. Ecography 42: 1728-1741. doi:10.1111/ecog.04528

Loreau, M. and de Mazancourt, C. (2008). Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments. American Naturalist 172: E48-E66. doi:10.1086/589746

Ong, J.J.L., Walter, J.A., Jensen, O.P. and Pinsky, M.L. (2021). Global hotspots of coherent marine fishery catches. Ecological Applications 31: e02321. doi:10.1002/eap.2321

Thibaut, L.M. and Connolly, S.R. (2013). Understanding diversity-stability relationships: towards a unified model of portfolio effects. Ecology Letters 16: 140-150. doi:10.1111/ele.12019

Examples

if (FALSE) { # \dontrun{
FAO27 <- fishing_area_data("Atlantic, Northeast", plot = FALSE)
catch_synchrony(FAO27$area_data, lang = "zh")
catch_synchrony(FAO27$area_data, by = "country", window = 15)
} # }