Mean Temperature of the Catch — mean_temperature

This function calculates the mean temperature of the catch (MTC) of each FAO fishing area in the data: the average preferred temperature of the species caught, weighted by their catch (Cheung et al. 2013), and compares it with the sea surface temperature (SST) of the area from a chosen SST product (see fao_area_sst() and fao_sst_products()). A rising MTC means that species of warmer waters make up a larger part of the catch.

Usage

mean_temperature_catch(
  area_data,
  version = "latest",
  min_coverage = 50,
  trend_from = 1970,
  use_match = c("exact", "synonym", "WoRMS valid name (exact)",
    "WoRMS valid name (synonym)", "manual"),
  match = NULL,
  sst = fao_setting("sst"),
  by = c("area", "division"),
  plot = TRUE,
  table = TRUE,
  lang = NULL,
  pptx = getOption("faofishingreport.pptx", FALSE),
  plot_show = fao_setting("plot_show"),
  label = NULL,
  data_dir = fao_data_dir()
)

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).
version: The FishBase and SeaLifeBase release, e.g. "26.06"; see fishbase_match(). Default is "latest".
min_coverage: The minimum share of the catch (in percent) made up by items with a preferred temperature. Years below it are faded in the charts, marked in the table and left out of the trends and correlations. Default is 50, a convention of this package (see Coverage); use 0 to keep all years.
trend_from: The first year of the second trend period. Default is 1970, the start of the period studied by Cheung et al. (2013).
use_match: The kinds of match (match_type of fishbase_match()) whose preferred temperature is used. Default is exact names, synonyms, valid names found in WoRMS and manual values.
match: The match table to use instead of running fishbase_match(): a data frame, or the path of a CSV file such as the species table of a previous run after checking it (see What the user must check). It needs the columns species_en, match_type and temp_pref_mean, and may have status, checked and note. Default is NULL.
sst: The SST to compare with (see fao_area_sst()): "ersst5" (NOAA ERSST v5, built into the package), the name of a product downloaded with download_sst_data(), the path of your own NetCDF file or folder of NetCDF files, FALSE for none, or a data frame with the columns area_code (or area_en), year and sst_mean (and optionally sst_p10 and sst_p90). Default is the sst setting of fao_settings(), "ersst5" unless changed. The SST data used is printed, shown in the chart legend and given in the column sst_source of the yearly table.
by: "area" (default) for the FAO major areas, or "division" for the divisions of the catch data of load_fao_divisions() (see Divisions).
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.
data_dir: The directory where downloaded data is stored. Default is fao_data_dir().

Value

Invisibly, a list of three data frames:

yearly: one row per area (or division) and year: MTC, coverage (percent), whether the coverage is below min_coverage, SST (mean and 10th-90th percentile range in the area), its product and the unit of the SST table used (sst_unit), number of items used, total catch and catch of the items used;
trend: one row per area and period: slopes of the MTC and SST (°C per decade), the Mann-Kendall test of the MTC, and the correlation of MTC and SST for the series and for their year-to-year differences;
species: one row per area and FAO species item: preferred temperature, catch, weight in and contribution (°C) to the MTC of the whole period, match, links, and the columns checked and note for the user's checks.

Charts are saved in figures/png/summary/: <label>_mtc.png (MTC and SST with its range in the area; for a single area with the coverage below) and, for several areas, <label>_mtc_coverage.png. Tables are saved in tables/: <label>_mtc.csv, <label>_mtc_trend.csv and <label>_mtc_species.csv. With by = "division", _mtc in the file names becomes _mtc_divisions.

Details

For each year $t$, $$MTC_t = \frac{\sum_i T_i C_{i,t}}{\sum_i C_{i,t}}$$ where $T_i$ is the preferred temperature of the FAO species item $i$ and $C_{i,t}$ its catch in year $t$; both sums are over the items that have a preferred temperature. The coverage of a year is the share of its catch made up by these items.

What the package does and what the user must check

The package computes the MTC, its trends and its comparison with SST. The preferred temperature of each species is not checked by the package: it is a first, automatic match of the FAO scientific name to FishBase or SeaLifeBase (fishbase_match()), given with the links needed to check it. Whether a match and its temperature are right must be judged by the user, from these pages and the literature, before the results are used:

Run the function, then open the species table (tables/<label>_mtc_species.csv). Items are sorted by catch, so the first rows matter most.
For each item used, open url (the FishBase or SeaLifeBase page of the matched species), search_url and worms_url (the World Register of Marine Species). Check that the matched species is the FAO item, and that its preferred temperature is plausible for the stock in the area.
Write who checked it, or the date, in the column checked. Correct a wrong value in temp_pref_mean and give the source in note (to use a value for an item that is not used, such as a genus, also set its match_type to "manual"); or record the correction in the corrections file of fishbase_match(), which every later run applies.
Run the function again with match = "<path of the checked table>".

Until every item used has been marked as checked, the function says so in its messages and in the caption of the charts.

Checking is needed because names do not always match between databases: FAO ASFIS names change slowly and conservatively, FishBase, SeaLifeBase and WoRMS follow different classifications, and synonyms and misspellings are common (Grenié et al. 2023). The preferred temperature itself is also uncertain (next section).

Preferred temperatures

The preferred temperature used is TempPrefMean of the FishBase or SeaLifeBase estimate table. FishBase gives it as an estimate from AquaMaps distribution maps (version 10/2019), computed with an unpublished R script (FishBase reference 123201: Froese 2020, pers. comm.); how the map cells are chosen and whether surface or bottom temperature is used is not documented. For some species it is well below the temperatures of the area where most of their catch comes from; for example, it is 10.3 °C for European pilchard (Sardina pilchardus), much of whose catch is taken off north-west Africa. Cheung et al. (2013) used instead the median of a temperature preference profile computed from modelled species distributions, and most regional studies used their values (e.g. Tsikliras and Stergiou 2014; Liang et al. 2018); Dimarchopoulou et al. (2022) and Lavin et al. (2023) used the FishBase mean, as here. The level of the MTC therefore depends on the source of the values, and the MTC of this function is not directly comparable with published values.

Only matched items whose match_type is in use_match are used: by default, items whose FAO scientific name is a valid species name ("exact"), a synonym in the synonym table of FishBase or SeaLifeBase ("synonym"), either of these after looking up the valid name in WoRMS ("WoRMS valid name (...)"), or set by the user ("manual"). Items above species level (e.g. "Sardinella spp" or "Marine fishes nei") are not used, as in the studies above, since the species that make up their catch are not known; some studies give genera the mean of the species of the genus found in the area (Dimarchopoulou et al. 2022).

Coverage

The items used never cover the whole catch, and published studies report coverage from below 30% to over 90% (e.g. Liang et al. 2018; Tsikliras et al. 2015). No published study sets a minimum coverage: min_coverage (default 50%) is a convention of this package, and should be set for each analysis. The coverage changes over time because more catch is reported by species instead of groups, without the earlier years being revised (Garibaldi 2012); such changes, and catches moved between areas or reported as "nei" (FAO 2024 reports 1.3 million tonnes of Chinese distant-water catch recorded as "nei" in Area 61), change the MTC without any change in temperature. Look at the coverage chart before reading a trend.

Trends and comparison with SST

Trends are calculated over all years and over the years from trend_from, using only years whose coverage is at least min_coverage: the slope of a linear regression (°C per decade) and a Mann-Kendall test (Kendall's tau between year and MTC) with its variance corrected for autocorrelation (Yue and Wang 2004; kendall_p_iid is the p value of the usual test), as in peak_trend(). The MTC is compared with the SST of the area by their slopes and by the Pearson correlation of the two series and of their year-to-year differences.

These are simple descriptive statistics. Cheung et al. (2013) estimated the rate of change of each large marine ecosystem with models that also included fishing effort, a large-scale ocean index and autocorrelation, and related the rates of MTC and SST across ecosystems; regional studies used lagged correlations, regressions and models (Tsikliras and Stergiou 2014; Liang et al. 2018; Kamberi et al. 2025). Both series are autocorrelated: the Mann-Kendall test takes this into account, but the p-values of the regression slope and of the correlations treat years as independent and are too small (Yue et al. 2002; Pyper and Peterman 1998). Two series that both rise over time are correlated even when they are not related; the correlation of differences avoids this, but can miss a relation that acts only over decades (Pyper and Peterman 1998). In the Adriatic Sea, the relation of MTC with SST was no longer significant once autocorrelation was accounted for (Kamberi et al. 2025).

Divisions

With by = "division" and catch data by division (load_fao_divisions(), e.g. the GFCM divisions of the Mediterranean and Black Sea; Tsikliras and Stergiou 2014 used GFCM data of the western, central and eastern Mediterranean), the MTC is calculated for each division and compared with the SST of the division (fao_area_sst(level = "division")). Divisions without a boundary on the FAO map use the SST of their subarea (e.g. 34.1.9 "Northern coastal, not known" uses 34.1, 51.3.1 Oman Sea uses 51.3); the column sst_unit gives the unit used. Codes that are not places (e.g. 37.0.0 Tunas, 37.9.0 Not known) have no SST. On the 2° grid of the built-in ERSST v5, some divisions have only a few cells (the function says which); a finer SST product, given as the path of its NetCDF files, suits them better.

Interpretation

A change in the MTC can come from a warmer or cooler ocean, but also from changes in the species fished, fishing grounds, gear, markets, management and reporting. For example, in the Arabian Gulf the MTC fell because of export demand for largehead hairtail (Alajmi et al. 2024), and in New Zealand fleets moving to deeper, colder waters lowered it (Lavin et al. 2023). Check which items drive a change with the species table (weight_pct, contribution_c) before attributing it to temperature. A preferred temperature describes the whole range of a species, not the stock in the area.

References

Alajmi, A., Ben-Hasan, A., Alrushaid, T., Vahabnezhad, A. and Pauly, D. (2024). Mean temperature of the catch index can be masked by changes in catch composition unrelated to ocean warming. Fisheries Management and Ecology 31: e12659. doi:10.1111/fme.12659

Cheung, W.W.L., Watson, R. and Pauly, D. (2013). Signature of ocean warming in global fisheries catch. Nature 497: 365-368. doi:10.1038/nature12156

Dimarchopoulou, D., Makino, M., Prayoga, M.R., Zeller, D., Vianna, G.M.S. and Humphries, A.T. (2022). Responses in fisheries catch data to a warming ocean along a latitudinal gradient in the western Pacific Ocean. Environmental Biology of Fishes 105: 1347-1362. doi:10.1007/s10641-021-01162-z

FAO (2024). The State of World Fisheries and Aquaculture 2024. Rome: FAO. doi:10.4060/cd0683en

Froese, R. (2020). Personal communication: R code (PrefTempBatch_5.R) to estimate preferred temperature from AquaMaps (version 10/2019). FishBase reference 123201. https://www.fishbase.se/references/FBRefSummary.php?ID=123201

Garibaldi, L. (2012). The FAO global capture production database: a six-decade effort to catch the trend. Marine Policy 36: 760-768. doi:10.1016/j.marpol.2011.10.024

Grenié, M., Berti, E., Carvajal-Quintero, J., Dädlow, G.M.L., Sagouis, A. and Winter, M. (2023). Harmonizing taxon names in biodiversity data: a review of tools, databases and best practices. Methods in Ecology and Evolution 14: 12-25. doi:10.1111/2041-210X.13802

Kamberi, E., Hala, E. and Möllmann, C. (2025). Impact of large-scale oceanic variability on Adriatic fisheries evidenced through the 'mean temperature of the catch' approach. Frontiers in Marine Science 12: 1719738. doi:10.3389/fmars.2025.1719738

Lavin, C.P., Pauly, D., Dimarchopoulou, D., Liang, C. and Costello, M.J. (2023). Fishery catch is affected by geographic expansion, fishing down food webs and climate change in Aotearoa, New Zealand. PeerJ 11: e16070. doi:10.7717/peerj.16070

Liang, C., Xian, W. and Pauly, D. (2018). Impacts of ocean warming on China's fisheries catches: an application of "mean temperature of the catch" concept. Frontiers in Marine Science 5: 26. doi:10.3389/fmars.2018.00026

Pyper, B.J. and Peterman, R.M. (1998). Comparison of methods to account for autocorrelation in correlation analyses of fish data. Canadian Journal of Fisheries and Aquatic Sciences 55: 2127-2140. doi:10.1139/f98-104

Tsikliras, A.C. and Stergiou, K.I. (2014). Mean temperature of the catch increases quickly in the Mediterranean Sea. Marine Ecology Progress Series 515: 281-284. doi:10.3354/meps11005

Tsikliras, A.C., Peristeraki, P., Tserpes, G. and Stergiou, K.I. (2015). Mean temperature of the catch (MTC) in the Greek Seas based on landings and survey data. Frontiers in Marine Science 2: 23. doi:10.3389/fmars.2015.00023

Yue, S. and Wang, C.Y. (2004). The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resources Management 18: 201-218. doi:10.1023/B:WARM.0000043140.61082.60

Yue, S., Pilon, P., Phinney, B. and Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes 16: 1807-1829. doi:10.1002/hyp.1095

Froese, R. and Pauly, D. (Eds.) FishBase. https://www.fishbase.se; Palomares, M.L.D. and Pauly, D. (Eds.) SeaLifeBase. https://www.sealifebase.se; WoRMS Editorial Board. World Register of Marine Species. https://www.marinespecies.org

Examples

if (FALSE) { # \dontrun{
FAO27 <- fishing_area_data("Atlantic, Northeast", plot = FALSE)
mtc <- mean_temperature_catch(FAO27$area_data, lang = "both")
mtc$trend
# The items with the largest catch: check their match and preferred temperature with the links
head(mtc$species[, c("species_name", "match_type", "temp_pref_mean", "catch_pct", "used", "url")], 20)

# After checking tables/Atlantic__Eastern_Central_mtc_species.csv (column `checked`, and
# corrected values with their source in `note`), run again with the checked table
mean_temperature_catch(FAO27$area_data, match = "tables/Atlantic__Northeast_mtc_species.csv")

# Other SST data: your own NetCDF file (or folder), or a product downloaded once
mean_temperature_catch(FAO27$area_data, sst = "D:/data/HadISST_sst.nc", label = "FAO27_hadisst")
download_sst_data("cobe2")
mean_temperature_catch(FAO27$area_data, sst = "cobe2", label = "FAO27_cobe2")

# The GFCM divisions of the Mediterranean and Black Sea
mean_temperature_catch(load_fao_divisions("GFCM"), by = "division")

# The period studied by Cheung et al. (2013)
mean_temperature_catch(subset(FAO27$area_data, Year >= 1970 & Year <= 2006),
                       label = "FAO27_1970_2006")

# All FAO major marine fishing areas, one chart panel per area
marine <- subset(load_fao_data(), FAO.major.fishing.area..Name. %in% fao_area_sst()$area_en)
mean_temperature_catch(marine, label = "marine_areas", lang = "zh")
} # }