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mean_temperature_catch(): 渔获平均温度

Source: vignettes/articles/zh/reference/mean_temperature_catch.Rmd
mean_temperature_catch.Rmd

英文原页：mean_temperature_catch()

说明

本函数计算数据中每个FAO渔区的渔获平均温度（MTC），即渔获物种偏好温度按渔获量加权的平均值（Cheung等，2013），并将其与所选SST产品给出的该渔区海表温度（SST）进行比较（见fao_area_sst()和fao_sst_products()）。MTC上升，说明暖水物种在渔获中所占的比例增大。

English

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.

用法 

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()
)

参数

area_data
FAO渔获数据：fishing_area_data()$area_data，或load_fao_data()的任意子集（例如一个国家，或几个渔区）。
English FAO catch data: fishing_area_data()$area_data, or any subset of load_fao_data() (e.g. one country, or several fishing areas).
version
FishBase和SeaLifeBase的版本，例如”26.06”；见fishbase_match()。默认为”latest”。
English The FishBase and SeaLifeBase release, e.g. “26.06”; see fishbase_match(). Default is “latest”.
min_coverage
有偏好温度的条目在渔获量中所占的最低比例（百分比）。低于此值的年份在图表中淡化显示，在表格中加以标记，并且不参与趋势和相关的计算。默认为50，这是本包的约定（见“覆盖率”一节）；设为0则保留所有年份。
English 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
第二个趋势时段的起始年份。默认为1970，即Cheung等（2013）研究时段的起始年份。
English The first year of the second trend period. Default is 1970, the start of the period studied by Cheung et al. (2013).
use_match
使用哪些匹配类型（fishbase_match()的match_type）的偏好温度。默认为精确名称、同物异名、在WoRMS中找到的有效名以及手动给出的数值（即”exact”、“synonym”、“WoRMS valid name (exact)”、“WoRMS valid name (synonym)”和”manual”）。
English 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
用来代替运行fishbase_match()的匹配表：一个数据框，或一个CSV文件的路径，例如上一次运行得到、并经过核对的物种表（见“本包做什么，用户必须核对什么”一节）。它必须有species_en、match_type和temp_pref_mean列，还可以有status、checked和note列。默认为NULL。
English 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
用于比较的SST（见fao_area_sst()）：“ersst5”（NOAA ERSST v5，包内自带）、用download_sst_data()下载的产品名称、你自己的NetCDF文件或存放NetCDF文件的文件夹的路径、FALSE（不比较），或者一个含area_code（或area_en）、year和sst_mean列（还可以有sst_p10和sst_p90列）的数据框。默认为fao_settings()中的sst设置，未更改时为”ersst5”。所用的SST数据会在信息中输出、在图例中注明，并记录在逐年表的sst_source列中。
English 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”（默认）表示按FAO主要渔区计算，“division”表示按load_fao_divisions()渔获数据中的分区计算（见“按分区计算”一节）。
English “area” (default) for the FAO major areas, or “division” for the divisions of the catch data of load_fao_divisions() (see Divisions).
plot
逻辑值。为TRUE（默认）时，图表保存在figures/png/summary/。
English A logical value. If TRUE (default), charts are saved in figures/png/summary/.
table
逻辑值。为TRUE（默认）时，表格保存在tables/。
English A logical value. If TRUE (default), tables are saved in tables/.
lang
图表文字和名称的语言：“en”（英文）、“zh”（中文）或”both”（中英双语）。默认为NULL，即使用fao_settings()设置的语言（未更改时为”en”）。
English 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
逻辑值。为TRUE时，图表还另存为可编辑的PowerPoint文件，保存在figures/pptx/summary/。默认为FALSE，可以用fao_settings(pptx = TRUE)为所有函数统一更改。
English 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
逻辑值。为TRUE时，每张图在保存的同时也画在绘图窗口中（例如RStudio的Plots窗格），便于立即查看。默认为FALSE，可以用fao_settings(plot_show = TRUE)为所有函数统一更改。无论是否显示，图表都会保存为文件（plot = TRUE）。
English 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
输出文件名中使用的名称。默认为数据所属的渔区（或国家）。
English The name used in the output file names. Default is the fishing area (or the country) of the data.
data_dir
下载数据的保存目录。默认为fao_data_dir()。
English The directory where downloaded data is stored. Default is fao_data_dir().

返回值

以不可见方式返回一个由三个数据框组成的列表：

  • yearly：每个渔区（或分区）每年一行：MTC、覆盖率（百分比）、覆盖率是否低于min_coverage、SST（渔区平均值及渔区内第10至第90百分位数的范围）、所用的SST产品和所用SST表中的空间单元（sst_unit）、所用条目数、总渔获量以及所用条目的渔获量；
  • trend：每个渔区每个时段一行：MTC和SST的斜率（°C/十年）、MTC的Mann-Kendall检验，以及MTC与SST在原序列之间和逐年差分之间的相关；
  • species：每个渔区每个FAO物种条目一行：偏好温度、渔获量、在整个时段MTC中的权重和贡献（°C）、匹配情况、链接，以及供用户记录核对情况的checked和note列。

图表保存在figures/png/summary/：<label>_mtc.png（MTC与SST及SST在渔区内的范围；只有一个渔区时，下方还有覆盖率），有多个渔区时还有<label>_mtc_coverage.png。表格保存在tables/：<label>_mtc.csv、<label>_mtc_trend.csv和<label>_mtc_species.csv。by = "division"时，文件名中的_mtc改为_mtc_divisions。

English

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.

详细说明

对于每一年t：

MTC_t = sum_i(T_i * C_i,t) / sum_i(C_i,t)

其中T_i是FAO物种条目i的偏好温度，C_{i,t}是它在第t年的渔获量；两个求和都只针对有偏好温度的条目。某一年的覆盖率，是这些条目的渔获量在该年渔获量中所占的比例。

English

For each year t, MTC_t = 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.

本包做什么，用户必须核对什么

本包计算MTC、它的趋势以及它与SST的比较。各物种的偏好温度没有经过本包核对：它是按FAO学名自动匹配FishBase或SeaLifeBase（fishbase_match()）得到的初步结果，并附有核对所需的链接。每个匹配及其温度是否正确，必须由用户根据这些网页和文献自行判断，然后才能使用结果：

  1. 运行本函数，然后打开物种表（tables/<label>_mtc_species.csv）。条目按渔获量排序，所以越靠前的行越重要。
  2. 对每个参与计算的条目，打开url（匹配到的物种在FishBase或SeaLifeBase中的页面）、search_url和worms_url（世界海洋物种名录WoRMS）。核对匹配到的物种是否就是这个FAO条目，以及它的偏好温度对该渔区的种群是否合理。
  3. 在checked列中写上核对人或日期。把错误的值在temp_pref_mean中改正，并在note中写明出处（如果要给原本不参与计算的条目使用数值，例如某个属，还要把它的match_type设为”manual”）；或者把更正记在fishbase_match()的更正文件中，以后每次运行都会应用。
  4. 用match = "<path of the checked table>"再次运行本函数。

在所有参与计算的条目都标为已核对之前，本函数会在信息和图表的说明文字中注明这一点。

之所以需要核对，是因为不同数据库之间的名称并不总是一致：FAO ASFIS名称的变动缓慢而保守，FishBase、SeaLifeBase和WoRMS采用不同的分类体系，同物异名和拼写错误也很常见（Grenié等，2023）。偏好温度本身也有不确定性（见下一节）。

English

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:

  1. Run the function, then open the species table (tables/<label>_mtc_species.csv). Items are sorted by catch, so the first rows matter most.
  2. 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.
  3. 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.
  4. 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).

偏好温度

所用的偏好温度是FishBase或SeaLifeBase的estimate表中的TempPrefMean。FishBase给出的这一数值是根据AquaMaps分布图（10/2019版）估算的，计算用的是一个未发表的R脚本（FishBase文献123201：Froese，2020，个人通讯）；地图上的网格如何选取、用的是表层温度还是底层温度，都没有文献说明。有些物种的这一数值远低于其大部分渔获所在海域的温度；例如沙丁鱼（European pilchard，Sardina pilchardus）为10.3 °C，而它的渔获有很大一部分来自非洲西北部外海。Cheung等（2013）用的则是根据模拟的物种分布计算的温度偏好剖面的中位数，大多数区域性研究也使用他们的数值（例如Tsikliras和Stergiou，2014；Liang等，2018）；Dimarchopoulou等（2022）和Lavin等（2023）则与本函数一样，使用FishBase的平均值。因此，MTC的水平取决于数值的来源，本函数的MTC不能直接与已发表的数值相比较。

只使用match_type属于use_match的已匹配条目。默认情况下包括：FAO学名是有效种名的条目（“exact”），FAO学名是FishBase或SeaLifeBase同物异名表中的同物异名的条目（“synonym”），在WoRMS中查到有效名后符合以上两者之一的条目（“WoRMS valid name (…)”），以及由用户设定的条目（“manual”）。种以上的条目（例如”Sardinella spp”或”Marine fishes nei”）不使用，这与上述研究一致，因为构成其渔获的物种是未知的；有些研究给属一级的条目赋予该属在该海域出现的物种的平均值（Dimarchopoulou等，2022）。

English

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).

覆盖率

参与计算的条目从来不能覆盖全部渔获，已发表的研究报告的覆盖率从低于30%到高于90%不等（例如Liang等，2018；Tsikliras等，2015）。没有任何已发表的研究规定最低覆盖率：min_coverage（默认50%）是本包的约定，应针对每项分析分别设定。覆盖率会随时间变化，因为越来越多的渔获按物种而不是按类群上报，而早年的数据并没有随之修订（Garibaldi，2012）；这类变化，以及在渔区之间转移或以”nei”（未另列明）上报的渔获（据FAO 2024年的报告，61区有130万吨中国远洋渔获被记为”nei”），都会在温度没有任何变化的情况下改变MTC。解读趋势之前，请先查看覆盖率图表。

English

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.

趋势及与SST的比较

趋势按全部年份和trend_from以来的年份分别计算，只使用覆盖率不低于min_coverage的年份：计算线性回归的斜率（°C/十年），并与peak_trend()一样进行Mann-Kendall检验（年份与MTC之间的Kendall τ），其方差按自相关做了校正（Yue和Wang，2004；kendall_p_iid是常规检验的p值）。MTC与渔区SST的比较，看两者的斜率，以及两个序列之间和它们的逐年差分之间的Pearson相关。

这些只是简单的描述性统计。Cheung等（2013）用同时纳入捕捞努力量、大尺度海洋指数和自相关的模型，估计每个大海洋生态系统的变化速率，并在各生态系统之间分析MTC与SST变化速率的关系；区域性研究使用了滞后相关、回归和模型（Tsikliras和Stergiou，2014；Liang等，2018；Kamberi等，2025）。两个序列都存在自相关：Mann-Kendall检验考虑了这一点，但回归斜率和相关系数的p值把各年视为相互独立，因而偏小（Yue等，2002；Pyper和Peterman，1998）。两个都随时间上升的序列，即使彼此无关，也会表现出相关；逐年差分的相关可以避免这一问题，但可能漏掉只在几十年尺度上起作用的关系（Pyper和Peterman，1998）。在亚得里亚海，考虑自相关之后，MTC与SST之间的关系不再显著（Kamberi等，2025）。

English

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).

按分区计算

by = "division"并使用按分区的渔获数据时（load_fao_divisions()，例如地中海和黑海的GFCM分区；Tsikliras和Stergiou（2014）用的是地中海西部、中部和东部的GFCM数据），本函数对每个分区计算MTC，并与该分区的SST（fao_area_sst(level = "division")）比较。在FAO地图上没有边界的分区使用所在亚区的SST（例如34.1.9 “Northern coastal, not known”使用34.1，51.3.1阿曼海使用51.3）；sst_unit列给出实际使用的单元。不是具体海域的代码（例如37.0.0 Tunas、37.9.0 Not known）没有SST。在内置ERSST v5的2°网格上，有些分区只有几个网格（少于5个时，本函数会指出是哪些分区）；这些分区更适合使用分辨率更高的SST产品（给出其NetCDF文件的路径）。

English

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.

结果解读

MTC的变化可能来自海洋变暖或变冷，也可能来自捕捞物种、渔场、渔具、市场、管理和统计上报方式的变化。例如，在阿拉伯湾，带鱼（largehead hairtail）的出口需求使MTC下降（Alajmi等，2024）；在新西兰，船队转向更深、更冷的水域，使MTC降低（Lavin等，2023）。把变化归因于温度之前，请先用物种表（weight_pct、contribution_c）查看是哪些条目驱动了这一变化。偏好温度描述的是一个物种的整个分布范围，而不是该渔区的种群。

English

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.

参考文献

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

示例 

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")