Climate Change

Reconstructing time series of fisheries catch for all countries of the world from 1950 (the first year that FAO published its ‘Yearbook' of global fisheries statistics) to 2010 was undertaken by fisheries ‘sectors'. However, because a standardized global definition of fishing sectors based on vessel size does not exist (for example, a vessel considered large-scale (industrial) in a developing country may be considered small-scale (artisanal) in developed countries), reconstructions utilize each country's individual definitions for sectors, or a regional equivalent. These are described in each country reconstruction publication underlying this work. We consider four sectors:

Industrial: large-scale fisheries (using trawlers, purse-seiners, longliners) with high capital input into vessel construction, maintenance and operation, and which may move fishing gear across the seafloor or through the water column using engine power (for example, demersal and pelagic trawlers), irrespective of vessel size. This corresponds to the ‘commercial' sectors of countries such as the USA;

Artisanal: small-scale fisheries whose catch is predominantly sold (hence they are also ‘commercial fisheries'), and which often use a large variety of generally static or stationary (passive) gears. Our definition of artisanal fisheries relies also on adjacency: they are assumed to operate only in domestic waters (that is, in their country's EEZ). Within their EEZ, they are further limited to a coastal area to a maximum of 50 km from the coast or to 200 m depth, whichever comes first. This area is defined as the Inshore Fishing Area (IFA)44 . Note that the definition of an IFA assumes the existence of a small-scale fishery, and thus unpopulated islands, although they may have fisheries in their EEZ (which by our definition are industrial, whatever the gear used), have no IFA;

Subsistence: small-scale non-commercial fisheries whose catch is predominantly consumed by the persons fishing it, and their families (this may also include the ‘take-home' fraction of the catch of commercial fishers, which usually by-passes reporting systems); and

Recreational: small-scale non-commercial fisheries whose major purpose is enjoyment.

In addition to the reconstructions by sector, we also assign catches to either ‘landings' (that is, retained and landed catch) or ‘discards' (that is, discarded catch), and label all catches as either ‘reported' or ‘unreported' with regards to national and FAO data. Thus, reconstructions present ‘catch' as the sum of ‘landings' plus ‘discards'.
Discarded fish and invertebrates are generally assumed to be dead, except for the US fisheries where the fraction of fish and invertebrates reported to survive is generally available on a per species basis45 . Due to a distinct lack of global coverage of information, we do not account for so-called under-water discards, or net-mortality of fishing gears46 . We also do not address mortality caused by ghost-fishing of abandoned or lost fishing gear47 .
For commercially caught jellyfishes (particularly Rhizostomeae, but also other taxa), it has been shown that over 2.5 time more are caught than reported to FAO (mostly as ‘Rhizostoma spp.')48 . This factor is used to estimate missing catches of unidentified jellyfish. However, this additional catch is, pending further study, not allocated to any specific country or FAO area, and is thus counted only in the world's total catch.
We exclude from consideration all catches of marine mammals, reptiles, corals, sponges and marine plants (the bulk of the plant material is not primarily used for human consumption, but for cosmetic or pharmaceutical use). In addition, we do not estimate catches made for the aquarium trade, which can be substantial in some areas in terms of number of individuals, but relatively small in overall tonnage, as most aquarium fish are small or juvenile specimens49 (link).
Most catch reconstructions consist of six steps15 :
(1) Identification, sourcing and comparison of baseline catch times series, that is, (a) FAO reported landings data by FAO statistical areas, taxon and year; and (b) national or regional data series by area, taxon and year. Implicit in this first step is that the spatial entity be identified and named that is to be reported on (for example, EEZ of Germany in the Baltic Sea), something that is not always obvious, and which poses problems to some of our external collaborators, notably those in countries with a claimed EEZ overlapping with that of their neighbour.
For most countries, the baseline data are the statistics reported by member countries to FAO. We treat all countries recognized in 2010 (or acting like independent countries with regards to fisheries) by the international community as having existed from 1950 to 2010. This is necessary, given our emphasis on ‘places', that is, on time-series of catches taken from specific ecosystems. This also applies to islands and other territories, many of which were colonies, and which have changed status and borders since 1950.
For several countries, the baseline data are provided by international bodies. In the case of EU countries, the baseline data originate from the International Council for the Exploration of the Sea (ICES), which maintains fisheries statistics by smaller statistical areas, as required given the Common Fisheries Policy of the EU. A similar area is the Antarctic waters and surrounding islands, whose fisheries are managed by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), where catch data are available by relatively small statistical areas50 .
When FAO data are used, care is taken to maintain their assignment to different FAO statistical areas for each country (Supplementary Fig. 1), as they often distinguish between strongly different ecosystems. For example, the Caribbean Sea versus the coast of the Eastern Central Pacific in the case of Panama, Costa Rica, Nicaragua, Honduras and Guatemala. For each maritime country, the area covered extends from the coastline to the edge of the EEZ, including any major coastal lagoons connected to the sea, and the mouths of rivers, that is, estuaries. However, freshwaters are excluded.
(2) Identification of sectors (for example, subsistence, recreational), time periods, species, gears and so on, not covered by (1), that is, missing data components. This is conducted via literature searches and consultations with local experts. This step is one where the contribution of local co-authors and experts is crucial. Potentially, all four sectors defined by us can occur in the marine fisheries of a given coastal country, with the distinction between large-scale and small-scale being the most important25 (link). For any entity, we check whether catches originating from the four sectors were included in the reported baseline of catch data, notably by examining their taxonomic composition, and any metadata, which were particularly detailed in the early decades of the FAO ‘Yearbooks'51 .
The absence of a taxon known to be caught in a country or territory from the baseline data (for example, cockles gleaned by women on the shore of an estuary)26 can also be used to identify a fishery that has been overlooked in the official data collection scheme, as can the absence of reef fishes in the coastal data of a Pacific Island state10 . To avoid double counting, tuna and other large pelagic fishes, unless known to be caught by a local small-scale fishery (and thus in the past not likely reported to a Regional Fisheries Management Organization or RFMO), are not included in this reconstruction step (see below under ‘High Seas and other catches of large pelagic fishes').
Finally, if gears are identified in national data, but a gear known to exist in a given country is not included, then it can be assumed that its catch has been missed, as documented for weirs (hadrah) in the Persian Gulf52 .
(3) Sourcing of alternative information sources on missing sectors identified in (2), via literature searches (peer-reviewed and grey) and consultations with local experts. Information sources include social science studies (anthropology, economics and so on), reports, data sets and expert knowledge. The major initial source of information for catch reconstructions is governments' websites and publications (specifically their Department of Fisheries or equivalent agency), both online and in hard copies. Contrary to what could be expected, it is often not the agency responsible for fisheries research and initial data collection that supplies the catch statistics to FAO, but other agencies, for example, statistical office or agency. As a result, much of the granularity of the original data (that is, catch by sector, by species or by gear) may be lost even before data are prepared for submission to FAO. Furthermore, the data request form sent by FAO each year to each country does not encourage improvements or changes in taxonomic composition, as the form that requests the most recent year's data contains the country's previous years' data in the same composition as submitted in earlier years. This encourages the pooling of detailed data at the national level into the taxonomic categories inherited through earlier (often decades old) FAO reporting schemes, as was discovered, for example, for Bermuda in the early 2000s (ref. 53 ). Thus, by getting back to the original data, much of the original granularity can be regained during reconstructions.
Additional sources of information on national catches are international organizations such as FAO, ICES or SPC (Secretariat of the Pacific Community), or a Regional Fisheries Management Organization (RFMO) such as NAFO (Northwest Atlantic Fisheries Organization), or CCAMLR54 , or current or past regional fisheries development and/or management projects (many of them launched and supported by FAO), such as the Bay of Bengal Large Marine Ecosystem project (BOBLME). All these organizations and projects issue reports and publications describing—sometimes in considerable details—the fisheries of their member countries. Another source of information is the academic literature, now widely accessible through Google Scholar.
A good source of information for the earlier decades (especially the 1950s and 1960s) for countries that were part of former colonial empires (especially British or French) are the colonial archives in London (British Colonial Office) and the ‘Archives Nationales d'Outre-Mer', in Aix-en-Provence, and the publications of ORSTOM (Office de la recherche scientifique et technique d'outre-mer), for former French colonies. A further source of information and data are non-fisheries sources, including household and/or nutritional surveys, which are occasionally used for estimating unreported subsistence catches. Our global network of local collaborators is also crucial in this respect, as they have access to key data sets, publications and local knowledge not available elsewhere, often in languages other than English.
Supplementary Figure 2 shows a plot of the publications used for slightly over 110 reconstructions against their date of publication. Although, recent publications predominate, older publications firmly anchor the 1950s catch estimates of many reconstructions. On average, around 35 unique publications were used per reconstruction (not counting online sources and personal communications).
Potential language bias is taken seriously in the Sea Around Us, to ensure that data are collated in languages other than English. Besides team members who read Chinese, others speak Arabic, Danish, Filipino/Tagalog, French, German, Hindi, Japanese, Portuguese, Russian, Spanish, Swedish and Turkish. To deal with other languages, research assistants are hired who speak, for example, Korean or Malay/Indonesian. We also rely on our multilingual network of colleagues and friends throughout the world, for example, for Greek or Thai. While it is true that English has now become the undisputed language of science55 , other languages are used by billions of people, and assembling knowledge about the fisheries of the world is not possible without the capacity to explore the literature in languages other than English.
(4) Development of data ‘anchor points' in time for each missing data item, and expansion of anchor point data to country-wide catch estimates. ‘Anchor' points are catch estimates usually pertaining to a single year and sector, and often to an area not exactly matching the limits of the EEZ or IFA in question. Thus, an anchor point pertaining to a fraction of the coastline of a given country may need to be expanded to the country as a whole. For expansion, we use fisher or population density, or relative IFA or shelf area as raising factor, as appropriate given the local condition. In all cases, we consider that case studies underlying or providing the anchor point data may had a case-selection bias (for example, representing an exceptionally good area or community for study, compared with other areas in the same country), and thus use raising factors very conservatively.
(5) Interpolation for time periods between data anchor points, either linearly or assumption based for commercial fisheries, and generally via per capita (or per fisher) catch rates for non-commercial sectors. Fisheries are often difficult to govern, as they are social activities involving multiple actors. In particular, fishing effort is often difficult to reduce, at least in the short term. Thus, if anchor points are available for years separated by multi-year intervals, it usually will be more reasonable to assume that the underlying fishing activity continues in the intervening years with no data. We tread this ‘continuity' assumption as a default proposition. Exceptions to such continuity assumptions are major environmental impacts such a hurricanes or tsunamis56 , or major socio-political disturbances, such as military conflicts or civil wars57 , which we explicitly consider with regards to the use of raising factors and the structure of time series estimates. In such cases, our reconstructions mark the event through a temporary change (for example, decline) in the catch time series, which is documented in the text of each catch reconstruction. At the very least, this provides pointers for future research on the relationship between fishery catches and natural catastrophes or conflicts. We note that the absence of such signals (such as a reduction in catch for a year or two) in the officially reported catch statistics for countries having experienced a major natural or socio-political disturbance can be a sign that their official catch data may not accurately reflect what occurs on the ground. This contributes to the emergence of ‘poor numbers'40 . Overall, our reconstructions assume—when no information to the contrary is available—that commercial catches (that is, industrial and artisanal) can be linearly interpolated between anchor points, while non-commercial catches (that is, subsistence and recreational) can generally be interpolated between anchor points using non-linear trends in human population numbers or number of fishers over time (via per capita rates).
Radical and rapid effort reductions as a result of an intentional policy decision and implementation do not occur widely. One example we are aware of is the trawl ban of 1980 in Western Indonesia58 . The ban had little or no impact on official Indonesian fisheries statistics for Western Indonesia, another indication that these statistics may have little to do with the realities on the ground. FAO hints at this being widespread in the Western Central Pacific and the Eastern Indian Ocean (the only FAO areas where reported catches appear to be increasing) when they note that ‘while some countries (i.e., the Russian Federation, India and Malaysia) have reported decreases in some years, marine catches submitted to FAO by Myanmar, Vietnam, Indonesia and China show continuous growth, i.e., in some cases resulting in an astonishing decadal increase (e.g., Myanmar up 121 percent, and Vietnam up 47 percent)'.42
(6) Estimation of total catch times series. A reconstruction is completed when the estimated catch time series derived through steps 2–5 are combined and harmonized with the reported catch of step 1. Generally, this results in an increase of the overall catch, but several cases exist where the reconstructed total catch is lower than the reported catch. The best documented case of this is that of mainland China14 (link), whose over-reported catches for local waters in the Northwest Pacific are compensated for by under-reported catches taken by Chinese distant water fleets fishing elsewhere. In the 2000s, Chinese distant water fleets operated in the EEZs of over 90 countries, that is, in most parts of the world's oceans5 . Harmonizing reconstructed catches with the reported baselines goes hand-in-hand with documenting the entire reconstruction procedure. Thus, every reconstruction is documented and published, either in the peer-reviewed scientific literature, or as detailed technical reports in the publicly accessible and indexed Fisheries Centre Research Reports series or the Fisheries Centre Working Paper series, or other regional organization reports (Supplementary Table 5).
Several reconstructions were conducted in the mid- to late 2000s, when official reported data (that is, FAO statistics or national data) were not available to 2010 (refs 15 , 59 ). All these cases are updated to 2010, in line with each country's individual reconstruction approach to estimating missing catch data. Thus, all reconstructions are brought to 2010 to ensure identical time coverage (Supplementary Table 5).
Since these six points were originally proposed, a seventh point has come to the fore that cannot be ignored10 :
(7) Quantifying the uncertainty associated with each reconstruction. In fisheries research, catch data are rarely associated with a measure of uncertainty, at least not in the form resembling confidence intervals. This may reflect the fact that the issue with catch data is not a lack of precision (that is, whether we could expect to produce similar results upon re-estimation), but about accuracy, that is, attempting to eliminate a systematic bias, a type of error which statistical theory does not really address.
We deal with this issue through a procedure related to ‘pedigrees'60 and the approach used by the Intergovernmental Panel on Climate Change to quantify the uncertainty in its assessments61 . The authors of the reconstructions are asked to attribute a ‘score' expressing their evaluation of the quality of the time series data to each fisheries sector (industrial, artisanal and so on) for each of the three time periods (1950–1969, 1970–1989 and 1990–2010). These ‘scores' are (1) ‘very low', (2) ‘low', (3) ‘high' and (4) ‘very high' (Table 1). There is a deliberate absence of an uninformative ‘medium' score, to avoid the effective ‘non-choice' that this option would represent. Each of these scores is assigned a percentage uncertainty range (Table 1). Thereafter, the overall mean weighted percentage uncertainty (over all countries and sectors) was computed (Fig. 1).

A detailed description of the data, analytical framework, and statistical methods, partly described in previous work,¹⁶ (link) is provided in the appendix.
We estimated location-specific associations using observed data on outdoor temperature and mortality. For this purpose, we obtained information from a dataset created through the Multi-Country Multi-City (MCC) Collaborative Research Network. The dataset is composed of observed daily time series of mean temperature and mortality counts for all causes or non-external causes only (International Classification of Diseases [ICD] codes 0–799 in ICD-9 and codes A00-R99 in ICD-10) in largely overlapping periods ranging from Jan 1, 1984, to Dec 31, 2015, in addition to location-specific meta-variables (appendix).
We computed future effects under alternative climate change scenarios using modelled climate and mortality projections. First, we obtained daily mean temperature series under scenarios of climate change consistent with the four representative concentration pathways (RCPs) defined in the 2014 IPCC report.² These four scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) correspond to increasing greenhouse gas concentration trajectories, and describe a range of changes in climate and related global warming, from mild (RCP2.6) to extreme (RCP8.5). We generated the temperature series under each RCP by general circulation models (GCMs), which offer a representation of past, current, and future climate dependent on greenhouse gas emissions. Specifically, projections for five GCMs, representative of the range of available climate models, were developed and made available by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP).¹⁷ (link) The ISI-MIP database provides daily mean temperature for historical (1960–2005) and projected (2006–99) periods, bias-corrected and downscaled at a 0·5° × 0·5° spatial resolution, as single runs of each GCM under each RCP. We extracted the modelled daily temperature series for each of the studied locations in the period 1990–2099 by linking the coordinates with the corresponding cell of the grid, and recalibrated the modelled series using the observed series.¹⁸ We computed projected daily series of all-cause mortality as the average observed counts for each day of the year, repeated along the same projection period (1990–2099).

The sites of isolation that represented Western Kenya were selected based on centric random systematic sampling from respective counties and subcounties (Figure 1). They were Lurambi (N 0° 0.29“; E 34° 69.71′) in Kakamega County, Emuhaya (N 0° 5.42′; E 34° 34.65′) in Vihiga County, Teso South (N 0° 33.729′; E 34° 16.21′) in Busia County, and Chaptais (N 0° 48.36′; E 34° 28.26′) in Bungoma County. Samples were collected during mid of June 2021. The main source of income of Western Kenya inhabitants is mixed agricultural farming [22 (link)]. Sugarcane, maize, beans, finger millets, bananas, and sweet potatoes are among the main food and cash crops grown in the region [23 ]. Western Kenya is typically hot and humid, with year-round rainfall. According to the World Bank Climate Change Knowledge Portal, it is indicated that it received average temperature of 21.28°C and an average rainfall of 2233.59 mm in the year 2021.

Plant and soil samples were taken at the elongation, heading, and ripening. Since climatic change treatment altered wheat development, soil and plant sampling was conducted based on the phenological stage. Wheat plants were randomly collected 1 m² from each plot, plant sample was separated into shoot and root. Root samples were rinsed with water to get rid of the soil. Meanwhile, the shoot and root samples were rinsed and de-enzyme at 105°C for 0.5 h, and then oven-dried at 70°C for 48 h. All plant samples were ground to 0.25 mm. For soil samples, five soil cores (0-15 cm) were taken and then homogenized to form a mixed soil sample. After removing visible residues and stones, soil samples were passed through a 2 mm sieve and maintained at 4°C before analysis.
Soil available P was extracted with NaHCO₃ and analyzed by a spectrophotometer (TU-1810, China). Soil available K concentrations were estimated using a flame photometer (FP6410, China) after extraction with 1 M ammonium acetate (NH₄OAc). Soil ${\text{NH}}_4^{+}$ -N and ${\text{NO}}_3^{-}$ N concentrations were determined using a subsection flow analysis instrument (Skalar, Netherlands).
Plant shoot and root samples were pretreated with H₂SO₄-H₂O₂, and analyzed for N concentrations by the Kjeldahl digestion method. Phosphorus concentrations were determined by a spectrophotometer (TU-1810, China), and K concentrations by a flame photometer (FP6410, China).