A database of global marine commercial, small-scale, illegal and unreported fisheries catch 1950–2014 / by Francisco Blaha

I wish I had more time to read, or more validly, I was more disciplined in my job to allow reading time! Yet again a recent paper by Reg A. Watson caught my attention. In this latest work, he harmonised global fisheries landing datasets from the best public sources, interpolate missing taxonomic data, then map the records to a grid of 30-min spatial cells so as to remain consistent with all available auxiliary data and make that dataset publically available. What a legend!

Examples of database use with mapped catch rates (kg km−2 yr−1). (a) Average annual reported catch rates (including IUU) for 2010–2014; (b) Average annual catch rate of discarded marine products 2000–2004; (c) Average catch rate of sharks and rays 2010–2014; (d) Average catch rate of tunas and billfish 2010–2014.

Examples of database use with mapped catch rates (kg km−2 yr−1).
(a) Average annual reported catch rates (including IUU) for 2010–2014; (b) Average annual catch rate of discarded marine products 2000–2004; (c) Average catch rate of sharks and rays 2010–2014; (d) Average catch rate of tunas and billfish 2010–2014.

Read below my shameless quoting of his paper or go (for free) to the original.

Abstract
As Global fisheries landings data from a range of public sources was harmonised and mapped to 30-min spatial cells based on the distribution of the reported taxa and the fishing fleets involved. This data was extended to include the associated fishing gear used, as well as estimates of illegal, unregulated and unreported catch (IUU) and discards at sea. Expressed as catch rates, these results also separated small-scale fisheries from other fishing operations. The dataset covers 1950 to 2014 inclusive.

Mapped catch allows study of the impacts of fisheries on habitats and fauna, on overlap with the diets of marine birds and mammals, and on the related use of fuels and release of greenhouse gases. The fine-scale spatial data can be aggregated to the exclusive economic zone claims of countries and will allow study of the value of landed marine products to their economies and food security, and to those of their trading partners.

Background & Summary
Fishing operations span the globe and occur in all but the deepest and most remote places in global oceans. Fishing remains central to the food security of many countries. It provides much needed protein and income to those with few alternatives. To wealthier nations it is associated with an extremely valuable and a highly globalised seafood trade. The world’s oceans hold continued promise to provide a range of vital services, and fishing will remain important.

Conflict for coastal land use, pollution and other increasing population-based demands are compounded by ocean acidification, warming, spread of pests, deoxygenation and toxic algae blooms. Humans need to guard marine resources, and mapping global fisheries is an important element. Fishing effort continues to increase, putting pressure on marine resources. Large ocean areas have been set aside from fishing as marine protected areas but placing these also requires knowledge of fishing pattern. Knowing the details of global fishing operations remains an important part of ensuring that the ocean’s services and productivity are not misused.

Examining the relationship between global fisheries and the marine environment, including its wildlife and sensitive habitats is challenging but is necessary before the impact on biodiversity and its values can be estimated. Publically available fisheries records are vague, especially in locating where fishing occurs. Nevertheless, it is vital to map fishing and use all available information to do so.

This information includes all public sources covering various spatial scales, and auxiliary data such as the distribution of the reported taxa, and information on the distribution of fishing fleets based on access rights and on their observed behaviour. Datasets have been compiled with increasing skill since 1999 and are renewed as more data become available.

The approach here is to use a harmonised global dataset from the best public sources, interpolate missing taxonomic data, then map the records to a grid of 30-min spatial cells so as to remain consistent with all available auxiliary data and to make that dataset publically available (Data Citation 1: Institute for Marine and Antarctic Studies, University of Tasmania http://dx.doi.org/10.4226/77/58293083b0515).

Data was sourced from a range of public sources (Fig. 1).

Fig 1: Flow diagram of data collation and processing

Fig 1: Flow diagram of data collation and processing

These were harmonised into a single global dataset with common coding. For each location and year, the best coverage from the available sources was selected and overlapping data removed. This dataset was filtered to retain only marine animals but excludes amphibians, reptiles, birds and mammals.

The records were mapped to candidate cells within a system of nearly 300 k global 30-min spatial cells using information on the reported fished taxon’s distribution, the behaviour and access of the reported fishing fleets and any area description provided. A portion of the reported landings represented by each record of the unmapped global dataset was mapped to each candidate cell following a gradient based on the reported taxon’s expected distribution based on depth, habitat and other requirements. Known quotas imposed on fishing fleets were applied.

The result was a mapped dataset of catch rates (tonnes per square km of ocean) for each spatial cell separated by year, fishing nation and fished taxa. This data set was further extended to breakdown the reported landings by fishing gear type based on associations with year/country/taxa. Following this, the catch rate of illegal and unreported landings was estimated for each data record. An estimate of discards (not necessarily of the reported taxa) is also made. Though much of the input landings would be derived from large-scale fishing operations it was possible to estimate rates from small-scale fishing and adjust catch rates to minimise duplicate reporting.

Non-overlapping data sources are selected as input (Fig. 2a). In general, the United Nation’s Food and Agriculture’s (FAO) dataset was the only source that provides global coverage but spatial resolution can be quite coarse. FAO’s various regional bodies provide finer spatial definition for several areas and those were used when available and possible. The breakdown of global tonnage represented by records (Fig. 2b) correlates generally to those developed by reconstructions of individual countries in another global dataset (SAUP), which uses different methodology. The number of database records varies spatially (Fig. 2c) and was impacted by the diversity and intensity of fishing and the level of management control. The number of different taxa reported also varies and is greater in coastal areas (Fig. 2d).

One of the likely conclusion of this mammoth dataset, is something we all suspect, that for more than a decade catches have plateaued and we are at maximum extraction capacity. Even if fishing effort still increasing (mostly thanks to subsidies) there is no "more" fish to take... what we have now is as good as it will get... if anything it will go down.