- EXPLORE GAPI
- EXPLORE RESULTS
The GAPI project developed specific criteria to ensure that the indicators were sufficiently rigorous. Included in these criteria are:
• Relevance or how well the indicator gauges the environmental impact at hand;
• Performance orientation or whether the indicator tracks actual, on-the-water performance;
• Transparency of formulas and data; and
• Availability of quality data.
In deriving the ten indicator formulae, construction of the wastes and the disease/parasites indicators proved especially challenging. Both are notoriously difficult to track in the environment and both lack broadly accepted metrics or impact models. While expert opinion was sought during the development of all indicators, dedicated expert workshops were convened to derive wastes and disease/parasites indicators. Both workshops were successful in developing rigorous, comprehensive indicators that are detailed in the Indicators section and the Report.
The overall score could be calculated by taking the average of the 10 individual indicator scores. However, doing so would ignore the fact that some indicators are more important than others in explaining the difference in performance among two or more players. A recent review of sustainability assessment methodologies (Singh et al., 2009) demonstrated that normalization and weighting of indicators used in sustainability assessments is typically associated with subjective judgments and reveals a high degree of arbitrariness without mentioning or systematically assessing critical assumptions. Even a decision not to weight the indicators is in fact weighting, in this case weights are set to zero. GAPI addresses this dilemma by shifting away from weighting based on the assumed magnitude of environmental impact of each indicator and instead employs a weighting scheme where the data, not the investigator, determine the degree of weighting for each indicator. Weights assigned to indicators reflect the ability of that indicator to discriminate the performance of any two players. An indicator for which all players score similarly will be weighted less than one which shows greater variation among the pool of players. The latter indicator has greater discriminatory power and therefore is accorded a statistically derived higher weight.
Weights are prescribed by Principal Component Analysis (PCA). Within GAPI, the 10 indicators generate a large “cloud” of data that has ten dimensions. PCA creates a lens through which one can view this complex set of data as simply and clearly as possible. PCA identifies trends in the data in order to determine how important each indicator is in describing the difference in performance between performers. PCA measures how much of the total variation in the data is explained by each indicator, thus providing a measure of each indicator’s relative importance or weight.
FCR stands for Feed Conversion Ratio, or (wet) Feed In: Fish Out. This generally refers to compound, or pelleted, feed.
FCR measures the amount of feed input relative to fish produced (Feed In: Fish Out), while the transfer coefficient is a calculation of Fish In: Fish Out ratio (note this is a measure of fish in, not feed in). The transfer coefficient measures the wild fish equivalent inputs to farmed fish outputs (Tacon and Metian 2008). The processing of wild fish into fish meal or oil for feed means that each kilogram of feed may contain much more than one kilogram of wild fish which is captured in the transfer coefficient
A series of expert consultations concluded that while it is important to look at all chemical inputs and outputs from aquaculture, antibiotics, antifoulants and parasiticides comprise the majority of discharges and impacts.
Unlike the case for biological oxygen demand, there is not a clear connection between farm density and impacts of antifoulant usage. In the absence of conclusive data it was decided that at this time BOD would be the only indicator to include a factor of farm density.
In 2007, the U.N. Food and Agriculture Organization (FAO), the World Health Organization (WHO), and the World Organization for Animal Health (OIE) hosted a joint meeting to assess the importance of key antibiotics in the treatment of human and animal disease. emphasis was placed on those antibiotics for which overuse / misuse could lead to the development of resistance. Two ratings emerged: a WHO rating of the importance of antibiotics in human use and an OIE rating of importance in veterinary use. WHO and OIE classify antibiotics as critically important, highly important, or important antimicrobials based on two sets of criteria (FAO 2008).
For antibiotics used in human medicine, the WHO classification criteria are:
Criterion 1: Sole therapy or one of a few alternatives to treat serious human disease.
Criterion 2: Antibacterial used to treat diseases caused by organisms that may be transmitted via non-human sources or diseases caused by organisms that may acquire resistance genes from non-human sources.
For antibiotics used in veterinary medicine, the OIE classification criteria are:
Criterion 1: Response rate to the questionnaire regarding Veterinary Critically Important Antimicrobials. This criterion was met when a majority of the respondents (more than 50%) identified the importance of the antimicrobial class in their response to the questionnaire.
Criterion 2: Treatment of serious animal disease and availability of alternative antimicrobials. This criterion was met when compounds within the class were identified as essential against specific infections and there was a lack of sufficient therapeutic alternatives.
Within each class if both criteria are met, the antibiotic is classified as critically important by WHO or OIE. For instance, if an antibiotic used in human medicine meets both WHO criteria, it is classified by the WHO as critically important. If one criterion is met, the antibiotic is classified as highly important in that category. If neither criterion is met, the antibiotic is classified as an important antimicrobial in that category. In order to assess the overall importance of antibiotics for both human and veterinary use, GAPI assigned scores to antibiotics based on their combined WHO and OIE classifications. The following table provides the joint WHO-OIE antibiotic importance scoring system used by GAPI.
GAPI Scoring System, as Classified by WHO and OIE
GAPI assessment is done on a species/country level. It is not possible to fairly and accurately characterize the hydrology of an entire coastline or the multiple coastlines one country may have. The Farm-Level Aquaculture Performance Index (FLAPI) project will offer a tool to delve down into this level of assessment.
A country’s cumulative score does reflect the full BOD impact of all of the farmed species (that GAPI assesses in that country). The country cumulative level is the most appropriate place to reflect the cumulative impact of all farms. In rare cases a country may have significant production of a species not assessed by GAPI (because production of that species globally is not great enough to satisfy the threshold for inclusion in GAPI). In such cases the BOD score is conservative with actual BOD impact being worse than indicated.
GAPI assumes nutrient loading spread across a large area will, on average, have a lesser impact than the same loading concentrated in a small area. In order to establish the area of impact, GAPI first identified farm locations using Google Earth images (Google Inc. 2009) imported into ArcGIS 9.2. For each site, the area of impact is assumed to be a 3 km radius buffer around the farm site. The effect that farm-derived nutrients have on the water column is well documented (Sarà 2007a) and has been measured up to and including distances of 1,000 m (Sarà et al. 2006). Ecological impacts are assumed to extend beyond this point and, as a result, farm-siting buffers in countries vary from 300 m (Nova Scotia) to 8 km (Scotland) (Ministry of Agriculture and Lands 2005). Because there are very few systematic data regarding how ecological effects vary with distance (Sarà 2007b), GAPI uses a median value of the siting regulations (3 km) to set the buffer for the area of overlap.
As depicted in the figure below, Area of Overlap is the sum of the areas of overlap of farm buffer zones. A higher Area of Overlap indicates a more concentrated effluent release. Since we were unable to identify the type of species farmed at each site, the total Area of Overlap of all farms for a country was adjusted based on the proportion of production comprised by each species in a given country. Similarly, we could not quantify the production magnitude at any particular farm. Therefore, GAPI assumes negligible impact of any single farm located beyond 3 km from the next closest farm. For this reason, the BOD indicator is a very conservative performance metric.
Assessment of the Area of Overlap in GAPI BOD Indicator
Not all fish removed from the wild would have survived if they were not harvested in the reduction fishery. The inclusion of the natural mortality factor avoids an overestimation of the impact of wild fish removed.
The Sustainability Score calculation for fish meal and fish oil ingredients is the same formula used to determine the Sustainability Score of wild fish inputs in the capture-based aquaculture indicator (CAP). The Sustainability Score of the fisheries supplying feed for an aquaculture system is the product of three factors: Harvest Performance, Stock Status, and an assessment of the Management Regime for each contributing fishery. These three measures are multiplied so that final sustainability scores most effectively discriminate between good and poor performances.
The percentage of catch of the fishery exceeding the set management catch limit. Where the actual catch is above the management limit, the harvest performance is:
Harvest Performance = Actual Catch - Mgmt Catch Limit x 100
However, where actual catch is below the management limit, the numerator is transformed to 1. Thus, the equation for harvest performance is:
Harvest Performance = 1 x 100
Management catch limit information is largely taken from FishSource (2010) (www.fishsource.org). GAPI’s first preference is to use the biological maximum sustainable yield (Bmsy) as the management catch limit for each species. However, if the Bmsy is unavailable, GAPI uses the total allowable catch (TAC). If the TAC is unavailable, GAPI uses the spawning stock biomass (SSB) or other available management catch limit. If no management catch limit was set for the assessment year (2007), GAPI uses the best of Bmsy, TAC, or other management catch limit (in order of preference) for the most recent year. If no management catch limit was ever set for the fishery, GAPI assumes that the management catch limit is zero. This leads to a harvest performance score where 100% of catch is considered to be over the management catch limit (i.e., the worst-case scenario).
In 2005, the FAO assigned categorical values to the health of fish stocks. The four categories ranged from underexploited to overexploited-depleted. Within GAPI, these categorical scores are converted to numeric scores between one and four, with one being the best performance (underexploited) and four being the worst performance (overexploited-depleted).
A 2009 Sustainable Fisheries Partnership (SFP) report examined the sustainability of world fisheries used for reduction purposes (e.g., aquaculture feeds). SFP used the setting of biological reference points (BRPs) as an indicator of the sustainability of reduction fisheries. BRPs can be derived using a variety of approaches. Ecosystem-based management is considered to be the best approach for setting upper target reference points, while B20 or biomass/recruitment models are considered the best approach for setting lower limit reference points.
Based on the BRP information provided in the SFP report, GAPI assigned a score between one and nine, where nine represents the worst possible performance (no upper and lower limits set) and one represents the best possible performance (use of ecosystem-based management and B20 to set upper and lower limits, respectively).
GAPI Management Scores
Although these practices affect the amount of copper introduced into the surrounding ecosystem, protocols vary greatly across the sector and are directly correlated with usage of antifoulants.
Proportion of aquaculture using antifoulant is currently the finest resolution of data available. Very few countries have regulations that require reporting of antifoulant usage, thus data was not available.
Currently copper is the most universally used antifoulant and its use is representative of antifoulant use in general. However other materials are gaining popularity and it is anticipated future GAPI updates will see a broadening of scope incorporated into this indicator.
NPP is “net primary productivity” and is a measure of how much ecological production is imbedded in the feed consumed by the production system. Solar energy (sunlight) is converted by photosynthesis in phyto [plant] -plankton into a biologically consumable form which is then distributed through the food chain by predation. The ECOE measures the magnitude of photosynthesis diverted from the wild ecosystem and appropriated by the aquaculture production system.
Inspired by the Marine Fish Invasiveness Screening Kit (MFISK) tool developed by Copp et al. (2007), the GAPI Invasiveness Score assesses the risks of impact of escape events within several broad categories. These include: domestication; climate; distribution; invasion elsewhere; undesirable traits; feeding traits; reproduction; and persistence attributes. For each species, a 26 point survey is conducted. Responses, typically scored as either 0 or 1, are summed to obtain the total GAPI Invasiveness Score. )
GAPI Invasiveness Score Questionaire
Until reporting of farms/countries improves we have to make use of the best available data. Currently averaging escapes across a year is the only option. GAPI is not intended to be a site-by-site or minute by minute reporting of impacts, but is rather meant to capture the mean impacts of the farming industry at scales relevant for species to species, country to country comparisons.
Industrial energy consumption is calculated as the energy use (MJ) embedded in feed used to produce one mT of fish in that country. Using values derived from a salmon life cycle analysis (LCA; a full “cradle to grave” accounting of environmental impacts), the knife coefficient represents the amount of industrial energy necessary for production of feed components and differing production systems (Ayer and Tyedmers 2009; Pelletier et al. 2009). The components are separated into livestock, plant, and marine. Energy input for each of these includes production, raw material processing/reduction, and feed milling. The coefficient only considers energy consumed during feed production and does not account for energy consumed in feed transport or any other energy pathway.
This estimate is derived from input data to Ecopath models (Christensen and Walters 2004), which use a trophic mass balance approach to quantify ecosystem biomasses of the world’s 66 large marine ecosystems (LMEs). Specific input data for LME models were obtained from Christensen et al. (2009) and published peer reviewed Ecopath ecosystem data for the relevant LME.
The proportion of the biomass of susceptible fish in the ecosystem is the proportion of species in the ecosystem that is susceptible to the pathogen in question. It is assumed that all members of a taxonomic Family are susceptible to a pathogen when two or more Genera are known to be susceptible (Lafferty, pers. comm. 2009).