Final Report Summary - IN EX FISH (Incorporating the extrinsic drivers into fisheries management)
The INEXFISH project aimed to incorporate the effects of extrinsic drivers, both anthropogenic and non-anthropogenic, into fisheries management and had the following four specific technical objectives:
1. to provide a state of the art review of the impact of anthropogenic and non-anthropogenic factors on the dynamics of fish stocks;
2. to develop a framework for the systematic evaluation of the impacts of these factors on the dynamics of exploited fish species;
3. to develop criteria for the selection of appropriate metrics, review available metrics of ecosystems status, select those that match the criteria and establish reference levels and
4. to incorporate the produced knowledge regarding anthropogenic and non-anthropogenic effects into fisheries management.
The fisheries management tools devised by INEXFISH aimed to specifically improve scientific knowledge that could be used for the development of advice from the International Council for the Exploration of the Sea (ICES) and the Scientific Advisory Committee of the General Fisheries Commission for the Mediterranean (GFCM).
The scientific review that took place as part of INEXFISH included information of both published and unpublished literature from the field and the laboratory and was available in the project website. This review, along with a specialised workshop, allowed for the determination of the extrinsic drivers, or factors, whose changes were most important for fish stocks and which were namely the following:
1. temperature, including ambient and sea surface temperature;
2. atmosphere-ocean system;
3. prey abundance, both in terms of species variety and quantitative abundance;
4. habitat structure;
5. toxic load, including incidence of eutrophication or body load of pollutants;
6. natural mortality;
7. degree of fish mortality.
Changes in population's dynamics as a function of biological processes could be identified by monitoring the above drivers. Their effects however were not generic, since it was proven that different stocks were affected to a lesser of greater extent depending on direct and indirect processes.
Determination of the major extrinsic factors provided the core of the framework for the examination of specific case studies in distinct, ecologically contrasted, geographical areas. The way management incorporated extrinsic drivers was reviewed, the effect of environmental factors on stocks was investigated and quantified, models were utilised and metrics were applied. Metrics were assessed, prior to their application, to determine if they could be used effectively and consistently, based on criteria such as representation of a concrete physical or biological property, availability of historical data, responsiveness after changes in the forcing factor and validity of the metric for many stocks and different areas. Care in the selection of species was also taken, to consider comparable species in the ecologically contrasting study areas. Additionally, some species were excluded from the analyses, since data were insufficient to allow for effective assessments.
INEXFISH project introduced a fisheries management approach which would take into account the extrinsic drivers influence. However, common metrics applicable across all ecosystems types should be carefully assessed, since population responses could differ between different cases.
Analyses in order to quantify the effect of extrinsic drivers to population dynamics were necessary, and were performed using a non-parametric technique called generalised additive modelling (GAM). The analyses resulted in objective selection of the most important between the various biologically relevant factors and allowed for the development of non-parametric, statistically ratified models which linked changes in biological processes to extrinsic factors. Metrics affecting the condition of the stock being modelled were predominantly affected by anthropogenic pressure; thus they could be easily incorporated into management practice. On the other hand, non-anthropogenic factors could not be controlled, only understood, so that management could better respond to changes in environmental conditions. Non-anthropogenic factors could be divided in two main categories, the ones that represented large scale indices of ocean-atmosphere interaction and those in specific areas which were often linked to a reproductive activity during a specific time period. The climate type of metrics dominated, since it was of direct or indirect biological relevance for most species and had sufficient time series data which could be employed in the analyses.
The derived S-Re GAM models were incorporated within a bio-economic fisheries and ecological systems simulation model, namely the fisheries library in R (FLR), in order to offer analyses of the metrics effect on population dynamics and define the potential for management response. FLR offered the advantage of having been thoroughly tested, understood and accepted by most European fisheries institutes. The sensitivity of the management regimes to the fluctuations of the extrinsic drivers could hence be evaluated.
The FLR model was used to determined how biological variation in biological processes affected population dynamics. Subsequently, the S-Re GAM models allowed for investigation of harvest control rules (HCR), which controlled fisheries exploitation. Seven different HCRs under numerous environmental conditions were studied. The environment proved to have considerable impact on all stocks, with changes in an unfavourable direction potentially leading to markedly low yields with high variations over time and increased stock collapse. A precautionary exploitation strategy could reduce, or even negate, these effects. It was concluded that, in cases of environmental conditions being poor for fish, the use of relevant environmental HCRs could increase yields by over 10 %; thus, they performed better compared to simple HCRs which did not take environmental limitations into account.
As each stock had different responses to factors' changes, the suggested approach for INEXFISH outcomes application in management was to examine scenarios based on the available models which would then allow robust environmental HCRs to be developed. A specific advisory framework could be developed, while the EU data collector regulations should include extrinsic drivers and organisations concerned for their collection should receive more support. Further attempts should be made to improve knowledge on phytoplankton and zooplankton, data on natural predators on fish, spatial and stratification changes in hydrological conditions and knowledge on toxic substances which could influence fish population. It was estimated necessary to continue efforts for the refined monitoring of drivers and models improvement, particularly under the concern of the probable climate change which could severely alter the current ecosystems behaviour and modify the extrinsic drivers' effects.
1. to provide a state of the art review of the impact of anthropogenic and non-anthropogenic factors on the dynamics of fish stocks;
2. to develop a framework for the systematic evaluation of the impacts of these factors on the dynamics of exploited fish species;
3. to develop criteria for the selection of appropriate metrics, review available metrics of ecosystems status, select those that match the criteria and establish reference levels and
4. to incorporate the produced knowledge regarding anthropogenic and non-anthropogenic effects into fisheries management.
The fisheries management tools devised by INEXFISH aimed to specifically improve scientific knowledge that could be used for the development of advice from the International Council for the Exploration of the Sea (ICES) and the Scientific Advisory Committee of the General Fisheries Commission for the Mediterranean (GFCM).
The scientific review that took place as part of INEXFISH included information of both published and unpublished literature from the field and the laboratory and was available in the project website. This review, along with a specialised workshop, allowed for the determination of the extrinsic drivers, or factors, whose changes were most important for fish stocks and which were namely the following:
1. temperature, including ambient and sea surface temperature;
2. atmosphere-ocean system;
3. prey abundance, both in terms of species variety and quantitative abundance;
4. habitat structure;
5. toxic load, including incidence of eutrophication or body load of pollutants;
6. natural mortality;
7. degree of fish mortality.
Changes in population's dynamics as a function of biological processes could be identified by monitoring the above drivers. Their effects however were not generic, since it was proven that different stocks were affected to a lesser of greater extent depending on direct and indirect processes.
Determination of the major extrinsic factors provided the core of the framework for the examination of specific case studies in distinct, ecologically contrasted, geographical areas. The way management incorporated extrinsic drivers was reviewed, the effect of environmental factors on stocks was investigated and quantified, models were utilised and metrics were applied. Metrics were assessed, prior to their application, to determine if they could be used effectively and consistently, based on criteria such as representation of a concrete physical or biological property, availability of historical data, responsiveness after changes in the forcing factor and validity of the metric for many stocks and different areas. Care in the selection of species was also taken, to consider comparable species in the ecologically contrasting study areas. Additionally, some species were excluded from the analyses, since data were insufficient to allow for effective assessments.
INEXFISH project introduced a fisheries management approach which would take into account the extrinsic drivers influence. However, common metrics applicable across all ecosystems types should be carefully assessed, since population responses could differ between different cases.
Analyses in order to quantify the effect of extrinsic drivers to population dynamics were necessary, and were performed using a non-parametric technique called generalised additive modelling (GAM). The analyses resulted in objective selection of the most important between the various biologically relevant factors and allowed for the development of non-parametric, statistically ratified models which linked changes in biological processes to extrinsic factors. Metrics affecting the condition of the stock being modelled were predominantly affected by anthropogenic pressure; thus they could be easily incorporated into management practice. On the other hand, non-anthropogenic factors could not be controlled, only understood, so that management could better respond to changes in environmental conditions. Non-anthropogenic factors could be divided in two main categories, the ones that represented large scale indices of ocean-atmosphere interaction and those in specific areas which were often linked to a reproductive activity during a specific time period. The climate type of metrics dominated, since it was of direct or indirect biological relevance for most species and had sufficient time series data which could be employed in the analyses.
The derived S-Re GAM models were incorporated within a bio-economic fisheries and ecological systems simulation model, namely the fisheries library in R (FLR), in order to offer analyses of the metrics effect on population dynamics and define the potential for management response. FLR offered the advantage of having been thoroughly tested, understood and accepted by most European fisheries institutes. The sensitivity of the management regimes to the fluctuations of the extrinsic drivers could hence be evaluated.
The FLR model was used to determined how biological variation in biological processes affected population dynamics. Subsequently, the S-Re GAM models allowed for investigation of harvest control rules (HCR), which controlled fisheries exploitation. Seven different HCRs under numerous environmental conditions were studied. The environment proved to have considerable impact on all stocks, with changes in an unfavourable direction potentially leading to markedly low yields with high variations over time and increased stock collapse. A precautionary exploitation strategy could reduce, or even negate, these effects. It was concluded that, in cases of environmental conditions being poor for fish, the use of relevant environmental HCRs could increase yields by over 10 %; thus, they performed better compared to simple HCRs which did not take environmental limitations into account.
As each stock had different responses to factors' changes, the suggested approach for INEXFISH outcomes application in management was to examine scenarios based on the available models which would then allow robust environmental HCRs to be developed. A specific advisory framework could be developed, while the EU data collector regulations should include extrinsic drivers and organisations concerned for their collection should receive more support. Further attempts should be made to improve knowledge on phytoplankton and zooplankton, data on natural predators on fish, spatial and stratification changes in hydrological conditions and knowledge on toxic substances which could influence fish population. It was estimated necessary to continue efforts for the refined monitoring of drivers and models improvement, particularly under the concern of the probable climate change which could severely alter the current ecosystems behaviour and modify the extrinsic drivers' effects.