Ecological risk assessment of marine resources caught as bycatch in industrial bottom trawl shrimp fishery in the Amazon Continental Shelf

Ecological risk assessment (ERA) has been widely used to assess species’ vulnerability to the impacts of fishing and then to prioritize any additional management actions to reduce impacts. The Ecological Risk Assessment for the Effects of the Fishing framework is based on a hierarchy of qualitative and semi-quantitative tools that work well in data-deficient situations. This study first used the Scale Intensity and Consequence (SICA) and Productive and Susceptibility Analyses (PSA) tools to evaluate the impacts of the industrial bottom trawl of southern brown shrimp on the Amazon Continental shelf in Northern Brazil. A total of 540 species were identified as having direct or indirect interaction with the trawls. The SICA identified that the main risk was related to fishing capture activities, potentially impacting the species’ population size. Of the 47 species evaluated in the PSA, 12 displayed low vulnerability, 23 displayed moderate vulnerability, and 12 displayed high vulnerability to the impacts of fishing. Future fisheries management should focus on reducing species vulnerability by prioritizing data collection for the most at-risk species. Also, fishing gear modification, such as bycatch exclusion devices (BRDs), should be employed to decrease the species’ vulnerability

Multidimensional Indicators to Assess the Sustainability of Demersal Small-Scale Fishery in the Azores

The Azorean demersal fishery sector is one of the most important in the archipelago. As a small-scale fishery, it plays an important role in the livelihood of the community, being a source of employment and income, and contributing to poverty alleviation. Because fisheries are a complex system, a multidisciplinary approach that includes socioeconomic indicators is required for a broader assessment of fishery sustainability. This study analyzes the Azorean bottom longline fishery using the Fishery Performance Indicators tool, regarding its ecology, economy, and community indicators. The findings indicated that the fishery is mostly sustainable, although there is still opportunity for improvement. Its ecological indicators had a good performance, mainly due to the effort and work of the scientific community that makes continuous studies to examine the state of its stocks. The economic indicators are in good condition as well, but some obstacles stopped the indicator from obtaining a better performance; mainly the landing volatility and the fishery's main source of capital (subsidies), which can make the fishery less competitive. Finally, its community indicator had a very good performance, which reflects the fishery's socioeconomic and cultural relevance for the Azores.info:eu-repo/semantics/publishedVersio

Workshop to apply thresholds for the preselected indicators for MSFD D3C3 (WKD3C3THRESHOLDS)

The WKD3C3THRESHOLDS meeting provided a platform for experts from the EU member states to meet and progress the assessment methodology on Criteria 3 of Descriptor 3 upon request by EC (DGENV). WKD3C3THRESHOLDS is the second of a series of three workshops (WKD3C3SCOPE, WKD3C3THRESHOLDS and WKD3SIMUL) to identify operational indicators for MSFD D3C3. The workshop was organised as a series of presentations of results with intermittent group discussions. The D3C3 indicators agreed at WKD3C3SCOPE were estimated and documented for a selection of stocks representing different life-histories (Tor a). Plots comparing indicators were investigated for stocks with all estimated indicators. The age structure indicators ABI, ASA, POS and SSB/R generally followed the temporal development of SSB and react similarly to F. A gap of up to 10 years was observed between changes in F and subsequent changes in age structure indicators for long-lived species while SSB responded quickly to changes in F. For medium-lived stocks, the four age structure indicators exhibited similar temporal patterns, with SSB divided by R tending to be more variable. Recruitment and mean weight at age documented shifts in productivity, impacting age structure indicators differently when changes occur. Plots of F, recruitment, weight at age and SSB are considered useful for understanding cases where changes in F do not impact SSB as expected (e.g. rebuilding does not occur or stock remains high in spite of high F). Higher proportions of older fish as measured by ABI/ASA/POS or SSB/R did not appear linked to an immediate increase recruitment. A comparison of length-based and age-based indicators for Mediterranean stocks was also conducted. Recruitment detection from survey time series showed uneven patterns over stocks and time series, and in some cases depended on survey timing. Length-based indicators exhibited weak consistency information from stock assessments, and confounding effects of biological variability and sampling timing on observed recruitment pulses were noted. The indicator L90R, calculated from the length-frequency distribution of fish larger than recruiting length, seemed to perform well among those inspected. Thresholds for the D3C3 indicators for stocks representing different life-histories, data availability and MSFD (sub)regions (when possible) were discussed (ToR b and c). The suggested thresholds covered all approaches identified by WKD3C3SCOPE. Clear thresholds where the indicator signifies stock productivity declines could not be identified from the data as none of the age structure indicators showed a positive correlation with stock productivity. As a result, threshold levels cannot be determined based on levels at which stock productivity is either impaired or enhanced. In the absence of clear relationships between the indicators and stock health, the workshop used varying percentages (10th percentile, median/50th percentile) of the simulated or observed distributions of indicators to determine good status of the indicator. The analyses presented emphasized the direct influence of recruitment and growth on fisheries yield and precautionary fishing mortality limits. Finally, a decision tree to choose a threshold setting method was proposed for further testing in WKSIMULD3 on the basis of listed pros and cons discussed by WKD3C3THRESHOLD participants. The SSB/R indicator responded to recruitment in an undesirable manner but there was insufficient evidence to determine which of the three remaining age structure indicators provided a higher signal to noise due to recruitment variability. Selectivity indicators under D3C3 were retained despite unclear guidance in the MSFD guidance document. The retained indicators for medium-lived stocks with age-based assessment data include ABI, POS, ASA, R, ASW, and Fjuv/Fapical. The value of age structure indicators as management indicators was unclear for short-lived and long-lived species. For short-lived species, no strong link was found between age-structure indicators and F or SSB, and high age at spawning may lead to senescence rather than increased viability of spawning products. For long-lived species, age structure indicators appeared to react substantially later than F and SSB, making their added value for management unclear. The definition of thresholds for these indicators will be further investigated in WKSIMULD3. The assessment of stock health under D3C3 relies on crucial data such as recruitment, weight at age, and size/age distribution (ToR d). In the absence of this information, D3C3 assessments cannot be conducted, and Member States were encouraged to enhance data collection efforts. For stocks with age-based assessments, these data are considered essential input and/or output for the assessment, and assessments based on age data are preferred over those based solely on length distributions for the estimation of age structure indicators. Finally, the group drafted a list of actions to be completed for the reparation of WKSIMULD3

Workshop on accounting for fishers and other stakeholders’ perceptions of the dynamics of fish stocks in ICES advice (WKAFPA)

The objective of the Workshop on accounting for fishers and other stakeholders’ perceptions of the dynamics of fish stocks in ICES advice (WKAFPA) was to identify where and how stake- holder information could be incorporated in the ICES fisheries advice process. It adopted an operational definition of the concept of perception, where perceptions result from observations, interpreted in light of experience, that can be supported by data, information and knowledge to generate evidence about them. Stakeholder information can be either structured (e.g. routinely collected information in a standardized format) or unstructured (e.g. experiential information) and either of those can inform decisions made during the production of ICES advice. Most notably, the group identified there was a need to engage with stakeholders earlier in the process, i.e. before benchmarks meetings take place and before preliminary assessment results are used as the basis to predict total allowable catches for upcoming advice (Figure 4.2). It was therefore recommended to include in the ICES process the organisation of pre-bench- mark/roadmap workshops where science and data needs of upcoming benchmarks can be iden- tified, followed by making arrangements how scientists and stakeholders can collaborate to ac- cess, prepare for use (where relevant) and document the structured and unstructured infor- mation well ahead of the benchmark meetings. It was also recommended to organise ‘sense-checking’ sessions with stakeholders when prelim- inary assessments are available but not yet used as the basis for advisory production. This would allow stakeholders and assessment scientists to verify available knowledge and data against stock perceptions and provide additional considerations relevant for the production of TAC ad- vice. Next to these two additional activities, it is recommended that communication on differ- ences in stakeholder perception or data derived perceptions are communicated within the ICES assessment reports as well as in the ICES advice in a transparent manner. Not only should dif- ferences or similarities be documented and communicated, in those cases where there are differ- ences in perception between ICES stock assessments and stakeholders, a working group, external to the assessment working groups, should evaluate these differences and describe whether these differences can be logically explained or require further investigation. This outcome of this pro- cess may potentially lead to new data collection or additional analyses suitable for input to benchmarks. Essential in this entire process is making sure the same language is spoken between scientists and stakeholders, that there are clear and transparent processes in place on how to deal with stakeholder information and communicate clearly how this information is used in the prepara- tion of ICES advice.info:eu-repo/semantics/publishedVersio

Workshop on accounting for fishers and other stakeholders’ perceptions of the dynamics of fish stocks in ICES advice (WKAFPA)

The objective of the Workshop on accounting for fishers and other stakeholders’ perceptions of the dynamics of fish stocks in ICES advice (WKAFPA) was to identify where and how stakeholder information could be incorporated in the ICES fisheries advice process. It adopted an operational definition of the concept of perception, where perceptions result from observations, interpreted in light of experience, that can be supported by data, information and knowledge to generate evidence about them. Stakeholder information can be either structured (e.g. routinely collected information in a standardized format) or unstructured (e.g. experiential information) and either of those can inform decisions made during the production of ICES advice. Most notably, the group identified there was a need to engage with stakeholders earlier in the process, i.e. before benchmarks meetings take place and before preliminary assessment results are used as the basis to predict total allowable catches for upcoming advice (Figure 4.2). It was therefore recommended to include in the ICES process the organisation of pre-benchmark/roadmap workshops where science and data needs of upcoming benchmarks can be identified, followed by making arrangements how scientists and stakeholders can collaborate to access, prepare for use (where relevant) and document the structured and unstructured information well ahead of the benchmark meetings. It was also recommended to organise ‘sense-checking’ sessions with stakeholders when preliminary assessments are available but not yet used as the basis for advisory production. This would allow stakeholders and assessment scientists to verify available knowledge and data against stock perceptions and provide additional considerations relevant for the production of TAC advice. Next to these two additional activities, it is recommended that communication on differences in stakeholder perception or data derived perceptions are communicated within the ICES assessment reports as well as in the ICES advice in a transparent manner. Not only should differences or similarities be documented and communicated, in those cases where there are differences in perception between ICES stock assessments and stakeholders, a working group, external to the assessment working groups, should evaluate these differences and describe whether these differences can be logically explained or require further investigation. This outcome of this process may potentially lead to new data collection or additional analyses suitable for input to benchmarks. Essential in this entire process is making sure the same language is spoken between scientists and stakeholders, that there are clear and transparent processes in place on how to deal with stakeholder information and communicate clearly how this information is used in the preparation of ICES advice.Peer reviewe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost