10 results on '"Tully, Oliver"'
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2. Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences.
- Author
-
UCL - SST/ELI/ELIC - Earth & Climate, Christie, Alec P, Abecasis, David, Adjeroud, Mehdi, Alonso, Juan C, Amano, Tatsuya, Anton, Alvaro, Baldigo, Barry P, Barrientos, Rafael, Bicknell, Jake E, Buhl, Deborah A, Cebrian, Just, Ceia, Ricardo S, Cibils-Martina, Luciana, Clarke, Sarah, Claudet, Joachim, Craig, Michael D, Davoult, Dominique, De Backer, Annelies, Donovan, Mary K, Eddy, Tyler D, França, Filipe M, Gardner, Jonathan P A, Harris, Bradley P, Huusko, Ari, Jones, Ian L, Kelaher, Brendan P, Kotiaho, Janne S, López-Baucells, Adrià, Major, Heather L, Mäki-Petäys, Aki, Martín, Beatriz, Martín, Carlos A, Martin, Philip A, Mateos-Molina, Daniel, McConnaughey, Robert A, Meroni, Michele, Meyer, Christoph F J, Mills, Kade, Montefalcone, Monica, Noreika, Norbertas, Palacín, Carlos, Pande, Anjali, Pitcher, C Roland, Ponce, Carlos, Rinella, Matt, Rocha, Ricardo, Ruiz-Delgado, María C, Schmitter-Soto, Juan J, Shaffer, Jill A, Sharma, Shailesh, Sher, Anna A, Stagnol, Doriane, Stanley, Thomas R, Stokesbury, Kevin D E, Torres Moreno, Aurora, Tully, Oliver, Vehanen, Teppo, Watts, Corinne, Zhao, Qingyuan, Sutherland, William J, UCL - SST/ELI/ELIC - Earth & Climate, Christie, Alec P, Abecasis, David, Adjeroud, Mehdi, Alonso, Juan C, Amano, Tatsuya, Anton, Alvaro, Baldigo, Barry P, Barrientos, Rafael, Bicknell, Jake E, Buhl, Deborah A, Cebrian, Just, Ceia, Ricardo S, Cibils-Martina, Luciana, Clarke, Sarah, Claudet, Joachim, Craig, Michael D, Davoult, Dominique, De Backer, Annelies, Donovan, Mary K, Eddy, Tyler D, França, Filipe M, Gardner, Jonathan P A, Harris, Bradley P, Huusko, Ari, Jones, Ian L, Kelaher, Brendan P, Kotiaho, Janne S, López-Baucells, Adrià, Major, Heather L, Mäki-Petäys, Aki, Martín, Beatriz, Martín, Carlos A, Martin, Philip A, Mateos-Molina, Daniel, McConnaughey, Robert A, Meroni, Michele, Meyer, Christoph F J, Mills, Kade, Montefalcone, Monica, Noreika, Norbertas, Palacín, Carlos, Pande, Anjali, Pitcher, C Roland, Ponce, Carlos, Rinella, Matt, Rocha, Ricardo, Ruiz-Delgado, María C, Schmitter-Soto, Juan J, Shaffer, Jill A, Sharma, Shailesh, Sher, Anna A, Stagnol, Doriane, Stanley, Thomas R, Stokesbury, Kevin D E, Torres Moreno, Aurora, Tully, Oliver, Vehanen, Teppo, Watts, Corinne, Zhao, Qingyuan, and Sutherland, William J
- Abstract
Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.
- Published
- 2020
3. Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
- Author
-
Christie, Alec P., Abecasis, David, Adjeroud, Mehdi, Alonso, Juan C., Amano, Tatsuya, Anton, Alvaro, Baldigo, Barry P., Barrientos, Rafael, Bicknell, Jake E., Buhl, Deborah A., Cebrian, Just, Ceia, Ricardo S., Cibils-martina, Luciana, Clarke, Sarah, Claudet, Joachim, Craig, Michael D., Davoult, Dominique, De Backer, Annelies, Donovan, Mary K., Eddy, Tyler D., Franca, Filipe M., Gardner, Jonathan P. A., Harris, Bradley P., Huusko, Ari, Jones, Ian L., Kelaher, Brendan P., Kotiaho, Janne S., Lopez-baucells, Adria, Major, Heather L., Maki-petays, Aki, Martin, Beatriz, Martin, Carlos A., Martin, Philip A., Mateos-molina, Daniel, Mcconnaughey, Robert A., Meroni, Michele, Meyer, Christoph F. J., Mills, Kade, Montefalcone, Monica, Noreika, Norbertas, Palacin, Carlos, Pande, Anjali, Pitcher, C. Roland, Ponce, Carlos, Rinella, Matt, Rocha, Ricardo, Ruiz-delgado, Maria C., Schmitter-soto, Juan J., Shaffer, Jill A., Sharma, Shailesh, Sher, Anna A., Stagnol, Doriane, Stanley, Thomas R., Stokesbury, Kevin D. E., Torres, Aurora, Tully, Oliver, Vehanen, Teppo, Watts, Corinne, Zhao, Qingyuan, Sutherland, William J., Christie, Alec P., Abecasis, David, Adjeroud, Mehdi, Alonso, Juan C., Amano, Tatsuya, Anton, Alvaro, Baldigo, Barry P., Barrientos, Rafael, Bicknell, Jake E., Buhl, Deborah A., Cebrian, Just, Ceia, Ricardo S., Cibils-martina, Luciana, Clarke, Sarah, Claudet, Joachim, Craig, Michael D., Davoult, Dominique, De Backer, Annelies, Donovan, Mary K., Eddy, Tyler D., Franca, Filipe M., Gardner, Jonathan P. A., Harris, Bradley P., Huusko, Ari, Jones, Ian L., Kelaher, Brendan P., Kotiaho, Janne S., Lopez-baucells, Adria, Major, Heather L., Maki-petays, Aki, Martin, Beatriz, Martin, Carlos A., Martin, Philip A., Mateos-molina, Daniel, Mcconnaughey, Robert A., Meroni, Michele, Meyer, Christoph F. J., Mills, Kade, Montefalcone, Monica, Noreika, Norbertas, Palacin, Carlos, Pande, Anjali, Pitcher, C. Roland, Ponce, Carlos, Rinella, Matt, Rocha, Ricardo, Ruiz-delgado, Maria C., Schmitter-soto, Juan J., Shaffer, Jill A., Sharma, Shailesh, Sher, Anna A., Stagnol, Doriane, Stanley, Thomas R., Stokesbury, Kevin D. E., Torres, Aurora, Tully, Oliver, Vehanen, Teppo, Watts, Corinne, Zhao, Qingyuan, and Sutherland, William J.
- Abstract
Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs. Randomised controlled experiments are the gold standard for scientific inference, but environmental and social scientists often rely on different study designs. Here the authors analyse the use of six common study designs in the fields of biodiversity conservation and social intervention, and quantify the biases in their estimates.
- Published
- 2020
- Full Text
- View/download PDF
4. Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
- Author
-
Christie, Alec P., Abecasis, David, Adjeroud, Mehdi, Alonso, Juan C., Amano, Tatsuya, Anton, Alvaro, Baldigo, Barry P., Barrientos, Rafael, Bicknell, Jake E., Buhl, Deborah A., Cebrian, Justin, Ceia, Ricardo S., Cibils-Martina, Luciana, Clarke, Sarah, Claudet, Joachim, Craig, Michael D., Davoult, Dominique, De Backer, Annelies, Donovan, Mary K., Eddy, Tyler D., França, Filipe M., Gardner, Jonathan P.A, Harris, Bradley P, Huusko, Ari, Jones, Ian L., Kelaher, Brendan P., Kotiaho, Janne S., López-Baucells, Adrià, Major, Heather L., Mäki-Petäys, Aki, Martín, Beatriz, Martín, Carlos A., Martin, Philip A., Mateos-Molina, Daniel, McConnaughey, Robert A., Meroni, Michele, Meyer, Christoph F.J., Mills, Kade, Montefalcone, Monica, Noreika, Norbertas, Palacín, Carlos, Pande, Anjali, Pitcher, Roland, Ponce, Carlos, Rinella, Matt, Rocha, Ricardo, Ruiz-Delgado, María C., Schmitter-Soto, Juan J., Shaffer, Jill A., Sharma, Shailesh, Sher, Anna A., Stagno, Doriane, Stanley, Thomas R., Stokesbury, Kevin D.E., Torres, Aurora, Tully, Oliver, Vehanen, Teppo, Watts, Corinne, Zhao, Qingyuan, J. Sutherland, William, Christie, Alec P., Abecasis, David, Adjeroud, Mehdi, Alonso, Juan C., Amano, Tatsuya, Anton, Alvaro, Baldigo, Barry P., Barrientos, Rafael, Bicknell, Jake E., Buhl, Deborah A., Cebrian, Justin, Ceia, Ricardo S., Cibils-Martina, Luciana, Clarke, Sarah, Claudet, Joachim, Craig, Michael D., Davoult, Dominique, De Backer, Annelies, Donovan, Mary K., Eddy, Tyler D., França, Filipe M., Gardner, Jonathan P.A, Harris, Bradley P, Huusko, Ari, Jones, Ian L., Kelaher, Brendan P., Kotiaho, Janne S., López-Baucells, Adrià, Major, Heather L., Mäki-Petäys, Aki, Martín, Beatriz, Martín, Carlos A., Martin, Philip A., Mateos-Molina, Daniel, McConnaughey, Robert A., Meroni, Michele, Meyer, Christoph F.J., Mills, Kade, Montefalcone, Monica, Noreika, Norbertas, Palacín, Carlos, Pande, Anjali, Pitcher, Roland, Ponce, Carlos, Rinella, Matt, Rocha, Ricardo, Ruiz-Delgado, María C., Schmitter-Soto, Juan J., Shaffer, Jill A., Sharma, Shailesh, Sher, Anna A., Stagno, Doriane, Stanley, Thomas R., Stokesbury, Kevin D.E., Torres, Aurora, Tully, Oliver, Vehanen, Teppo, Watts, Corinne, Zhao, Qingyuan, and J. Sutherland, William
- Abstract
Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.
- Published
- 2020
5. Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
- Author
-
Christie, Alec P., Abecasis, David, Adjeroud, Mehdi, Alonso, Juan C., Amano, Tatsuya, Anton, Alvaro, Baldigo, Barry P., Barrientos, Rafael, Bicknell, Jake E., Buhl, Deborah A., Cebrian, Justin, Ceia, Ricardo S., Cibils-Martina, Luciana, Clarke, Sarah, Claudet, Joachim, Craig, Michael D., Davoult, Dominique, De Backer, Annelies, Donovan, Mary K., Eddy, Tyler D., França, Filipe M., Gardner, Jonathan P.A, Harris, Bradley P, Huusko, Ari, Jones, Ian L., Kelaher, Brendan P., Kotiaho, Janne S., López-Baucells, Adrià, Major, Heather L., Mäki-Petäys, Aki, Martín, Beatriz, Martín, Carlos A., Martin, Philip A., Mateos-Molina, Daniel, McConnaughey, Robert A., Meroni, Michele, Meyer, Christoph F.J., Mills, Kade, Montefalcone, Monica, Noreika, Norbertas, Palacín, Carlos, Pande, Anjali, Pitcher, Roland, Ponce, Carlos, Rinella, Matt, Rocha, Ricardo, Ruiz-Delgado, María C., Schmitter-Soto, Juan J., Shaffer, Jill A., Sharma, Shailesh, Sher, Anna A., Stagno, Doriane, Stanley, Thomas R., Stokesbury, Kevin D.E., Torres, Aurora, Tully, Oliver, Vehanen, Teppo, Watts, Corinne, Zhao, Qingyuan, J. Sutherland, William, Christie, Alec P., Abecasis, David, Adjeroud, Mehdi, Alonso, Juan C., Amano, Tatsuya, Anton, Alvaro, Baldigo, Barry P., Barrientos, Rafael, Bicknell, Jake E., Buhl, Deborah A., Cebrian, Justin, Ceia, Ricardo S., Cibils-Martina, Luciana, Clarke, Sarah, Claudet, Joachim, Craig, Michael D., Davoult, Dominique, De Backer, Annelies, Donovan, Mary K., Eddy, Tyler D., França, Filipe M., Gardner, Jonathan P.A, Harris, Bradley P, Huusko, Ari, Jones, Ian L., Kelaher, Brendan P., Kotiaho, Janne S., López-Baucells, Adrià, Major, Heather L., Mäki-Petäys, Aki, Martín, Beatriz, Martín, Carlos A., Martin, Philip A., Mateos-Molina, Daniel, McConnaughey, Robert A., Meroni, Michele, Meyer, Christoph F.J., Mills, Kade, Montefalcone, Monica, Noreika, Norbertas, Palacín, Carlos, Pande, Anjali, Pitcher, Roland, Ponce, Carlos, Rinella, Matt, Rocha, Ricardo, Ruiz-Delgado, María C., Schmitter-Soto, Juan J., Shaffer, Jill A., Sharma, Shailesh, Sher, Anna A., Stagno, Doriane, Stanley, Thomas R., Stokesbury, Kevin D.E., Torres, Aurora, Tully, Oliver, Vehanen, Teppo, Watts, Corinne, Zhao, Qingyuan, and J. Sutherland, William
- Abstract
Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.
- Published
- 2020
6. Quantifying and addressing the prevalence and bias of study designs in the environmental and social sciences
- Author
-
Christie, Alec P., Abecasis, David, Adjeroud, Mehdi, Alonso, Juan C., Amano, Tatsuya, Anton, Alvaro, Baldigo, Barry P., Barrientos, Rafael, Bicknell, Jake E., Buhl, Deborah A., Cebrian, Justin, Ceia, Ricardo S., Cibils-Martina, Luciana, Clarke, Sarah, Claudet, Joachim, Craig, Michael D., Davoult, Dominique, De Backer, Annelies, Donovan, Mary K., Eddy, Tyler D., França, Filipe M., Gardner, Jonathan P.A, Harris, Bradley P, Huusko, Ari, Jones, Ian L., Kelaher, Brendan P., Kotiaho, Janne S., López-Baucells, Adrià, Major, Heather L., Mäki-Petäys, Aki, Martín, Beatriz, Martín, Carlos A., Martin, Philip A., Mateos-Molina, Daniel, McConnaughey, Robert A., Meroni, Michele, Meyer, Christoph F.J., Mills, Kade, Montefalcone, Monica, Noreika, Norbertas, Palacín, Carlos, Pande, Anjali, Pitcher, Roland, Ponce, Carlos, Rinella, Matt, Rocha, Ricardo, Ruiz-Delgado, María C., Schmitter-Soto, Juan J., Shaffer, Jill A., Sharma, Shailesh, Sher, Anna A., Stagno, Doriane, Stanley, Thomas R., Stokesbury, Kevin D.E., Torres, Aurora, Tully, Oliver, Vehanen, Teppo, Watts, Corinne, Zhao, Qingyuan, J. Sutherland, William, Christie, Alec P., Abecasis, David, Adjeroud, Mehdi, Alonso, Juan C., Amano, Tatsuya, Anton, Alvaro, Baldigo, Barry P., Barrientos, Rafael, Bicknell, Jake E., Buhl, Deborah A., Cebrian, Justin, Ceia, Ricardo S., Cibils-Martina, Luciana, Clarke, Sarah, Claudet, Joachim, Craig, Michael D., Davoult, Dominique, De Backer, Annelies, Donovan, Mary K., Eddy, Tyler D., França, Filipe M., Gardner, Jonathan P.A, Harris, Bradley P, Huusko, Ari, Jones, Ian L., Kelaher, Brendan P., Kotiaho, Janne S., López-Baucells, Adrià, Major, Heather L., Mäki-Petäys, Aki, Martín, Beatriz, Martín, Carlos A., Martin, Philip A., Mateos-Molina, Daniel, McConnaughey, Robert A., Meroni, Michele, Meyer, Christoph F.J., Mills, Kade, Montefalcone, Monica, Noreika, Norbertas, Palacín, Carlos, Pande, Anjali, Pitcher, Roland, Ponce, Carlos, Rinella, Matt, Rocha, Ricardo, Ruiz-Delgado, María C., Schmitter-Soto, Juan J., Shaffer, Jill A., Sharma, Shailesh, Sher, Anna A., Stagno, Doriane, Stanley, Thomas R., Stokesbury, Kevin D.E., Torres, Aurora, Tully, Oliver, Vehanen, Teppo, Watts, Corinne, Zhao, Qingyuan, and J. Sutherland, William
- Abstract
Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.
- Published
- 2020
7. Estimating seabed pressure from demersal trawls, seines, and dredges based on gear design and dimensions
- Author
-
Eigaard, Ole R., Bastardie, Francois, Breen, Mike, Dinesen, Grete E., Hintzen, Niels T., Laffargue, Pascal, Mortensen, Lars O., Nielsen, J. Rasmus, Nilsson, Hans C., O'Neill, Finbarr G., Polet, Hans, Reid, David G., Sala, Antonello, Skold, Mattias, Smith, Chris, Sorensen, Thomas K., Tully, Oliver, Zengin, Mustafa, Rijnsdorp, Adriaan D., Eigaard, Ole R., Bastardie, Francois, Breen, Mike, Dinesen, Grete E., Hintzen, Niels T., Laffargue, Pascal, Mortensen, Lars O., Nielsen, J. Rasmus, Nilsson, Hans C., O'Neill, Finbarr G., Polet, Hans, Reid, David G., Sala, Antonello, Skold, Mattias, Smith, Chris, Sorensen, Thomas K., Tully, Oliver, Zengin, Mustafa, and Rijnsdorp, Adriaan D.
- Abstract
This study assesses the seabed pressure of towed fishing gears and models the physical impact (area and depth of seabed penetration) from trip-based information of vessel size, gear type, and catch. Traditionally fishing pressures are calculated top-down by making use of large-scale statistics such as logbook data. Here, we take a different approach starting from the gear itself (design and dimensions) to estimate the physical interactions with the seabed at the level of the individual fishing operation. We defined 14 distinct towed gear groups in European waters (eight otter trawl groups, three beam trawl groups, two demersal seine groups, and one dredge group), for which we established gear “footprints”. The footprint of a gear is defined as the relative contribution from individual larger gear components, such as trawl doors, sweeps, and groundgear, to the total area and severity of the gear's impact. An industry-based survey covering 13 countries provided the basis for estimating the relative impact-area contributions from individual gear components, whereas sediment penetration was estimated based on a literature review. For each gear group, a vessel size–gear size relationship was estimated to enable the prediction of gear footprint area and sediment penetration from vessel size. Application of these relationships with average vessel sizes and towing speeds provided hourly swept-area estimates by métier. Scottish seining has the largest overall gear footprint of ∼1.6 km2 h−1 of which 0.08 km2 has an impact at the subsurface level (sediment penetration ≥ 2 cm). Beam trawling for flatfish ranks low when comparing overall footprint size/hour but ranks substantially higher when comparing only impact at the subsurface level (0.19 km2h−1). These results have substantial implications for the definition, estimation, and monitoring of fishing pressure indicators, which are discussed in the context of an ecosystem approach to fisheries management.
- Published
- 2016
- Full Text
- View/download PDF
8. A correction to “Estimating seabed pressure from demersal trawls, seines and dredges based on gear design and dimensions”†
- Author
-
Eigaard, Ole R., Bastardie, Francois, Breen, Mike, Dinesen, Grete E., Hintzen, Niels T., Laffargue, Pascal, Mortensen, Lars O., Rasmus Nielsen, J., Nilsson, Hans, O'neill, Finbarr G., Polet, Hans, Reid, David G., Sala, Antonello, Sköld, Mattias, Smith, Chris, Sørensen, Thomas K., Tully, Oliver, Zengin, Mustafa, Rijnsdorp, Adriaan D., Eigaard, Ole R., Bastardie, Francois, Breen, Mike, Dinesen, Grete E., Hintzen, Niels T., Laffargue, Pascal, Mortensen, Lars O., Rasmus Nielsen, J., Nilsson, Hans, O'neill, Finbarr G., Polet, Hans, Reid, David G., Sala, Antonello, Sköld, Mattias, Smith, Chris, Sørensen, Thomas K., Tully, Oliver, Zengin, Mustafa, and Rijnsdorp, Adriaan D.
- Published
- 2016
9. Estimating seabed pressure from demersal trawls, seines, and dredges based on gear design and dimensions
- Author
-
Eigaard, Ole Ritzau, Bastardie, Francois, Breen, Mike, Dinesen, Grete E., Hintzen, Niels T., Lafargue, Pascal, Mortensen, Lars Olof, Nielsen, J. Rasmus, Nilson, Hans C., O'Neil, Finbarr G., Polet, Hans, Reid, David G., Sala, Antonello, Sköld, Mattias, Smith, Chris, Sørensen, Thomas Kirk, Tully, Oliver, Zenging, Mustafa, Rijnsdorp, Adriaan D., Eigaard, Ole Ritzau, Bastardie, Francois, Breen, Mike, Dinesen, Grete E., Hintzen, Niels T., Lafargue, Pascal, Mortensen, Lars Olof, Nielsen, J. Rasmus, Nilson, Hans C., O'Neil, Finbarr G., Polet, Hans, Reid, David G., Sala, Antonello, Sköld, Mattias, Smith, Chris, Sørensen, Thomas Kirk, Tully, Oliver, Zenging, Mustafa, and Rijnsdorp, Adriaan D.
- Abstract
This study assesses the seabed pressure of towed fishing gears and models the physical impact (area and depth of seabed penetration) from trip-based information of vessel size, gear type, and catch. Traditionally fishing pressures are calculated top-down by making use of large-scale statistics such as logbook data. Here, we take a different approach starting from the gear itself (design and dimensions) to estimate the physical interactions with the seabed at the level of the individual fishing operation. We defined 14 distinct towed gear groups in European waters (eight otter trawl groups, three beam trawl groups, two demersal seine groups, and one dredge group), for which we established gear “footprints”. The footprint of a gear is defined as the relative contribution from individual larger gear components, such as trawl doors, sweeps, and groundgear, to the total area and severity of the gear's impact. An industry-based survey covering 13 countries provided the basis for estimating the relative impact-area contributions from individual gear components, whereas sediment penetration was estimated based on a literature review. For each gear group, a vessel size–gear size relationship was estimated to enable the prediction of gear footprint area and sediment penetration from vessel size. Application of these relationships with average vessel sizes and towing speeds provided hourly swept-area estimates by métier. Scottish seining has the largest overall gear footprint of ∼1.6 km2 h−1 of which 0.08 km2 has an impact at the subsurface level (sediment penetration ≥ 2 cm). Beam trawling for flatfish ranks low when comparing overall footprint size/hour but ranks substantially higher when comparing only impact at the subsurface level (0.19 km2h−1). These results have substantial implications for the definition, estimation, and monitoring of fishing pressure indicators, which are discussed in the context of an ecosystem approach to fisheries management
- Published
- 2016
10. Crustacean fisheries
- Author
-
Tully, Oliver, Freire, Juan, Addison, Julian, Tully, Oliver, Freire, Juan, and Addison, Julian
- Published
- 2003
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