Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale
Abstract Nutrient-related environmental degradation in coastal waters is a continuing global problem. Bivalve shellfish farms show nutrient removal capabilities similar to some traditional management strategies and in some places have been incorporated into nutrient management programs to help achie...
Ausführliche Beschreibung
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| Autor*in: |
Bayer, Skylar R. [verfasserIn] Cubillo, Alhambra M. [verfasserIn] Rose, Julie M. [verfasserIn] Ferreira, Joao G. [verfasserIn] Dixon, Mark [verfasserIn] Alvarado, Annita [verfasserIn] Barr, Janine [verfasserIn] Bernatchez, Genevieve [verfasserIn] Meseck, Shannon [verfasserIn] Poach, Matthew [verfasserIn] Pousse, Emilien [verfasserIn] Wikfors, Gary H. [verfasserIn] Bricker, Suzanne [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2024 |
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| Übergeordnetes Werk: |
Enthalten in: Estuaries and coasts - Springer US, 2006, 47(2024), 5 vom: 15. Mai, Seite 1184-1198 |
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| Übergeordnetes Werk: |
volume:47 ; year:2024 ; number:5 ; day:15 ; month:05 ; pages:1184-1198 |
| Links: |
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| DOI / URN: |
10.1007/s12237-024-01354-7 |
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| Katalog-ID: |
SPR056340680 |
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| 245 | 1 | 0 | |a Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale |
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| 520 | |a Abstract Nutrient-related environmental degradation in coastal waters is a continuing global problem. Bivalve shellfish farms show nutrient removal capabilities similar to some traditional management strategies and in some places have been incorporated into nutrient management programs to help achieve water quality goals. Bioextractive nutrient removal varies by farmed species and is influenced by environment parameters; thus, data and information for both are needed to estimate nutrient mitigation potential of shellfish farms. The Farm Aquaculture Resource Management (FARM) model, calibrated for farmed species, uses local environmental and farming practice data to simulate interactions between the farmed population and the local environment and to optimize cultivation practices for economic gain. We calibrated the model to predict nitrogen removal by Eastern oyster (Crassostrea virginica) farms with specific field and experimental data on oysters, their local environment, and farm practices in Long Island Sound, CT, USA. Previous FARM applications were not validated for nitrogen removal with local data. In the harvest when ready (HWR) model scenario (oysters are harvested when they reach harvest size), the farm removed 159 kg N $ ha^{−1} $ $ year^{−1} $ while the non-HWR scenario (all oysters are harvested at one time) removed 274 kg N $ ha^{−1} $ $ year^{−1} $. These estimates are within the range of previously reported in-water bioextraction studies in the Northeastern USA. The robust outputs from this validated model can be reliably used in marine spatial planning efforts and by nutrient managers to predict the nitrogen removal benefits that could be achieved through new or expanded eastern oyster farms in eutrophic environments. | ||
| 650 | 4 | |a Nutrient bioextraction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Nutrient removal |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Nitrogen budget |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Oyster production model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Oyster aquaculture |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Long Island Sound |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Cubillo, Alhambra M. |e verfasserin |4 aut | |
| 700 | 1 | |a Rose, Julie M. |e verfasserin |4 aut | |
| 700 | 1 | |a Ferreira, Joao G. |e verfasserin |4 aut | |
| 700 | 1 | |a Dixon, Mark |e verfasserin |4 aut | |
| 700 | 1 | |a Alvarado, Annita |e verfasserin |4 aut | |
| 700 | 1 | |a Barr, Janine |e verfasserin |4 aut | |
| 700 | 1 | |a Bernatchez, Genevieve |e verfasserin |4 aut | |
| 700 | 1 | |a Meseck, Shannon |e verfasserin |4 aut | |
| 700 | 1 | |a Poach, Matthew |e verfasserin |4 aut | |
| 700 | 1 | |a Pousse, Emilien |e verfasserin |4 aut | |
| 700 | 1 | |a Wikfors, Gary H. |e verfasserin |4 aut | |
| 700 | 1 | |a Bricker, Suzanne |e verfasserin |4 aut | |
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10.1007/s12237-024-01354-7 doi (DE-627)SPR056340680 (SPR)s12237-024-01354-7-e DE-627 ger DE-627 rakwb eng 550 VZ 38.90 bkl 42.94 bkl Bayer, Skylar R. verfasserin (orcid)0000-0003-4128-9536 aut Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Nutrient-related environmental degradation in coastal waters is a continuing global problem. Bivalve shellfish farms show nutrient removal capabilities similar to some traditional management strategies and in some places have been incorporated into nutrient management programs to help achieve water quality goals. Bioextractive nutrient removal varies by farmed species and is influenced by environment parameters; thus, data and information for both are needed to estimate nutrient mitigation potential of shellfish farms. The Farm Aquaculture Resource Management (FARM) model, calibrated for farmed species, uses local environmental and farming practice data to simulate interactions between the farmed population and the local environment and to optimize cultivation practices for economic gain. We calibrated the model to predict nitrogen removal by Eastern oyster (Crassostrea virginica) farms with specific field and experimental data on oysters, their local environment, and farm practices in Long Island Sound, CT, USA. Previous FARM applications were not validated for nitrogen removal with local data. In the harvest when ready (HWR) model scenario (oysters are harvested when they reach harvest size), the farm removed 159 kg N $ ha^{−1} $ $ year^{−1} $ while the non-HWR scenario (all oysters are harvested at one time) removed 274 kg N $ ha^{−1} $ $ year^{−1} $. These estimates are within the range of previously reported in-water bioextraction studies in the Northeastern USA. The robust outputs from this validated model can be reliably used in marine spatial planning efforts and by nutrient managers to predict the nitrogen removal benefits that could be achieved through new or expanded eastern oyster farms in eutrophic environments. Nutrient bioextraction (dpeaa)DE-He213 Nutrient removal (dpeaa)DE-He213 Nitrogen budget (dpeaa)DE-He213 Oyster production model (dpeaa)DE-He213 Oyster aquaculture (dpeaa)DE-He213 Long Island Sound (dpeaa)DE-He213 Cubillo, Alhambra M. verfasserin aut Rose, Julie M. verfasserin aut Ferreira, Joao G. verfasserin aut Dixon, Mark verfasserin aut Alvarado, Annita verfasserin aut Barr, Janine verfasserin aut Bernatchez, Genevieve verfasserin aut Meseck, Shannon verfasserin aut Poach, Matthew verfasserin aut Pousse, Emilien verfasserin aut Wikfors, Gary H. verfasserin aut Bricker, Suzanne verfasserin aut Enthalten in Estuaries and coasts Springer US, 2006 47(2024), 5 vom: 15. Mai, Seite 1184-1198 (DE-627)51010830X (DE-600)2229170-2 1559-2731 nnns volume:47 year:2024 number:5 day:15 month:05 pages:1184-1198 https://dx.doi.org/10.1007/s12237-024-01354-7 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2939 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 38.90 VZ 42.94 VZ AR 47 2024 5 15 05 1184-1198 |
| spelling |
10.1007/s12237-024-01354-7 doi (DE-627)SPR056340680 (SPR)s12237-024-01354-7-e DE-627 ger DE-627 rakwb eng 550 VZ 38.90 bkl 42.94 bkl Bayer, Skylar R. verfasserin (orcid)0000-0003-4128-9536 aut Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Nutrient-related environmental degradation in coastal waters is a continuing global problem. Bivalve shellfish farms show nutrient removal capabilities similar to some traditional management strategies and in some places have been incorporated into nutrient management programs to help achieve water quality goals. Bioextractive nutrient removal varies by farmed species and is influenced by environment parameters; thus, data and information for both are needed to estimate nutrient mitigation potential of shellfish farms. The Farm Aquaculture Resource Management (FARM) model, calibrated for farmed species, uses local environmental and farming practice data to simulate interactions between the farmed population and the local environment and to optimize cultivation practices for economic gain. We calibrated the model to predict nitrogen removal by Eastern oyster (Crassostrea virginica) farms with specific field and experimental data on oysters, their local environment, and farm practices in Long Island Sound, CT, USA. Previous FARM applications were not validated for nitrogen removal with local data. In the harvest when ready (HWR) model scenario (oysters are harvested when they reach harvest size), the farm removed 159 kg N $ ha^{−1} $ $ year^{−1} $ while the non-HWR scenario (all oysters are harvested at one time) removed 274 kg N $ ha^{−1} $ $ year^{−1} $. These estimates are within the range of previously reported in-water bioextraction studies in the Northeastern USA. The robust outputs from this validated model can be reliably used in marine spatial planning efforts and by nutrient managers to predict the nitrogen removal benefits that could be achieved through new or expanded eastern oyster farms in eutrophic environments. Nutrient bioextraction (dpeaa)DE-He213 Nutrient removal (dpeaa)DE-He213 Nitrogen budget (dpeaa)DE-He213 Oyster production model (dpeaa)DE-He213 Oyster aquaculture (dpeaa)DE-He213 Long Island Sound (dpeaa)DE-He213 Cubillo, Alhambra M. verfasserin aut Rose, Julie M. verfasserin aut Ferreira, Joao G. verfasserin aut Dixon, Mark verfasserin aut Alvarado, Annita verfasserin aut Barr, Janine verfasserin aut Bernatchez, Genevieve verfasserin aut Meseck, Shannon verfasserin aut Poach, Matthew verfasserin aut Pousse, Emilien verfasserin aut Wikfors, Gary H. verfasserin aut Bricker, Suzanne verfasserin aut Enthalten in Estuaries and coasts Springer US, 2006 47(2024), 5 vom: 15. Mai, Seite 1184-1198 (DE-627)51010830X (DE-600)2229170-2 1559-2731 nnns volume:47 year:2024 number:5 day:15 month:05 pages:1184-1198 https://dx.doi.org/10.1007/s12237-024-01354-7 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2939 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 38.90 VZ 42.94 VZ AR 47 2024 5 15 05 1184-1198 |
| allfields_unstemmed |
10.1007/s12237-024-01354-7 doi (DE-627)SPR056340680 (SPR)s12237-024-01354-7-e DE-627 ger DE-627 rakwb eng 550 VZ 38.90 bkl 42.94 bkl Bayer, Skylar R. verfasserin (orcid)0000-0003-4128-9536 aut Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Nutrient-related environmental degradation in coastal waters is a continuing global problem. Bivalve shellfish farms show nutrient removal capabilities similar to some traditional management strategies and in some places have been incorporated into nutrient management programs to help achieve water quality goals. Bioextractive nutrient removal varies by farmed species and is influenced by environment parameters; thus, data and information for both are needed to estimate nutrient mitigation potential of shellfish farms. The Farm Aquaculture Resource Management (FARM) model, calibrated for farmed species, uses local environmental and farming practice data to simulate interactions between the farmed population and the local environment and to optimize cultivation practices for economic gain. We calibrated the model to predict nitrogen removal by Eastern oyster (Crassostrea virginica) farms with specific field and experimental data on oysters, their local environment, and farm practices in Long Island Sound, CT, USA. Previous FARM applications were not validated for nitrogen removal with local data. In the harvest when ready (HWR) model scenario (oysters are harvested when they reach harvest size), the farm removed 159 kg N $ ha^{−1} $ $ year^{−1} $ while the non-HWR scenario (all oysters are harvested at one time) removed 274 kg N $ ha^{−1} $ $ year^{−1} $. These estimates are within the range of previously reported in-water bioextraction studies in the Northeastern USA. The robust outputs from this validated model can be reliably used in marine spatial planning efforts and by nutrient managers to predict the nitrogen removal benefits that could be achieved through new or expanded eastern oyster farms in eutrophic environments. Nutrient bioextraction (dpeaa)DE-He213 Nutrient removal (dpeaa)DE-He213 Nitrogen budget (dpeaa)DE-He213 Oyster production model (dpeaa)DE-He213 Oyster aquaculture (dpeaa)DE-He213 Long Island Sound (dpeaa)DE-He213 Cubillo, Alhambra M. verfasserin aut Rose, Julie M. verfasserin aut Ferreira, Joao G. verfasserin aut Dixon, Mark verfasserin aut Alvarado, Annita verfasserin aut Barr, Janine verfasserin aut Bernatchez, Genevieve verfasserin aut Meseck, Shannon verfasserin aut Poach, Matthew verfasserin aut Pousse, Emilien verfasserin aut Wikfors, Gary H. verfasserin aut Bricker, Suzanne verfasserin aut Enthalten in Estuaries and coasts Springer US, 2006 47(2024), 5 vom: 15. Mai, Seite 1184-1198 (DE-627)51010830X (DE-600)2229170-2 1559-2731 nnns volume:47 year:2024 number:5 day:15 month:05 pages:1184-1198 https://dx.doi.org/10.1007/s12237-024-01354-7 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2939 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 38.90 VZ 42.94 VZ AR 47 2024 5 15 05 1184-1198 |
| allfieldsGer |
10.1007/s12237-024-01354-7 doi (DE-627)SPR056340680 (SPR)s12237-024-01354-7-e DE-627 ger DE-627 rakwb eng 550 VZ 38.90 bkl 42.94 bkl Bayer, Skylar R. verfasserin (orcid)0000-0003-4128-9536 aut Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Nutrient-related environmental degradation in coastal waters is a continuing global problem. Bivalve shellfish farms show nutrient removal capabilities similar to some traditional management strategies and in some places have been incorporated into nutrient management programs to help achieve water quality goals. Bioextractive nutrient removal varies by farmed species and is influenced by environment parameters; thus, data and information for both are needed to estimate nutrient mitigation potential of shellfish farms. The Farm Aquaculture Resource Management (FARM) model, calibrated for farmed species, uses local environmental and farming practice data to simulate interactions between the farmed population and the local environment and to optimize cultivation practices for economic gain. We calibrated the model to predict nitrogen removal by Eastern oyster (Crassostrea virginica) farms with specific field and experimental data on oysters, their local environment, and farm practices in Long Island Sound, CT, USA. Previous FARM applications were not validated for nitrogen removal with local data. In the harvest when ready (HWR) model scenario (oysters are harvested when they reach harvest size), the farm removed 159 kg N $ ha^{−1} $ $ year^{−1} $ while the non-HWR scenario (all oysters are harvested at one time) removed 274 kg N $ ha^{−1} $ $ year^{−1} $. These estimates are within the range of previously reported in-water bioextraction studies in the Northeastern USA. The robust outputs from this validated model can be reliably used in marine spatial planning efforts and by nutrient managers to predict the nitrogen removal benefits that could be achieved through new or expanded eastern oyster farms in eutrophic environments. Nutrient bioextraction (dpeaa)DE-He213 Nutrient removal (dpeaa)DE-He213 Nitrogen budget (dpeaa)DE-He213 Oyster production model (dpeaa)DE-He213 Oyster aquaculture (dpeaa)DE-He213 Long Island Sound (dpeaa)DE-He213 Cubillo, Alhambra M. verfasserin aut Rose, Julie M. verfasserin aut Ferreira, Joao G. verfasserin aut Dixon, Mark verfasserin aut Alvarado, Annita verfasserin aut Barr, Janine verfasserin aut Bernatchez, Genevieve verfasserin aut Meseck, Shannon verfasserin aut Poach, Matthew verfasserin aut Pousse, Emilien verfasserin aut Wikfors, Gary H. verfasserin aut Bricker, Suzanne verfasserin aut Enthalten in Estuaries and coasts Springer US, 2006 47(2024), 5 vom: 15. Mai, Seite 1184-1198 (DE-627)51010830X (DE-600)2229170-2 1559-2731 nnns volume:47 year:2024 number:5 day:15 month:05 pages:1184-1198 https://dx.doi.org/10.1007/s12237-024-01354-7 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2939 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 38.90 VZ 42.94 VZ AR 47 2024 5 15 05 1184-1198 |
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10.1007/s12237-024-01354-7 doi (DE-627)SPR056340680 (SPR)s12237-024-01354-7-e DE-627 ger DE-627 rakwb eng 550 VZ 38.90 bkl 42.94 bkl Bayer, Skylar R. verfasserin (orcid)0000-0003-4128-9536 aut Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Nutrient-related environmental degradation in coastal waters is a continuing global problem. Bivalve shellfish farms show nutrient removal capabilities similar to some traditional management strategies and in some places have been incorporated into nutrient management programs to help achieve water quality goals. Bioextractive nutrient removal varies by farmed species and is influenced by environment parameters; thus, data and information for both are needed to estimate nutrient mitigation potential of shellfish farms. The Farm Aquaculture Resource Management (FARM) model, calibrated for farmed species, uses local environmental and farming practice data to simulate interactions between the farmed population and the local environment and to optimize cultivation practices for economic gain. We calibrated the model to predict nitrogen removal by Eastern oyster (Crassostrea virginica) farms with specific field and experimental data on oysters, their local environment, and farm practices in Long Island Sound, CT, USA. Previous FARM applications were not validated for nitrogen removal with local data. In the harvest when ready (HWR) model scenario (oysters are harvested when they reach harvest size), the farm removed 159 kg N $ ha^{−1} $ $ year^{−1} $ while the non-HWR scenario (all oysters are harvested at one time) removed 274 kg N $ ha^{−1} $ $ year^{−1} $. These estimates are within the range of previously reported in-water bioextraction studies in the Northeastern USA. The robust outputs from this validated model can be reliably used in marine spatial planning efforts and by nutrient managers to predict the nitrogen removal benefits that could be achieved through new or expanded eastern oyster farms in eutrophic environments. Nutrient bioextraction (dpeaa)DE-He213 Nutrient removal (dpeaa)DE-He213 Nitrogen budget (dpeaa)DE-He213 Oyster production model (dpeaa)DE-He213 Oyster aquaculture (dpeaa)DE-He213 Long Island Sound (dpeaa)DE-He213 Cubillo, Alhambra M. verfasserin aut Rose, Julie M. verfasserin aut Ferreira, Joao G. verfasserin aut Dixon, Mark verfasserin aut Alvarado, Annita verfasserin aut Barr, Janine verfasserin aut Bernatchez, Genevieve verfasserin aut Meseck, Shannon verfasserin aut Poach, Matthew verfasserin aut Pousse, Emilien verfasserin aut Wikfors, Gary H. verfasserin aut Bricker, Suzanne verfasserin aut Enthalten in Estuaries and coasts Springer US, 2006 47(2024), 5 vom: 15. Mai, Seite 1184-1198 (DE-627)51010830X (DE-600)2229170-2 1559-2731 nnns volume:47 year:2024 number:5 day:15 month:05 pages:1184-1198 https://dx.doi.org/10.1007/s12237-024-01354-7 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2939 GBV_ILN_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 38.90 VZ 42.94 VZ AR 47 2024 5 15 05 1184-1198 |
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|
| author |
Bayer, Skylar R. |
| spellingShingle |
Bayer, Skylar R. ddc 550 bkl 38.90 bkl 42.94 misc Nutrient bioextraction misc Nutrient removal misc Nitrogen budget misc Oyster production model misc Oyster aquaculture misc Long Island Sound Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale |
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Bayer, Skylar R. |
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550 VZ 38.90 bkl 42.94 bkl Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale Nutrient bioextraction (dpeaa)DE-He213 Nutrient removal (dpeaa)DE-He213 Nitrogen budget (dpeaa)DE-He213 Oyster production model (dpeaa)DE-He213 Oyster aquaculture (dpeaa)DE-He213 Long Island Sound (dpeaa)DE-He213 |
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ddc 550 bkl 38.90 bkl 42.94 misc Nutrient bioextraction misc Nutrient removal misc Nitrogen budget misc Oyster production model misc Oyster aquaculture misc Long Island Sound |
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ddc 550 bkl 38.90 bkl 42.94 misc Nutrient bioextraction misc Nutrient removal misc Nitrogen budget misc Oyster production model misc Oyster aquaculture misc Long Island Sound |
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ddc 550 bkl 38.90 bkl 42.94 misc Nutrient bioextraction misc Nutrient removal misc Nitrogen budget misc Oyster production model misc Oyster aquaculture misc Long Island Sound |
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Elektronische Aufsätze Aufsätze Elektronische Ressource |
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Estuaries and coasts |
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550 - Earth sciences & geology |
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Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale |
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(DE-627)SPR056340680 (SPR)s12237-024-01354-7-e |
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Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale |
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Bayer, Skylar R. |
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Estuaries and coasts |
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Estuaries and coasts |
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eng |
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2024 |
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Bayer, Skylar R. Cubillo, Alhambra M. Rose, Julie M. Ferreira, Joao G. Dixon, Mark Alvarado, Annita Barr, Janine Bernatchez, Genevieve Meseck, Shannon Poach, Matthew Pousse, Emilien Wikfors, Gary H. Bricker, Suzanne |
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550 VZ 38.90 bkl 42.94 bkl |
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Elektronische Aufsätze |
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Bayer, Skylar R. |
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10.1007/s12237-024-01354-7 |
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(ORCID)0000-0003-4128-9536 |
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(orcid)0000-0003-4128-9536 |
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550 |
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verfasserin |
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refining the farm aquaculture resource management model for shellfish nitrogen removal at the local scale |
| title_auth |
Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale |
| abstract |
Abstract Nutrient-related environmental degradation in coastal waters is a continuing global problem. Bivalve shellfish farms show nutrient removal capabilities similar to some traditional management strategies and in some places have been incorporated into nutrient management programs to help achieve water quality goals. Bioextractive nutrient removal varies by farmed species and is influenced by environment parameters; thus, data and information for both are needed to estimate nutrient mitigation potential of shellfish farms. The Farm Aquaculture Resource Management (FARM) model, calibrated for farmed species, uses local environmental and farming practice data to simulate interactions between the farmed population and the local environment and to optimize cultivation practices for economic gain. We calibrated the model to predict nitrogen removal by Eastern oyster (Crassostrea virginica) farms with specific field and experimental data on oysters, their local environment, and farm practices in Long Island Sound, CT, USA. Previous FARM applications were not validated for nitrogen removal with local data. In the harvest when ready (HWR) model scenario (oysters are harvested when they reach harvest size), the farm removed 159 kg N $ ha^{−1} $ $ year^{−1} $ while the non-HWR scenario (all oysters are harvested at one time) removed 274 kg N $ ha^{−1} $ $ year^{−1} $. These estimates are within the range of previously reported in-water bioextraction studies in the Northeastern USA. The robust outputs from this validated model can be reliably used in marine spatial planning efforts and by nutrient managers to predict the nitrogen removal benefits that could be achieved through new or expanded eastern oyster farms in eutrophic environments. © The Author(s) 2024 |
| abstractGer |
Abstract Nutrient-related environmental degradation in coastal waters is a continuing global problem. Bivalve shellfish farms show nutrient removal capabilities similar to some traditional management strategies and in some places have been incorporated into nutrient management programs to help achieve water quality goals. Bioextractive nutrient removal varies by farmed species and is influenced by environment parameters; thus, data and information for both are needed to estimate nutrient mitigation potential of shellfish farms. The Farm Aquaculture Resource Management (FARM) model, calibrated for farmed species, uses local environmental and farming practice data to simulate interactions between the farmed population and the local environment and to optimize cultivation practices for economic gain. We calibrated the model to predict nitrogen removal by Eastern oyster (Crassostrea virginica) farms with specific field and experimental data on oysters, their local environment, and farm practices in Long Island Sound, CT, USA. Previous FARM applications were not validated for nitrogen removal with local data. In the harvest when ready (HWR) model scenario (oysters are harvested when they reach harvest size), the farm removed 159 kg N $ ha^{−1} $ $ year^{−1} $ while the non-HWR scenario (all oysters are harvested at one time) removed 274 kg N $ ha^{−1} $ $ year^{−1} $. These estimates are within the range of previously reported in-water bioextraction studies in the Northeastern USA. The robust outputs from this validated model can be reliably used in marine spatial planning efforts and by nutrient managers to predict the nitrogen removal benefits that could be achieved through new or expanded eastern oyster farms in eutrophic environments. © The Author(s) 2024 |
| abstract_unstemmed |
Abstract Nutrient-related environmental degradation in coastal waters is a continuing global problem. Bivalve shellfish farms show nutrient removal capabilities similar to some traditional management strategies and in some places have been incorporated into nutrient management programs to help achieve water quality goals. Bioextractive nutrient removal varies by farmed species and is influenced by environment parameters; thus, data and information for both are needed to estimate nutrient mitigation potential of shellfish farms. The Farm Aquaculture Resource Management (FARM) model, calibrated for farmed species, uses local environmental and farming practice data to simulate interactions between the farmed population and the local environment and to optimize cultivation practices for economic gain. We calibrated the model to predict nitrogen removal by Eastern oyster (Crassostrea virginica) farms with specific field and experimental data on oysters, their local environment, and farm practices in Long Island Sound, CT, USA. Previous FARM applications were not validated for nitrogen removal with local data. In the harvest when ready (HWR) model scenario (oysters are harvested when they reach harvest size), the farm removed 159 kg N $ ha^{−1} $ $ year^{−1} $ while the non-HWR scenario (all oysters are harvested at one time) removed 274 kg N $ ha^{−1} $ $ year^{−1} $. These estimates are within the range of previously reported in-water bioextraction studies in the Northeastern USA. The robust outputs from this validated model can be reliably used in marine spatial planning efforts and by nutrient managers to predict the nitrogen removal benefits that could be achieved through new or expanded eastern oyster farms in eutrophic environments. © The Author(s) 2024 |
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Refining the Farm Aquaculture Resource Management Model for Shellfish Nitrogen Removal at the Local Scale |
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https://dx.doi.org/10.1007/s12237-024-01354-7 |
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Cubillo, Alhambra M. Rose, Julie M. Ferreira, Joao G. Dixon, Mark Alvarado, Annita Barr, Janine Bernatchez, Genevieve Meseck, Shannon Poach, Matthew Pousse, Emilien Wikfors, Gary H. Bricker, Suzanne |
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Cubillo, Alhambra M. Rose, Julie M. Ferreira, Joao G. Dixon, Mark Alvarado, Annita Barr, Janine Bernatchez, Genevieve Meseck, Shannon Poach, Matthew Pousse, Emilien Wikfors, Gary H. Bricker, Suzanne |
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| doi_str |
10.1007/s12237-024-01354-7 |
| up_date |
2024-07-03T21:46:47.820Z |
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1803596026685161472 |
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| score |
7.4003115 |