Relationships between the eDNA concentration obtained from metabarcoding and stream fish abundance estimated by the removal method under field conditions
Abstract Estimating abundance or biomass using eDNA metabarcoding is a powerful emerging tool that may provide an alternative to conventional laborious methods for biological monitoring. However, inferring aquatic macroorganism abundance or biomass using eDNA concentrations remains challenging, espe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hikaru Nakagawa [verfasserIn] Keitaro Fukushima [verfasserIn] Masaru Sakai [verfasserIn] Luhan Wu [verfasserIn] Toshifumi Minamoto [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Environmental DNA - Wiley, 2019, 4(2022), 6, Seite 1369-1380 |
|---|---|
| Übergeordnetes Werk: |
volume:4 ; year:2022 ; number:6 ; pages:1369-1380 |
| Links: |
|---|
| DOI / URN: |
10.1002/edn3.346 |
|---|
| Katalog-ID: |
DOAJ083770267 |
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| 520 | |a Abstract Estimating abundance or biomass using eDNA metabarcoding is a powerful emerging tool that may provide an alternative to conventional laborious methods for biological monitoring. However, inferring aquatic macroorganism abundance or biomass using eDNA concentrations remains challenging, especially in lotic environments, because of several potential confounding factors. In this study, we tested whether quantitative eDNA metabarcoding that uses internal standard DNA can be used to estimate the abundance of four fish species. We collected eDNA samples and concurrently estimated fish densities using the conventional removal method in small tributaries in four seasons during a year. The effects of potential confounding factors, including the body mass of the individuals, water temperature, and discharge volume, were assessed using an allometric scaling model. We found an increasing trend of eDNA concentration against the increase in abundance across all species. In the most abundant species, a significant increase in the precision of predicted abundance was achieved by considering confounding factors, such as season and discharge. Although this study successfully determined the relationships between eDNA concentration and fish abundance under lotic field conditions, it also identified several limitations of quantitative eDNA metabarcoding. The relationship between eDNA concentration and fish abundance in rare species showed significant variances in the regression. More sequencing depth may be necessary to detect rare species sufficiently. The eDNA concentration estimation error effect was significant, particularly among the samples that showed the same abundance figures by direct capture estimation. The utilization of quantitative eDNA metabarcoding may be suitable for organisms that are expected to have a substantial variation in their population density. More comparative studies with various conventional methods would be informative, especially in lotic field environments, to overcome these limitations and achieve wider applications of eDNA metabarcoding in future research and monitoring. | ||
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10.1002/edn3.346 doi (DE-627)DOAJ083770267 (DE-599)DOAJd9a9b535b9914a029f59a5a4b6696061 DE-627 ger DE-627 rakwb eng GE1-350 QR100-130 Hikaru Nakagawa verfasserin aut Relationships between the eDNA concentration obtained from metabarcoding and stream fish abundance estimated by the removal method under field conditions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Estimating abundance or biomass using eDNA metabarcoding is a powerful emerging tool that may provide an alternative to conventional laborious methods for biological monitoring. However, inferring aquatic macroorganism abundance or biomass using eDNA concentrations remains challenging, especially in lotic environments, because of several potential confounding factors. In this study, we tested whether quantitative eDNA metabarcoding that uses internal standard DNA can be used to estimate the abundance of four fish species. We collected eDNA samples and concurrently estimated fish densities using the conventional removal method in small tributaries in four seasons during a year. The effects of potential confounding factors, including the body mass of the individuals, water temperature, and discharge volume, were assessed using an allometric scaling model. We found an increasing trend of eDNA concentration against the increase in abundance across all species. In the most abundant species, a significant increase in the precision of predicted abundance was achieved by considering confounding factors, such as season and discharge. Although this study successfully determined the relationships between eDNA concentration and fish abundance under lotic field conditions, it also identified several limitations of quantitative eDNA metabarcoding. The relationship between eDNA concentration and fish abundance in rare species showed significant variances in the regression. More sequencing depth may be necessary to detect rare species sufficiently. The eDNA concentration estimation error effect was significant, particularly among the samples that showed the same abundance figures by direct capture estimation. The utilization of quantitative eDNA metabarcoding may be suitable for organisms that are expected to have a substantial variation in their population density. More comparative studies with various conventional methods would be informative, especially in lotic field environments, to overcome these limitations and achieve wider applications of eDNA metabarcoding in future research and monitoring. absolute density abundance biomass multiple linear regression multiple species quantitative environmental DNA metabarcoding Environmental sciences Microbial ecology Keitaro Fukushima verfasserin aut Masaru Sakai verfasserin aut Luhan Wu verfasserin aut Toshifumi Minamoto verfasserin aut In Environmental DNA Wiley, 2019 4(2022), 6, Seite 1369-1380 (DE-627)1683467949 (DE-600)3001165-6 26374943 nnns volume:4 year:2022 number:6 pages:1369-1380 https://doi.org/10.1002/edn3.346 kostenfrei https://doaj.org/article/d9a9b535b9914a029f59a5a4b6696061 kostenfrei https://doi.org/10.1002/edn3.346 kostenfrei https://doaj.org/toc/2637-4943 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 6 1369-1380 |
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10.1002/edn3.346 doi (DE-627)DOAJ083770267 (DE-599)DOAJd9a9b535b9914a029f59a5a4b6696061 DE-627 ger DE-627 rakwb eng GE1-350 QR100-130 Hikaru Nakagawa verfasserin aut Relationships between the eDNA concentration obtained from metabarcoding and stream fish abundance estimated by the removal method under field conditions 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Estimating abundance or biomass using eDNA metabarcoding is a powerful emerging tool that may provide an alternative to conventional laborious methods for biological monitoring. However, inferring aquatic macroorganism abundance or biomass using eDNA concentrations remains challenging, especially in lotic environments, because of several potential confounding factors. In this study, we tested whether quantitative eDNA metabarcoding that uses internal standard DNA can be used to estimate the abundance of four fish species. We collected eDNA samples and concurrently estimated fish densities using the conventional removal method in small tributaries in four seasons during a year. The effects of potential confounding factors, including the body mass of the individuals, water temperature, and discharge volume, were assessed using an allometric scaling model. We found an increasing trend of eDNA concentration against the increase in abundance across all species. In the most abundant species, a significant increase in the precision of predicted abundance was achieved by considering confounding factors, such as season and discharge. Although this study successfully determined the relationships between eDNA concentration and fish abundance under lotic field conditions, it also identified several limitations of quantitative eDNA metabarcoding. The relationship between eDNA concentration and fish abundance in rare species showed significant variances in the regression. More sequencing depth may be necessary to detect rare species sufficiently. The eDNA concentration estimation error effect was significant, particularly among the samples that showed the same abundance figures by direct capture estimation. The utilization of quantitative eDNA metabarcoding may be suitable for organisms that are expected to have a substantial variation in their population density. More comparative studies with various conventional methods would be informative, especially in lotic field environments, to overcome these limitations and achieve wider applications of eDNA metabarcoding in future research and monitoring. absolute density abundance biomass multiple linear regression multiple species quantitative environmental DNA metabarcoding Environmental sciences Microbial ecology Keitaro Fukushima verfasserin aut Masaru Sakai verfasserin aut Luhan Wu verfasserin aut Toshifumi Minamoto verfasserin aut In Environmental DNA Wiley, 2019 4(2022), 6, Seite 1369-1380 (DE-627)1683467949 (DE-600)3001165-6 26374943 nnns volume:4 year:2022 number:6 pages:1369-1380 https://doi.org/10.1002/edn3.346 kostenfrei https://doaj.org/article/d9a9b535b9914a029f59a5a4b6696061 kostenfrei https://doi.org/10.1002/edn3.346 kostenfrei https://doaj.org/toc/2637-4943 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 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_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2022 6 1369-1380 |
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Hikaru Nakagawa |
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Hikaru Nakagawa misc GE1-350 misc QR100-130 misc absolute density misc abundance misc biomass misc multiple linear regression misc multiple species misc quantitative environmental DNA metabarcoding misc Environmental sciences misc Microbial ecology Relationships between the eDNA concentration obtained from metabarcoding and stream fish abundance estimated by the removal method under field conditions |
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GE1-350 QR100-130 Relationships between the eDNA concentration obtained from metabarcoding and stream fish abundance estimated by the removal method under field conditions absolute density abundance biomass multiple linear regression multiple species quantitative environmental DNA metabarcoding |
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Relationships between the eDNA concentration obtained from metabarcoding and stream fish abundance estimated by the removal method under field conditions |
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Relationships between the eDNA concentration obtained from metabarcoding and stream fish abundance estimated by the removal method under field conditions |
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Hikaru Nakagawa |
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Hikaru Nakagawa Keitaro Fukushima Masaru Sakai Luhan Wu Toshifumi Minamoto |
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relationships between the edna concentration obtained from metabarcoding and stream fish abundance estimated by the removal method under field conditions |
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Relationships between the eDNA concentration obtained from metabarcoding and stream fish abundance estimated by the removal method under field conditions |
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Abstract Estimating abundance or biomass using eDNA metabarcoding is a powerful emerging tool that may provide an alternative to conventional laborious methods for biological monitoring. However, inferring aquatic macroorganism abundance or biomass using eDNA concentrations remains challenging, especially in lotic environments, because of several potential confounding factors. In this study, we tested whether quantitative eDNA metabarcoding that uses internal standard DNA can be used to estimate the abundance of four fish species. We collected eDNA samples and concurrently estimated fish densities using the conventional removal method in small tributaries in four seasons during a year. The effects of potential confounding factors, including the body mass of the individuals, water temperature, and discharge volume, were assessed using an allometric scaling model. We found an increasing trend of eDNA concentration against the increase in abundance across all species. In the most abundant species, a significant increase in the precision of predicted abundance was achieved by considering confounding factors, such as season and discharge. Although this study successfully determined the relationships between eDNA concentration and fish abundance under lotic field conditions, it also identified several limitations of quantitative eDNA metabarcoding. The relationship between eDNA concentration and fish abundance in rare species showed significant variances in the regression. More sequencing depth may be necessary to detect rare species sufficiently. The eDNA concentration estimation error effect was significant, particularly among the samples that showed the same abundance figures by direct capture estimation. The utilization of quantitative eDNA metabarcoding may be suitable for organisms that are expected to have a substantial variation in their population density. More comparative studies with various conventional methods would be informative, especially in lotic field environments, to overcome these limitations and achieve wider applications of eDNA metabarcoding in future research and monitoring. |
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Abstract Estimating abundance or biomass using eDNA metabarcoding is a powerful emerging tool that may provide an alternative to conventional laborious methods for biological monitoring. However, inferring aquatic macroorganism abundance or biomass using eDNA concentrations remains challenging, especially in lotic environments, because of several potential confounding factors. In this study, we tested whether quantitative eDNA metabarcoding that uses internal standard DNA can be used to estimate the abundance of four fish species. We collected eDNA samples and concurrently estimated fish densities using the conventional removal method in small tributaries in four seasons during a year. The effects of potential confounding factors, including the body mass of the individuals, water temperature, and discharge volume, were assessed using an allometric scaling model. We found an increasing trend of eDNA concentration against the increase in abundance across all species. In the most abundant species, a significant increase in the precision of predicted abundance was achieved by considering confounding factors, such as season and discharge. Although this study successfully determined the relationships between eDNA concentration and fish abundance under lotic field conditions, it also identified several limitations of quantitative eDNA metabarcoding. The relationship between eDNA concentration and fish abundance in rare species showed significant variances in the regression. More sequencing depth may be necessary to detect rare species sufficiently. The eDNA concentration estimation error effect was significant, particularly among the samples that showed the same abundance figures by direct capture estimation. The utilization of quantitative eDNA metabarcoding may be suitable for organisms that are expected to have a substantial variation in their population density. More comparative studies with various conventional methods would be informative, especially in lotic field environments, to overcome these limitations and achieve wider applications of eDNA metabarcoding in future research and monitoring. |
| abstract_unstemmed |
Abstract Estimating abundance or biomass using eDNA metabarcoding is a powerful emerging tool that may provide an alternative to conventional laborious methods for biological monitoring. However, inferring aquatic macroorganism abundance or biomass using eDNA concentrations remains challenging, especially in lotic environments, because of several potential confounding factors. In this study, we tested whether quantitative eDNA metabarcoding that uses internal standard DNA can be used to estimate the abundance of four fish species. We collected eDNA samples and concurrently estimated fish densities using the conventional removal method in small tributaries in four seasons during a year. The effects of potential confounding factors, including the body mass of the individuals, water temperature, and discharge volume, were assessed using an allometric scaling model. We found an increasing trend of eDNA concentration against the increase in abundance across all species. In the most abundant species, a significant increase in the precision of predicted abundance was achieved by considering confounding factors, such as season and discharge. Although this study successfully determined the relationships between eDNA concentration and fish abundance under lotic field conditions, it also identified several limitations of quantitative eDNA metabarcoding. The relationship between eDNA concentration and fish abundance in rare species showed significant variances in the regression. More sequencing depth may be necessary to detect rare species sufficiently. The eDNA concentration estimation error effect was significant, particularly among the samples that showed the same abundance figures by direct capture estimation. The utilization of quantitative eDNA metabarcoding may be suitable for organisms that are expected to have a substantial variation in their population density. More comparative studies with various conventional methods would be informative, especially in lotic field environments, to overcome these limitations and achieve wider applications of eDNA metabarcoding in future research and monitoring. |
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Relationships between the eDNA concentration obtained from metabarcoding and stream fish abundance estimated by the removal method under field conditions |
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