Using data calibration to reconcile outputs from different survey methods in long-term or large-scale studies
Abstract Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jones, Christopher S. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Environmental monitoring and assessment - Springer International Publishing, 1981, 194(2022), 3 vom: 14. Feb. |
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| Übergeordnetes Werk: |
volume:194 ; year:2022 ; number:3 ; day:14 ; month:02 |
| Links: |
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| DOI / URN: |
10.1007/s10661-021-09727-2 |
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OLC2078039284 |
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| 520 | |a Abstract Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach. | ||
| 650 | 4 | |a Data calibration | |
| 650 | 4 | |a Model prediction | |
| 650 | 4 | |a Monitoring | |
| 650 | 4 | |a Vegetation change | |
| 650 | 4 | |a Grazing | |
| 650 | 4 | |a Double sampling | |
| 700 | 1 | |a Duncan, David H. |0 (orcid)0000-0003-4411-8214 |4 aut | |
| 700 | 1 | |a Morris, William K. |4 aut | |
| 700 | 1 | |a Robinson, Doug |4 aut | |
| 700 | 1 | |a Vesk, Peter A. |0 (orcid)0000-0003-2008-7062 |4 aut | |
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10.1007/s10661-021-09727-2 doi (DE-627)OLC2078039284 (DE-He213)s10661-021-09727-2-p DE-627 ger DE-627 rakwb eng 333.7 VZ Jones, Christopher S. verfasserin (orcid)0000-0003-2833-0194 aut Using data calibration to reconcile outputs from different survey methods in long-term or large-scale studies 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach. Data calibration Model prediction Monitoring Vegetation change Grazing Double sampling Duncan, David H. (orcid)0000-0003-4411-8214 aut Morris, William K. aut Robinson, Doug aut Vesk, Peter A. (orcid)0000-0003-2008-7062 aut Enthalten in Environmental monitoring and assessment Springer International Publishing, 1981 194(2022), 3 vom: 14. Feb. (DE-627)130549649 (DE-600)782621-7 (DE-576)476125413 0167-6369 nnns volume:194 year:2022 number:3 day:14 month:02 https://doi.org/10.1007/s10661-021-09727-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL AR 194 2022 3 14 02 |
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10.1007/s10661-021-09727-2 doi (DE-627)OLC2078039284 (DE-He213)s10661-021-09727-2-p DE-627 ger DE-627 rakwb eng 333.7 VZ Jones, Christopher S. verfasserin (orcid)0000-0003-2833-0194 aut Using data calibration to reconcile outputs from different survey methods in long-term or large-scale studies 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach. Data calibration Model prediction Monitoring Vegetation change Grazing Double sampling Duncan, David H. (orcid)0000-0003-4411-8214 aut Morris, William K. aut Robinson, Doug aut Vesk, Peter A. (orcid)0000-0003-2008-7062 aut Enthalten in Environmental monitoring and assessment Springer International Publishing, 1981 194(2022), 3 vom: 14. Feb. (DE-627)130549649 (DE-600)782621-7 (DE-576)476125413 0167-6369 nnns volume:194 year:2022 number:3 day:14 month:02 https://doi.org/10.1007/s10661-021-09727-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL AR 194 2022 3 14 02 |
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10.1007/s10661-021-09727-2 doi (DE-627)OLC2078039284 (DE-He213)s10661-021-09727-2-p DE-627 ger DE-627 rakwb eng 333.7 VZ Jones, Christopher S. verfasserin (orcid)0000-0003-2833-0194 aut Using data calibration to reconcile outputs from different survey methods in long-term or large-scale studies 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2022 Abstract Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach. Data calibration Model prediction Monitoring Vegetation change Grazing Double sampling Duncan, David H. (orcid)0000-0003-4411-8214 aut Morris, William K. aut Robinson, Doug aut Vesk, Peter A. (orcid)0000-0003-2008-7062 aut Enthalten in Environmental monitoring and assessment Springer International Publishing, 1981 194(2022), 3 vom: 14. Feb. (DE-627)130549649 (DE-600)782621-7 (DE-576)476125413 0167-6369 nnns volume:194 year:2022 number:3 day:14 month:02 https://doi.org/10.1007/s10661-021-09727-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-FOR SSG-OLC-IBL AR 194 2022 3 14 02 |
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Using data calibration to reconcile outputs from different survey methods in long-term or large-scale studies |
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Abstract Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach. © The Author(s) 2022 |
| abstractGer |
Abstract Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach. © The Author(s) 2022 |
| abstract_unstemmed |
Abstract Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach. © The Author(s) 2022 |
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| title_short |
Using data calibration to reconcile outputs from different survey methods in long-term or large-scale studies |
| url |
https://doi.org/10.1007/s10661-021-09727-2 |
| remote_bool |
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| author2 |
Duncan, David H. Morris, William K. Robinson, Doug Vesk, Peter A. |
| author2Str |
Duncan, David H. Morris, William K. Robinson, Doug Vesk, Peter A. |
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| doi_str |
10.1007/s10661-021-09727-2 |
| up_date |
2024-07-03T18:31:47.220Z |
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