Resurveying historical vegetation data — opportunities and challenges
Background: Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location...
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| Veröffentlicht in: | Applied vegetation science Jg. 20; H. 2; S. 164 - 171 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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United States
John Wiley & Sons Ltd
01.04.2017
Wiley Subscription Services, Inc |
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| ISSN: | 1402-2001, 1654-109X |
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| Abstract | Background: Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location, in addition to observer bias and seasonal bias, may add significant errors to temporal change. These errors may have major implications for the reliability of studies on long-term environmental change and deserve closer attention of vegetation ecologists. Methods: Vegetation data obtained from the resurveying of non-permanently marked plots are assessed for their potential to study environmental change effects on plant communities and the challenges the use of such data have to meet. We describe the properties of vegetation resurveys, distinguishing basic types of plots according to relocation error, and we highlight the potential of such data types for studying vegetation dynamics and their drivers. Finally, we summarize the challenges and limitations of resurveying non-permanently marked vegetation plots for different purposes in environmental change research. Results and conclusions: Re-sampling error is caused by three main independent sources of error: error caused by plot relocation, observer bias and seasonality bias. For relocation error, vegetation plots can be divided into permanent and non-permanent plots, while the latter are further divided into quasi-permanent (with approximate relocation) and non-traceable (with random relocation within a sampled area) plots. To reduce the inherent sources of error in resurvey data, the following precautions should be followed: (i) resurvey historical vegetation plots whose approximate plot location within a study area is known; (ii) consider all information available from historical studies in order to keep plot relocation errors low; (iii) resurvey at times of the year when vegetation development is comparable to the historical survey to control for seasonal variability in vegetation; (iv) retain a high level of experience of the observers to keep observer bias low; and (v) edit and standardize data sets before analyses. |
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| AbstractList | Background: Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location, in addition to observer bias and seasonal bias, may add significant errors to temporal change. These errors may have major implications for the reliability of studies on long-term environmental change and deserve closer attention of vegetation ecologists. Methods: Vegetation data obtained from the resurveying of non-permanently marked plots are assessed for their potential to study environmental change effects on plant communities and the challenges the use of such data have to meet. We describe the properties of vegetation resurveys, distinguishing basic types of plots according to relocation error, and we highlight the potential of such data types for studying vegetation dynamics and their drivers. Finally, we summarize the challenges and limitations of resurveying non-permanently marked vegetation plots for different purposes in environmental change research. Results and conclusions: Re-sampling error is caused by three main independent sources of error: error caused by plot relocation, observer bias and seasonality bias. For relocation error, vegetation plots can be divided into permanent and non-permanent plots, while the latter are further divided into quasi-permanent (with approximate relocation) and non-traceable (with random relocation within a sampled area) plots. To reduce the inherent sources of error in resurvey data, the following precautions should be followed: (i) resurvey historical vegetation plots whose approximate plot location within a study area is known; (ii) consider all information available from historical studies in order to keep plot relocation errors low; (iii) resurvey at times of the year when vegetation development is comparable to the historical survey to control for seasonal variability in vegetation; (iv) retain a high level of experience of the observers to keep observer bias low; and (v) edit and standardize data sets before analyses. Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location, in addition to observer bias and seasonal bias, may add significant error to temporal change. These errors may have major implications for the reliability of studies on long-term environmental change and deserve closer attention of vegetation ecologists. Vegetation data obtained from the resurveying of non-permanently marked plots are assessed for their potential to study environmental-change effects on plant communities and the challenges the use of such data have to meet. We describe the properties of vegetation resurveys distinguishing basic types of plots according to relocation error, and we highlight the potential of such data types for studying vegetation dynamics and their drivers. Finally, we summarise the challenges and limitations of resurveying non-permanently marked vegetation plots for different purposes in environmental change research. Resampling error is caused by three main independent sources of error: error caused by plot relocation, observer bias, and seasonality bias. For relocation error, vegetation plots can be divided into permanent and non-permanent plots, while the latter are further divided into quasi-permanent (with approximate relocation) and non-traceable (with random relocation within a sampled area) plots. To reduce the inherent sources of error in resurvey data, the following precautions should be followed: (i) resurvey historical vegetation plots whose approximate plot location within a study area is known; (ii) consider all information available from historical studies in order to keep plot relocation errors low; (iii) resurvey at times of the year when vegetation development is comparable to the historical survey to control for seasonal variability in vegetation; (iv) keep a high level of experience of the observers to keep observer bias low; and (v) edit and standardise datasets before analyses. Background Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location, in addition to observer bias and seasonal bias, may add significant errors to temporal change. These errors may have major implications for the reliability of studies on long-term environmental change and deserve closer attention of vegetation ecologists. Methods Vegetation data obtained from the resurveying of non-permanently marked plots are assessed for their potential to study environmental change effects on plant communities and the challenges the use of such data have to meet. We describe the properties of vegetation resurveys, distinguishing basic types of plots according to relocation error, and we highlight the potential of such data types for studying vegetation dynamics and their drivers. Finally, we summarize the challenges and limitations of resurveying non-permanently marked vegetation plots for different purposes in environmental change research. Results and conclusions Re-sampling error is caused by three main independent sources of error: error caused by plot relocation, observer bias and seasonality bias. For relocation error, vegetation plots can be divided into permanent and non-permanent plots, while the latter are further divided into quasi-permanent (with approximate relocation) and non-traceable (with random relocation within a sampled area) plots. To reduce the inherent sources of error in resurvey data, the following precautions should be followed: (i) resurvey historical vegetation plots whose approximate plot location within a study area is known; (ii) consider all information available from historical studies in order to keep plot relocation errors low; (iii) resurvey at times of the year when vegetation development is comparable to the historical survey to control for seasonal variability in vegetation; (iv) retain a high level of experience of the observers to keep observer bias low; and (v) edit and standardize data sets before analyses. Resurveying historical vegetation plots provides a unique opportunity to estimate vegetation and environmental changes over the past decades. This paper describes the properties of vegetation resurveys distinguishing basic types of plots according to relocation error and it highlights the potential of such data types for studying vegetation dynamics and their drivers. Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location, in addition to observer bias and seasonal bias, may add significant error to temporal change. These errors may have major implications for the reliability of studies on long-term environmental change and deserve closer attention of vegetation ecologists.BACKGROUNDResurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location, in addition to observer bias and seasonal bias, may add significant error to temporal change. These errors may have major implications for the reliability of studies on long-term environmental change and deserve closer attention of vegetation ecologists.Vegetation data obtained from the resurveying of non-permanently marked plots are assessed for their potential to study environmental-change effects on plant communities and the challenges the use of such data have to meet. We describe the properties of vegetation resurveys distinguishing basic types of plots according to relocation error, and we highlight the potential of such data types for studying vegetation dynamics and their drivers. Finally, we summarise the challenges and limitations of resurveying non-permanently marked vegetation plots for different purposes in environmental change research.MATERIAL & METHODSVegetation data obtained from the resurveying of non-permanently marked plots are assessed for their potential to study environmental-change effects on plant communities and the challenges the use of such data have to meet. We describe the properties of vegetation resurveys distinguishing basic types of plots according to relocation error, and we highlight the potential of such data types for studying vegetation dynamics and their drivers. Finally, we summarise the challenges and limitations of resurveying non-permanently marked vegetation plots for different purposes in environmental change research.Resampling error is caused by three main independent sources of error: error caused by plot relocation, observer bias, and seasonality bias. For relocation error, vegetation plots can be divided into permanent and non-permanent plots, while the latter are further divided into quasi-permanent (with approximate relocation) and non-traceable (with random relocation within a sampled area) plots. To reduce the inherent sources of error in resurvey data, the following precautions should be followed: (i) resurvey historical vegetation plots whose approximate plot location within a study area is known; (ii) consider all information available from historical studies in order to keep plot relocation errors low; (iii) resurvey at times of the year when vegetation development is comparable to the historical survey to control for seasonal variability in vegetation; (iv) keep a high level of experience of the observers to keep observer bias low; and (v) edit and standardise datasets before analyses.RESULTS AND CONCLUSIONSResampling error is caused by three main independent sources of error: error caused by plot relocation, observer bias, and seasonality bias. For relocation error, vegetation plots can be divided into permanent and non-permanent plots, while the latter are further divided into quasi-permanent (with approximate relocation) and non-traceable (with random relocation within a sampled area) plots. To reduce the inherent sources of error in resurvey data, the following precautions should be followed: (i) resurvey historical vegetation plots whose approximate plot location within a study area is known; (ii) consider all information available from historical studies in order to keep plot relocation errors low; (iii) resurvey at times of the year when vegetation development is comparable to the historical survey to control for seasonal variability in vegetation; (iv) keep a high level of experience of the observers to keep observer bias low; and (v) edit and standardise datasets before analyses. Background Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and environmental changes over the past decades. Most historical plots, however, are not permanently marked and uncertainty in plot location, in addition to observer bias and seasonal bias, may add significant errors to temporal change. These errors may have major implications for the reliability of studies on long‐term environmental change and deserve closer attention of vegetation ecologists. Methods Vegetation data obtained from the resurveying of non‐permanently marked plots are assessed for their potential to study environmental change effects on plant communities and the challenges the use of such data have to meet. We describe the properties of vegetation resurveys, distinguishing basic types of plots according to relocation error, and we highlight the potential of such data types for studying vegetation dynamics and their drivers. Finally, we summarize the challenges and limitations of resurveying non‐permanently marked vegetation plots for different purposes in environmental change research. Results and conclusions Re‐sampling error is caused by three main independent sources of error: error caused by plot relocation, observer bias and seasonality bias. For relocation error, vegetation plots can be divided into permanent and non‐permanent plots, while the latter are further divided into quasi‐permanent (with approximate relocation) and non‐traceable (with random relocation within a sampled area) plots. To reduce the inherent sources of error in resurvey data, the following precautions should be followed: (i) resurvey historical vegetation plots whose approximate plot location within a study area is known; (ii) consider all information available from historical studies in order to keep plot relocation errors low; (iii) resurvey at times of the year when vegetation development is comparable to the historical survey to control for seasonal variability in vegetation; (iv) retain a high level of experience of the observers to keep observer bias low; and (v) edit and standardize data sets before analyses. Resurveying historical vegetation plots provides a unique opportunity to estimate vegetation and environmental changes over the past decades. This paper describes the properties of vegetation resurveys distinguishing basic types of plots according to relocation error and it highlights the potential of such data types for studying vegetation dynamics and their drivers. |
| Author | Schei, Fride H. Kapfer, Jutta Kopecký, Martin Hédl, Radim Jurasinski, Gerald Grytnes, John-Arvid |
| AuthorAffiliation | 2 Institute of Botany, The Czech Academy of Sciences, Lidická 25/27, 60200 Brno, Czech Republic 1 Department of Biology, University of Bergen, Thormøhlensgate 53A, 5020 Bergen, Norway 3 Department of Botany, Palacký University, Šlechtitelů 27, 78371 Olomouc, Czech Republic 4 Landscape Ecology and Site Evaluation, University of Rostock, 18059 Rostock, Germany 5 Norwegian Institute of Bioeconomy Research, Holtveien 66, 9016 Tromsø, Norway 7 Norwegian Institute of Bioeconomy Research, Fanaflaten 4, 5244 Fana, Norway 6 Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, CZ-165 21, Praha 6-Suchdol, Czech Republic |
| AuthorAffiliation_xml | – name: 3 Department of Botany, Palacký University, Šlechtitelů 27, 78371 Olomouc, Czech Republic – name: 6 Department of Forest Ecology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamýcká 129, CZ-165 21, Praha 6-Suchdol, Czech Republic – name: 2 Institute of Botany, The Czech Academy of Sciences, Lidická 25/27, 60200 Brno, Czech Republic – name: 1 Department of Biology, University of Bergen, Thormøhlensgate 53A, 5020 Bergen, Norway – name: 4 Landscape Ecology and Site Evaluation, University of Rostock, 18059 Rostock, Germany – name: 7 Norwegian Institute of Bioeconomy Research, Fanaflaten 4, 5244 Fana, Norway – name: 5 Norwegian Institute of Bioeconomy Research, Holtveien 66, 9016 Tromsø, Norway |
| Author_xml | – sequence: 1 givenname: Jutta surname: Kapfer fullname: Kapfer, Jutta – sequence: 2 givenname: Radim surname: Hédl fullname: Hédl, Radim – sequence: 3 givenname: Gerald surname: Jurasinski fullname: Jurasinski, Gerald – sequence: 4 givenname: Martin surname: Kopecký fullname: Kopecký, Martin – sequence: 5 givenname: Fride H. surname: Schei fullname: Schei, Fride H. – sequence: 6 givenname: John-Arvid surname: Grytnes fullname: Grytnes, John-Arvid |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30245580$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Copyright © 2017 International Association for Vegetation Science 2016 International Association for Vegetation Science |
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| Keywords | long-term vegetation dynamics pseudo-turnover non-permanent plots quasi-permanent plots semi-permanent plots Environmental change observer bias relocation error vegetation resampling non-traceable plots |
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| Snippet | Background: Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation... Background Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation... Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation and... Background Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation... BACKGROUND: Resurveying historical vegetation plots has become more and more popular in recent years as it provides a unique opportunity to estimate vegetation... |
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| SubjectTerms | Bias data collection Environmental change Environmental changes Environmental effects Historical account Long‐term vegetation dynamics Non‐permanent plots Non‐traceable plots Observer bias Plant communities Pseudo‐turnover Quasi‐permanent plots Relocation Relocation error seasonal variation Seasonal variations Semi‐permanent plots SPECIAL FEATURE: VEGETATION RESURVEY Studies surveys uncertainty Vegetation Vegetation re‐sampling |
| Title | Resurveying historical vegetation data — opportunities and challenges |
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