Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects models
In natural resource management, reliable monitoring data is necessary to make informed management decisions. Multiple observers collecting data for a monitoring program can introduce variance due to between-observer differences which reduces data reliability. We introduce an application of mixed-eff...
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Elsevier
2025-03-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954124004886 |
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| author | Leah T. Dreesmann Timothy R. Johnson Jason W. Karl |
| author_facet | Leah T. Dreesmann Timothy R. Johnson Jason W. Karl |
| author_sort | Leah T. Dreesmann |
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| description | In natural resource management, reliable monitoring data is necessary to make informed management decisions. Multiple observers collecting data for a monitoring program can introduce variance due to between-observer differences which reduces data reliability. We introduce an application of mixed-effects models with relaxed homogeneous variance assumptions (called heterogeneous-variance mixed-effects models) to quantify between-observer variance in natural resource monitoring programs with large spatial and temporal extents. We used monitoring data from the Bureau of Land Management's Assessment, Inventory, and Monitoring program to describe and demonstrate the use of the method. In two example analyses, we identified differences in observer variance across regions and years and associated the differences in these examples with features of the AIM monitoring program. These examples illustrate several potential uses of the models which include calculating the magnitude of observer variance to guide indicator selection and appropriate indicator estimate calculations, determining appropriate data aggregation and comparison, understanding the influences of changes to a monitoring program on observer variance, identifying previously unknown influences on observer variance, and suggesting practices or further changes to monitoring programs to reduce observer variance in future data collection efforts. This statistical technique requires a dataset with a large number of samples and observers and is therefore best suited to large monitoring programs. Reliable metadata about observers and the program history is also required to build the model and interpret the results. The model could also be expanded to answer additional questions about the influence of observers on monitoring data. |
| format | Article |
| id | doaj-art-e82d8a675c914dcc9367ef48254463ed |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-e82d8a675c914dcc9367ef48254463ed2025-01-19T06:24:37ZengElsevierEcological Informatics1574-95412025-03-0185102946Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects modelsLeah T. Dreesmann0Timothy R. Johnson1Jason W. Karl2Department of Forest, Rangeland, and Fire Science, University of Idaho, 875 Perimeter Drive MS 1135, Moscow, ID 83844, USA; Department of Mathematics and Statistical Science, University of Idaho, 875 Perimeter Drive MS 1104, Moscow, ID 83844, USA; Corresponding author at: Department of Forest, Rangeland, and Fire Science, University of Idaho, 875 Perimeter Drive MS 1135, Moscow, ID 83844, USA.Department of Mathematics and Statistical Science, University of Idaho, 875 Perimeter Drive MS 1104, Moscow, ID 83844, USADepartment of Forest, Rangeland, and Fire Science, University of Idaho, 875 Perimeter Drive MS 1135, Moscow, ID 83844, USAIn natural resource management, reliable monitoring data is necessary to make informed management decisions. Multiple observers collecting data for a monitoring program can introduce variance due to between-observer differences which reduces data reliability. We introduce an application of mixed-effects models with relaxed homogeneous variance assumptions (called heterogeneous-variance mixed-effects models) to quantify between-observer variance in natural resource monitoring programs with large spatial and temporal extents. We used monitoring data from the Bureau of Land Management's Assessment, Inventory, and Monitoring program to describe and demonstrate the use of the method. In two example analyses, we identified differences in observer variance across regions and years and associated the differences in these examples with features of the AIM monitoring program. These examples illustrate several potential uses of the models which include calculating the magnitude of observer variance to guide indicator selection and appropriate indicator estimate calculations, determining appropriate data aggregation and comparison, understanding the influences of changes to a monitoring program on observer variance, identifying previously unknown influences on observer variance, and suggesting practices or further changes to monitoring programs to reduce observer variance in future data collection efforts. This statistical technique requires a dataset with a large number of samples and observers and is therefore best suited to large monitoring programs. Reliable metadata about observers and the program history is also required to build the model and interpret the results. The model could also be expanded to answer additional questions about the influence of observers on monitoring data.http://www.sciencedirect.com/science/article/pii/S1574954124004886Observer errorNon-sampling errorVariance modelingBayesian analysisMonitoringNatural resource management |
| spellingShingle | Leah T. Dreesmann Timothy R. Johnson Jason W. Karl Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects models Ecological Informatics Observer error Non-sampling error Variance modeling Bayesian analysis Monitoring Natural resource management |
| title | Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects models |
| title_full | Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects models |
| title_fullStr | Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects models |
| title_full_unstemmed | Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects models |
| title_short | Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects models |
| title_sort | quantifying observer variance in expansive monitoring program indicator data with heterogeneous variance mixed effects models |
| topic | Observer error Non-sampling error Variance modeling Bayesian analysis Monitoring Natural resource management |
| url | http://www.sciencedirect.com/science/article/pii/S1574954124004886 |
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