Comparison of Spatially and Nonspatially Explicit Nonlinear Mixed Effects Models for Norway Spruce Individual Tree Growth under Single-Tree Selection
<i>Background and Objectives:</i> Continuous cover forestry is of increasing importance, but operational forest growth models are still lacking. The debate is especially open if more complex spatial approaches would provide a worthwhile increase in accuracy. Our objective was to compare...
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| Format: | Article |
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MDPI AG
2020-12-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/11/12/1338 |
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| author | Simone Bianchi Mari Myllymaki Jouni Siipilehto Hannu Salminen Jari Hynynen Sauli Valkonen |
| author_facet | Simone Bianchi Mari Myllymaki Jouni Siipilehto Hannu Salminen Jari Hynynen Sauli Valkonen |
| author_sort | Simone Bianchi |
| collection | DOAJ |
| description | <i>Background and Objectives:</i> Continuous cover forestry is of increasing importance, but operational forest growth models are still lacking. The debate is especially open if more complex spatial approaches would provide a worthwhile increase in accuracy. Our objective was to compare a nonspatial versus a spatial approach for individual Norway spruce tree growth models under single-tree selection cutting. <i>Materials and Methods</i>: We calibrated nonlinear mixed models using data from a long-term experiment in Finland (20 stands with 3538 individual trees for 10,238 growth measurements). We compared the use of nonspatial versus spatial predictors to describe the competitive pressure and its release after cutting. The models were compared in terms of Akaike Information Criteria (AIC), root mean square error (RMSE), and mean absolute bias (MAB), both with the training data and after cross-validation with a leave-one-out method at stand level. <i>Results</i>: Even though the spatial model had a lower AIC than the nonspatial model, RMSE and MAB of the two models were similar. Both models tended to underpredict growth for the highest observed values when the tree-level random effects were not used. After cross-validation, the aggregated predictions at stand level well represented the observations in both models. For most of the predictors, the use of values based on trees’ height rather than trees’ diameter improved the fit. After single-tree selection cutting, trees had a growth boost both in the first and second five-year period after cutting, however, with different predicted intensity in the two models. <i>Conclusions</i>: Under the research framework here considered, the spatial modeling approach was not more accurate than the nonspatial one. Regarding the single-tree selection cutting, an intervention regime spaced no more than 15 years apart seems necessary to sustain the individual tree growth. However, the model’s fixed effect parts were not able to capture the high growth of the few fastest-growing trees, and a proper estimation of site potential is needed for uneven-aged stands. |
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| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T14:01:49Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-2e2857cf7b9540d7bcfd83768e0ed4702023-11-21T01:03:13ZengMDPI AGForests1999-49072020-12-011112133810.3390/f11121338Comparison of Spatially and Nonspatially Explicit Nonlinear Mixed Effects Models for Norway Spruce Individual Tree Growth under Single-Tree SelectionSimone Bianchi0Mari Myllymaki1Jouni Siipilehto2Hannu Salminen3Jari Hynynen4Sauli Valkonen5Natural Resources Institute Finland (LUKE), FI-00790 Helsinki, FinlandNatural Resources Institute Finland (LUKE), FI-00790 Helsinki, FinlandNatural Resources Institute Finland (LUKE), FI-00790 Helsinki, FinlandNatural Resources Institute Finland (LUKE), FI-00790 Helsinki, FinlandNatural Resources Institute Finland (LUKE), FI-00790 Helsinki, FinlandNatural Resources Institute Finland (LUKE), FI-00790 Helsinki, Finland<i>Background and Objectives:</i> Continuous cover forestry is of increasing importance, but operational forest growth models are still lacking. The debate is especially open if more complex spatial approaches would provide a worthwhile increase in accuracy. Our objective was to compare a nonspatial versus a spatial approach for individual Norway spruce tree growth models under single-tree selection cutting. <i>Materials and Methods</i>: We calibrated nonlinear mixed models using data from a long-term experiment in Finland (20 stands with 3538 individual trees for 10,238 growth measurements). We compared the use of nonspatial versus spatial predictors to describe the competitive pressure and its release after cutting. The models were compared in terms of Akaike Information Criteria (AIC), root mean square error (RMSE), and mean absolute bias (MAB), both with the training data and after cross-validation with a leave-one-out method at stand level. <i>Results</i>: Even though the spatial model had a lower AIC than the nonspatial model, RMSE and MAB of the two models were similar. Both models tended to underpredict growth for the highest observed values when the tree-level random effects were not used. After cross-validation, the aggregated predictions at stand level well represented the observations in both models. For most of the predictors, the use of values based on trees’ height rather than trees’ diameter improved the fit. After single-tree selection cutting, trees had a growth boost both in the first and second five-year period after cutting, however, with different predicted intensity in the two models. <i>Conclusions</i>: Under the research framework here considered, the spatial modeling approach was not more accurate than the nonspatial one. Regarding the single-tree selection cutting, an intervention regime spaced no more than 15 years apart seems necessary to sustain the individual tree growth. However, the model’s fixed effect parts were not able to capture the high growth of the few fastest-growing trees, and a proper estimation of site potential is needed for uneven-aged stands.https://www.mdpi.com/1999-4907/11/12/1338Norway spruceindividual based modelcontinuous cover forestryboreal forestry |
| spellingShingle | Simone Bianchi Mari Myllymaki Jouni Siipilehto Hannu Salminen Jari Hynynen Sauli Valkonen Comparison of Spatially and Nonspatially Explicit Nonlinear Mixed Effects Models for Norway Spruce Individual Tree Growth under Single-Tree Selection Forests Norway spruce individual based model continuous cover forestry boreal forestry |
| title | Comparison of Spatially and Nonspatially Explicit Nonlinear Mixed Effects Models for Norway Spruce Individual Tree Growth under Single-Tree Selection |
| title_full | Comparison of Spatially and Nonspatially Explicit Nonlinear Mixed Effects Models for Norway Spruce Individual Tree Growth under Single-Tree Selection |
| title_fullStr | Comparison of Spatially and Nonspatially Explicit Nonlinear Mixed Effects Models for Norway Spruce Individual Tree Growth under Single-Tree Selection |
| title_full_unstemmed | Comparison of Spatially and Nonspatially Explicit Nonlinear Mixed Effects Models for Norway Spruce Individual Tree Growth under Single-Tree Selection |
| title_short | Comparison of Spatially and Nonspatially Explicit Nonlinear Mixed Effects Models for Norway Spruce Individual Tree Growth under Single-Tree Selection |
| title_sort | comparison of spatially and nonspatially explicit nonlinear mixed effects models for norway spruce individual tree growth under single tree selection |
| topic | Norway spruce individual based model continuous cover forestry boreal forestry |
| url | https://www.mdpi.com/1999-4907/11/12/1338 |
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