Prediction of forest nutrient and moisture regimes from understory vegetation with random forest classification models
The proper choice of the tree species to be grown in a specific forest site requires a good knowledge of the tree species autecology and a comprehensive description of the local environmental conditions. In Belgium (Western Europe), ecological forest site are classified according to three major grad...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X22009190 |
| _version_ | 1828219040495042560 |
|---|---|
| author | Lisein Jonathan Fayolle Adeline Legrain Andyne Prévot Céline Claessens Hugues |
| author_facet | Lisein Jonathan Fayolle Adeline Legrain Andyne Prévot Céline Claessens Hugues |
| author_sort | Lisein Jonathan |
| collection | DOAJ |
| description | The proper choice of the tree species to be grown in a specific forest site requires a good knowledge of the tree species autecology and a comprehensive description of the local environmental conditions. In Belgium (Western Europe), ecological forest site are classified according to three major gradients: climate, soil nutrient (fertility) and soil moisture regimes. Understory indicator species are used by practitioners to determine nutrient and moisture regimes, but requires a significant expertise of forest ecosystems. The present work aims in a first instance at modelling the nutrient and moisture regimes based on species composition. Secondly, a practical decision support tool is developped and made available in order to predict forest nutrient and moisture regime starting from a floristic relevé. To do so, we collected floristic relevés representing understory vegetation diversity in Belgium and covering all the nutrient and moisture gradient. The combination of soil and topographic measurements with the indicator plants presence/absence support forest scientists in inferring a nutrient and moisture regime to each relevé. The resulting dataset was balanced along the different nutrient or moisture regimes and Random Forest classification models were trained in order to predict the forest site characteristic from indicator species presence (or absence). One model was fitted for the prediction of the nutrient regime, exclusively based on the floristic information. A second one was trained to classify the moisture regime. Accurate predictions confirms the appropriate use of indicator species for the Belgian forest site classification. The two models are intregrated in a web application dedicated to forest practionners. This website enables the automatic determination of nutrient and moisture regimes from the species list of a floristic relevé. |
| first_indexed | 2024-04-12T16:11:30Z |
| format | Article |
| id | doaj.art-1259bb091e264584b49cbf5c87cc6753 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-04-12T16:11:30Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-1259bb091e264584b49cbf5c87cc67532022-12-22T03:25:53ZengElsevierEcological Indicators1470-160X2022-11-01144109446Prediction of forest nutrient and moisture regimes from understory vegetation with random forest classification modelsLisein Jonathan0Fayolle Adeline1Legrain Andyne2Prévot Céline3Claessens Hugues4Liège University – Faculty of Gembloux Agro-Bio Tech – Unit of Forest Resources Management, Belgium; Corresponding author.Liège University – Faculty of Gembloux Agro-Bio Tech – Unit of Forest Resources Management, BelgiumLiège University – Faculty of Gembloux Agro-Bio Tech – Unit of Forest Resources Management, BelgiumForêt.Nature asbl, BelgiumLiège University – Faculty of Gembloux Agro-Bio Tech – Unit of Forest Resources Management, BelgiumThe proper choice of the tree species to be grown in a specific forest site requires a good knowledge of the tree species autecology and a comprehensive description of the local environmental conditions. In Belgium (Western Europe), ecological forest site are classified according to three major gradients: climate, soil nutrient (fertility) and soil moisture regimes. Understory indicator species are used by practitioners to determine nutrient and moisture regimes, but requires a significant expertise of forest ecosystems. The present work aims in a first instance at modelling the nutrient and moisture regimes based on species composition. Secondly, a practical decision support tool is developped and made available in order to predict forest nutrient and moisture regime starting from a floristic relevé. To do so, we collected floristic relevés representing understory vegetation diversity in Belgium and covering all the nutrient and moisture gradient. The combination of soil and topographic measurements with the indicator plants presence/absence support forest scientists in inferring a nutrient and moisture regime to each relevé. The resulting dataset was balanced along the different nutrient or moisture regimes and Random Forest classification models were trained in order to predict the forest site characteristic from indicator species presence (or absence). One model was fitted for the prediction of the nutrient regime, exclusively based on the floristic information. A second one was trained to classify the moisture regime. Accurate predictions confirms the appropriate use of indicator species for the Belgian forest site classification. The two models are intregrated in a web application dedicated to forest practionners. This website enables the automatic determination of nutrient and moisture regimes from the species list of a floristic relevé.http://www.sciencedirect.com/science/article/pii/S1470160X22009190Floristic relevéBioindicatorForest siteNutrient regimeMoisture regimeWestern Europe |
| spellingShingle | Lisein Jonathan Fayolle Adeline Legrain Andyne Prévot Céline Claessens Hugues Prediction of forest nutrient and moisture regimes from understory vegetation with random forest classification models Ecological Indicators Floristic relevé Bioindicator Forest site Nutrient regime Moisture regime Western Europe |
| title | Prediction of forest nutrient and moisture regimes from understory vegetation with random forest classification models |
| title_full | Prediction of forest nutrient and moisture regimes from understory vegetation with random forest classification models |
| title_fullStr | Prediction of forest nutrient and moisture regimes from understory vegetation with random forest classification models |
| title_full_unstemmed | Prediction of forest nutrient and moisture regimes from understory vegetation with random forest classification models |
| title_short | Prediction of forest nutrient and moisture regimes from understory vegetation with random forest classification models |
| title_sort | prediction of forest nutrient and moisture regimes from understory vegetation with random forest classification models |
| topic | Floristic relevé Bioindicator Forest site Nutrient regime Moisture regime Western Europe |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X22009190 |
| work_keys_str_mv | AT liseinjonathan predictionofforestnutrientandmoistureregimesfromunderstoryvegetationwithrandomforestclassificationmodels AT fayolleadeline predictionofforestnutrientandmoistureregimesfromunderstoryvegetationwithrandomforestclassificationmodels AT legrainandyne predictionofforestnutrientandmoistureregimesfromunderstoryvegetationwithrandomforestclassificationmodels AT prevotceline predictionofforestnutrientandmoistureregimesfromunderstoryvegetationwithrandomforestclassificationmodels AT claessenshugues predictionofforestnutrientandmoistureregimesfromunderstoryvegetationwithrandomforestclassificationmodels |