Observed variation in soil properties can drive large variation in modelled forest functioning and composition during tropical forest secondary succession
© 2019 The Authors. New Phytologist © 2019 New Phytologist Trust.
| Publié dans: | The New phytologist. - 1979. - 223(2019), 4 vom: 30. Sept., Seite 1820-1833 |
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| Auteur principal: | |
| Autres auteurs: | , , , , , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2019
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| Accès à la collection: | The New phytologist |
| Sujets: | Journal Article Research Support, U.S. Gov't, Non-P.H.S. ED2−MEND−N-COM ecosystem composition forest biomass soil nutrients soil texture spatial variation terrestrial ecosystem modelling tropical forests |
| Résumé: | © 2019 The Authors. New Phytologist © 2019 New Phytologist Trust. Censuses of tropical forest plots reveal large variation in biomass and plant composition. This paper evaluates whether such variation can emerge solely from realistic variation in a set of commonly measured soil chemical and physical properties. Controlled simulations were performed using a mechanistic model that includes forest dynamics, microbe-mediated biogeochemistry, and competition for nitrogen and phosphorus. Observations from 18 forest inventory plots in Guanacaste, Costa Rica were used to determine realistic variation in soil properties. In simulations of secondary succession, the across-plot range in plant biomass reached 30% of the mean and was attributable primarily to nutrient limitation and secondarily to soil texture differences that affected water availability. The contributions of different plant functional types to total biomass varied widely across plots and depended on soil nutrient status. In Central America, soil-induced variation in plant biomass increased with mean annual precipitation because of changes in nutrient limitation. In Central America, large variation in plant biomass and ecosystem composition arises mechanistically from realistic variation in soil properties. The degree of biomass and compositional variation is climate sensitive. In general, model predictions can be improved through better representation of soil nutrient processes, including their spatial variation |
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| Description: | Date Completed 27.02.2020 Date Revised 30.09.2020 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1469-8137 |
| DOI: | 10.1111/nph.15848 |