С�����Ƶ

The Tropical Forest Regrowth (TFR) model is derived from the Multiple Element Limitation (MEL) model (Rastetter and Shaver 1992; Rastetter et al. 2013) to investigate the responses of secondary tropical forests. The main differences between the structure of this model and MEL version IV (Rastetter et al. 2013) are: 1) MEL includes a coupled water cycle while this model only includes indices for runoff and soil moisture that influence soil processes, 2) MEL includes separate state variables for soil inorganic nitrogen (NO₃^- and NH₄⁺), while they are lumped into total inorganic N in this model, 3) MEL has separate state variables for various forms of mineral P, while this model only simulates PO₄^3-, and 4) MEL has a separate state variable for dissolved organic matter (DOM), while this model simulates DOM losses directly from the SOM pools.  These simplifications reduce the number of parameters to be estimated and processes for which input data are needed (a limitation in tropical forests) while still allowing for understanding of the general patterns of secondary forest regrowth in the tropics.

Application

Nagy, R.C., E. Rastetter, C. Neill, and S. Porder (in review). Nutrient limitation in tropical secondary forests following different management practices.  Ecological Applications.

Secondary forests now make up more than half of all tropical forests, and constraints on their biomass accumulation will influence the strength of the terrestrial carbon (C) sink in the coming decades.  However the variance in secondary tropical forest biomass for a given stand age and climate is high and understanding of constraints is limited.

We asked how well a biogeochemical model can explain variation in secondary tropical forest biomass recovery. We built a new model based on the multiple element limitation (MEL) model (Rastetter and Shaver 1992; Rastetter et al. 2013) to study changes in N and P, productivity, and C storage in lowland tropical forest regrowth.  Our purpose was to understand how nutrient limitation evolves following disturbance as tropical secondary forests regrow.  Specifically, we asked: 1) How does secondary forest biogeochemical cycling proceed from a wide range of post-disturbance conditions such as those after harvest, blowdown, selective logging, and slash and burn? 2) Can the model capture the range of dynamics observed in secondary forest chronosequences and provide an explanation for the observed variance in biomass recovery?  3) Can tropical soil degradation during pasture use explain patterns of forest regrowth following pasture abandonment?

We parameterized our model based on a Large-Scale Biosphere-Atmosphere (LBA) site in eastern Amazonia.  The site, Caxiuanã, is located at 1˚43' 3.5"S, 51˚27' 36"W in Pará, Brazil at 15 m a.s.l. near the mouth of the Amazon River (Malhi et al. 2009).  This site is a mature lowland tropical forest with an average leaf area index (LAI) of 5.1 and average canopy height of 30-35 m (Restrepo-Coupe et al. 2013).

The model predicted that N limited the rate of forest recovery in the first few decades following harvest, but that this limitation switched to P approximately 30-40 years after abandonment, consistent with field data on N and P cycling from secondary tropical forest chronosequences.  Simulated biomass recovery agreed well with field data of biomass recovery following harvest (R²=0.80).  Model results showed that if all biomass remained on site following a severe disturbance such as blowdown, regrowth approached pre-disturbance biomass in 80-90 years.  Recovery was fast following selective logging, which represented only a minor decrease in ecosystem nutrient stocks.  Field data from regrowth on abandoned pastures were consistent with simulated losses of nutrients in soil organic matter, particularly P.  Following all of these forest disturbances except blowdown, tropical forest regrowth exhibited retrogression that included reduced biomass caused by nutrient loss through harvest, sequestration in secondary minerals, and leaching.  Differences in nutrient availability accounted for 49-94% of the variance in secondary forest biomass C at a given stand age.

Downloads

The TFR model is written in Lazarus v1.4.2, an open source IDE for object oriented Pascal. It uses the model development framework, Modelshell, also written in Lazarus v1.4.2. To run the model download the zip file and follow the instructions in the included readme file. The compressed file below contains instructions and sample files for running TFR, the full source code, and a Windows 64 bit executable.

Modelshell files

Download the parameter, driver and output files for all simulations in Nagy, et. al., in review.

Citations

Malhi, Y., L.E.O.C. Aragão, D.B. Metcalfe, R. Paiva, C.A. Quesada, S. Almeida, L. Anderson, P. Brando, J.Q. Chambers, A.C.L. da Costa, L.R. Hutyra, P. Oliveira, S. Patiño, E.H. Pyle, A.L. Robertson, and L.M. Teixeira. 2009. Comprehensive assessment of carbon productivity, allocation and storage in three Amazonian forests. Global Change Biology 15: 1255-1274.

Rastetter, E.B. and G.R. Shaver. 1992. A model of multiple element limitation for acclimating vegetation. Ecology 73: 1157-1174.

Rastetter, E.B., R.D. Yanai, R.Q. Thomas, M.A. Vadeboncoeur, T.J. Fahey, M.C. Fisk, B.L. Kwiatkowski, and S.P. Hamburg. 2013. Recovery from disturbance requires resynchronization of ecosystem nutrient cycles. Ecological Applications 23(3): 621-642.

Restrepo-Coupe, N., H.R. da Rocha, L.R. Hutyra, A.C. da Araujo, L.S. Borma, B. Chrisoffersen, O.M.R. Cabral, P.B. da Camargo, F.L. Cardoso, A.C.L. da Costa, D.R. Fitzjarrald, M.L. Goulden, B. Kruijt, J.M.F. Maia, Y.S. Malhi, A.O. Manzi, S.D. Miller, A.D. Nobre, C. von Randow, L.D. Abreu Sá, R.K. Sakai, J. Tota, S.C. Wofsy, F.B. Zanchi, and S.R. Saleska. 2013. What drives the seasonality of photosynthesis across the Amazon basin? A cross-site analysis of eddy flux tower measurements from the Brasil flux network. Agricultural and Forestry Meteorology 182-183: 128-144.

This material is based in part upon work supported by the National Science Foundation under grant #s DEB 0949420 to E.R., DEB 0949370, DEB 1257391 and ICN 1342953 to C.N., and DEB 0918387 to S.P.  Additional support was provided by EPA STAR Award # FP-91749001-0 to R.C.N.  This work was also supported by Earth Lab at the University of Colorado-Boulder as part of the Grand Challenge Initiative (). Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, the Environmental Protection Agency or the University of Colorado-Boulder.