Implementation of the perfect plasticity approximation with biogeochemical compartments in R

Modeling forest ecosystems is a landmark challenge in science, due to the complexity of the processes involved and their importance in predicting future planetary conditions. While there are a number of open‐source forest biogeochemistry models, few papers exist detailing the software development approach used to develop these models. This has left many forest biogeochemistry models large, opaque and/or difficult to use, typically implemented in compiled languages for speed. Here, we present a forest biogeochemistry model from the SORTIE‐PPA class of models, PPA‐SiBGC. Our model is based on the perfect plasticity approximation with simple biogeochemistry compartments and uses empirical vegetation dynamics rather than detailed prognostic processes to drive the estimation of carbon and nitrogen fluxes. This allows our model to be used with traditional forest inventory data, making it widely applicable and simple to parameterize. We detail the conceptual design of the model as well as the software implementation in the R language for statistical computing. Our aim is to provide a useful tool for the biogeochemistry modeling community that demonstrates the importance of vegetation dynamics in biogeochemical models.

[1]  Stephen Sitch,et al.  Evaluation of terrestrial carbon cycle models through simulations of the seasonal cycle of atmospheric CO2: First results of a model intercomparison study , 1998 .

[2]  S. Manabe.  CLIMATE AND THE OCEAN CIRCULATION1 , 1969 .

[3]  C. Potter,et al.  Global patterns of carbon dioxide emissions from soils on a 0.5-degree-grid-cell basis , 1995 .

[4]  S. Malyshev,et al.  Scaling from individual trees to forests in an Earth system modeling framework using a mathematically tractable model of height-structured competition , 2015 .

[5]  S. Wofsy,et al.  Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2 , 2009 .

[6]  A. Dalcher,et al.  A Simple Biosphere Model (SIB) for Use within General Circulation Models , 1986 .

[7]  R. Macarthur,et al.  COMPETITION, HABITAT SELECTION, AND CHARACTER DISPLACEMENT IN A PATCHY ENVIRONMENT. , 1964, Proceedings of the National Academy of Sciences of the United States of America.

[8]  S. Pacala,et al.  Forest models defined by field measurements : Estimation, error analysis and dynamics , 1996 .

[9]  Sergey Levine,et al.  Adapting Deep Visuomotor Representations with Weak Pairwise Constraints , 2015, WAFR.

[10]  Ii.,et al.  CLIMATE AND THE OCEAN CIRCULATION , 1969 .

[11]  J. Dushoff,et al.  SCALING FROM TREES TO FORESTS: TRACTABLE MACROSCOPIC EQUATIONS FOR FOREST DYNAMICS , 2008 .

[12]  J. L. Clutter.  Compatible growth and yield models for loblolly pine , 1963 .

[13]  Ann Henderson-Sellers,et al.  Biosphere-atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model , 1986 .

[14]  J. R. Wallis,et al.  Some ecological consequences of a computer model of forest growth , 1972 .

[15]  A. J. Lotka.  Analytical Note on Certain Rhythmic Relations in Organic Systems , 1920, Proceedings of the National Academy of Sciences.

[16]  J. W. Moser,et al.  Deriving Growth and Yield Functions for Uneven-Aged Forest Stands , 1969 .

[17]  T. Malthus.  An essay on the principle of population, as it affects the future improvement of society , 2006 .

[18]  Jerome K. Vanclay,et al.  Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests , 1994 .

[19]  Bruno Moulia,et al.  Wind loads and competition for light sculpt trees into self-similar structures , 2017, Nature Communications.

[20]  Hong S. He,et al.  Are more complex physiological models of forest ecosystems better choices for plot and regional predictions? , 2016, Environ. Model. Softw..

[21]  N. Strigul.  Individual-Based Models and Scaling Methods for Ecological Forestry: Implications of Tree Phenotypic Plasticity , 2012 .

[22]  S. Levin,et al.  Terrestrial models and global change: challenges for the future , 1998 .

[23]  R. Levins.  The strategy of model building in population biology , 1966 .

[24]  Benjamin Smith,et al.  Vegetation demographics in Earth System Models: A review of progress and priorities , 2018, Global change biology.

[25]  H. Buckley,et al.  When a Foundation Crumbles: Forecasting Forest Dynamics Following the Decline of the Foundation Species Tsuga Canadensis , 2017 .

[26]  R. Buckman.  Growth and Yield of Red Pine in Minnesota , 1962 .

[27]  N. Strigul,et al.  A forest biogeochemistry model intercomparison on the East Coast of the United States , 2018 .

[28]  C. Canham,et al.  Measurement and modeling of spatially explicit variation in light transmission through interior cedar-hemlock forests of British Columbia , 1999 .

[29]  C. Potter,et al.  Interannual variability in global soil respiration, 1980–94 , 2002 .

[30]  H. Bugmann.  A Review of Forest Gap Models , 2001 .

[31]  J. Mbabazi.  Forecasting Forest Futures: A Hybrid Modelling Approach to the Assessment of Sustainability of Forest Ecosystems and Their Values , 2011 .

[32]  Juan A. Blanco,et al.  Complexity in modelling forest ecosystems: How much is enough?☆ , 2008 .

[33]  Alan R. Ek,et al.  Process-based models for forest ecosystem management: current state of the art and challenges for practical implementation. , 2000, Tree physiology.

[34]  H. Shugart.  A Theory of Forest Dynamics , 1984 .

[35]  G. Domke,et al.  Toward inventory-based estimates of soil organic carbon in forests of the United States. , 2017, Ecological applications : a publication of the Ecological Society of America.

[36]  M. P.R.,et al.  A METHOD FOR SCALING VEGETATION DYNAMICS: THE ECOSYSTEM DEMOGRAPHY MODEL (ED) , 2022 .

[37]  L. Heath,et al.  Updated generalized biomass equations for North American tree species , 2014 .

[38]  H. Shugart.  A Theory of Forest Dynamics , 1984 .