Scaling From Trees to Forests: Tractable Macroscopic Equations for Forest Dynamics

Strigul, Nikolay; Pristinski, Denis; Purves, Drew W.; Dushoff, Jonathan; Pacala, Stephen W.

doi:10.1890/08-0082.1

Cited by 232 publications

(364 citation statements)

References 84 publications

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“…First, the PPA model provides an accurate approximation to the dynamics of SORTIE when SORTIE is modified to include a realistic level of crown plasticity (19). Second, we found that a perfect-plasticity canopy model accurately reproduced the observed species and size dependencies of crown radius, crown depth, and canopy status (understory vs. canopy) in US forests (18).…”

Section: The Ppa Modelmentioning

confidence: 74%

“…The first step to creating the PPA from SORTIE was to replace this assumption of perfect rigidity with the assumption of perfect plasticity. According to perfect plasticity, each tree can place its crown area anywhere in the horizontal plane (19), altering its crown shape or even breaking its crown into discontinuous fragments. The assumption is spatially implicit, so the placement of the parcels of crown is not specified.…”

Section: The Ppa Modelmentioning

confidence: 99%

“…The height Z* needs to satisfy the logical condition that the total sun-exposed crown area is less than or equal to the ground area. Thus, Z* depends in a simple way on the height and crown geometry of the trees in the stand (18,19).…”

Section: The Ppa Modelmentioning

confidence: 99%

“…Unlike virtual forest models, the PPA can be parameterized from forest inventory data, is simple enough to simulate at broad geographic scales, and is mathematically tractable (18)(19)(20). The purpose of the work presented here was to assess whether the PPA can predict the dynamics of real forest communities.…”

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confidence: 99%

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Predicting and understanding forest dynamics using a simple tractable model

Purves

Lichstein²,

Strigul³

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

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The perfect-plasticity approximation (PPA) is an analytically tractable model of forest dynamics, defined in terms of parameters for individual trees, including allometry, growth, and mortality. We estimated these parameters for the eight most common species on each of four soil types in the US Lake states (Michigan, Wisconsin, and Minnesota) by using short-term (<15-year) inventory data from individual trees. We implemented 100-year PPA simulations given these parameters and compared these predictions to chronosequences of stand development. Predictions for the timing and magnitude of basal area dynamics and ecological succession on each soil were accurate, and predictions for the diameter distribution of 100-year-old stands were correct in form and slope. For a given species, the PPA provides analytical metrics for early-successional performance (H 20, height of a 20-year-old open-grown tree) and late-successional performance (Ẑ *, equilibrium canopy height in monoculture). These metrics predicted which species were early or late successional on each soil type. Decomposing Ẑ * showed that (i) succession is driven both by superior understory performance and superior canopy performance of late-successional species, and (ii) performance differences primarily reflect differences in mortality rather than growth. The predicted late-successional dominants matched chronosequences on xeromesic (Quercus rubra) and mesic (codominance by Acer rubrum and Acer saccharum) soil. On hydromesic and hydric soils, the literature reports that the current dominant species in old stands (Thuja occidentalis) is now failing to regenerate. Consistent with this, the PPA predicted that, on these soils, stands are now succeeding to dominance by other latesuccessional species (e.g., Fraxinus nigra, A. rubrum).climate change ͉ community ecology ͉ SORTIE ͉ TASS ͉ trade-offs

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Section: The Ppa Modelmentioning

confidence: 74%

Section: The Ppa Modelmentioning

confidence: 99%

Section: The Ppa Modelmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting and understanding forest dynamics using a simple tractable model

Purves

Lichstein²,

Strigul³

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

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228

203

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“…Further embedding evolution in ecological thinking would allow greater appreciation of the relationship between biological processes at the individual level and the population, community or ecosystem results of these processes. If novel environmental change imposes selection pressures that cause the components of an ecological system to evolve and if this evolutionary process affects the way that system functions, then evolution cannot be ignored.Despite the desirability of including evolution in ecological models, the most successful group of process-based models in ecology-the terrestrial biosphere models do not do so [27][28][29][30][31]. This may be due to the fact that their raison d'être is to predict changes in the forest community and so even though they have long simulated run times-typically 1000 years, the organisms within them also have long generation times.…”

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confidence: 99%

Predictive ecology: systems approaches

Evans

Norris

Benton

2012

Phil. Trans. R. Soc. B

117

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The world is experiencing significant, largely anthropogenically induced, environmental change. This will impact on the biological world and we need to be able to forecast its effects. In order to produce such forecasts, ecology needs to become more predictive-to develop the ability to understand how ecological systems will behave in future, changed, conditions. Further development of process-based models is required to allow such predictions to be made. Critical to the development of such models will be achieving a balance between the brute-force approach that naively attempts to include everything, and over simplification that throws out important heterogeneities at various levels. Central to this will be the recognition that individuals are the elementary particles of all ecological systems. As such it will be necessary to understand the effect of evolution on ecological systems, particularly when exposed to environmental change. However, insights from evolutionary biology will help the development of models even when data may be sparse. Process-based models are more common, and are used for forecasting, in other disciplines, e.g. climatology and molecular systems biology. Tools and techniques developed in these endeavours can be appropriated into ecological modelling, but it will also be necessary to develop the science of ecoinformatics along with approaches specific to ecological problems. The impetus for this effort should come from the demand coming from society to understand the effects of environmental change on the world and what might be performed to mitigate or adapt to them.Keywords: ecological modelling; prediction; climate change; evolution; systems biology; global circulation model 'We appeal to the notorious fact that ZOOLOGY, soon after the commencement of the latter half of the last century, was falling abroad, weighed down and crushed, as it were, by the inordinate number and manifoldness of facts and phenomena apparently separate, without evincing the least promise of systematizing itself by any inward combination, any vital interdependence of its parts'. Samuel Taylor Coleridge [1] The ability to predict, to forecast how a system might behave in the future, is a key feature of any science. Prediction is, according to, the Nobel laureate, immunologist and philosopher of science, Peter Medawar, a 'property that sets the genuine sciences apart from those that arrogate to themselves the title without really earning it' [2]. Some subjects, e.g. economics, engineering, climatology, routinely make predictions that others can use; and people are prepared to rely on them as a guide to the future, despite them sometimes, in the light of experience, being inaccurate. It was a premise of the Royal Society discussion meeting, upon which this volume is based, that ecology needs to become more predictive, especially in the context of creating an understanding of the way in which biological systems might respond to environmental change. It is well understood that making predictions about how any system wi...

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The role of local‐scale heterogeneities in terrestrial ecosystem modeling

et al. 2015

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The coarse‐grained spatial representation of many terrestrial ecosystem models hampers the importance of local‐scale heterogeneities. To address this issue, we combine a range of observations (forest inventories, eddy flux tower data, and remote sensing products) and modeling approaches with contrasting degrees of abstraction. The following models are selected: (i) Lund‐Potsdam‐Jena (LPJ), a well‐established, area‐based, dynamic global vegetation model (DGVM); (ii) LPJ‐General Ecosystem Simulator, a hybrid, individual‐based approach that additionally considers plant population dynamics in greater detail; and (iii) distributed in space‐LPJ, a spatially explicit version of LPJ, operating at a fine spatial resolution (100 m × 100 m), which uses an enhanced hydrological representation accounting for lateral connectivity of surface and subsurface water fluxes. By comparing model simulations with a multivariate data set available at the catchment scale, we argue that (i) local environmental and topographic attributes that are often ignored or crudely represented in DGVM applications exert a strong control on terrestrial ecosystem response; (ii) the assumption of steady state vegetation and soil carbon pools at the beginning of simulation studies (e.g., under “current conditions”), as embedded in many DGVM applications, is in contradiction with the current state of many forests that are often out of equilibrium; and (iii) model evaluation against vegetation carbon fluxes does not imply an accurate simulation of vegetation carbon stocks. Having gained insights about the magnitude of aggregation‐induced biases due to smoothing of spatial variability at the catchment scale, we discuss the implications of our findings with respect to the global‐scale modeling studies of carbon cycle and we illustrate alternative ways forward.

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Scaling From Trees to Forests: Tractable Macroscopic Equations for Forest Dynamics

Cited by 232 publications

References 84 publications

Predicting and understanding forest dynamics using a simple tractable model

Predicting and understanding forest dynamics using a simple tractable model

Predictive ecology: systems approaches

The role of local‐scale heterogeneities in terrestrial ecosystem modeling

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