A Method for Scaling Vegetation Dynamics: The Ecosystem Demography Model (ED)

Abstract

The problem of scale has been a critical impediment to incorporating important fine-scale processes into global ecosystem models. Our knowledge of fine-scale physiological and ecological processes comes from a variety of measurements, ranging from forest plot inventories to remote sensing, made at spatial resolutions considerably smaller than the large scale at which global ecosystem models are defined. In this paper, we describe a new individual-based, terrestrial biosphere model, which we label the ecosystem demography model (ED). We then introduce a general method for scaling stochastic individual-based models of ecosystem dynamics (gap models) such as ED to large scales. The method accounts for the fine-scale spatial heterogeneity within an ecosystem caused by stochastic disturbance events, operating at scales down to individual canopy-tree-sized gaps. By conditioning appropriately on the occurrence of these events, we derive a sizeand age-structured (SAS) approximation for the first moment of the stochastic ecosystem model. With this approximation, it is possible to make predictions about the large scales of interest from a description of the fine-scale physiological and population-dynamic processes without simulating the fate of every plant individually. We use the SAS approximation to implement our individual-based biosphere model over South America from 15Њ N to 15Њ S, showing that the SAS equations are accurate across a range of environmental conditions and resulting ecosystem types. We then compare the predictions of the biosphere model to regional data and to intensive data at specific sites. Analysis of the model at these sites illustrates the importance of fine-scale heterogeneity in governing large-scale ecosystem function, showing how population and community-level processes influence ecosystem composition and structure, patterns of aboveground carbon accumulation, and net ecosystem production.

Revisiting the Two-Layer Hypothesis: Coexistence of Alternative Functional Rooting Strategies in Savannas

“…I assumed a maximum rooting depth of 70 cm and divided the soil space into eight layers, as follows: 0–5 cm, 5–10 cm, and in 10-cm increments thereafter. The soil moisture dynamics in each layer i are determined by an ordinary differential equation:where S i is the relative moisture saturation of soil layer i , I i is infiltration (in mm d −1 ) from the layer above (in the case of layer 1, I is rainfall input), K sat (in mm d −1 ) and τ are texture-dependent parameters that determine the rate of water infiltration to deeper layers [39], E i is evaporation, T i,k is transpiration (both in mm d −1 ) of rooting profile k from soil layer i , n is soil porosity, D i is the depth of soil layer i (in mm), and S f is the field capacity of the soil. The second term on the r.h.s.…”

Synergistic Ecoclimate Teleconnections from Forest Loss in Different Regions Structure Global Ecological Responses

“…Because any given tree loss event has the potential to affect ecoclimate teleconnections, and tree loss is occurring simultaneously in disparate regions, assessments are needed that account for concurrent tree loss events to assess whether ecological responses differ from those of individual loss events. Ecological effects can vary among remote teleconnected locations [14] and can be highly sensitive to changing climate regimes [27], although mechanisms associated with given ecological responses have not generally been explicitly evaluated. To address future challenges related to climate change and land use, it will be important to assess the local impacts of tree loss on climate, climatic and ecological consequences, as well as the mechanisms that drive ecological responses in remote teleconnected areas.…”

Integrating Evolution into Ecological Modelling: Accommodating Phenotypic Changes in Agent Based Models

“…It is difficult to envisage how such forecasts could be produced using anything other than computer models and that, because of the need to project them into novel future conditions, such models would need to be process-based rather than phenomenological [1,4]. While for long-lived organisms (relative to the length of the model run) it may be possible to ignore evolutionary change (as is done for trees in forest gap models [34,35], in most cases it will be desirable to include the capacity for adaptation through either phenotypic plasticity or evolution. This seems to be done only rarely [5].…”