LD1-4: Mammalian herbivores contribute to the resilience
of the floodplain hardwood-conifer mosaic by accelerating the turnover of early
successional stands (where establishment can occur) and increasing the
landscape distribution of late-successional white spruce stands (which provide
a conifer seed source).
Research Methods
Modeling
We wish to conduct field experiments and modeling in parallel. Our modeling efforts will serve as much both to generate hypothesies and for synthesis. Thus, modeling and fieldwork will be structured as interactive activities as opposed to the common research scenario of separate, non-overlapping activities. We feel this interactive approach is critical to the successful synthesis of the proposed science. We plan to employ a modified version of a spatially explicit model, the Alaskan Frame-based Ecosystem Model Code (ALFRESCO), of transient vegetation dynamics developed for transition states of ecological processes.
We have previously developed a floodplain forest primary succession model version that examines the interactive effects of fluvial dynamics and herbivory as primary drivers of spatio-temporal vegetation composition. Here we propose to develop a detailed model picture of the establishment and growth of white spruce, based on field measurements including species physiology and demography, microclimate, and soil chemistry. The model will simulate the life history of white spruce from seed production through establishment, and its response to disturbance and disturbance-induced changes in microclimate (mediated by changes in species abundance and community structure).
Frame-based modeling paradigm
The
frame-based modeling paradigm can be thought of as an extension of the concept
of cellular automata. In this
application, a landscape matrix is composed of discrete cells called
frames. As with cellular automata, each
frame has a state and a set of rules governing its behavior. In contrast to cellular automata, however,
where cells typically store only small amounts of data, frames can store
considerable amounts of data, e.g. age, date of last disturbance, site
conditions, growth conditions, cumulative climate variables, etc. This does not imply, however, that a cell
exists in isolation. The rules
governing a frame can also depend on the state (including internal data) of the
adjacent cells.
Frame
based modeling differs most from cellular automata, however, in that each cell
may have one of a number of sets of rules applied depending on its
frame-type. When a cell changes
frame-type the whole paradigm it is working under shifts, i.e. both the data it
stores and the rules it operates by.
This facilitates modeling of complex systems because it is possible to
consider only the most dominant processes governing a cell and the data
associated with those processes, and assign that cell the relevant
frame-type. The frame then operates
under that paradigm until the rules indicate it is time to change to a
different frame type.
Fig. 1. Conceptual
diagram of frame-based model. Colored boxes represent frame types (i.e.,
floodplain community types). Arrows identify transitions, which are labeled by
primary operating processes. System drivers include flooding, herbivory, and
fire.
ALFRESCO can be used as an example to clarify the frame-based paradigm. Our basic model consists of five frame-types representing idealized floodplain community types: bare silt, willow, alder, poplar, and white spruce. The design of each frame-type is derived to account for the most important processes occurring in that frame type given our scale of interest and objectives. Each cell is assigned an initial frame-type, and during model execution may or may not change frame type depending on a number of various, and sometimes complicated, factors (see conceptual model Fig. 1).
Model components:
Our model will simulate primary floodplain forest succession from initial colonization of bare silt bars to mature climax white spruce forest. The model will focus on the primary life history stages of white spruce from seed production to seedling establishment – identifying the factors and processes that control both the reproductive success and rate of transition from newly exposed silt bars to mature commercially valuable stands. We will use the fieldwork to inform the model and the model to direct the fieldwork, in an iterative, adaptive fashion.
The focus of this study will be to develop a detailed story of early successional dynamics on floodplain communities and then extrapolate forward in time to make inferences on the fate of mature white spruce stands – valued in the local/regional timber products market. The model will aim to identify interactions and feedbacks between herbivory, microclimate, and succession. Important components of the life history story include:
- Seed Production – episodic, with large variance in numbers and viability.
- Germination – disturbance induced direct mortality and response to specific microclimatic factors, which are mediated by canopy structure, density, etc.
- Survival – similar if not the same as the germination component.
- Establishment – site conditions, disturbance effects, and canopy development.
- Disturbance – we will model disturbance effects on stand development, but not the physical processes of disturbance per se; flooding, insects, fire, herbivory, and logging.
Model simulations:
- Examine interactions between combinations of initial site conditions, disturbance type and effects, and evaluate the response in terms of stand development to a commercially valuable status[sensitivity analysis].
- Test the possible implications of a changing climate and disturbance regime, and future sustained increases in logging pressure[scenario development].