Transcript Modeling Aquatic vegetation for Comal and San Marcos River

Modeling aquatic vegetation
for Comal and San Marcos
river systems
Todd M. Swannack, Ph.D.
US Army Engineer Research and Development Center
Environmental Laboratory
Vicksburg, MS
Vegetation in the River systems
• Comal and San Marcos vegetation is incredibly dynamic
(influenced by physical environment, existing distributions,
and human-driven factors)
• Major species for each system
Comal
• Hygrophila
• Ludwigia
• Sagittaria
• Vallisneria
• Bryophytes
• Cabomba
San Marcos
• Wild Rice
• Hydrilla
• Hygrophila
• Potamogeton
• Sagittaria
• Vallisneria
• Ludwigia
• Models need to capture dynamism of the system
Vegetation Changes 2003 – 2005
Summer 2003
Fall 2003
Spring 2004
Fall 2004
Spring 2005
Fall 2005
Model structure
• Spatially-explicit, agent-based model, programmed in
Netlogo
• Prototype: Old Channel, Comal River
• Spatial domain and scale: same as fountain darter
model, cell size of 0.25m2 (can upscale if needed)
• Temporal scale: varying, depending on the process
within the model. Also scalable (e.g., darter-plant
interactions may occur on a time scale that we
haven’t considered yet)
Major Processes
• Growth/Senescence (intra-cell dynamics)
• Dispersal (inter-cell dynamics)
• Recolonization after disturbance event (intercell dynamics)
Growth Modeling
• ERDC/MEGAPLANT
• Mass-balance, carbon flow, biomass
model (100+ parameters)
• Simulates above & belowground
biomass for single species
• Designed to simulate conditions in
which plants can persist or when plants
produce excessive biomass
• Inputs: temperature, irradiance, water
depth & transparency
• Outputs: biomass in various states
(tubers, roots, leaves & shoots)
Growth Modeling
• Cons of these models
• Spatially-implicit
• Dispersal is not in the model
• Single species (no competition)
• No current links b/w biomass and
existing data (spatial coverage)
• As they currently exist, ERDC models do
not address ecosystem-level questions
being asked for this project, and are over
parameterized for those questions
• Computationally intractable at fine
spatial scale of fountain darter model
Current Growth Modeling
• Simplify growth models to
capture critical components
• Add characteristics for
structure, native/non-native to
agent-class
• Current data indicate darters
are more often found in native
species (e.g., Vallsneria vs
Hygrophilia, which are
structurally similar)
• Simulate intracell growth
• N = biomass in cell i
• r = intrinsic growth rate
• κ = carrying capacity for each
cell (going to try to link this to
percent cover)
𝑵𝒊,𝒕+𝟏 = 𝑵𝒊,𝒕 + 𝒓𝒊 ∙ 𝑵𝒊,𝒕 ∙ 𝟏 − 𝑵𝒊,𝒕 ∙ 𝜿−𝟏
Dispersal Modeling
• What’s the probability that a plant
in cell j is colonized by plants from
cell i?
• Following Wang et al 2010, 2012
• Calculates dispersal based on
lognormal dispersal kernal(kji)
• D = Euclidian distance b/w j & I
• S = constant (shape parameter,
assumed 1)
• L = dispersal velocity (scale
parameter)
• Recolonization will be treated the
same way
: 𝑁𝑗,𝑡+1 = 𝑘𝑗𝑖 ∙ 𝑁𝑗,𝑡
Conceptual Model
𝑞
𝑁𝑖,𝑡+1 = 𝑁𝑖,𝑡 + 𝑟𝑖 ∙ 𝑁𝑖,𝑡 ∙ 1 − 𝑁𝑖,𝑡 ∙ κ−1 +
𝑘𝑗𝑖 ∙ 𝑁𝑗,𝑡
𝑗=1,𝑗≠𝑖
Integrated System
Intracell growth
: 𝑁𝑖,𝑡+1 = 𝑁𝑖,𝑡 + 𝑟𝑖 ∙ 𝑁𝑖,𝑡 ∙ 1 − 𝑁𝑖,𝑡 ∙ κ−1
Intercell dispersal
: 𝑁𝑗,𝑡+1 = 𝑘𝑗𝑖 ∙ 𝑁𝑗,𝑡
Model Programing
1 Initialization
2 Input
2.1 Input vegetation maps
2.2 Input physical parameters
2.3 Input water depths and water velocities
3 Submodels
3.1 Update physical parameters (daily)
3.2 Calculate growth and senescence
3.3 Calculate dispersal
3.4 Update aggregated variable (output)
Model evaluation
• Submodels will be evaluated for ability to simulate field
dynamics within reasonable range of error
• System model will be evaluated using pattern-oriented
modeling (following work of Grimm, Railsback, Topping,
and colleagues)
• Basic principle is to identify emergent patterns
that result from interactions of model
components and compare those patterns to
patterns in the real system
Questions?
• Contact information:
• [email protected]
• 601-415-3509