![Page 1: Modeling aquatic vegetation for Comal and San Marcos river systems Todd M. Swannack, Ph.D. US Army Engineer Research and Development Center Environmental](https://reader036.vdocument.in/reader036/viewer/2022070410/56649f175503460f94c2e28f/html5/thumbnails/1.jpg)
Modeling aquatic vegetation for Comal and San Marcos river systems
Todd M. Swannack, Ph.D.US Army Engineer Research and Development CenterEnvironmental LaboratoryVicksburg, MS
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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
• Models need to capture dynamism of the system
Comal• Hygrophila• Ludwigia• Sagittaria• Vallisneria• Bryophytes• Cabomba
San Marcos• Wild Rice• Hydrilla• Hygrophila• Potamogeton• Sagittaria• Vallisneria• Ludwigia
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Vegetation Changes 2003 – 2005
Summer 2003 Fall 2003 Spring 2004 Fall 2004 Spring 2005 Fall 2005
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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)
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Major Processes• Growth/Senescence (intra-cell dynamics)
• Dispersal (inter-cell dynamics)
• Recolonization after disturbance event (inter-cell dynamics)
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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)
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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
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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)
𝑵 𝒊 , 𝒕+𝟏=𝑵 𝒊 , 𝒕+𝒓 𝒊 ∙𝑵 𝒊 ,𝒕 ∙ [𝟏−𝑵 𝒊 , 𝒕 ∙𝜿−𝟏 ]
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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
:
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Conceptual Model
: :
𝑁 𝑖 ,𝑡+1=𝑁 𝑖 , 𝑡+𝑟 𝑖∙𝑁 𝑖 ,𝑡 ∙ [1−𝑁 𝑖 ,𝑡 ∙ κ− 1 ]+ ∑
𝑗=1, 𝑗≠ 𝑖
𝑞
𝑘 𝑗𝑖 ∙𝑁 𝑗 ,𝑡
Intracell growth Intercell dispersal
Integrated System
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1 Initialization
2 Input2.1 Input vegetation maps
2.2 Input physical parameters
2.3 Input water depths and water velocities
3 Submodels3.1 Update physical parameters (daily)
3.2 Calculate growth and senescence
3.3 Calculate dispersal
3.4 Update aggregated variable (output)
Model Programing
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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