Modeling aquatic vegetation for Comal and San Marcos river systems

1. 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

2. 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 • San Marcos • Wild Rice • Hydrilla • Hygrophila • Potamogeton • Sagittaria • Vallisneria • Ludwigia • Comal • Hygrophila • Ludwigia • Sagittaria • Vallisneria • Bryophytes • Cabomba

3. Vegetation Changes 2003 – 2005 Summer 2003 Fall 2003 Fall 2004 Spring 2004 Spring 2005 Fall 2005

4. 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)

5. Major Processes • Growth/Senescence (intra-cell dynamics) • Dispersal (inter-cell dynamics) • Recolonization after disturbance event (inter-cell dynamics)

6. 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)

7. 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

8. 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)

9. Dispersal Modeling • What’s the probability that a plant in cell j is colonized by plants from celli? • 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 :

10. Conceptual Model Integrated System Intracell growth Intercell dispersal : :

11. Model Programing 1 Initialization 2 Input 2.1 Input vegetation maps 2.2 Input physical parameters 2.3 Input water depths and water velocities 3Submodels 3.1 Update physical parameters (daily) 3.2 Calculate growth and senescence 3.3 Calculate dispersal 3.4 Update aggregated variable (output)

12. 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

13. Questions? • Contact information: • Todd.m.swannack@usace.army.mil • 601-415-3509