1. Modelling Algal Sloughing in Streams
Nicholas J. Banish

2. Slough + Transport PAR PARₒ N P N N e^⁻k(z) P P P ᴢPAR
Solar data can be input directly, or also modeled using latitude, time of year, and cloud cover
PAR
PARₒ
= fixed fraction of global horizontal solar radiation N P N
particle absorb + scatter N
e^⁻k(z) P P P
ᴢPAR
Threshold biomass
Biomass modeled using physiology functions for maximal algal standing crop
Slough + Transport
Timing
Magnitude
Duration
Composition
(AFDM g m¯²)
100
300
200
Cladophora BIomass
Phenology

3. Factors that Influence benthic algae
Exogenous Drivers
Endogenous Drivers
Water Chemistry
(nutrients, TN, TP, DOC, etc.)
Phenology Template
Algal Physiology
Physical environment
(substrate, depth)
Light
Nutrients (allocation and turnover)
Light (P-I relationships, light use efficiency)
Temperature (metabolism, effect on kinetics)
Net Biomass Accural

4. Domain and Objective Domain Objective:
Timing , magnitude, duration of algal transport events
Examines mass-sloughing and transport of attached benthic algae in rivers of western Montana with high abundance of Cladophora and the character of episodes of algal drift. Additionally can quantify material flux (OM, contaminants)
The model is a predictor of the effect of light conditions in streams to the occurrence of one phase of lotic algal phenology. It takes into account regulated hydrographs of these streams, but negates hydraulic influences on sloughing, and instead incorporates plant physiology response to light.
Regulated, low, clear baseflows are exemplary growing conditions. Ideal for use on streams that are characterized by prolonged steady and baseflows.
Objective:
It quantifies the level of biomass given (Flynn, Chapra, Suplee), but also predicts that at a given level of biomass during baseflow conditions, light attenuation in the form of self-shading triggers autogenic mass-sloughing
The model attempts to predict, given loadings of biomass and site characteristics , the timing, magnitude, and duration of episodes of algal drift.
If warranted, the composition of that material for a load calculation.

5. Assumptions:
The model assumes that sloughing occurs due to excessive algae growth and is a community response to light-limitation
Self-shading is the causal mechanism
Light is predominant driver that causes sloughing
not flow, not shear, not changes in these; not grazing, not epiphyte loading influencing drag or capturing photons

6. Spatial and Temporal Scales
Spatial scale:
Constraints on the spatial scale of this model deal with the size of stream in question . Model application is limited primarily by depth.
Rivers deeper than 1 m have high light attenuation, so role of light in triggering sloughing is thought not to be of great importance. Model is specific to shallow rivers with low clear base flows.
Substratum composition must be relatively large and immobile.
Temporal Scale:
Constraints of temporal scale deal with algal phenology; the timing of onset of Cladophora bloom is largely a function hydrograph character.
Timing of the hydrograph is important.
Earlier the peak,= earlier the bloom.
Longer sustained duration of low flow= larger and more dominant the bloom.
Specific to high-magnitude nuisance bloom years.
Also doesn’t really work well to predict heterogeneous algal patch dynamics, specific to monotypic Cladophora patches

7. What are high densities?
mid-July, Upper Clark Fork River at Bear Gulch Bridge
~ 90 g/m2 AFDM; Chla ~ 200 mg/m2
peak biomass ranges from g/m2 AFDM; Chla in excess of 600 mg m2 for UCFR

8. Algal Physiology
Cladophora biomass in regulated hydrograph systems limited by light and nutrient availability

9. Light
Light limitation determined by amount of PAR reaching the river bottom
exponential decay function of the form PAR(z)=p(initial) e^-k(z)
par (O) = light at surface, PAR(z) is light at z meters below surface.
k is light extinction coefficient
Effect of light on photosynthesis is modeled by Michaelis–Menten half-saturation model
single parameter P-I CURVE relfect saturation or photoinhibition
Absorption and Scattering by particles in water “partial extinction coefficients”
light extinction coefficients for separate elements that attenuate light delivery
for water and color of water
inorganic solids such as sand and clay particles cause light scattering
organic detritus
phytoplankton
These are “forcing functions”, manipulated based can changes in conditions (more material, more light attenuation) *positive feedback mechanism*

10. Similarities in physiognomic structure
(vertical development and stratification)
Nutrients
Light
Light
Light limiting (sun/shade fraction)
Nutrients
(sediments)
Nutrients
Idealized structure and resource supply in benthic algae and terrestrial plants. Shaded triangles indicate direction of resource attenuation. In algae, both light and nutrients enter the assemblage from above, suggesting similar adaptive traits for the acquisition of both nutrients. In contrast, different adaptive traits and patterns of energy allocation are required for the acquisition of light and nutrients by terrestrial plants.

11. Nutrients Droop (1973) model for nutrient limitation:
algae cell quotas are for N and P
no co-limitation
also exchange of nutrient balance that reflects of internal nutrient stores and allocation for growth, and excretion (mineralization)
Adaption of M-M enzyme kinetic model

12. Model Inputs Output: biomass = Chla/AFDM standing crop
area of streambed occupied by Cladophora
Nutrient conditions for inorganic N and P in water column
concentrations of light scattering particles and assc. light extinction parameters
temperature, govern kinetics
physical data (depth)
Output:
Slough (timing, magnitude, duration) = f(Peak Biomass, % Coverage, H₂O nutrient conc., kPAR, °C, z)

13. Slough + Transport PAR PARₒ N P N N e^⁻k(z) P P P ᴢPAR
Solar data can be input directly, or also modeled using latitude, time of year, and cloud cover
PAR
PARₒ
= fixed fraction of global horizontal solar radiation N P N
particle absorb + scatter N
e^⁻k(z) P P P
ᴢPAR
Threshold biomass
Biomass modeled using physiology functions for maximal algal standing crop
Slough + Transport
Timing
Magnitude
Duration
Composition
(AFDM g m¯²)
100
300
200
Cladophora BIomass
Phenology

14. Theory: Patch Succession? Slough deposition maintenance hypothesis
Metals contaminant transport via algal drift? insects emerging at this time have elevated levels of metals the export to terrestrial environment?
A drift dodging trout (nuisance or enhanced conveyor belt?)
Nutrient trapping, ecosystem engineer?