Predictive 

Vegetation 

Modeling: 

Progress and 

Opportunities for 

Growth 

Brad Butterfield1 and Emily Palmquist2 

1 Department of Biological Sciences, Northern Arizona University 

2 US Geological Survey, Southwest Biological Science Center, Grand 

Canyon Monitoring and Research Center, Flagstaff 

This information is preliminary and is subject to revision. It is being provided to meet the need for timely best science. The 

information is provided on the conditions that neither the U.S. Geological Survey nor the U.S. Government shall be held liable 

for any damages resulting from the authorized or unauthorized use of the information. 
Project C: Riparian Vegetation Monitoring and 

Research

• Project Elements and Objectives

• C.1 Ground-based Riparian Vegetation Monitoring

• C.2 Determining Hydrological Tolerances and Management Tools for Plant 

Species of Interest

• C.3 Predictive Models and Synthesis

• C.4 Vegetation Management Decision Support

• Funding amount and source: $267,602 (AMP)

• Cooperators: Northern Arizona University

• LTEMP Resource goals: “Maintain native vegetation and wildlife 

habitat, in various stages of maturity, such that they are diverse, 

healthy, productive, self-sustaining, and ecologically appropriate.”

Preliminary Information – Subject to Revision. Not for Citation or Distribution.
Riparian Vegetation: 

Interface Between 

Aquatic and Terrestrial 

Systems

Vegetation interacts with multiple 

resources

• Sand

• Recreation

• Archaeological and Tribal

Photo credit: Brad Butterfield Preliminary Information – Subject to Revision. Not for Citation or Distribution.
Ground-Based Monitoring

• Backbone of our modeling

• More than 17000 plots surveyed since 2014

• From near channel to pre-dam floodplain habitats

• Sandbars, debris fans, and channel margins

• NAU sandbars and throughout the Canyon

Photo credit: Grand Canyon 

Monitoring and Research Center Preliminary Information – Subject to Revision. Not for Citation or Distribution.
We’re Good at Predicting Current Vegetation

• Vegetation composition is highly 

predictable based on current 

hydrology and climate in the Colorado 

River ecosystem (CRe)

• But large departures from the current 

conditions will require extrapolation

The 

Environment 

We Know

The Environment We Might Experience

Hydrology

Climate

Preliminary Information – Subject to Revision. Not for Citation or Distribution.
Veg Modeling Challenges and Solutions

Challenge Solution

• Altered hydrograph

• Correlation ≠ causation

• Vegetation interacts with other 

resources (e.g. sand)

• Use data from other river 

systems with similar species, but 

different hydrographs

• Conduct experiments that 

control for multiple correlated 

factors

• Develop dynamic, integrative 

models across systems

Preliminary Information – Subject to Revision. Not for Citation or Distribution.
1 | Models from Ground-Based Monitoring 

Preliminary Information – Subject to Revision. Not for Citation or Distribution. Photo credit: Grand Canyon Monitoring and Research Center
Riparian Hydrological Zones

1 | Models from Ground-Based Monitoring 

Preliminary Information – Subject to Revision. Not for Citation or Distribution. Photo credit: Victor Leshyk
1 | Models from Ground-Based Monitoring 

Strong effects of:

• Elevation above river stage

• Minimum temperature

Preliminary Information – Subject to Revision. Not for Citation or Distribution.
Arrowweed (Pluchea sericea)

1 | Models from Ground-Based Monitoring 

River Stage (m)

Annual Frequency

How Much Suitable Habitat?

y

Veg Monitoring + Environmental Data Predicted Habitat Suitabilit

Preliminary Information – Subject to Revision. Not for Citation or Distribution.

Arrowweed at Kwagunt Marsh

Predicted Habitat Suitability
1 | Models from Ground-Based Monitoring 

How Much Suitable Habitat?

Veg Monitoring + Environmental Data

River Stage (m)

Annual Frequency

Arrowweed (Pluchea sericea)

Predicted Habitat Suitability

Preliminary Information – Subject to Revision. Not for Citation or Distribution.

Arrowweed at Kwagunt Marsh

Predicted Habitat Suitability
How Much Suitable Habitat?

Arrowweed at Kwagunt Marsh

Predicted Habitat Suitability

Arrowweed (Pluchea sericea)

Predicted Habitat Suitability

River Stage (m)

Annual Frequency

Preliminary Information – Subject to Revision. Not for Citation or Distribution.

Veg Monitoring + Environmental Data

1 | Models from Ground-Based Monitoring 
1 | Models from Ground-Based Monitoring 

How Much Suitable Habitat?

Veg Monitoring + Environmental Data

Arrowweed at Kwagunt Marsh

Predicted Habitat Suitability

Arrowweed (Pluchea sericea)

Predicted Habitat Suitability

River Stage (m)

Annual Frequency

Preliminary Information – Subject to Revision. Not for Citation or Distribution.
1 | Models from Ground-Based Monitoring 

How Much Suitable Habitat?

Veg Monitoring + Environmental Data

Arrowweed at Kwagunt Marsh

Predicted Habitat Suitability

Arrowweed (Pluchea sericea)

Predicted Habitat Suitability

Preliminary Information – Subject to Revision. Not for Citation or Distribution.
1 | Models from Ground-Based Monitoring 

Flow Scenarios

• Can generate composite 

metrics (e.g. native riparian 

shrubs, species richness, 

etc.)

• Project under different flow 

scenarios

• E.g. Kasprak, Sankey and 

Butterfield. 2021. 

Environmental Research 

Letters

Preliminary Information – Subject to Revision. Not for Citation or Distribution.
2 | Expanding Predictive Ability

Butterfield, Palmquist and Yackulic. 2022. River Research and Applications Preliminary Information – Subject to Revision. 

Not for Citation or Distribution.
2 | Expanding Predictive Ability 

Flow Seasonality has Shaped the CRe Species 

Pool

• Models predict which species occur most 

frequently in the CRe

• Predicted species richness is greatest with 

high summer flows

• Species that have expanded in recent 

decades (e.g. Baccharis, arrowweed) are 

predicted to respond positively to high 

summer flows

High Streamflow Month

Low Streamflow Month

Predicted Habitat Suitability 

under Different Flow Scenarios

Preliminary Information – Subject to Revision. Not for Citation or Distribution.
2 | Expanding Predictive Ability 

Drought or Inundation: Which Determines 

Species Distributions?

Preliminary Information – Subject to Revision. Not for Citation or Distribution. Image credit: Victor Leshyk
2 | Expanding Predictive Ability 

Breaking the Correlation between Drought 

and Inundation with Controlled Experiments

Photo credit: Grand Canyon Monitoring and Research Center Preliminary Information – Subject to Revision. Not for Citation or Distribution.
2 | Expanding Predictive Ability 

Photo credit: Grand Canyon Monitoring and Research Center

Measurements

• Stomatal conductance to water vapor

• New root growth

Preliminary Information – Subject to Revision. Not for Citation or Distribution.
2 | Expanding Predictive Ability 

Shaped by Inundation, 

Vulnerable to Drought?

• Divergent responses to drought

• Predict idiosyncratic responses 

to low flows

• More systematic responses to 

inundation

• Inundation has shaped the 

current vegetation, but perhaps 

less relevant in the future

Preliminary data, do not cite

BAEM = Baccharis emoryii, EQFE = Equisetum x ferrissii, PHAU = Phragmites australis, PLSE = Pluchea

sericea, PRGL = Prosopis glandulosa, SAEX = Salix exigua, SEGR = Senegalia greggii, TARA = Tamarix

Preliminary Information – Subject to Revision. Not for Citation or Distribution.
3 | Integration: Complementary Approaches

Tool Strengths Weaknesses

Hydrological Niche Modeling 

(from ground-based 

monitoring data)

Fine-resolution prediction of 

species responses to dam 

operations

Limits to extrapolation under 

new environmental conditions

Hydrograph Seasonality

(from rivers across the 

western US)

Extrapolate to new climate 

conditions and dam 

operations based on extensive 

data

Coarse resolution

Mechanistic Modeling

(from controlled experiments)

Differentiate among drivers 

that may exhibit different 

collinearity or range in the 

future (i.e. low-flow vs. highflow anomalies) 

Connecting controlled 

experiments to real-world 

predictions

3 | Integration 

Preliminary Information – Subject to Revision. Not for Citation or Distribution.
3 | Integration 

Additional Vegetation Models and Data Types

• Other models I did not touch on today

• Emily’s Bayesian models differentiate habitat suitability from abundance

• Models that incorporate traits to improve predictions for uncommon species

• Models that allow us to infer interactions (competition and facilitation) 

among species

• Experiments

• Spring disturbance flows

(manuscript in review)

• Hydropeaking greenhouse experiment

coming summer of ‘23

Preliminary Information – Subject to Revision. Not for Citation or Distribution. Photo credit: Freshwaters Illustrated
3 | Integration 

Biological-Physical 

Linkages

• Aeolian and alluvial processes have 

strong feedbacks with the biological 

system

• Plants trap both wind- and waterborne 

sediment

• Changes in sediment affect plant growth 

and survival

• Having all three components 

interacting in a dynamic model would 

be a big step forward in predictive 

ability and mechanistic understanding

Preliminary Information – Subject to Revision. Not for Citation or Distribution. Photo credit: Brad Butterfield