Dynamics of Giant Kelp Forests: The Engineer of California

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Dynamics of Giant Kelp Forests The Engineer of
Californias Nearshore Ecosystems Dave Siegel,
Kyle Cavanaugh, Brian Kinlan, Dan Reed, Phaedon
Kyriakidis, Stephane Maritorena, Steve Gaines,
Kristin Landgren UC Santa BarbaraDick
Zimmerman Victoria HillOld Dominion University
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Macrocystis pyrifera Giant Kelp

• High economic ecological importance
• Ecosystem engineer of the nearshore ecosystems
• Source of natural products
• Dominant canopy forming macroalga in So Cal
• Highly dynamic
• Plant lifespans 2.5 years
• Frond life spans 4 months
• Fronds growth can be 0.5 m/day

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Macrocystis Fish Stocks

• Growth and mortality regulated by water temp,
  nutrients, light, depth, bottom type, predation,
  wave action

PDO Shift
El Nino
El Nino
Kelp biomass data from Kelco visual estimates
Fish observations from Brooks et al 2002
Reed et al. 2006
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Macrocystis Dynamics

• Growth
• Nutrients seawater temperature
• Mortality / Disturbance
• Wave action (esp. storms), senescence, predation,
  DOC release, etc.
• Colonization
• Spore dispersal, benthic light levels, depth,
  substrate type, etc.

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Research Goals

• Understand variability of giant kelp canopy cover
  carbon biomass
• High resolution satellite imagery (SPOT, AVIRIS,
  etc.) informed by SBC-LTER observations
• Develop models of kelp forest dynamics
• Light utilization gross / net primary
  production
• Patch dynamics models of canopy cover

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Research Area
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Remote Sensing of Macrocystis with Multispectral
Imagery

• Surface canopy of giant kelp exhibits high near
  infrared (NIR) reflectance
• SPOT imagery well suited to differentiate kelp

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Methods Canopy Cover

1. Perform dark pixel atmospheric correction
2. Principal components analysis to separate
   residual surface signal (PC1) from kelp (PC2)

PC band 1

• Positive contribution from all 3 bands
• Glint, sediment loads, atmosphere variations,
  etc.

False color SPOT image (8/15/2006)
PC band 2

• High NIR, low green and red reflectance
• Kelp

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Methods Canopy Cover Classification

• Minimum kelp threshold value selected from
  99.9th-tile value of offshore (35-60 m) pixels

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Validation Canopy Cover

• Cover measurements compared with high resolution
  2004 CDFG aerial kelp survey

SPOT Oct 29, 2004
CDFG Sept-Nov 2004
r2 0.98 p 0
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Kelp Occupation Frequency Jan 2006- May 2007

• 8 image dates
• 39 of occupied pixels were present in at least
  half the scenes
• 4 of pixels were present across all dates

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Kelp Forest Biomass

• Useful for understanding modeling ecosystem
  interactions
• NPP, turnover, export, etc.
• Difficult to measure directly
• Time and effort intensive
• BUT SBC-LTER does monthly surveys

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Research Area
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SBC-LTER Diver Surveys

• Monthly measurements of kelp forest attributes at
  Arroyo Quemado, Arroyo Burro Mohawk Area
• Assessment of areal kelp biomass, frond/blade
  density, net primary production, etc.
• Sampling for 160 m2 transect
• About 16 SPOT 5 pixels

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Seasonal kelp biomass changes along 3 LTER
transects

• Maximums in late 2002
• Wave driven seasonality apparent

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Methods Biomass

• Normalized Difference Vegetation Index (NDVI)
• (NIR-RED)
• (NIRRED)
• Calculated for areas of kelp cover

NDVI Transform
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Empirical Estimation of Kelp Biomass from SPOT

• Provides path to the remote estimation of kelp
  biomass (kg/m2)
• Enables
• regional assessment
• high temporal resolution views with multiple
  scenes

r2 0.71
r2 0.71 n 37
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Seasonal Kelp Forest Changes
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Regional Kelp Biomass

• Created from biomass-NDVI relationship for areas
  of kelp cover

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Validation using Visual Biomass Observations
r2 0.73 p lt 110-7
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• Spectra obtained from airborne inaging
  spectrometers are similar to lab measures of
  individual kelp blade reflectances

Optical estimates of kelp physiological state
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Area and Productivity Estimates Depend on Spatial
Resolution

• Bias increases as spatial resolution decreases
• Not a linear function of spatial resolution
• Resolution classes result from
• inherent scale of kelp patches
• spectral averaging as pixel size increases

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Metapopulation Modeling
Patch 16
Patch 17
gt500 m
Patch 18
Patch 19
Bed 28
Bed 27
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Next Steps

• Acquire as much imagery as possible
• Characterize kelp forest variability
• Patch-level description of occupancy, etc.
• Estimate regional scale kelp forest NPP
• Assess disturbance factors (waves, etc.)
• Space/time modeling of kelp cover biomass
• Predict probability of where / when kelp changes
• Driven by substrate / disturbance / etc.
• Compare kelp gross photosynthesis to NPP

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Thank You!!
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ISP Alginates Visual Kelp Biomass
Kelp canopy biomass, 34-year monthly time series
1000
100
10
0
Canopy Biomass (tons/km coast)
Raw data provided by D. Glantz, ISP Alginates,
Inc. Santa Barbara Coastal LTER
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Regional Kelp Biomass
11/2004 15000 metric tons
UCSB
11/2006 7800 metric tons
04/2007 22358 metric tons