Dynamics of Giant Kelp Forests: The Engineer of California’s Nearshore Ecosystems Dave Siegel, Kyle Cavanaugh, Brian Kinlan, Dan Reed, Phaedon Kyriakidis,
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Dynamics of Giant Kelp Forests: The Engineer of California’s
Nearshore Ecosystems
Dave Siegel, Kyle Cavanaugh, Brian Kinlan, Dan Reed, Phaedon Kyriakidis, Stephane Maritorena, Steve Gaines, Kristin Landgren
UC Santa Barbara
Dick Zimmerman & Victoria HillOld Dominion University
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
Macrocystis & Fish Stocks
• Growth and mortality regulated by water temp, nutrients, light, depth, bottom type, predation, wave action
Kelp biomass data from Kelco visual estimates; Fish observations from Brooks et al 2002
Reed et al. [2006]
El N
ino
El N
ino
PD
O S
hift
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.
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
Research Area
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
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
PC band 2
False color SPOT image(8/15/2006)
• Positive contribution from all 3 bands• Glint, sediment loads, atmosphere variations, etc.
• High NIR, low green and red reflectance• Kelp
Methods: Canopy Cover Classification
• Minimum kelp threshold value selected from 99.9th%-tile value of offshore (35-60 m) pixels
Validation: Canopy Cover
• Cover measurements compared with high resolution 2004 CDFG aerial kelp survey
SPOT: Oct 29, 2004
CDFG: Sept-Nov 2004
r2 = 0.98p ~ 0
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
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…
Research Area
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
Seasonal kelp biomass changes along 3 LTER transects
• Maximums in late 2002• Wave driven seasonality apparent
Methods: Biomass• Normalized Difference Vegetation Index (NDVI)
(NIR-RED)(NIR+RED)
• Calculated for areas of kelp cover
NDVITransform
Empirical Estimation of Kelp Biomass from SPOT
r2 = 0.71
• Provides path to the remote estimation of kelp biomass (kg/m2)
• Enables …regional assessmenthigh temporal
resolution views with multiple scenes
r2 = 0.71n = 37
Seasonal Kelp Forest Changes
Regional Kelp Biomass
• Created from biomass-NDVI relationship for areas of kelp cover
Nov. 2004: 15000 ton
Nov. 2006: 7800 ton
April 2007: 22350 ton
0
5000
10000
15000
20000
25000
Dec-05 Mar-06 Jun-06 Sep-06 Dec-06 Mar-07 Jun-07
Biomass Along SB Coastline
Validation using Visual Biomass Observations
r2 = 0.73p < 1*10-7
Spectra obtained from airborne inaging spectrometers are similar to lab measures of individual kelp blade reflectances:
Spectra obtained from airborne inaging spectrometers are similar to lab measures of individual kelp blade reflectances:
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
400 500 600 700 800 900
Wavelength (nm)
Rrs (sr
-1)
Mature
Immature
Senescent
PHILLS Canopy
Optical estimates of kelp physiological state
Area and Productivity Estimates Depend on Spatial Resolution
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
• 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 increases1
1.5
2
2.5
3
0 50 100 150 200 250
Spatial Resolution (m2)
Normalized BiasArea
Biomass
Metapopulation ModelingMetapopulation Modeling
>500 m
Bed 28
Bed 27
Patch 17
Patch 18
Patch 16
Patch 19
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
Thank You!!
– 1000
– 100
– 10
– 0
Canopy Biomass(tons/km coast)
36.5°N
35.9°N
35.3°N
34.7°N
34.4°N
34.1°N
33.7°N
33.4°N
32.6°N
32.0°N
31.5°N
30.9°N
30.5°N
29.6°N
LatLocation
Carmel Bay
Pt.Buchon
Pt.Purisima
Coal Oil Pt.
Palos Verdes
San Onofre
Pt.Loma
Pta.San Jose
Pta.San Carlos
ISP Alginates Visual Kelp BiomassISP Alginates Visual Kelp Biomass
Raw data provided by D. Glantz, ISP Alginates, Inc. & Santa Barbara Coastal LTER
Kelp canopy biomass, 34-year monthly time series
Regional Kelp Biomass
UCSB
11/2004: 15000 metric tons
11/2006: 7800 metric tons
04/2007: 22358 metric tons
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