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]

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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.

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

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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:

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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

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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

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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