# Presentation: 530814

## 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 HillOld 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 Nino

- El Nino

- PDO Shift

## 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.

## Slide 5

## 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 

- Perform dark pixel atmospheric correction
- 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 = 0.98
- p ~ 0

**Notes:**
Y = 0.76x+1.2
Outliers removed: r2 = .98, slope insignificantly different than 1

## 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

**Notes:**
Confusing

## 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

- NDVI
- Transform

## Empirical Estimation of Kelp Biomass from SPOT

- r2 = 0.71

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

- r2 = 0.71
- n = 37

## Seasonal Kelp Forest Changes

## Regional Kelp Biomass

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

## Validation using Visual Biomass Observations

- r2 = 0.73
- p < 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:**

**Optical estimates of kelp** **physiological state**

## 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**

## Metapopulation Modeling

**Metapopulation Modeling**

**>500 m**

**Bed 28**

**Bed 27**

***Patch 17***

***Patch 18***

***Patch 16***

***Patch 19***

**Notes:**
FIGURE 1

## 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!!

## ISP Alginates Visual Kelp Biomass

**–**** ****1000**

**–**** ****100**

**–**** ****10**

**–**** ****0**

*Canopy Biomass*
*(tons/km coast)*

**ISP 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***

**Notes:**
We do that, and here’s what we get - a picure of the data that is much easier to interpret, is much more amenable to statistical analyses that require an exhaustively sampled grid,  and is not biased by clustering of samples are hi or low areas.

You can see from this that kelp forest biomass is highly variable over space - ntoe the ~100 km distribution of “hotspots” and time - note the strong effects of el nino’s in 1982-3, and 1997-98, and the more moderate el nino in 1991

## Regional Kelp Biomass

- UCSB

- 11/2004: 15000 metric tons

- 11/2006: 7800 metric tons

- 04/2007: 22358 metric tons