Towards community-based approaches to estimating NPP & NCP from remotely-sensed optical properties...

Towards community-based approaches to estimating NPP &

NCP from remotely-sensed optical properties

Rick A. ReynoldsScripps Institution of OceanographyUniversity of California San [email protected]

ICESOCC | SIO | 22 Sep 2014

2NPP models

Optical measurements are a key component to estimating NPP & NCP at multiple temporal and spatial scales

General form of NPP modelsNet Production = [Biomass * Light absorption * Quantum yield] – (Respiration)

Can be coupled with additional models to estimate NCP

All phytoplankton “Biomass” is considered equal! Differences in community composition, photophysiology, and size structure are

ignored

𝑁𝑃𝑃=𝐶∗∫4 00

700

𝐸𝑜 ( 𝜆 )𝑎 h𝑝∗ ( 𝜆 )𝑑 𝜆∗𝜙𝑐−𝑅


3The ocean is not homogenous

57 biogeochemical provinces identified by Longhurst et al. (1995)

81 provinces classified from satellite data (Oliver and Irwin, 2008)


4Can optics be used to identify communities?

Significant advances in discriminating oceanic communities from optical measurements

Several approaches abundance based single species blooms dominant functional

groups size structure

Potential for mapping communities and improving NPP & NCP model estimates

available at www.ioccg.org


5Example community-based approach to NPP

Uitz et al. 2006; IOCCG 2014

Chla distributions partitioned into 3 distinct phytoplankton size classes based on total abundance Micro (diatoms and

dinoflagellates) Nano (prymnesiophytes) Pico (prokaryotes and

picoeukaryotes)


6

Extended to NPP Class-specific

photophysiology NPP partitioned among size

classes SO results seem

reasonable Micro 30-50% of NPP in

spring-summer Nano dominate seasonal

blooms But

No SO data in parameterization

Valid with changing ocean?

Example community-based approach to NPP

Uitz et al. 2010

TOTAL

Micro

Nano

Pico

Dec-Feb climatology of NPPfor 1998-2007


7Optical-based classifications

Discrimination of communities through HCA of optical data

Pigment-based clusters used as a reference

Non-bloom conditions Chla range 0.1-0.6 mg m-3

Torrecilla et al. 2011


8Good agreement between pigments and optics

High degrees of similarity between classifications derived from pigments and optics

Phytoplankton absorption coefficient better than Rrs

Best results obtained using derivatives of high spectral resolution data

Pigment-based clusters

Dominant marker pigments

Station

Fuco ≈ MV-Chlb A

DV-Chla > Zea B

DV-Chla ≈ Zea C1, C2, C3, C4

19’-Hexfuco > Zea D

19’-Hexfuco > Fuco E

Zea ≈ 19’-Hexfuco FTorrecilla et al. 2011

-based clusters𝑎 h𝑝′ ′ (𝜆)


9IOPs and planktonic community Particle IOPs closely linked

to planktonic constituents spectral absorption coefficient

directly linked to phytoplankton pigments and cell size

spectral scattering coefficient sensitive to particle size distribution

Can be obtained from in situ from sensors on various platforms, or from ocean color inversion models

Bricaud et al. 2004

Kostadinov et al. 2009

Specific absorption spectra of major phytoplankton pigments

Model simulations of particle backscattering shapein relation to particle size distribution


10IOPs as community indicators

Recent work in Arctic suggests that IOPs can discriminate particle assemblages 7 planktonic assemblages

identified in Chukchi and Beaufort Seas, each with distinct biogeochemical characteristics

Input to hierarchical cluster analysis216 normalized spectra of bbp(l):ap(l)

War

d’s

link

age

dist

ance

Dendrogram obtained from HCA of bbp(l):ap(l)

Neukermans et al. 2014 Ocean Sciences Meeting

Min

eral

Mix

Pico

+Det

ritus

-1

Micro

Phyt

o-1

Micro

Phyt

o-2

Pico

+Nan

o Ph

yto

Detrit

us-2

BeaufortSea

Alaska

Canada

Mackenzie R.

Par

ticl

e ba

cksc

atte

ring

to a

bsor

ptio

n(d

imen

sion

less

)


11Approach applicable to S. Ocean?

Phytoplankton are generally dominant contributors to nonwater absorption in SO 0.0 0.5 1.0

0.0

0.5

1.00.0

0.5

1.0

0.0 0.5 1.00.0

0.5

1.00.0

0.5

1.0

0.63

0.12

0.25

0.19

0.19

0.62

CDOMPhytoplankton

Non

alga

l par

ticle

s

Non

alga

l par

ticle

s Phytoplankton

CDOM

Relative contributions to anw(440)S. Ocean Arctic

Reynolds et al., IOCCG in press

10-2 10-1 100 101 10210-4

10-3

10-2

10-1Ross SeaAPFZ

Chl [mg m-3]

b bp(5

55)

[m-1

]

Reynolds et al. 2001

Regional variability in particle size distribution and backscattering has been observed


12Towards community-based approach

Optics-derived community indicators can be used to assess community

distributions improve NPP and NCP

estimates validate or assimilate into

numerical models monitor changes in SO

environment and biodiversity

Ward et al. 2012

Satellite-derived Modeled


13Outstanding questions

What communities can we identify optically in the S. Ocean? Do current approaches work in the SO? How do optical communities relate to other indicators of

community structure? (genomics, pigments, size structure) Consistent with environmental patterns? (macro and

micronutrients, light and mixing, grazing pressure) Can we associate these communities with specific biogeochemical

behavior?

Do these community distributions change over time and space? Are there any trends in planktonic community distributions? What are the potential implications?


14Acknowledgements

Collaborators M. Babin, G. Mitchell, G. Neukermans, J. Piera, D. Stramski,

E. Torrecilla, J. Uitz

NASA Programs Ocean Biology and Biogeochemistry Biodiversity and Ecological Forecasting Cryospheric Sciences

Towards community-based approaches to estimating NPP & NCP from remotely-sensed optical properties...

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