Predictive relationships between seabed species &

environment

Predictive relationships between seabed species &

environment

• Roland Pitcher, GBR Roland Pitcher, GBR • Peter Lawton, GoMA Peter Lawton, GoMA • Lew Incze, GoMALew Incze, GoMA• Stephen Smith, DFO, CanStephen Smith, DFO, Can• Nick Ellis, CSIRO, AusNick Ellis, CSIRO, Aus• Tom Shirley, GoMEXTom Shirley, GoMEX• Chin-Lei Wei, Comarge/GoMEXChin-Lei Wei, Comarge/GoMEX

Main contact: Roland PitcherMain contact: Roland Pitcher

Objectives:Objectives:For defined seabed “biological patterns” (individ. species, multi-species For defined seabed “biological patterns” (individ. species, multi-species assemblages, and some diversity attributes) sampled over mesoscales:assemblages, and some diversity attributes) sampled over mesoscales:

1.1. Examine extent to which physical surrogates may explain biological Examine extent to which physical surrogates may explain biological

patterns; patterns; 2.2. Rank importance of physical variables for structuring biological patterns;Rank importance of physical variables for structuring biological patterns;3.3. Examine common biological responses to physical gradients;Examine common biological responses to physical gradients;4.4. Identify critical values for physical variables corresponding to 'threshold' Identify critical values for physical variables corresponding to 'threshold'

changeschanges

Key Approaches:Key Approaches: Bootstrapped randomized tree-based Bootstrapped randomized tree-based methodmethod

Scope of analysis:Scope of analysis:GBR: Databases available (sled and trawl sampling/28 physical environment GBR: Databases available (sled and trawl sampling/28 physical environment

variables); prior development of statistical approach and simulations.variables); prior development of statistical approach and simulations.

GoMA: Benthic invertebrate database of Theroux & Wigley (1998) being GoMA: Benthic invertebrate database of Theroux & Wigley (1998) being cleaned; coverages of physical datasets for Gulf of Maine being pursued.cleaned; coverages of physical datasets for Gulf of Maine being pursued.

GoMEX: Databases for 4 largest surveys of continental shelf benthos collated; GoMEX: Databases for 4 largest surveys of continental shelf benthos collated; accompanying oceanographic & physical data being pursued.accompanying oceanographic & physical data being pursued.

Comarge: Deep GoMEX Biodiversity project already has suitable biological Comarge: Deep GoMEX Biodiversity project already has suitable biological and physical databases in placeand physical databases in place

Still open to discussion with other programsStill open to discussion with other programs

|MUD.P<22.0416

SW.CHLA.AV<0.60197

CRS.S.AV<34.9244

STN.DEPTH<-29.1M.BSTRESS<0.24556

CRS.SI.AV<3.39321

2.29900.8121 1.8260

0.9318 1.4300 2.9500

4.0890

02

46

8

0.0 0.1 0.2 0.3 0.4 0.5 0.6

01

23

45

6

Benthic stress

De

nsi

ty

Ra

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Density of splitsDensity of dataRatio of densitiesRatio=1

0.0

00

.05

0.1

00

.15

0.2

0

0 10 20 30 40 50 60

01

23

45

6

Mud (%)

De

nsi

ty

Ra

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Density of splitsDensity of dataRatio of densitiesRatio=1

0 10 20 30 40 50 60

02

46

810

12

Mud (%)

Cum

ulat

ive

impo

rtanc

e

0.0 0.1 0.2 0.3 0.4 0.5

01

23

45

Benthic stress

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1

2

3 A

4 A

1.1. Biomass distribution for 1 sp displayed on coverage of % mudBiomass distribution for 1 sp displayed on coverage of % mud

2.2. Single tree model for same sp (build randomized forest of Single tree model for same sp (build randomized forest of 1000’s)1000’s)

3.3. ““Importance” weighted split points and data frequency for two Importance” weighted split points and data frequency for two physical variables (A: mud; B: benthic stress) for 10 spp.physical variables (A: mud; B: benthic stress) for 10 spp.

4.4. Cumulative distribution of weighted splits for same variablesCumulative distribution of weighted splits for same variables

3 B

4 B

5. Pattern of biological change-response on the top 8 physical 5. Pattern of biological change-response on the top 8 physical gradients for 90 species, scaled by aggregate variable importancegradients for 90 species, scaled by aggregate variable importance

Transform to ecological gradients to characterise (map) region Transform to ecological gradients to characterise (map) region directly from physical surrogates…directly from physical surrogates…

Compare/contrast/accumulate across Programs…Compare/contrast/accumulate across Programs…

55

Planned timelinesPlanned timelinesPlanned timelinesPlanned timelines

Oct 2008: First workshop (Halifax, NS, Canada)Oct 2008: First workshop (Halifax, NS, Canada)

Feb – March, 2009: Final collation, simulations and statistical Feb – March, 2009: Final collation, simulations and statistical analyses analyses

April 1, 2009: key analytical results & emerging synthesis points to April 1, 2009: key analytical results & emerging synthesis points to Paul Snelgrove Paul Snelgrove

Late April – early May, 2009: Team workshop, Saint Andrews, NB, Late April – early May, 2009: Team workshop, Saint Andrews, NB, Canada or Portland, ME, USACanada or Portland, ME, USA

Mid April – mid July: Roland Pitcher on sabbatical in Canada Mid April – mid July: Roland Pitcher on sabbatical in Canada Mid August: Mid August: draft manuscript for publicationdraft manuscript for publication

Planned outputs: Cross-region Synthesis paper, other papers Planned outputs: Cross-region Synthesis paper, other papers (technical, within region, detailed)(technical, within region, detailed)