How much does water depth control facies distribution?

A case study from the Florida Shelf

Gene Rankey

The Assumption “Topography is a primary driver of

lateral facies change” Tinker and Kerans (2002)

“Here we simply accept the premise that facies and groups of lithofacies elements in fact do reflect

changing water depths” (Diedrich and Wilkinson, 1999).

Rankey Facies-Water Depth, CSL Annual Review

Purposes

• Explore relations between bottom types and the water depths at which they occur

• Test facies-scale ‘interpretability’ of ancient analogs

• Quantify possible uncertainties

Rankey Facies-Water Depth, CSL Annual Review

Major Findings• Benthic habitats & facies occur across a spectrum of water depths

• Not considerably distinct from a random distribution

• Variables other than water depth have an important - even dominant - influence on the distribution of facies on this shelf

• Interpretations and process-based forward models that simulate analogous systems should include the possibility of variable

responses to water depth

Rankey Facies-Water Depth, CSL Annual Review

Methods

• Regional map of benthic habitats

• Bathymetric maps

• Compare using GIS

•Analyze statistically

Rankey Facies-Water Depth, CSL Annual Review

Location and Data

Rankey Facies-Water Depth, CSL Annual Review

Habitats, Facies, and Water Depth

Habitats Interpreted Facies

Present across spectrum of WDRankey Facies-Water Depth, CSL Annual Review

Habitats, Facies, and Water DepthH

abit

ats

Water Depth

A

B

C

0-2 2-4 4-6

1.0

0.4 0.6

0.33 0.33 0.34

Predictable, “Deterministic”

Unpredictable, “NOT Deterministic”

“Given habitat A, how well can we predict WD?”

Rankey Facies-Water Depth, CSL Annual Review

Quantifying Dependence

n

i

n

j

ijij rr1 1

sys )log(*H

Shannon-Weaver diversitymeasures contingencies

rij = probability of facies i occurring in water depth j

H = 0 indicates a perfectly ordered system (facies i occurs only in water depth j).

Rankey Facies-Water Depth, CSL Annual Review

Quantifying Dependence

)H / (H -1 E max

Shannon Evennessmeasures contingencies relative to random

• Hmax = ‘random’ : any water depth is equally probable

• E ranges from zero (H=Hmax) to one• E values near one - the system is not diverse (H=0),

but one class dominates

Rankey Facies-Water Depth, CSL Annual Review

Quantifying Dependence

• E=1 means high predictability: given a condition (facies/habitat), we can predict

the result (e.g., water depth) with certainty

• E is proportional to the percentage that uncertainty has been reduced from the maximum.

Rankey Facies-Water Depth, CSL Annual Review

Habitats, Facies, and Water Depth

Habitats

Wat

er D

epth

A B C

0-2

2-4

4-6 1.0

0.4 0.6

0.33 0.33 0.34

Predictable, “Deterministic”

Unpredictable, “NOT Deterministic”

“Given water depth A, how well could we predict habitats?”

Rankey Facies-Water Depth, CSL Annual Review

Facies Diversity and Water Depth

• Deterministic component is highest in deeper water• Decreases with decreasing water depth

•Shallow water more heterogeneous

Summary & Implications•Benthic habitats occur across a spectrum of water depths

• Not significantly distinct from a random distribution

•Variables other than water depth have an important - even dominant - influence on the distribution of facies on this

shelf - Energy, spatial context, geologic history

• Quantified uncertainty in interpretation of ancient analogs and in populating forward models

Rankey Facies-Water Depth, CSL Annual Review…Next steps TBA tomorrow….