User-Friendly Multivariate Analysis for Linking Predictive

Water Quality Models to Biological Data

Janna Owens

Water Quality Monitoring

Physical, chemical and biological assessments

Calculate environmental impacts Create models of water processes

as predictive tools for physical/chemical data

Ideally, a compatible framework would integrate biological data

PRIMER software

Plymouth Research Routines in Multivariate Ecological Research

Coherent strategy for interpretation of community structure

Wide range univariate/multivariate routines

Ease of use and comprehension

Predictive Model

0.00

1.00

2.00

3.00

4.00

5.00

2 -Year 5 -Year 10 -Year 25 -Year 50 -Year 100 -Year

Per

cen

tag

e In

crea

se

Peak Flow (cfs)

Runoff Depth (in)

Deterministic models do not directly evaluate larger biological organisms

Won’t simulate many aspects of complex community

Statistical data modeling integrates biological and environmental variables

Basic methodologies: Cluster and Ordination

Aquatic Biological Modeling

techniques to classify objects

Biological classification verified by environmental variables

Difficult to use with environmental gradients

Requires extensive database

Mutivariate data presented in 2 dimensions

Sample (dis)similarity represented by proximity in space

Determines variables that affect biological data

Spatial distortion possible without caution

Cluster vs. Ordination

Cluster Analysis

?

Hierarchical Cluster

Stable Unstable

Ordination Analysis

Multi-Variable

PCA

PC Eigenvalue %Variation Cum.

%Variation1 199 39.0 39.02 130 25.5 64.53 68.6 13.4 77.94 35.7 7.0 84.95 20.3 4.0 88.9

PCA cont.

G H

L

S

P

G

L S

P

V

G

H

L

S

P

V

G H

L

S P

V

G H L

S

P

V

G H L

S

P

V G

H C

L

M

S

P

G H

C

L M

S

P

G H

C

L

M S

P

Stable sites:

Less Urbanization

Unstable sites:

More Urbanization

MDS

MDS Trajectory

ANOSIM

Dominance Curves

3

~ 67%

~ 27%

Sites

Species rank by abundance

Cu

mu

lati

ve D

om

inan

ce (

%)

Aggregation

  ;-)  ;-)

Phylogenetic Diversity

TAXTEST

95%

Sample Spreadsheet

Applications

More productive data mining Allow merging of historical and

diverse sample efforts Comparison to a variety of predictive

models to assess trends Universal comprehension

Acknowledgments US EPA Region IV Dr. Andrew Simon, USDA, National

Sedimentation Lab Drs. Angus and Marion, UAB Clarke, K.R. and Warwick, R.M. 1993.

Change in Marine Communities: An approach to Statistical Analysis and Interpretation, Bourne Press Ltd., Bournemouth, U.K.

Questions?