Predicting Cold Water Fish Community Presence In New Hampshire for

Implementation of Dissolved Oxygen Criteria

Hel

lgat

e, D

ead

Dia

mon

d R

iver

By

David Neils

Background

• CWA requires states to report on status of waters [305(b)/303(d) report]

• NH DES establishes water quality standards by which to make assessments of water quality (Env-Ws 1700)

• Specifically, Env-Ws 1703.07 which outlines NH’s dissolved oxygen criteria:

Waterbody Classification

Daily Average Instantaneous Minimum

Class A 75% saturation 6 mg/L

Class B 75% saturation 5 mg/L

Cold Water Fish Spawning Areas*

7-day mean - > 9.5 mg/L 8 mg/L

* Period from Oct. 1 – May 14 or June 30 for spring / late hatch fall spawners

Current application of cold water fish spawning area DO criteria

• Approximately 10,000 miles of streams

• In 2004 305(b)/303(d) equated to 3,189 assessment units

• 24 units (<1%) assessed as cold water fish spawning areas using more stringent DO criteria (= 47.5 miles)

Current application is restricted to fisheries where field collections of cold water gamefish indicate successful spawning (i.e presence of YOY and/or multiple year classes).

Obviously highly accurate, but limited in statewide application.

Community Classification – An alternative approach

Basic premise: biological communities are, in part, structured by the physical and chemical environmental conditions in which they live. If distinct biological communities can be identified, then the variables that define them can be defined.

Biological Community A

Environmental ConditionsWater-based

Climate – coldPhysical location – far north

Biological Community B

Environmental ConditionsTerrestrial

Climate – warmPhysical location – southern hemisphere

Classifying Stream Fish Communities

• Not a new concept – has been widely researched and utilized as tool for grouping similar fish community types

• Context for current application is identification of similar community types for purpose of assessing water quality (i.e. cold water fish communities require higher DO levels for natural populations to persist)

• Additional application is identification of similar community types for purpose of community condition assessment – biological indices or models built specifically to determine condition of different types of communities

Objectives

• To determine if a model could be built that predicts where cold water fish communities occur or should occur

• Decide what variables are important in determining the presence or absence of cold water fish communities

• Assess the practicality of applying the model’s results statewide

Dataset

• NH DES biomonitoring fish collections 1997 – 2003

• Limited to 1st – 4th order streams sampled from June – August

• 186 stations included in analysis (eliminated sites known to have significant human disturbance

• Broken in calibration (152 sites) and validation (34 sites) datasets

• Analysis based on presence / absence occurrence

• A minimum of 5 individuals collected at site to be considered “present”

Walker Brook, Mason, NH

Identification of target species

Need to identify what species define coldwater fish communities (Not “classification” in strict sense)

Requirements for target species:

Cold water “specialists” >30 occurrences in dataset Known to have statewide distribution Native to NH

Result: Brook Trout (Salvalinus fontinalis) and Slimy Sculpin (Cotus cognatus)

Brook Trout (Salvalinus fontinalis)

Habitat (From Scarola) : “requires year-round supply of cold, oxygenated water and sufficient areas of gravel on which to spawn. Without these it will not survive”

Reproduction: Fall spawner (Oct. – Nov.); eggs develop through winter and larvae (yoy) emerge in early spring.

Slimy Sculpin (Cottus cognatus)

Habitat: Small rocky bottomed streams; strictly limited to cold water

Reproduction: Spring spawner (Apr. - May); eggs develop in 3 – 4 weeks followed by larval emergence.

Selection of Predictor Variables

• Permanence – Variables that resist change

Good Example: Elevation

Bad Example: Substrate composition

• Ease of collection – Variables that can be obtained quickly, accurately

Good Example: Latitude

Bad Example: Flood prone width

• Natural range of variability – Variables that are robust

Good Example: Watershed size

Bad Example: Stream bank slope

Requirements Candidate Variables

• Latitude – dd.dddd

• Longitude – dd.dddd

• Elevation – feet

• Watershed Area – square miles

• Major River Basin – Merrimack, Piscataqua, Saco, Connecticut, Androscoggin

• Ecological Drainage Unit (EDU) – Androscoggin, Upper CT, Lower CT, Merrimack/Coastal

Latitude (dd.dddd)

42.843

43.243.443.643.8

4444.2

Present Absent

Longitude (dd.dddd)

71

71.2

71.4

71.6

71.8

72

Present Absent

Elevation (ft)

0

200

400

600

800

1000

1200

Present Absent

Watershed Area (mi2)

0

10

20

30

40

50

60

Present Absent

* *

*

Categorical Variables:

EDU: Frequency distribution significantly different than that expected by chance

Major River Basin: Frequency distribution significantly different than that expected by chance

Results: Independent Variable ExaminationContinuous Variables:

Var

iabl

e M

eans

CW community present

CW community absent

WMNF Boundaries

• More frequent in north

• More frequent at higher elevations

• More frequent in smaller watersheds

• Less frequent in Merrimack and Coastal areas

Distribution of calibration dataset cold and non-cold water fish communities sampled by NH DES biomonitoring unit 1997 – 2003.

OK – So 5 of 6 variables are show differences b/t cold and non-cold water fish communities, but how do the variables inter-relate?

We need another analysis tool…

Logistic Regression: yes, I’ll spare you the details

What you need to know:

• Each variable is examined for its relative importance (similar to step-wise linear regression)

• Regression equation assigns each site a probability (0 – 1) of being a cold (1) or non-cold (0) water fish community based on important variables

• Predictive accuracy (i.e. # correct predictions) of model as measure of success

Simultaneous Variable Consideration

0

0.2

0.4

0.6

0.8

1

Pro

ba

bil

ity

of

oc

cu

rre

nc

e

Environmental Gradient

Predict Present P(present) > 0.50

Predict Absent P(present) < 0.50

Logistic Regression Overview

S-shaped predict

ive cu

rve re

sultin

g

from R

egression E

quation

Individual Point

P(present) =1

1 + exp (-α – β1X1-.. βiXi)

Preliminary Model Results

Logistic Model Summaries

Independent Variable(s)

Model Chi-square df Sig.

-2 log likelihood

Δ in -2 log likelihood

Model 1 Latitude 62.54 1 <0.001 147.756 ------

Model 2Latitude,

Bioregion 79.492 4 <0.001 130.804 16.952*

Model 3

Latitude, Bioregion, Elevation 87.005 5 <0.001 123.291 7.513*

Neither watershed size or drainage basin explained significant portion of variation

* sig. change w/ 1 df

Preliminary Model Results

Model Variables

# correct predictions

(n=152)

% correct predictions

1 Latitude 121 79.6

2Latitude, EDU

122 80.3

3Latitude, EDU, Elevation

122 80.3

Select Model 1 for simplicity – Latitude is the overwhelming predictor of cold water fish community presence / absence.

For every 1 degree change in latitude, 14x change in expected fish community type.

Cold Water Fish Community Predictive Regression Equation

• Predictive model based solely on latitude

• Nearly 80% accurate

• P(present) threshold of 0.50 = latitudinal breakpoint @ ~43.7oN

0

0.2

0.4

0.6

0.8

1

Latitude (dd.dddd)

Pro

bab

ility

of o

ccu

rren

ce

P(present) = 1 1+exp[4.395 + 2.641(latitude)]

Observed Present

0

0.2

0.4

0.6

0.8

1

42.5 43 43.5 44 44.5 45 45.5

Latitude (dd.dddd)

P(p

rese

nt)

Predict Present

Predict AbsentErrors

Observed Absent

0

0.2

0.4

0.6

0.8

1

42.5 43 43.5 44 44.5 45 45.5

Latitude (dd.dddd)

P(p

rese

nt

Predict Absent

Predict Present Errors

Exploring Predictive Errors

Type I – Reject null hypothesis when it is trueFor model – predicting CW present when observed absent

Observed Absent

0

0.2

0.4

0.6

0.8

1

42.5 43 43.5 44 44.5 45 45.5

Latitude (dd.dddd)

P(p

rese

nt

Predict Absent

Predict Present Errors

Type II - Do not reject null hypothesis when falseFor model – predicting CW absent when observed present

Minimizing Type II errors is more protective (i.e. captures all sites where CW observed present), but run high risk of applying unnecessarily strict standard

Minimizing Type I errors is less protective, minimizes chances of applying strict DO std. to non-CW communities

Observed Present

0

0.2

0.4

0.6

0.8

1

42.5 43 43.5 44 44.5 45 45.5

Latitude (dd.dddd)

P(p

rese

nt)

Predict Present

Predict AbsentErrors

Model Adjustments

Probability threshold adjusted to compare results:

* Remember above line = predicted present; below line = predicted absent

0

0.2

0.4

0.6

0.8

1

42.5 43 43.5 44 44.5 45 45.5

Latitude (dd.dddd)

P(p

rese

nt)

Obs - Absent

Obs - Present

Few Pink (min. Type I)

Few Yellow (min. Type II)

Final Model Adjustments

• Maximum predictive accuracy for validation dataset achieved at 70% probability threshold (72.1%; 25 of 34 sites).

• At the 70% threshold model predictions were better than those made by chance.

• Inaccurate predictions occurred more frequently at sites observed to have cold water fish communities but predicted to have non-cold cold water fish communities (Type II; 6/15) vs. sites observed to have non-cold water fish communities but predicted to have cold water fish communities (Type I; 3/19).

Final Recommendation: Utilize model based on latitude with a 70% probability of occurrence threshold

Reality….Or what the results look like on a map

0

0.2

0.4

0.6

0.8

1

42.7

42.9

43.1

43.3

43.5

43.7

43.9

44.1

44.3

44.5

44.7

44.9

45.1

Latitude (dd.dddd)

P(present) =

1 1+exp[4.395 + 2.641(latitude)]

N 43.9850

Logistic Regression Function

Model Prediction Correspondence with Water Quality DataSpecific Conductance

0

20

40

60

80

100

120

140

Predicted Observed

mir

com

hos

Present

Absent

pH

6.5

6.6

6.7

6.8

6.9

7

Predicted Observed

Units Present

Absent

Dissolved Oxygen

7

7.5

8

8.5

9

9.5

10

Predicted Observed

mg/L Present

Absent

8.0 mg/L – Instantaneous Minimum

9.5 mg/L – 7-day Mean Minimum

• Cold and Non-cold communities above 6.5 pH criteria

• Cold higher than non-cold

• Non-cold almost twice as high as cold (predicted and observed)

• The model is “conservative” in nature

• Additional refinement is suggested

• Best professional judgment (better known as common sense) should be included

• Models aren’t always right

• This is a “first approximation”

• Can we agree that all areas predicted as “cold” should be regulated as such

Policy Application / Consideration

• Broader implementation of water quality criteria

• Potential utilization as regulatory requirement

• Trade-off between over vs. under protective criteria

• Requires acceptance by regulating entity

Technical Application / Consideration

• Model improvement requires collection of additional “supplementary” data

• Utilization of statewide fish data would be beneficial

• Current analysis includes some sites with stocked gamefish

• Is there a better way?

An added bonus (or the real reason you might want to know where to find cold water fish communities):

David Neils

Biomonitoring Program

NH Dept. Env. Services

29 Hazen Dr.

Concord, NH

dneils@des.state.nh.us

603.271.8865