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Designing Effective Monitoring Programs for Fish Population Response to Habitat Restoration Designing Effective Monitoring Programs for Fish Population Response to Habitat Restoration

Designing Effective Monitoring Programs for Fish Population Response to Habitat Restoration - PowerPoint Presentation

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Uploaded On 2018-11-07

Designing Effective Monitoring Programs for Fish Population Response to Habitat Restoration - PPT Presentation

John Sweka USFWS Northeast Fishery Center Lamar PA What is the goal of fish habitat restoration efforts and partnerships To create morebetter fish habitat To enhancerecoverrestore fish populations ID: 720399

control monitoring time population monitoring control population time power habitat abundance treatment site restoration scale variation interest size design

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Slide1

Designing Effective Monitoring Programs for Fish Population Response to Habitat Restoration

John

Sweka

USFWS – Northeast Fishery Center

Lamar, PASlide2
Slide3

What is the goal of fish habitat restoration efforts and partnerships?

To create more/better fish habitat

To enhance/recover/restore fish populationsSlide4

How do we know if we met our goal?

We need to monitorSlide5

Adaptive Management

DOI Technical Guide

Example Models:

More Large Woody Debris = More Brook Trout

Lower Sediment Load = More Brook TroutSlide6

Issues of spatial scale

Defining the population of interest

Depends on the goal of the habitat restoration work

Reach population vs. stream population vs. range wide population

Often a mismatch between the population of interest and spatial scale of habitat restoration and monitoring

Can lead to erroneous conclusions about the effectiveness of habitat restorationSlide7

300 m

Brook trout abundance

A

0

= 15

A

1

= 30

100% increase !!!!!!Slide8

300 m

6000 m

If abundance throughout the rest of the steam stays the same, this is only a 5% increase.Slide9

Issues of spatial scale

Life history information can inform decisions on spatial scale of monitoring

Home range – Does monitoring encompass the home range of individuals in the population?

Schooling behavior – Is the species of interest patchily distributed?

Migration – Does the species of interest migrate from one habitat to another while completing its life cycle?Slide10

A good example of monitoring at the population scale

Liermann

, M. and P.

Roni

. 2008. More sites or more years? Optimal study design for monitoring fish response to watershed restoration. North American Journal of Fisheries Management 28: 935-943.

“…the only way to assess the population

level effects

of watershed scale restoration is to monitor at the population level.”

Monitored salmon

smolt

migration from small streams with and without habitat restoration.

Employed knowledge of life history information (migration)

Replicated experiment with controlsSlide11

Issues of temporal scale

How does the duration of monitoring compare to the life history of the species of interest?

Generation Time – amount of time it takes one cohort to grow up and replace another; can be calculated from a life table or a Leslie matrix

YOY

Age1

Age2

Age3

Age4+

YOY

0.00

37.50

56.25

97.50

150.00

Age1

0.06

0.00

0.00

0.00

0.00

Age2

0.00

0.10

0.00

0.00

0.00

Age3

0.00

0.00

0.10

0.00

0.00

Age4+

0.00

0.00

0.00

0.10

0.01

Population growth rate(

λ

) = 1.6, generation time = 2.14Slide12

Issues of temporal scale

If habitat restoration has a population level effect, we would not expect to begin seeing any real change until the expected generation time is reached

Likely longer due to variation in other uncontrollable factors (e.g. rainfall, flow, temperature, predation etc.)

Length of monitoring can greatly influence conclusions that are drawnSlide13

Sweka

, J.A. and K.J. Hartman. 2006. Effects of large woody debris addition on stream habitat and brook trout populations in Appalachian streams. Hydrobiologia 559: 363-378.Slide14

Sweka

, J.A. and K.J. Hartman. 2006. Effects of large woody debris addition on stream habitat and brook trout populations in Appalachian streams. Hydrobiologia 559: 363-378.Slide15

Types of Monitoring Designs

Before-After Design

Time

Abundance

Assumes everything but the treatment remained constant through time

Best if there is many years of pre- and post- data

Simply compare pre- and post- mean abundanceSlide16

Types of Monitoring Designs

Pre/Post Pairs

Time

Abundance

Allows assessment of site-to-site variation

Temporal scale may not be long enough

Ignores any regional trends in abundance that may existSlide17

Types of Monitoring Designs

Before-After-Control-Impact Design (BACI)

Time

Abundance

Has an independent control site

Best if there are several years of pre- and post- data

Interested in the difference between treatment and control

Embraces natural variation

Control site

Treatment siteSlide18

Types of Monitoring Designs

Before-After-Control-Impact Design (BACI)

Time

Abundance

Control can have higher or lower abundance than the treatment site

Control site

Treatment siteSlide19

Types of Monitoring Designs

Before-After-Control-Impact Design (BACI)

Time

Abundance

Can have several treatment and control sites

Control sites

Treatment sitesSlide20

Types of Monitoring Designs

Real World Case

Time

Abundance

Control site

Treatment site

Limited funding, 1 year pre-, couple years post-

Add a control – separate natural variation from treatment variation

Continue monitoring treatment and control – look for treatment x time interactionSlide21

Power and Sample Size

Power

– the probability of correctly rejecting the null hypothesis of no change (no difference) when some specified alternative is correct

How much Power do you need?

Depends on the consequences

Law of diminishing returns – rate of increase in power decreases with increasing sample size

Increasing power can be costlySlide22

Power and Sample Size

How many samples should I take to detect a difference?

Choice of alpha (chance of falsely rejecting

H

o

)

Whether the test will be one- or two-tailed

Value of the alternative

H

a

(desired difference to detect)

Choice of design

Some assumption about the behavior of the variation in the data (

e.g

variance proportional to mean)

Estimate of the variation (standard

deveiation

or variance)

Slide23

Power and Sample Size

Guidelines for choosing an

effect size

(

Gerow

2007)

Small Effect

– the smallest difference that elicits your interest

Large Effect

– the smallest difference that you would definitely not want to fail to detect

Medium Effect

- the average of small and large effectsSlide24

Power and Sample Size

Gerow

, K. G. 2007. Power and sample size estimation techniques for fisheries management: Assessment and a new computational tool. North American Journal of Fisheries Management 27: 397 – 404.

Gerrodette

, T. 1987. A power analysis for detecting trends. Ecology 68: 1364-1372.Slide25

Conclusions

Effective monitoring starts with a clearly defined population of interest and goals

Incorporate the life history, home range, and behavior of the target species

Have a control and avoid

psuedoreplication

View monitoring as an experiment (hypothesis testing)

Use power analysis to inform study design

Funding timelines don’t match biological timelines

Additional partnerships

Creative ways to extend funding

Work with funding sources for changeSlide26

Questions??