An overview of Enhanced Delta Smelt Monitoring (EDSM) …deltacouncil.ca.gov/sites/default/files/2017/12/2017 LOBO_IRP_EDSM... · One WIIN mandate (amongst many): “(Sec. 4010) The

Ken Newman, USFWS, Lodiwith Denise Barnard and Lara Mitchell

5 December 2017

An overview of Enhanced Delta Smelt Monitoring (EDSM) Program

An overview of Enhanced Delta Smelt Monitoring (EDSM) Program

U.S. Fish & Wildlife Service

Outline

I. Objectives – why EDSM exists

II. EDSM Design: (a) Phases, (b) Study Area, (c) Strata, (d) GRTS selection, (e) Data Collection, (f) Data Analysis

III. Results

IV. Comments, Concerns, Criticisms

V. Final Remarks

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I. Objectives

1. Estimate total Delta Smelt (DS) abundance for all life stages on a weekly to bi‐weekly basis, along with standard errors

2. Estimate spatial dist’n of DS at relevant spatial‐temporal resolution, e.g., proportion of adults at risk of entrainment

3. Provide early warning of potential adult DS entrainment

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I. Objectives

4. Provide data for estimating and modeling adult entrainment

5. To compare EDSM estimates with those from existing surveys

6. To provide data for independent assessment of Life Cycle Model predictions

7. To potentially assist other studies, e.g., USBR Directed Outflow Project

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1. Objectives and WIIN ActStart of EDSM and passage of Water Infrastructure Improvements for the Nation (WIIN) Act nearly coincided in December 2016.

One WIIN mandate (amongst many): “(Sec.4010) The bill requires Interior to conduct, with respect to Delta smelt, increased monitoring anda distribution study.”

EDSM is potentially the means of fulfilling this mandate (a view partially echoed by letters to FWS Regional Director from Sen. Feinstein, Coalition for a Sustainable Delta, and the Metropolitan Water District: with improvements suggested)

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Existing Long term Monitoring Surveys

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Spring Sampling- Larvae + Post-Larvae (20mm)

Summer Sampling-Juveniles (TNS)

Fall Sampling- Sub-Adults (FMWT)

Winter/Spring Sampling-Adults (SKT)

Mature/Spawning Larvae Juveniles Sub-AdultsJan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

19591960….….1967….….….1980…..…..…..…..…..1995…………2001

Now

Summer TownetSurvey Fall

MidwaterTrawl

Survey

20mm LarvalSurvey

Spring Kodiak Trawl Survey 7

Existing Surveys


19591960….….1967….….….1980…..…..…..…..…..1995…………2001

Now


MidwaterTrawl

Survey

20mm LarvalSurvey


Existing Surveys

(bi-weekly)

(monthly)

(monthly)

(bi-weekly)


19591960….….1967….….….1980…..…..…..…..…..1995…………2001

Now


MidwaterTrawl

Survey

20mm LarvalSurvey


Existing Surveys

(bi-weeklyEfficient3 tows)

(monthlyInefficient1 tow)

(monthlyefficient1 tow)

(bi-weeklyInefficient3 tows)


Problems with Existing Surveys

FMWT+SKT (adult), DS surveys: not fine enough temporal resolution

TNS+FMWT: inefficient for DS

All 4 surveys’ sample locations are judgment, not probability, samples

Chance of False Negative with single tows (FMWT+SKT) higher than with multiple tows

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II. EDSM Design

Three Phases:

Phase 1: December – March (adults)

Phase 2: April – June (post‐larvae)

Phase 3: July – November (juveniles + sub‐adults)

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II. EDSM Design

Study Area is Dynamic, but “generally” the waters occupied by DS

Defined by area and by depth

Phases 1+3: ~4m

Phase 2: 10m

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II. EDSM Design

Stratification: Also Dynamic

13Phase 1 Phase 2


II. EDSM Design

Sampling Schedule:

Weekly, field crews on the water 4‐5 days per week

(implicitly a temporal stratification)

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II. EDSM Design

Sample Site Selection:

Within each stratum, equal probability Generalized Random Tesselation Stratified (GRTS; Stevens and Olsen 2004) sampling.

Plusses: spatially dispersed probability sample

Minuses: complex 15


GRTS vs Systematic or IndependentRandom sample

Systematic Sample => spatially dispersed but only 1st point is random—difficult to estimate variance.

Independent Random Sample => each point is random, but can lead to clumping.

Olsen, et 2012

IRSGRTS


II. EDSM Design: GRTS 4 Steps

1. Divide stratum into nested sub‐areas with numerical addresses (recursive partitioning): e.g., 2 layers of partitions with 4 components

0.00, 0.01,…,0.30,0.31,0.32,0.33

2. Randomize top layer, then randomize w/i 2nd layer, etc(hierarchical randomization)

3. Take systematic sample along a line constructed from the hierarchically randomized partitions

4. Reorder the selected points (sub‐areas) so that any given sequence is spatially balanced (reverse hierarchical ordering)

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Phase 1 Example

●

Western Delta

●

Suisun Bay

●

Suisun Marsh

●

Lower

●

SacramentoLower San

Joaquin

●

Liberty Island/ Cache Slough

●

Sac Deep Water Shipping Channel

●

Upper Sacramento

●

Eastern Delta

●

Southern Delta

1

2

3

4

5

6

78910

11

1213

14

15

16

Suisun Bay4 primary sites, 12 oversample

sitesN

P

Slips & LaunchesP Pittsburgh


II. EDSM Design

ALL WY 2017 Phase 1 Sample Locations:

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II. EDSM DesignData Collection: Phases 1 & 3

Fish with Kodiak Trawl

Stopping rule

Sample at least twice, stop if 1 DS caught.

Else sample till 1 DS caught or #tows=qmax, qmax=5 or 8.

(If 1st tow yields 24 DS or more, stop.)21


Purpose of stopping rules

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Decrease probability of false absences Minimize take


II. EDSM DesignData Collection: Phase 2

Use 20mm larval net.

Sample exactly twice.(stopping rule cannot be used)

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II. EDSM DesignData Collection: Phase 2

And some “experimental” shallow water sampling: 1/3 size larval net off side of boat.

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Shallow Water:

9 sample days21 sites36 samples


Oblique Surface

II. EDSM DesignExperimental Gear comparisons

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June & July6 sample days9 sites64 samples

20mm Kodiak

Large net

Small net


Gear comparisons Results

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0

50

100

150

200

250

300

June June July July

DSM

Catch/Volum

e

KDTRS SurfaceL SurfaceL Oblique

Small & SurfaceLarge 20mm/Surface

Small 20mm/Surface

Large 20mm/Oblique


II. EDSM Design: Data Analysis

Numbers caught in a given tow follow a Zero Inflated Negative Binomial (ZINB).

Catch ~ 0 + (1‐ 0) NegBin( v, ),

where 0 = probability no fish present

and v=volume sampled

Fish density: = (1‐ 0)

=> abundance: = * Volume

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Estimation of the parameters, 0, is done via Maximum likelihood.

Likelihood for Phases 1 and 3 accounts for stopping rules (Tech Note 23).

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= (1‐ 0) => = * Volume

Standard errors of based on std errors of 0 , (using “delta” method).

Confidence intervals assume estimates’ sampling distribution is lognormal (bypass embarrassment of negative abundance estimates)

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III. Results: Summary

Phase 1 WY 17:

Phase 2 WY 17:

Phase 3 WY 17/18:

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Dec 15 2016–Mar 30 201759 sample days426 sites2158 samples (tows)148 fish

Apr 24 2017–Jun 28 2017≈32 sample days197 sites391 samples (tows)20 fish

Jul 3 2017–Nov 24 2017≈86 sample days≈488 sites≈2759 samples (tows)≈362 fish


III. Results

Weekly reports:

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III. Results

Includes Tables with estimates of abundance.

E.g., Phase 1

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III. Results

and Figures showing catches

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III. Results

and Figures showing estimates over time

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III. Results

and for Phase 1, estimates of the proportion “at risk” of entrainment:

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1. Considerable week to week variation in .

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9726

35,862



“Bottom line” is that DS are increasingly sparse and numbers caught are alarmingly low despite “intensive” sampling.

Weekly catches over 22 weeks :

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Jul: 52, 32, 77, 66 Aug: 15, 7, 9, 12, 3 Sep: 9, 8, 21, 4

Oct: 2, 39, 1, 2, 1, 1 Nov: 1,0,1,3



Fixes?

a. Week‐stratum‐specific values are assumed for parameters => may start assuming common 0 for strata in a week.

b. Inadequate sample size? Sample size determination is needed/overdue. Expensive?

c. Alternative analysis‐more model‐based, smooth estimates over week.

d. Alternate stratification?

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2. Sample size determination (from Phase 1)‐Per stratum h=> 20% trimmed mean and 0

‐ Τ for stratum h = * 1 0,h *

‐ Calculate expected number of tows E[q] for Zero Inflated Poission (ZIP)

‐ Sample volume per site = E[q] * v, average tow volume (for either 5 minute or 10 minute tows).

‐ , the total number of sample = /

‐Between sample unit variation in catch, Var( ), is estimated using the ZIP dist’n

‐ Var( Τ * Var( )/n, n = #sample sites

=> T = ∑ Τ , Var Τ ∑ Var( Τ , CV =VarT

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2. Sample size

determination

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Coefficient of Variation (CV)of Τ vs Total Sample Size

“Mean” Density and Mean

40% CV w/ 24Sites, 5 minute tows



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Coefficient of Variation (CV)of Τ vs Total Sample Size

“Low” Density and Median

Over 100% CV w/ 24Sites, 5 minute tows



3. Phase 2 catches adjusted upward accounting for gear selectivity => this will change based on new gear analyses.

Logistic to Dome‐Shaped:

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4. Phase 1 and 3 assume 100% effective gear=> debatable.

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5. Study area too small: systematically excluding near shore areas.

Not sampling waters < 2m depth =>

Excluding 6% of the total Bay‐Delta region considered “suitable” for Delta Smelt.

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6. “Take”.

‐ Compare numbers killed to numbers available: 148 + 20 + 362 = 530 caught over 1 year period compared to Τ = 10’s of 1000s?

(Better: adjust for life stage)

‐ Fraction of water sampled: 24 sites * 4 tows * 3000 m3 => 288,000 m3.

Volume to 10m depth = 1,744,080,840m3

=> 0.00017 of volume sampled.

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‐ SOPs changed in multiple ways to lessen take:(1) having stopping rules (2) 5 minute tows in high density areas (vs 10 minute tows elsewhere) (3) occasionally reduce tow times to as low as 2.5 minutes when the risk of catching large #DS is high (4) forego a 2nd tow if 1st tow caught > 24 DS

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Benefit of SOP changes on Take:Predicted Phase 3 catch if (a) all tows were 10 minutes long (b) a 2nd tow had been done when the first catch was more than 24.Observed 365 DS

Predicted 1006 DS

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7. Have yet to estimate Entrainment (adults)…working on it:

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V. Final Remarks

DS are becoming increasingly sparse, hard to find, few being caught.

1. Alternative sampling procedure? E.g., a few relatively long transects across a stratum (picking up gear after 5 minutes, check for “large” catches, then repeat)

2. Contract a GRTS expert (Stevens or Olson, say) to get 2nd opinion.

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Questions? Comments?

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Documents

An overview of Enhanced Delta Smelt Monitoring (EDSM) …deltacouncil.ca.gov/sites/default/files/2017/12/2017 LOBO_IRP_EDSM... · One WIIN mandate (amongst many): “(Sec. 4010) The