Rio Grande Silvery Minnow Reproductive Monitoring(2001–2019)

Robert K. Dudley1,2, Adam L. Barkalow1, Tessia O. Robbins1, Steven P. Platania1,2, and Gary C. White1,3

1 American Southwest Ichthyological Researchers (ASIR); 800 Encino Place NE, Albuquerque, NM, 871022 Museum of Southwestern Biology (Fishes), UNM; MSC03-2020, Albuquerque, NM, 871313 Dept. of Fish, Wildlife, and Conservation Biology, CSU; 10 Wagar, Fort Collins, CO, 80523

Historical

Current

Native Distribution (Hybognathus amarus)

Experimental

Rio Grande PS Reproductive Guild:

Rio Grande Silvery Minnow Hybognathus amarus

Speckled Chub Macrhybopsis aestivalisRio Grande Shiner Notropis jemezanus

Phantom Shiner N. orca

Pecos Bluntnose Shiner N. simus pecosensis

Rio Grande Bluntnose Shiner N. s. simus

Life History of Rio Grande Silvery Minnow(Mortensen et al., 2019)

Cochiti InflowOtowi GageUSGS 08313000 1895–2017

Floodplain Activation

FloodRecession

Baseflow

BaseflowMonsoons

Snowmelt Runoff

PeakFlow

Reproductive Monitoring Program Objectives

1. Characterize the timing, duration, and magnitude of spawning by Rio

Grande Silvery Minnow (RGSM) in the Middle Rio Grande, and assess

differences across reaches and years.

2. Examine the relationships between flow, temperature, and RGSM

spawning within years.

3. Evaluate the influence of seasonal flows (e.g., magnitude and duration)

on RGSM egg passage rates across years.

4. Provide insight into key environmental factors affecting trends in the

temporal and spatial spawning patterns of RGSM.

Sampling Sites

Albuquerque (Angostura Reach)

Sevilleta (Isleta Reach)

San Marcial (San Acacia Reach)

Sampling Methods

Sampling equipment:• Moore Egg Collectors (MEC)

• Two per site

• Mechanical flow meters

• Volume sampled

Sampling intensity:• Daily: 6 hours/day

• Weekly: 7 days/week

• Annually: 50 days/year

Daily samples of drifting eggs:• Number of eggs (#): n

• Volume sampled (m3): V

• Density: D = ((n/V)· 100)

• Discharge (m3/s): Q

• Passage Rate: Pe = ((D/100)· Q)

Estimated Egg Passage Rate (eggs / s)

LCI = exp log E(x)( )-1.96 ´SE E(x)( ) / E(x)éë

ùû

UCI = exp log E(x)( )+1.96 ´SE E(x)( ) / E(x)éë

ùû

E(x) = d exp m +s 2

2

é

ëê

ù

ûú

Egg Passage Rates(All Sampling Sites)

• Seasonal timing/duration of

spawning (ca. late April to

early June) was similar across

sampling sites.

• Egg passage rates at

Sevilleta and San Marcial were

consistently higher than at

Albuquerque.

• Egg passage rate trends,

based on the three sampling

sites, were very similar across

years.

Year

10-3

10-2

10-1

100

101

102

103

104

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

10-5

10-4

10-3

10-2

10-1

100

101

102

103

E(x

) (e

gg

s /

s)

10-3

10-2

10-1

100

101

102

103

Albuquerque

San Marcial

Sevilleta

Q (

cfs

)

0

500

1000

1500

1 4 7 10 13 16 19 22 25 28 31 3 6 9 12 15 18 21 24

Egg

s p

er

100 m

3

10-2

10-1

100

101

102

103

Te

mp

. (°

C)

15.0

20.0

25.0

30.0

Discharge

Egg Density

Temperature

May June

Discharge, Eggs, and Temperature(San Marcial: 2012)

Egg Occurrence Probability and %Δ Discharge(San Marcial: 2003–2019)

Percentage change in mean daily discharge

-50 -25 0 25 50 75 100 125 150 175 200 225 250 275 300

Occurr

ence p

robabili

ty

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Egg Occurrence Probability and Temperature(San Marcial: 2003–2019)

Mean daily water temperature (°C)

14 15 16 17 18 19 20 21 22 23 24 25 26

Occurr

ence p

robabili

ty

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Densities of RGSM and Discharge(2003–2019)

Dis

ch

arg

e (

cubic

fe

et p

er

se

co

nd

)

0

1000

2000

3000

4000

5000

6000

Year

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

E( x

) (f

ish

pe

r 1

00

m2)

10-2

10-1

100

101

102

103

E(x) = 0 E(x) = 0

Dis

ch

arg

e (

cubic

fe

et p

er

se

co

nd

)

0

1000

2000

3000

4000

5000

6000

Year

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

E( x

) (e

ggs / s

)

10-3

10-2

10-1

100

101

102

103

E(x) = 0 E(x) = 0

Egg Passage Rates and Discharge(San Marcial: 2003–2019)

Maximum Q (cfs)

0 2500 5000

E(x

) (e

gg

s /

s)

10-3

10-2

10-1

100

101

102

Days Q > 500 cfs

0 10 20 30 40 50

Days Q > 1,500 cfs

0 10 20 30 40 50

Days Q > 2,500 cfs

0 10 20 30 40 50

E(x

) (e

gg

s /

s)

10-3

10-2

10-1

100

101

102

Mean daily Q (cfs)

0 1000 2000 3000 4000

Egg Passage Rate vs. Discharge(San Marcial: 2003–2019)

• Each model included both δ (occurrence probability) and μ (lognormal

passage rate) with a single covariate for each estimated parameter

(e.g., δ[SANmean] μ[SAN>1,500]).

• Covariates representing various spring runoff conditions were included

in models.

• Hydraulic covariates included both fixed effects (i.e., covariate explains

variation) and random effects (i.e., random error [R] around covariate).

• Goodness-of-fit statistics (log-likelihood and Akaike’s information

criterion [AICc]) were used to assess the fit of data to various models.

Modeling the Spawning Ecology of RGSM(San Marcial: 2003–2019)

Model Estimates and Discharge(San Marcial: 2003–2019)

Model logLike K AICc wi

δ(SAN>2,500+R) μ(SANmax+R) 1,322.69 9 1,341.00 0.3214

δ(SAN>2,500+R) μ(SANmean+R) 1,323.35 9 1,341.65 0.2315

δ(SAN>2,500+R) μ(SAN>1,500+R) 1,325.00 9 1,343.30 0.1016

δ(SAN>2,500+R) μ(SAN>500+R) 1,325.08 9 1,343.38 0.0975

δ(SANmean+R) μ(SANmean+R) 1,327.01 9 1,345.31 0.0371

δ(SANmean+R) μ(SANmax+R) 1,327.04 9 1,345.34 0.0367

δ(R) μ(SANmean+R) 1,329.42 8 1,345.66 0.0312

δ(SAN>1,500+R) μ(SANmean+R) 1,328.16 9 1,346.46 0.0209

δ(SANmean+R) μ(SAN>1,500+R) 1,328.62 9 1,346.93 0.0166

δ(SAN>1,500+R) μ(SANmax+R) 1,328.74 9 1,347.04 0.0157

Ecological Model Results for RGSM(San Marcial: 2003–2019)

Conclusions and Implications

1. We found that egg occurrence probabilities were higher during years

with reduced and truncated spring flows, and that egg passage rates

were lower during years with elevated and extended spring flows.

2. It is likely that the proportion of individuals retained and successfully

recruited upstream is related to the complexity of instream habitat

conditions and the availability of nursery habitats (i.e., spring runoff).

3. Ongoing efforts to restore dynamic river flows, reconnect fragmented

reaches, and reestablish a functional floodplain should help to promote

resilient and self-sustaining populations of RGSM.

4. Continued study of the key factors that control this complex aquatic

ecosystem will be essential for developing and implementing

successful strategies for the long-term recovery of RGSM.

Acknowledgements

• Field, Data, & Laboratory: Leiah Atchison, Martinique Chavez, Michael Farrington, Tanner Germany,

Alexander Harrison, Jennifer Kennedy, Jacob Mortensen, Alexander Schroeder, Andrea Urioste, and Aaron

Wedemeyer (ASIR & Museum of Southwestern Biology [UNM])

• Curation of Specimens: Alexandra Snyder (Museum of Southwestern Biology [UNM])

• Land Access & Sampling: Dionne Epps (Open Space Division [City of Albuquerque]), Kathy Granillo

and Jon Erz (Sevilleta NWR), and Susan Woods (U.S. Bureau of Reclamation [USBR])

• Technical & Contract Support: Jennifer Bachus and Mary Maestas (USBR)

• Report Review: Eric Gonzales (USBR) and Joel Lusk (USFWS)

• Fish Sampling & Collection Permits: Handling and collection of Rio Grande Silvery Minnow was

authorized by the USFWS (Permit TE001623-4). The N.M. Department of Game and Fish authorized our

handling and collection of all other native and nonnative fishes (Permit 1896).

• Support & Funding: The Middle Rio Grande Endangered Species Collaborative Program has provided

valuable scientific input on this research program since 2001. This study was funded by the USBR, and the

Albuquerque Area Office and Salt Lake City Regional Office administered all funds (Contract R17PC00033:

Requisition 0040426606).