1. Differentiating Resident from Migrant Lake Sturgeon in the Lower Niagara River
Gregory R. Jacobs1, John A. Sweka1, Dimitry Gorsky2,
Michelle Casto-Yerty2, and Betsy Trometer2
1 – USFWS Northeast Fishery Center, Lamar, PA
2 – USFWS Lower Great Lakes Fish and Wildlife Conservation Office, Amherst, NY

2. Overview
Overview
Population assessment of lake sturgeon in the Lower Niagara River
Mark-recapture
Sex, stage, contaminants
Habitat selection
Herein:
Can fixed-station telemetry detect differences in habitat selection of spawning vs. non-spawning lake sturgeon?

3. Methods

4. Study Site Lower Niagara River Methods Lower Middle Upper
Niagara Falls NY ON
Lake Ontario
Lake Erie
Lower
Upper
Middle
Lower Niagara River

5. Radio Telemetry Telemetry Equipment What does detection mean?
Methods
Radio Telemetry
Telemetry Equipment
ATS R4500 Radio Receiver
8-receiver array
Distributed among 3 river areas
Max Depth = e(Distance)
At 100 m, 8 m max depth
Range < River Cross-section
Max Depth: 15 – 50 m
Channel width: 175 – 700 m
What does detection mean?
Assume: presence in 100 m radius
Lower Niagara River
Lake Ontario ON NY
Lake Erie

6. Lake Sturgeon Baited set lines Biological Data, PIT Radio-tag implant
Methods
Lake Sturgeon
Baited set lines
Biological Data, PIT
Radio-tag implant
616 day, ↑ range, trailing whip, VHF
Implanted April, 2012
Sex & stage determination
Blood Serum T & E2, Ultrasound, Biopsy
Study Period: Apr-18 to Jul-30, 2012

7. Models Considered Occupancy Modeling
Methods
Models Considered
Occupancy Modeling
Site is unit if inference
Detection probability modeled explicitly
Keep on back burner...
Discrete Choice Modeling (Kneib et al. 2011)
Fish is unit of inference
Presence-only method, site & individual effects
Initially, assume whole river ‘available’

8. Discrete Choice Model Simultaneous analysis of r habitat types
Methods
Discrete Choice Model
Simultaneous analysis of r habitat types
Model coefficients = additive effects on η
One habitat type as reference category

9. Results

10. Telemetry Summary 2012-tagged fish (30)
Results
Telemetry Summary
2012-tagged fish (30)
27 relocated Apr-18 to Jul-30, 2012 (104 days)
21 kept for analysis
Last Day Observed
Reproductive Status
Day of year
Mature M
Mature F
Immature N 9 3 TL
1,363 mm
1,574 mm
1,387 mm Wt
18.4 kg
33.5 kg
19.8 kg
Days
14.6
21.3
13.7
Range
33.8
42.3
56.8

11. Discrete Choice Model Unconditional model Hypothetical model
Results
Discrete Choice Model
Covariate Effect
Day of Year
Nonlinear Time Effect
Unconditional model
Only random fish effect
No overall preference
Hypothetical model
η = β + Rstatus +
Time (non-linear) +
Fish (random intercept)
Still no significant selection (β) ...

12. Discrete Choice Results
Covariate Effect
Day of Year
Nonlinear Time Effect
Covariate Effect
Individual Fish Effect
Immature M F

13. Discussion

14. Lower Niagara River: no
Results
Can we differentiate migrants from residents using fixed-station telemetry?
Lower Niagara River: no
Individual heterogeneity >> covariate effects
Work-in-progress
Low sample size (n=30, too few 2011 returns)
Single year, (not quite) 2 seasons
Coarse-scale covariates (at best)

15. Results
Can we differentiate migrants from residents using fixed-station telemetry?
General approach
Promising analytical framework
Applicable to large-system fixed telemetry

16. Acknowledgements
Molly Webb, Mariah Talbott, Eli Cureton, USFWS Bozeman, MT
Dave Smith, Mary Rocky, USGS Leetown, WV
Field Crew: Todd Duval, Brian Layton, Shana Chapman, Paul Bigaj, USFWS Amherst, NY
Geoffrey H. Groocock, DVM, PhD, consulted on surgical methods
Chris Castiglione, USFWS Amherst, NY

18. Discrete Choice Coefficients
Variable Coeff. SD P 95% CI β (Mid) β (Uppr) M (Mid) M (Uppr) F (Mid) F (Uppr)

19. Habitat Variables Substrate type River Kilometer Temperature
Side-scan sonar bottom mapping
Bottom types: Hard , Intermediate , Soft
River Kilometer
Distance from Lake Ontario
Temperature
Buffalo, NY station
Other variables
Bathymetry, flow regime,
disturbance