1

40 West 20th Street

New York, NY 10011

(212) 727-2700

Fax (212) 727-1773

July 19, 2013

Submitted via electronic mail to nmfs.ner.batchfmpbiop@noaa.gov

Mr. John K. Bullard, Regional Administrator

National Marine Fisheries Service – NOAA 

55 Great Republic Drive

Gloucester, MA 01930

RE: ENDANGERED SPECIES ACT SECTION 7 CONSULTATION BIOLOGICAL 

OPINION -- Endangered Species Act Section 7 Consultation on the Continued

Implementation of Management Measures for the Northeast Multispecies, Monkfish, 

Spiny Dogfish, Atlantic Bluefish, Northeast Skate Complex, Mackerel/Squid/Butterfish, 

and Summer Flounder/Scup/Black Sea Bass Fisheries [Consultation No. 

F/NER/2012/01956] 

Dear Regional Administrator Bullard:

On behalf of the Natural Resources Defense Council (NRDC), I submit the following comments 

on the draft “batched” biological opinion (BiOp) developed as part of the Section 7 consultation 

referenced above. Our comments pertain solely to the Atlantic sturgeon. As an overall matter, 

the failure of the draft BiOp to call for any measures to reduce Atlantic sturgeon bycatch in the 

six federal fisheries in which the species is primarily caught is profoundly discouraging. 

Bycatch was considered the highest threat to the species at the time of the listing, and bycatch in 

these six fisheries in particular has been documented as a big source of the problem, with 

thousands of Atlantic sturgeon caught each year. The decision not to require any bycatch 

reduction rests on estimates of population abundance and bycatch mortality and thresholds for 

acceptable fishing mortality about which we have significant concerns, as we discuss below. 

The failure to require bycatch reduction is made that much more disheartening by the 

considerable research over the last several years on methods and gear to reduce bycatch mortality

and because we increasingly know where and when the Atlantic sturgeon bycatch hotspots occur 

(e.g., large-mesh sink gillnets off the Virginia and North Carolina coasts in the late winter and 

early spring). Bycatch is, in many ways, the easiest source of significant Atlantic sturgeon 

mortality to address in order to ensure the species’ recovery – but, if this BiOp is finalized in its 

current form, this will not happen. We ask that you revisit the methodologies relied upon in the 
2

draft BiOp to address the issues identified below. We also request that at least the population 

estimates for each DPS be peer-reviewed before the BiOp is finalized. 

By letter dated May 20, 2013, NRDC sent a request to NMFS pursuant to the Freedom of 

Information Act for records relating to J. Kocik, C. Lipsky, T. Miller, P. Rago, and G. Shepherd. 

2013. An Atlantic Sturgeon Population Index for ESA Management Analysis. Northeast 

Fisheries Science Center Reference Document 13-06 (Atlantic Sturgeon Population Index). We 

have not yet received the requested records, although the statutory deadline for this response has 

elapsed. NRDC reserves the right to supplement these comments based on our review of the 

requested records once we receive them.

We attach two reports and incorporate them herein by reference. The first is a report from Dr. 

Ellen K. Pikitch, Executive Director of the Institute for Ocean Conservation Science and 

Professor at the School of Marine and Atmospheric Sciences at Stony Brook University. Dr. 

Pikitch is recognized as an international expert in ocean conservation science and has authored 

and edited more than 100 articles and books on fisheries science and management, including 

more than a dozen relating to sturgeon. Dr. Pikitch’s primary research interests include 

vulnerable and ecologically important marine species, including sturgeon. She has served as 

member of the IUCN Sturgeon Specialist Group, including as Chair of the Stock Assessment and 

Recovery Sub-Committee, since 2002. Dr. Pikitch received a Ph.D. in Zoology from Indiana 

University and M.A. and B.S. degrees in Mathematics from the City College of New York. 

Dr. Pikitch’s report examines the bycatch mortality rates relied on the draft BiOp as the basis for 

concluding whether or not to recommend action to reduce bycatch. For reasons detailed in the 

report, Dr. Pikitch concludes that these threshold mortality rates (≤14% for population 

maintenance and ≤5% for population recovery) are not based on the best available science. Dr. 

Pikitch recommends alternative threshold mortality rates that are significantly better supported 

by the technical literature and that have been consistently relied on by regulatory agencies in the 

past (at least until the draft BiOp).

The second report is from Dr. Elizabeth A. Babcock, Assistant Professor in the Division of 

Marine Biology and Fisheries, Rosenstiel School of Marine and Atmospheric Sciences, 

University of Miami. Dr. Babcock is a highly-skilled and well-published quantitative fisheries 

scientist, with significant experience in fisheries stock assessments and analysis of bycatch 

monitoring systems. She received her Ph.D. from the University of Washington. 

Dr. Babcock’s report, which was previously provided to Regional Administrator Bullard on 

May17, 2013, contains a review of the Atlantic Sturgeon Population Index. Dr. Babcock 

identifies a number of flaws with the Atlantic Sturgeon Population Index, including an incorrect 

key equation and the authors’ failure to show that their use of tag recovery data provides accurate 

estimates of encounter rates in the relevant fisheries (for example, the tag recovery data relied on 

appears to be from a mix of fisheries, primarily in state waters, while the bycatch estimates are 

exclusively from federal waters). Because of these flaws, Dr. Babcock recommends that the 

ASPI index not be used either as an absolute estimate of abundance or as a relative index of 

abundance. NRDC is concerned that the draft BiOp suggests to the public that the ASPI 

population estimates are more reliable than is the case and recommends that either the discussion 
3

of the ASPI be removed from the document (as it is not being relied on for regulatory purposes) 

or a more complete explanation of its shortcomings be included.

NRDC provides the following additional comments concerning the draft BiOp:

Acceptable fishing mortality rates

• With respect to the threshold fishing mortality rates relied on by the draft BiOp, the literature 

from which these rates are derived makes clear that these rates assume that fishing mortality 

is the sole, or at least the primary, source of anthropogenic mortality (ASMFC 2007; 

Boreman 1997). But it is widely-recognized, including by NMFS, that Atlantic sturgeon 

suffer from other significant sources of anthropogenic mortality to varying degrees from 

varying sources depending on the DPS, such as mortality from impaired habitat, dredging, 

and vessel strikes. For example, vessel strikes are considered chief threats to Atlantic

sturgeon in the Delaware River (Brown and Fox 2010; ASSRT 2007) and in the James River

(Balazik et al. 2012; ASSRT 2007). These additional sources of mortality need to be taken 

into account in the final biological opinion, such as by reducing the acceptable fishing 

mortality rates or setting an acceptable mortality rate that incorporates all sources of 

mortality. Although NMFS asserts that it is unable to quantify the effects of non-fishing 

threats (Draft BiOp at 129), this is not true for at least some threats. Atlantic sturgeon 

mortality from vessel strikes, for example, has been quantified in certain locations, as noted 

above. In any event, the ESA places an obligation on NMFS to err on the side of protecting 

listed species and to develop necessary information and conduct projections to fully account 

for threats. See, e.g., Conner v. Burford, 848 F.2d 1441, 1454 (9th Cir. 1988); Natural Res. 

Defense Council v. Kempthorne, 506 F. Supp. 2d 322, 360 (E.D. Cal. 2007).

• The acceptable fishing mortality rates relied on in the draft BiOp are derived from 

information about the Hudson River Atlantic sturgeon population. This river population is 

widely-considered to be the healthiest of current U.S. Atlantic sturgeon populations. Smaller 

populations “would be expected to be disproportionately affected by bycatch as proportional 

removals have larger negative effects on less productive populations (ASMFC 2007, citing 

Policansky and Magnuson 1998). As discussed in Dr. Pikitch’s attached report (see also 

Moyer et al. 2012), the acceptable fishing mortality rates must be set in a precautionary 

manner to take into account the increased risk incurred by these smaller populations. 

In this regard, the Draft BiOp fails specifically to adequately account for the heightened 

vulnerability and the importance of the Delaware River Atlantic sturgeon population, once 

the nation’s largest. The Delaware is one of two rivers in the New York Bight DPS with a 

spawning population – and one of only four such rivers between Maine and Virginia. In the 

final Atlantic sturgeon listing rule, the agency described the Delaware population as “very 

small and extremely vulnerable to any sources of anthropogenic mortality.” The draft BiOp 

(at p. 127) recognizes that the loss of the Delaware River population “will negatively impact 

the persistence and variability of the DPS as a whole.” As noted above, the Delaware River 

Atlantic sturgeon population is also subject to multiple threats, with NMFS noting in the final 

listing rule that the loss of just a few adult female Atlantic sturgeon from the spawning 

population as a result of vessel strikes would hinder its recovery. Nevertheless, the draft 
4

BiOp fails to consider in any specific detail the threat posed to the Delaware River Atlantic

sturgeon population by bycatch in the six federal fisheries. 

Northeast Area Monitoring and Assessment Program (NEAMAP)-derived population estimates

• We have significant concerns about how the NEAMAP survey information was relied on in 

the draft BiOp. While we recognize the importance of providing protections to listed species 

on the basis of the best available scientific information, rather than waiting for potentially 

better information, we fear that in this instance that there is high likelihood that the 

population estimates are incorrect at the DPS level and are contributing to decisions not to 

take protective measures. Specifically:

o The length data from the NEAMAP survey strongly suggests that it is selective for 

younger fish, even accounting for what is likely a skewed population age structure as 

a result of selective fishing mortality on juveniles and young adults. Taylor (2010), 

on slide 11, indicates that, out of 79 sturgeons caught in the NEAMAP surveys, 69, or 

87%, are less than 150 cm, which means they are juveniles. The remaining sturgeons 

caught appear to be ages 10-14, with nothing older. This means that the survey may 

be able to provide a reasonable index of juvenile abundance but it does not tell us 

how many adult sturgeons are out there. This is of particular concern because of the 

high fishing mortality on juveniles and young adults.

o As shown on the BiOp at p. 124-25, the error range associated with the population 

estimates derived from the NEAMAP surveys is quite large, no doubt in large part as 

a result of the rare frequency of sturgeon encounters (NMFS 2012). But this means 

that there is substantial likelihood, recognized by the survey itself, that the actual 

number of Atlantic sturgeon is substantially lower (or higher) than that being assumed 

by NMFS, which has selected the mean estimate for use for management. For each 

DPS, the level at which management decisions are being made, the risk of error is 

higher.

o In assigning the NEAMAP-derived population estimate to specific DPSs, NMFS 

appears to have applied DPS composition numbers derived from the NMFS observer 

program. The problem with this is that it necessarily assumes that the NEAMAP 

survey is sampling from the different DPSs identically to the NMFS observer 

program. This almost certainly is not the case, as can be seen by comparing the 

locations of the NEAMAP Atlantic sturgeon encounters shown on page 8 of Taylor 

(2010) to those of the NMFS observer program encounters shown on page 6 of 

Damon-Randall et al. (2013). For example, the NEAMAP survey found a large 

number of fish off the south shore of Long Island but relatively few around Delaware 

Bay and off the NC coast compared to the NMFS observer program. If the NEAMAP 

survey is sampling from healthier populations in higher numbers, this will lead to 

overly-optimistic population estimates for the weaker DPSs. 

• It is not entirely clear how NMFS derived “adult equivalents” for purposes of estimating 

population levels (and bycatch mortality). Because the population estimates and the bycatch 
5

mortality estimates are derived from fishing gear (in the case of the NMFS observer 

program) or trawl survey gear (in the case of the NEAMAP survey) that is selective to the 

older age classes of subadults, it is important that this selectivity is accounted for when 

reducing subadult numbers by factoring in natural mortality in order to determine “adult 

equivalents.” 

Bycatch mortality estimates

• We have significant concerns about the methodology used in the draft BiOp for assigning

bycatch mortality estimates to each DPS. NMFS appears to have allocated proportions of the 

total bycatch mortality estimates from NEFSC (2011) to the specific DPSs based on the 

genetic testing of 173 Atlantic sturgeons sampled through the NMFS observer program. 

Although the methodology for selecting the 173 NMFS observer program specimens utilized 

in this Mixed Stock Analysis (MSA) is not set out in the Draft BiOp or in Damon-Randall, et 

al. (2013), no information is provided to establish that these 173 observer program specimens 

adequately represent the DPS make-up of Atlantic sturgeon bycatch mortality across the 

batched fisheries. We know that Atlantic sturgeon, particularly juveniles, are found 

disproportionately around their natal rivers. If these are also high bycatch mortality areas, 

then there will be a disproportionate impact. 

• We do not understand the basis for applying the MSA-derived DPS percentages to bycatch in 

the South Atlantic shark and shrimp fisheries. These fisheries do not operate in much of the 

region sampled for purposes of the MSA analysis (such as the Gulf of Maine, to pick the 

most obvious example). It is virtually certain that bycatch in these fisheries is comprised of 

more Atlantic sturgeon from the more southern DPSs than is assumed in MSA. For example, 

Atlantic sturgeon bycatch off the southern Virginia/North Carolina coast in the winter was 

found to be comprised of 47-57% Atlantic sturgeon from the Chesapeake Bay DPS, 25-35% 

from the South Atlantic DPS, and 12-22% from the New York Bight DPS (Damon-Randall, 

et al. 2013). 

• The Draft BiOp (p. 129) assumes zero bycatch in the batched fisheries in the southeast 

region, i.e., south of Cape Hatteras, thus underestimating Atlantic sturgeon bycatch mortality. 

This is of particular concern for the more southern DPSs, as bycatch in the southeast fisheries 

will be comprised of higher proportions of Atlantic sturgeon from these DPSs. This needs to 

be corrected in the final BiOp.

• In calculating bycatch mortality for specific DPSs, the Draft BiOp assumes the same bycatch 

mortality rates in warmer water (such as is more prevalent in the more southern DPSs) as 

colder water, even though it recognizes (at pp. 246 & 248), that mortality is higher in warmer 

water .

• For all DPSs, the Draft BiOp assumes 50% of bycatch mortality occurs in state fisheries. 

Available information indicates that this level may not be high enough for the more southern 

DPSs. ASSRT (2007) and the draft BiOp document significant bycatch in state fisheries that 

will disproportionately affect the Chesapeake, Carolina, and South Atlantic DPSs. There has 

been concern for some time, for example, about bycatch of aggregating juveniles in 
6

nearshore waters of Virginia and North Carolina, in which there are active fisheries (Laney 

2007). Sufficient information is available to quantify such bycatch, providing the benefit of 

the doubt to the listed species in instances of limited data, rather than rely on a single 

coastwide assumption. Such quantification was done by the ASSRT (2007) and by the 

agency as part of the listing process, and more information has become available since the 

listing occurred. 

• The draft BiOp fails to account for bycatch mortality in recreational fisheries, even though it, 

for example, characterizes (at p. 189) Atlantic sturgeon bycatch in the recreational shad 

fishery in southern Maine as an “acute problem.” 

• The draft BiOp’s bycatch mortality estimates assume that all mortality is immediate and 

recorded by observers. Available information (Stein et al. 2004; Collins et al. 1996) 

indicates that post-release mortality is likely to occur. NMFS should incorporate an 

assumption for such post-release mortality into the bycatch mortality estimates. 

• The draft BiOp’s bycatch estimates fail to account for the “observer effect,” by which 

fishermen change normal fishing activities to avoid bycatch when carrying an observer. The 

possible existence of an observer effect could be analyzed by comparing characteristics of 

observed trips with characteristics of unobserved trips. It is suspicious, for example, that 

observed Atlantic sturgeon bycatch dramatically declined in 2010 (NEFSC 2011), the year 

the species listing became under consideration, relative to earlier years. 

Incidental Take Statement

• NRDC believes that it is vital that incidental take reporting occur more than every five years, 

considering that the listing occurred very recently and that the BiOp relies on a significant 

amount of newly-developed information and analyses. Similarly, it is important that NMFS 

provide monthly summaries and an annual report of observed Atlantic sturgeon takes in New 

England and Mid-Atlantic fisheries. 

• The Incidental Take Statement, and the biological opinion more generally, should be 

explicitly conditioned on the upcoming Atlantic sturgeon stock assessment, and should be reopened if the stock assessment provides different population and fishing mortality estimates 

than relied on in the biological opinion. 

Literature Cited

Atlantic States Marine Fisheries Commission. 1998. Atlantic Sturgeon Stock Assessment Peer 

Review Report. March 1998.

Atlantic States Marine Fisheries Commission. 2007. Estimation of Atlantic Sturgeon Bycatch in 

Coastal Atlantic Commercial Fisheries of New England and the Mid-Atlantic. Special Report to 

the ASMFC Atlantic Sturgeon Management Board. August 2007.
7

Atlantic Sturgeon Status Review Team. 2007. Status Review of Atlantic Sturgeon (Acipenser 

oxyrinchus oxyrinchus). Report to National Marine Fisheries Service, Northeast Regional Office. 

February 23, 2007. 

Balazik, M.T., K.J. Reine, A.J. Spells, C.A. Fredrickson, M.L. Fine, G.C. Garman and S.P. 

McIninch. 2012. The Potential for Vessel Interactions with Adult Atlantic Sturgeon in the James 

River, Virginia. North American Journal of Fisheries Management 32:6, 1062-1069. 

Boreman, J. 1997. Sensitivity of North American sturgeons and paddlefish to fishing mortality. 

Environmental Biology of Fishes 48: 399-405.

Brown, J.J., and G.W. Murphy. 2010. Atlantic sturgeon vessel-strike mortalities in the Delaware 

estuary. Fisheries Vol. 35 No. 2: 72-83.

Clark, W.P. 2002. F35% revisited ten years later. North American Journal of Fisheries 

Management 22:251-257.

Collin, M.R., S.G. Rogers, and T.J. Smith. 1996. Bycatch of sturgeons along the southern 

Atlantic coast of the USA. North American Journal of Fisheries Management 16: 24-29.

Damon-Randall, K., M. Colligan, and J. Crocker. 2013. Composition of Atlantic Sturgeon in 

Rivers, Estuaries and in Marine Waters. National Marine Fisheries Service, Protected Resources 

Division. February 2013.

Dorn, W.M. 2002. Advice on West Coast rockfish harvest rates from Bayesian meta-analysis of 

stock-recruitment relationships. North American Journal of Fisheries Management 22:280-300.

Doroshev, S., J. Van Eenennaam, G. Watson, and G. Woberg. 1994. Reproductive conditions of 

the Atlantic sturgeon (Acipenser oxyrhynchus) stock in the Hudson River. Final Report to the 

Hudson River Foundation, New York.

Eyler, S., M. Mangold, and S. Minkkinen. 2009. Atlantic Coast Sturgeon Tagging Database. 

Summary Report. U.S. Fish and Wildlife Service. Maryland Fishery Resources Office, 

Annapolis, MD.

Goodyear, C.P. 1993. Spawning stock biomass per recruit in fisheries management: foundation 

and current use. p. 67-81. In S. J. Smith, J.J. Hunt and D. Rivard [ed.] Risk evaluation and 

biological reference points for fisheries management. Can. Spec. Publ. Fish. Aquat. Sci. 120.

Gross, M.R., J. Repka, C., T. Robertson, D.H. Secor, and W. Van Winkle. 2002. Sturgeon 

conservation: insights from elasticity analysis. Pages 13-30 in W. Van Winkle, editor. Biology, 

management, and protection of North American sturgeon. American Fisheries Society, 

Symposium 28, Bethesda, MD. 

Kahnle, A.W., K. A. Hattala, and K.A. McKown. 2007. Status of Atlantic Sturgeon of the 

Hudson River Estuary, New York, USA. American Fisheries Society Symposium 56: 347-363.
8

Laney, R.W., J.E. Hightower, B.R. Versak, M.F. Mangold, W.W. Cole, Jr., S.E. Winslow. 2007. 

Distribution, habitat use, and size of Atlantic sturgeon captured during cooperative winter 

tagging cruises, 1988-2006. American Fisheries Society Symposium 56: 167-182.

Miller, T. and G. Shepard. 2011. Summary of Discard Estimates for Atlantic Sturgeon. 

Population Dynamics Branch, Northeast Fisheries Science Center. 19 August 2011.

Moyer, G.R., J.A. Sweka, and D.L. Peterson. 2012. Past and present processes influencing 

genetic diversity and effective population size in a natural population of Atlantic sturgeon. 

Transactions of the American Fisheries Society 141:1, 56-57.

National Marine Fisheries Service. 2012. Endangered Species Act Section 7 Consultation 

Biological Opinion. National Marine Fisheries Service, Northeast Fisheries Science Center. 

April 2013.

National Marine Fisheries Service and the Atlantic States Marine Fisheries Commission. 2013. 

Workshop of Sea Turtle and Atlantic Sturgeon Bycatch Reduction in Gillnet Fisheries. Ocean 

City, Maryland. 22-23 January 2013.

North Carolina Division of Marine Fisheries. 2012. Potential Management Options to Reduce 

Sea Turtle and Atlantic Sturgeon Interactions in the Estuarine Gill Net Fishery. Issue Paper. July 

5, 2012.

Taylor, K. 2010. ASMFC Atlantic Sturgeon Management and Survey Updates. Atlantic States 

Marine Fisheries Commission. 8 February 2010.

* * *

Thank you for the opportunity to provide these comments.

Respectfully yours, 

Bradford H. Sewell

Senior Attorney

Attachments
1

Attachment 1

Report re the Endangered Species Act Section 7 Consultation on the Continued Implementation 

of Management Measures for the Northeast Multispecies, Monkfish, Spiny Dogfish, Atlantic 

Bluefish, Northeast Skate Complex, Mackerel/Squid/Butterfish, and Summer 

Flounder/Scup/Black Sea Bass Fisheries [Consultation No. F/NER/2012/01956]

For the Natural Resources Defense Council

July 19, 2013

Dr. Ellen Pikitch

Professor and Executive Director, Institute for Ocean Conservation Science

School of Marine and Atmospheric Sciences, Stony Brook University

Stony Brook, New York

Email: ellen.pikitch@stonybrook.edu

* * *

I was asked for my expert opinion on the following question:

Are the acceptable fishing mortality rates utilized in the draft Biological Opinion (BiOp) for 

population maintenance and for population recovery of Atlantic sturgeon, F=0.14 and 0.05

respectively, based on the best available science?

No, in my opinion, these fishing mortality rates are not based on the best available science for 

the following reasons.

The F=0.05 and F=0.14 fishing mortality rates are based upon reference mortality rates for 

Atlantic sturgeon recovery and maintenance, respectively, proposed by Boreman (1997). 

Specifically, Boreman (1997) proposed that maintaining spawning at 50% of eggs per recruit 

EPRmax (EPR50%) would be sufficient to allow recovery of the Atlantic sturgeon population in the 

Hudson River. Boreman’s rationale for the EPR50% level cites work by Goodyear (1993) (which 

the BiOp also cites), and aspects of the life history of Atlantic sturgeon, which is long-lived, has 

a late age of first maturity, and spawns only periodically once mature. These life history 

attributes, which are characteristic of sturgeon as a group, result in these species being highly 

vulnerable to fishing and slow to recover once depleted (Pikitch et al. 2005). Further support 

and/or reiteration of Boreman (1997) for a EPR50% level as a maximum mortality rate for 

recovery is provided by ASMFC (1998), ASSRT (2007), and Kahnle et al. (2007). Boreman 

calculated an estimate of EPR50% = 0.05 using preliminary data provided by Kahnle (see 

acknowledgements in Boreman), and other life history data available at the time of analysis. 

Subsequently, Kahnle et al. (in ASMFC 1998 and in Kahnle et al. 2007), using updated and more 

complete data from their Hudson River studies, as well as reproductive information from 

Doroshov (1997) estimated EPR50% = 0.03 for Atlantic sturgeon for the age-independent M = 

0.07 scenario (note, Boreman (1997) also used M=0.07 for all ages). Both ASMFC (1998) and 

ASSRT (2007) used F=0.03 corresponding to EPR50%. Further, Kahnle et al. (2007) estimated 

EPR50% = 0.02 for a scenario where M varies with age of sturgeon (decreasing with advancing 
2

age) while still averaging M = 0.07. In my opinion, the latter, age-varying scenario is a more 

realistic depiction of sturgeon natural mortality and, for that matter, the mortality pattern to be 

expected for most fishes. 

My opinion is that NMFS, in its BiOp, should have relied upon the Kahnle et al. (2007) study as 

the best available source of EPR estimates or it needed to provide an adequate explanation for 

choosing different values. Specifically, an upper bound for sturgeon recovery of F = 0.03 = 

EPR50% and a more likely and precautionary target level of F = 0.02 (based on the age-varying 

natural mortality scenario) should have been selected. In fact, NMFS relied on the Kahnle et al.

(2007) age-varying natural mortality scenario in another section of the BiOp (see bottom of p.

272) for calculations of “adult equivalents.” 

Boreman (1997) suggested that 20% of EPRmax (EPR20%) would be sufficient to maintain the 

population and estimated a corresponding F of 0.14. Kahnle et al. (2007) estimated F = 0.08 

corresponding to EPR20% using their updated and more robust methodology for the constant M 

scenario. Kahnle et al. (2007) did not provide an estimate of F = EPR20% for the age-varying M 

scenario, and I would expect it to be substantially lower than 0.08.

Nevertheless, I do not believe that a target rate of EPR20% is adequately precautionary for 

maintaining Atlantic sturgeon populations in all distinct population segments (DPSs). The 

EPR20% target comes from Goodyear (1993), which considers neither the unusual combination of 

morphology, habits and life history characteristics that make Atlantic sturgeon highly vulnerable 

to fishing mortality nor the significantly depressed condition of Atlantic sturgeon populations. 

Many of the remaining spawning populations are far below historical size and have skewed size 

and age structure because of overfishing and other anthropogenic sources of mortality that puts 

them at greater risk of extirpation. In addition, as noted by Kahnle et al. (2007), a target of F =

EPR50% is consistent with recommendations for maintaining healthy biomass (Clark 2002) or 

obtaining MSY (Dorn 2002) for the comparably long-lived and late maturing Sebastes species 

off the U.S. Pacific Coast. Moreover, ASMFC (2007), the ASSRT (2007), and Kahnle et al. 

(2007) all use F = 0.03 = EPR50% as the maximum fishing mortality rate for both maintaining and 

recovering the Atlantic sturgeon population. In my opinion, the BiOp should have done so as 

well. 

Literature Cited

Atlantic States Marine Fisheries Commission. 1998. Atlantic Sturgeon Stock Assessment Peer 

Review Report. March 1998.

Atlantic States Marine Fisheries Commission. 2007. Estimation of Atlantic Sturgeon Bycatch in 

Coastal Atlantic Commercial Fisheries of New England and the Mid-Atlantic. Special Report to 

the ASMFC Atlantic Sturgeon Management Board. August 2007.

Atlantic Sturgeon Status Review Team. 2007. Status Review of Atlantic Sturgeon (Acipenser 

oxyrinchus oxyrinchus). Report to National Marine Fisheries Service, Northeast Regional Office. 

February 23, 2007. 
3

Boreman, J. 1997. Sensitivity of North American sturgeons and paddlefish to fishing mortality. 

Environmental Biology of Fishes 48: 399-405.

Clark, W.P. 2002. F35% revisited ten years later. North American Journal of Fisheries 

Management 22:251-257.

Dorn, W.M. 2002. Advice on West Coast rockfish harvest rates from Bayesian meta-analysis of 

stock-recruitment relationships. North American Journal of Fisheries Management 22:280-300.

Doroshev, S., J. Van Eenennaam, G. Watson, and G. Woberg. 1994. Reproductive conditions of 

the Atlantic sturgeon (Acipenser oxyrhynchus) stock in the Hudson River. Final Report to the 

Hudson River Foundation, New York.

Goodyear, C.P. 1993. Spawning stock biomass per recruit in fisheries management: foundation 

and current use. p. 67-81. In S. J. Smith, J.J. Hunt and D. Rivard [ed.] Risk evaluation and 

biological reference points for fisheries management. Can. Spec. Publ. Fish. Aquat. Sci. 120.

Kahnle, A.W., K. A. Hattala, and K.A. McKown. 2007. Status of Atlantic Sturgeon of the 

Hudson River Estuary, New York, USA. American Fisheries Society Symposium 56: 347-363.

Pikitch E.K., Doukakis P., Lauck L., Chakrabarty P. and D.L. Erickson. 2005. Status, trends and 

management of sturgeon and paddlefish fisheries. Fish and Fisheries. 6:233-265.

* * *

Ellen Pikitch

Professor and Executive Director, 

Institute for Ocean Conservation Science

Stony Brook University, Stony Brook, New York
1

Review of: Kocik J, Lipsky C, Miller T, Rago P, and Shepherd G. 2013. An Atlantic Sturgeon 

Population Index for ESA Management Analysis. Northeast Fisheries Science Center Reference 

Document 13-06. 

Elizabeth A. Babcock 

Division of Marine Biology and Fisheries, Rosenstiel School of Marine and Atmospheric 

Sciences, University of Miami, 4600 Rickenbacker Cswy, Miami Fl 33133. 

May 16, 2013 

1. Summary and conclusions 

The document presents a novel methodology for estimating the abundance of Atlantic sturgeon 

using a combination of tag-recapture data and bycatch estimates from the commercial fisheries. 

The tagging data are used to estimate the encounter rate, which is the fraction of sturgeon that are 

captured in the fishery as bycatch (whether or not they survive). The bycatch estimates are 

adjusted to account for the fact that not all fish caught are killed, and then scaled by the 

encounter rates to estimate total sturgeon abundance. The estimated abundance is referred to as 

the Atlantic Sturgeon Population Index (ASPI); however, the number is described as an absolute 

estimate of sturgeon abundance (N) rather than an index of relative abundance. The uncertainty 

in the ASPI estimate is calculated using a standard Monte Carlo simulation method. 

The overall approach of combining bycatch estimates with tag-recapture estimates of encounter 

rates to estimate abundance is a viable one, provided sufficient data exists and an appropriate 

methodology is developed and followed. For rarely-caught species it is necessary to use all 

sources of information, and tagging data can be informative about encounter rates , in the 

fisheries in which the tag recoveries occurred. However, I believe the equations that were used in 

this document, notably equation (2) and equation (4), are incorrect. If this methodology is to be 

used for stock assessment, the authors must justify their methodology by describing the 

derivations of the equations in more detail, and they should demonstrate the effectiveness of the 

approach with simulated data. In addition, the authors should provide detailed justifications for 

all the decisions taken in the development of tag-recapture and bycatch estimation sub-models. 

Specifically, the authors must demonstrate that the tag recovery data used provides accurate 

estimates of the encounter rates in the relevant fisheries. I have serious concerns that this is not 

the case. 

2. ASPI Methodology 

Step 1. Minimum population size from bycatch data and encounter rate data 

The first component of the ASPI index is an estimate of the minimum population size nt, which 

is the number of fish that have ever been caught that are still alive at the end of year t. The 

equation is: 

(2) 
2

where μt is the encounter rate in the fishery in year t, Ct, is the bycatch in year t, θ is the fraction 

of the bycatch that die, and M is the natural mortality rate. The equation says that the number at 

the end of year t+1 is equal to the number that were alive at the end of year t and not captured in 

year t+1, plus the number of fish caught for the first time in year t+1 that survived the capture 

process (both reduced by natural mortality). Fish that had been caught before and were caught 

again in year t+1 are subtracted out (=μtnt(1-θ) ). Removing fish that had been caught before is 

equivalent to assuming that, although fish may survive a single capture, they always die the 

second time they are captured. The assumption that fish that are caught a second time are 

removed from the population may or may not be justified. Except for this assumption, the 

method to calculate minimum population size seems reasonable. 

The bycatch estimates were made by using a generalized linear model to estimate sturgeon 

bycatch in observed trips, using the catch of other species as explanatory variables, and then 

using that model to predict catches throughout each fishery. This approach seems reasonable in 

general, but I have not reviewed the methods paper (Miller and Shepherd 2011) in detail. One 

concern is that, based on Miller and Shepherd (2011), the bycatch rates apparently cover federal 

fisheries only, and do not include fisheries occurring in state waters, such as in rivers, estuaries 

and coastal waters. The encounter rates were estimated with tagging data, which apparently 

cover both state and federal fisheries. 

Step 2. Encounter rate from tag-recapture data 

The second component of the ASPI estimate is an encounter rate estimated from tag-recapture 

data, using the Baranov catch equation. The Baranov catch equation is used when fishing 

mortality rates are constant throughout the year, to convert instantaneous mortality rates (fishing 

mortality F and natural mortality M) into the fraction of the population that is taken by the 

fishery in each year (the harvest rate μ). From a fish population at the beginning of the year (N), 

the fraction that survive is ݁ିሺிାெሻ

, so that the fraction that die is ൫1 െ ݁ିሺிାெሻ൯. Since the 

fraction of total mortality that is fishing mortality is ቀ ி

ிାெቁ, the fraction of the population that 

will be caught in the fishery is: 

 (R1) ߤ௧ ൌ ி೟

ி೟ାெ ൫1 െ ݁ିሺி೟ାெሻ

൯ . 

The total catch is just this harvest rate1

 multiplied by the number of fish in the population at the 

beginning of the year: 

(R2) ܥ௧ ൌ ߤ௧ܰ௧

To use the Baranov catch equation to estimate encounter rates from tagged fish, it is also 

necessary to include estimates of reporting rates and tag loss rates. For example, for the 

component of the fishery that is not covered by the observers, the expected number of tag returns 

is: 

                                                            

1

 “Harvest rate” is the usual term for the fraction of fish caught in the fishery (μ) when all fish that are caught are 

killed. “Encounter rate” is a more general term which allows the possibility that some fish survive capture. The tagrecapture data is being used to estimate an encounter rate, not a harvest rate. 
3

(unnumbered 

equation on page 4) 

where λ is the tag reporting rate, ρ is the fraction of fishing effort covered by observers, and 

Z=F+M+L, where L is the instantaneous tag loss rate. Because it is assumed that tags are 

removed from the fishery when they are caught, the F in this equation is approximately the same 

as the encounter rate μ in the fishery, whether fish survive or die. 

The model presented in the paper estimated harvest rates μ rather than F from this model. 

Depending on how tag loss and survival probabilities were included, the μ estimated from the tag 

recapture data may not exactly correspond to the fishery encounter rate. However, it would be 

straightforward to adjust the model to estimate a μ that corresponds exactly to the fishery 

encounter rate after adjusting for tag loss. The tag loss rates have been estimated using doubletagging experiments. 

Tag-recapture encounter rates are an unbiased estimate of encounter rates in a particular fishery, 

assuming that: (1) only tags returned by that fishery are included in the analysis, (2) tag loss and 

tag-reporting rates are correctly estimated, and (3) tagged fish are randomly distributed in the 

population. The description of the tagging program implies that tags may have been returned 

from fisheries in state fisheries (e.g., rivers, estuaries, and coastal waters), not just the federal 

fishery, which would violate the first assumption. It was not possible to tell from the document 

whether the second and third assumptions were met. Provided that all these potential biases are 

addressed, the tag-recapture analysis could provide useful estimates of encounter rates. 

Step 3. Combining the minimum population size with the encounter rate to calculated ASPI 

The minimum population size estimate is used to calculate the ASPI abundance estimate using 

equation 4: 

(4) 

No derivation is provided for this equation. It does not appear to be correct. 

The most obvious way to calculate N from μ would be to use the Baranov catch equation. In a 

fishery in which all fish that are captured are killed, the catch is equal to the numbers times the 

harvest rate (equation R2) so that: 

(R3) ܰ௧ ൌ ஼೟

ఓ೟

In a bycatch fishery where some of the fish survive capture, F is the instantaneous encounter rate 

and θ is the fraction of fish that die when captured, the fraction of fish that are caught and die 

would be: 

(R4) ߤௗ௜௘ௗ ൌ ிఏ

ிఏାெ ൫1 െ ݁ିሺிఏାெሻ

൯
4

This is the standard Baranov catch equation, except that the fishing mortality rate has been split 

into an encounter rate component and a bycatch mortality component. 

The total bycatch rate, including both those that die and those that survive would be the same 

number divided by the fraction that die: 

(R5) ߤ௧௢௧௔௟ ൌ ி

ிఏାெ ൫1 െ ݁ିሺிఏାெሻ

൯

Note that the “fishing mortality rate” in the exponent of ൫1 െ ݁ିሺிఏାெሻ൯ includes only the 

fraction that die (Fθ). Those that survive are not removed from the population, so they are not 

included in this expression. 

Thus, given an estimate of the total encounter rate ߤ௧௢௧௔௟ the correct estimate of N would be: 

(R6) ܰ௧ ൌ ஼೟೚೟ೌ೗

ఓ೟೚೟ೌ೗

or, alternatively 

(R7) ܰ௧ ൌ ஼೏೐ೌ೏

ఏఓ೟೚೟ೌ೗

The encounter rate calculated from the tag-recapture data appears to be approximately equal to 

ߤ௧௢௧௔௟, so either equation R6 or equation R7 could be used to calculate N. 

Using the minimum population size (nt) in the place of catch, as in equation 4, introduces a bias 

into the estimate of Nt by adding in the fish that were caught in previous year and not caught this 

year and also subtracting the previously caught fish that were caught again this year. Thus, 

depending on how many fish have been caught in the past, the ASPI index can be either 

positively or negatively biased. 

To determine the implications of using the ASPI approach (equation 2 and 4), rather than the 

standard Baranov catch equation to calculate Nt, I simulated a fishery and calculated estimated 

numbers (Figure 1). The derivation of equation 2 implies that the total bycatch should be used 

to calculate the minimum population size. However, page 6, paragraph 4 states that only dead 

discards were used. I did not follow why this was done, but I calculated the minimum population 

size both ways. I also estimated abundance based on equations R5 and R6. In this example, I 

ignored tag loss, but see the appendix for the case with tag loss, and R code to generate these 

results. Because the abundance, number of tags, fishing mortality rate, natural mortality rate and 

bycatch survival rate are all set arbitrarily, the magnitudes of the error in my results are not 

informative. However, a valid estimator should be able to produce unbiased estimates from such 

simple, simulated data. 
5

1

1

1

1

2 4 6 8 10

0e+00 2e+05 4e+05 6e+05

Year

Abundance

2 2 2 2 2 2 2 2 2 2

3333333333 4444444444

1

2

3

4

True population

n/mu (C.total)

n/mu (C.dead)

C.total/mu

C.dead/(mu*theta) 

Figure 1. In this simulated data, the true 

population (black line), fishing mortality 

rate, tag loss rate, natural mortality rate 

and bycatch survival rate are all constant; 

and a constant number of fish are tagged 

every year. The alternative population 

estimates are calculated by dividing some 

number by the encounter rates calculated 

from tagging. The numerator is: (1) 

minimum population size calculated from 

total bycatch using equation 2; (2) 

minimum population size calculated from 

dead bycatch using equation 2 (used in the 

paper); (3) total bycatch; and (4) dead 

bycatch adjusted by θ. 

The performance of the various estimation methods depends on the assumptions about encounter 

rates, number of tags, trend in abundance, and tag loss, but it is clear that the methods proposed 

in the paper (equation 2 and 4) can be biased both in absolute numbers and in trend. Simply 

dividing the total bycatch by the encounter rate generates an unbiased estimate of total numbers, 

provided that the encounter rate was calculated correctly. 

The method of dividing the total bycatch by the estimated encounter rate was used in the ASPI 

paper, in Table 14. Although the authors state that this estimate would be biased by the live 

release of sturgeons, I see no reason why it would be. Provided that the tag-recapture model is 

used to estimate encounter rate (not harvest rate), correctly accounting for tag losses, and 

provided that both the discard estimates and the tag recaptures come from the exact same fishery, 

the total discards divided by encounter rate should be an unbiased estimate of total numbers in 

the population targeted by that fishery. 

Because of the poor performance of the ASPI index on simulated data, and the fact that the logic 

behind it is not well described, I would not recommend using this number, either as an absolute 

estimate of abundance or as a relative index of abundance, until these issues can be addressed. 

To justify this method, I suggest the authors spell out the derivation of equations 2 and 4, and 

explain why they do not just use the standard Baranov catch equation. Also, they should show 

that the method performs well on simulated data. The simulated data exercise that I did may not 

be correct if I did not correctly understand all the elements of the model. Therefore, I suggest 

that the authors should produce their own simulated data test. 

3. Other comments on the paper 

Although the ASPI index calculation was problematic, several of the aspects of the analysis were 

well done and provide useful information. The tag-recapture analysis, if (1) tag returns from the 
6

correct fishery components are used, (2) tag loss and reporting rates are correctly estimated, and 

(3) it is shown that tagged fish are randomly distributed, can provide useful estimates of 

encounter rates. The estimates of bycatch from the federal fisheries, although they were based 

on relatively small samples, were made using a reasonable model-based estimation procedure. 

The Monte Carlo simulation method that was used to estimate the uncertainty in the estimate of 

the ASPI index was appropriate and correctly implemented. 

The approach used in this paper has potential to produce accurate estimates of total abundance 

and the uncertainty in abundance, so I encourage the authors to correct the ASPI equations, and 

the other possible sources of bias I discuss, and re-do the analysis. I have the following 

additional comments. 

(1) Given that the index is sensitive to bycatch mortality θ, this parameter should have a 

variance in the Monte Carlo simulations. 

(2) The paper needs a better explanation of why the number of recaptures by observers had 

to be scaled down. 

(3) The discussion of the current draft says that the estimated total abundance is biased low, 

because the bycatch does not include rivers and estuaries, while the tag-recapture 

encounter rate does. Why is this? Are tagged fish that are returned by the fisheries 

operating in state waters, including rivers and estuaries, included in the tag-recapture data 

set used in the model? This would appear to be a serious bias, considering that the 

databases used to estimate bycatch do not include inshore state fisheries. Is it possible to 

use only the recaptures from the fishery operating federal waters in this analysis? 

Alternatively, tag-recapture data from all fisheries could be used, if bycatch numbers and 

tag reporting rates could be estimated for all fisheries. 

(4) A sensitivity analysis of the tag-recapture submodel would be useful. Are tags randomly 

distributed across the population? If not, how is this accounted for in the model? 

(5) A more detailed justification of the model-based bycatch estimates is necessary. Miller 

and Shepherd (2011) does not provide much detail on model selection or diagnostics. 

(6) The authors might want to consider using the tagging data and bycatch estimates in an 

integrated assessment model, or else combining the tagging and bycatch information into 

an assessment using Bayesian models (e.g. Michielsens et al. 2008. Combining multiple 

Bayesian data analyses in a sequential framework for quantitative fisheries stock 

assessment. Can. J. Fish. Aquat. Sci. 65: 962–974). Such an integrated analysis would 

allow the survey data to be used as well, and would more accurately propagate 

uncertainties in parameter estimates into the final abundance estimates. 

Appendix. 

Figure 1, above, did not include tag loss. If there is tag loss, and we calculate encounter rates as 

the number of recaptures in each year divided by the number of tagged fish in each year, then the 

encounter rate estimate will be biased. However, we can use the Baranov catch equation 

iteratively to solve for F, and then recalculate the encounter rate excluding tag loss to get the 

correct number. Figure 2 shows the estimates with an instantaneous tag loss rate of 0.4. 

Dividing the catch by the encounter rate not adjusted by tag loss gives the correct trend, but 

overestimates absolute abundance. The adjusted harvest rate gives the correct results. 
7

Figure 2. The simulated data are the same 

as in Figure 1, except that there is now a 

tag loss rate. Line 5 refers to the total 

bycatch divided by the encounter rate 

corrected for tag loss. 

R code to generate simulated data and abundance estimates. This code generates Figure 2. To 

generate Figure 1, change the value of “loss” to zero. 

##### Arbitrary input parameters. Change these to see effect of tag loss, 

#etc. on results 

theta=0.2 #fraction of caught fish that die 

FF=rep(0.01,10) #Instantaneous fishing encounter rate 

MM=0.05 #Natural mortality rate 

loss=0.4 #Tag loss rate 

R.tag=rep(100,10) # Number of tagged fish 

N.tot=rep(200000,10) # Total number of fish 

## Alternative versions of the Baranov catch equation 

catchfunc.dead=function(FF,MM,NN,theta=1) { 

 #F and M throughout year. N at beginning of year 

 #Calculates the number of fish that are caught and die 

 theta*FF/(FF*theta+MM)*NN*(1-exp(-(FF*theta+MM))) 

} 

catchfunc.tot=function(FF,MM,NN,theta=1) { 

 #F and M throughout year. N at beginning of year 

 #Calculates the number of fish that are caught, alive or dead 

 FF/(FF*theta+MM)*NN*(1-exp(-(FF*theta+MM))) 

} 

catchfunc.tag=function(FF,MM,NN,loss) { 

 #F and M throughout year. N at beginning of year 

 #Calculates the number of fish tagged fish caught 

 FF/(FF+MM+loss)*NN*(1-exp(-(FF+MM+loss))) 

} 

Ffunc=function(i) { 

 #Calculates F iteratively from the Baranov catch equation, given mu 

 minfunc=function(x) abs((x/(x+MM+loss))*(1-exp(-(x+MM+loss)))-mu.tag[i]) 

 temp=nlminb(mu.tag[i],minfunc,lower=1e-7,upper=5) 

 temp$par 

} 

1

1

1

2 4 6 8 10

0e+00 2e+05 4e+05 6e+05

Year

Abundance

2 2 2 2 2 2 2 2 2 2

3333333333 4444444444

5555555555

1

2

3

4

5

True population

n/mu (C.total)

n/mu (C.dead)

C.total/mu

C.dead/(mu*theta) 

Corrected est
8

#### Calculate the number of tagged fish, and number of recaptures 

# Assume tags are removed from fish at recapture, so that they are 

# removed from the population at rate F 

N.tag[1]= R.tag[1] 

for(i in 2:10) N.tag[i]=R.tag[i]+N.tag[i-1]*exp(-(FF[i-1]+MM)) 

C.tag=catchfunc.tag(FF,MM,N.tag,loss) 

# Calculated live and dead discards 

C.tot=catchfunc.tot(FF,MM,N.tot,theta) 

C.dead=catchfunc.dead(FF,MM,N.tot,theta) 

# Calculate encounter rate from tagging data 

mu.tag=C.tag/N.tag 

# Calculate minimum population size from equation 1, and equation 2 

# Then calculate estimated population size from equation 4 

N.min1=C.tot*(1-theta)*exp(-MM) #Equation 1 

N.min2=N.min1[1] # Equation 2 applied to total catch 

for(i in 1:9) N.min2[i+1]=((1-mu.tag[i+1])*N.min2[i]+(C.tot[i+1]-

mu.tag[i+1]*N.min2[i])*(1-theta))*exp(-MM) 

N.tot.est1=N.min2/mu.tag #Equation 4 

N.min3=N.min1[1] # Equation 2 applied to dead catch only 

for(i in 1:9) N.min3[i+1]=((1-mu.tag[i+1])*N.min3[i]+(C.dead[i+1]-

mu.tag[i+1]*N.min3[i])*(1-theta))*exp(-MM) 

N.tot.est2=N.min3/mu.tag #Equation 4 

#Calculate N based on bycatch divided by encounter rate 

N.tot.est3=C.tot/mu.tag 

N.tot.est4=C.dead/(theta*mu.tag) 

# The following correctly accounts for tag loss 

F.tag=NULL 

for(i in 1:10) F.tag[i]=Ffunc(i) 

mu.dead=(F.tag*theta)/(F.tag*theta+MM)*(1-exp(-(FF*theta+MM))) 

N.tot.est5=C.dead/mu.dead 

# The following makes a plot of the results 

if(loss==0) { 

matplot(1:10,cbind(N.tot.est1,N.tot.est2,N.tot.est3,N.tot.est4),bty="n",ylim=

c(0,3*N.tot[1]),type="b",col=2:6,xlab="Year",ylab="Abundance") 

 abline(2e5,0) 

 legend("topright",c("True population","n/mu (C.total)","n/mu 

(C.dead)","C.total/mu","C.dead/(mu*theta) 

"),pch=c(NA,as.character(1:4)),col=1:4,lty=c(1,rep(NA,4)),cex=0.9) 

} else { 

matplot(1:10,cbind(N.tot.est1,N.tot.est2,N.tot.est3,N.tot.est4,N.tot.est5),bt

y="n",ylim=c(0,3*N.tot[1]),type="b",col=2:6,xlab="Year",ylab="Abundance") 

 abline(2e5,0) 

 legend("topright",c("True population","n/mu (C.total)","n/mu 

(C.dead)","C.total/mu","C.dead/(mu*theta) ","Corrected 

est"),pch=c(NA,as.character(1:5)),col=1:6,lty=c(1,rep(NA,5)),cex=0.9) 

}