1 40 West 20 th 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 M anagement Measures for the Northeast Multispecies, Monkf ish, 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 Natu ral Resources Defe nse 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 Atla ntic 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 l isting, 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

ear ly 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 th e methodologies relied upon in the2 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 t o 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 Docume nt 13

- 06 (Atlantic Sturgeon Population Index). W e 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 record s 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 Atmosphe ric 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 relati ng 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

Reco very Sub

- Committee, since 2002. Dr. Pikitch received a Ph.D. in Zoology from Indiana University and M.A. and B.S. degrees in Mathematics fro m the City College of New York.

Dr. Pikitch ’s report examines the bycatch mortality rates relied on the draft B iOp 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 base d on the best available science. Dr. Pikitch recommends alternative threshold mortality rates that are significantly better support ed by the technical literature and that have been consistently relied on by regulatory agencies in the past (at least u ntil 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 autho rs’ 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 bycat ch 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 sugge sts to the public that the ASPI population estimates are more reliable than is the case and recommends that either the discussion3 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 make s 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

s uffer 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 t o 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 t he 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 l east some threats. Atlantic sturgeon mortality from vessel strikes , for example, has been q uantified 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 n ecessary information and conduct projections to fully account
for threats. See, e.g., Conner v. Burford , 848 F.2d 1441, 1454 (9 th Cir. 1988); Natural Res. Defense Council v. Kempthorne , 506 F. Supp. 2d 322, 360 (E.D. Cal. 2007).

• The acceptable fishing mo rtality 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. S maller 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 als o 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 betwe en 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 th e 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 tha t 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 draft4 BiOp fails to consider in any specific detail the threat posed to the Delaware River Atla ntic sturgeon population by bycatch in the six federal fisheries.

N ortheast Area Monitoring and Assessment Program (N EAMAP )

- derived population estimates

• We have significant concerns about how the NEAMAP survey information was relied on in
the draft BiO p. 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 ou t 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 dou bt 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) tha n 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). F or 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 es timates and the bycatch5 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 m ortality e stimates

• 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 p rogram. 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 th e New York Bight DPS ( Damon

- Randall, et al. 2013).

• T he 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 particu lar 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.

• I n 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 in6 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 ASS RT (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 b ycatch 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 o f 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 considerat ion, relative to earlier years

.

Incidental Take Statement

• NRDC b elieves 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 im portant that NMFS provide monthly summaries and an annual report of observed Atlantic sturgeon takes in New England and Mi d

- 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 re

opened if the stock assessment provides different population and fishing mortality estimates than relied on in the biological opinion.

Literature Cited

Atlantic States Marine Fisheries C ommission. 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. Sp ecial 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 Of fice. 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 mortali ties in the Delaware
estuary. Fisheries Vol. 35 No. 2: 72

- 83.

Clark, W.P. 2002. F 35% 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 sou thern
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 Ee nennaam, 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 Coa st 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: in sights 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 A tlantic 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 stur geon 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 Scie nce 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 Socie ty 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 Atlan tic 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,

Brad ford H. Sewell Senior Attorney

Attachments1 A t t a c h m e n t 1

Report re the Endangered Species Act Secti on 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 Atmospher ic Sciences, Stony Brook University Stony Brook, New York

E mail: 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.

T he F=0.05 and F=0.14 fishing mortality rates are based upon reference mortality rates for Atlantic sturgeon recover y and maintenance , respectively

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

EPR
max (EPR 50% ) would be sufficient to allow recovery of the Atlantic sturgeon population in the
Hudson River. Boreman’s rationale for the EPR 50% level cites work by Goodyear (1993) (which the BiOp also cites), and aspects of the life hist ory of Atlantic sturgeon, which i s 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 bei ng highly vulnerable to fishing and slow to recover once depleted (Pikitch et al

. 2005 ) . Further support and/or reiteration of Boreman (1997) for a EPR 50% level as a maximum mortality ra te for recovery is provided by ASMFC ( 1998 ), ASSRT ( 2007 ), and Kahnle et al. ( 2007 ) . Boreman
calculated an estimate of EPR 50% = 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 in formation from Doroshov (1997) estimated EPR 50% = 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 EPR 50%

. Further, Kahnle et al . ( 2007 ) estimated

EPR 50% = 0.02 for a scenario where M varies with age of sturgeon (decreasi ng with advancing2 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) stud y 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 =

EPR 50% 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) suggest ed that 20% of EPR
max (EPR 20% ) w ould be sufficient to maintain the population and estimated a corresponding F of 0.14 . Kahnle et al. (2007) estimated F = 0.08 corresponding to EPR 20% u s ing their updated a nd more robust methodology for the constant M scenario. Kahnle et al. (2007) did not provide an estimate of F = EPR 20% 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 EPR 20% is adequately precautionary for maintaining Atlantic sturgeon populations in all distinct population segments (DPSs)

. The

EPR 20% 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 si gnificantly 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 = EPR 50% i s consistent with recommendations for maintaining healthy biomass (Clark 2002 ) or obtaining MSY (Dorn 2002) f or 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 = EPR 50% as the maximum fishing mortality rate for both maintaining and recovering the Atlantic sturgeon populat ion . 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. F 35% 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 rela tionships. 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

H udson 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 fish eries 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 York1 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, Ro senstiel School of Mari ne 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 es timating the abundance of Atlantic sturgeon using a combination of tag-recaptu re data and bycatch estimates from the commercial fisheries. The tagging data are used to estimate the encounte r 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 abunda nce. The estimated abundance is referred to as the Atlantic Sturgeon Popul ation Index (ASPI); however, the number is described as an absolute estimate of sturgeon abundance ( N

) rather than an index of rela tive abundance. The uncertainty in the ASPI estimate is calculated using a standard Monte Carlo simulation method.

The overall approach of combining bycatch estimat es with tag-recapture estimates of encounter rates to estimate abundance is a viable one, prov ided 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 mu st 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 au thors should provide detailed justifications for
all the decisions taken in the de velopment 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 fish eries. I have serious c oncerns 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 n t , which
is the number of fish that have ever been caught that are still aliv e at the end of year t . The equation is:

(2)2 where μ t is the encounter rate in the fishery in year t

, C t , 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). Fi sh that had been caught before and were caught again in year t+1 are subtracted out (= μ t n t (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 popul ation 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 ot her species as explanatory variables, and then using that model to predict catches throughout ea ch fishery. This approach seems reasonable in general, but I have not review ed the methods paper (Miller an d 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 occurri ng 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 Ba ranov catch equation is used when fishing mortality rates are constant thr oughout the year, to convert instan taneous mortality rates (fishing mortality F and natural mortality M ) into the fraction of the popul ation 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 ݁ ି ሺ ிାெ ሻ , so that the fraction that die is ൫1 െ ݁

ି ሺ ிାெ ሻ ൯ . 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) ߤ ௧ ൌ ி ೟

ி

೟ ାெ ൫1 െ ݁

ି ሺ ி ೟ ାெ ሻ ൯

.

The total catch is just this harvest rate 1 multiplied by the number of fish in the population at the beginning of the year:

(R2) ܥ ௧ ൌ ߤ ௧ ܰ ௧

To use the Baranov catch equation to estimate en counter rates from tagged fish, it is also necessary to include estimates of reporting rates and tag loss ra tes. 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 ef fort 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 return ed 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 th e tagging program implies that tags may have been returned from fisheries in state fisheries (e.g., rivers, es tuaries, 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 me t. Provided that all th ese potential biases are addressed, the tag-recapture analysis could pr ovide 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 ki lled, 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 capture d, the fraction of fish that are caught and die
would be:

(R4) ߤ ௗ௜௘ௗ ൌ ிఏ

ிఏାெ ൫1 െ ݁

ି ሺ ிఏାெ ሻ ൯4 This is the standard Baranov catch equation, except that the fishi ng mortality rate has been split
into an encounter rate component and a bycatch mortality component.

The total bycatch rate, including bo th those that die and those th at survive would be the same number divided by the fraction that die:

(R5) ߤ ௧௢௧௔௟ ൌ ி

ிఏାெ ൫1 െ ݁

ି ሺ ிఏାெ ሻ ൯

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

ି ሺ ிఏାெ ሻ ൯ includes only the fraction that die ( F θ ). Those that survive are not remove d from the population, so they are not included in this expression.

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

(R6) ܰ ௧ ൌ ஼ ೟೚೟ೌ೗

ఓ ೟೚೟ೌ೗

or, alternatively
(R7) ܰ ௧ ൌ ஼ ೏೐ೌ೏ ఏఓ ೟೚೟ೌ೗

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

ߤ ௧௢௧௔௟ , so either equation R6 or equati on R7 could be used to calculate N

.

Using the minimum population size ( n t ) in the place of catch, as in equation 4, introduces a bias
into the estimate of N t by adding in the fish that were caught in previous year and not caught this year and also subtracting the pr eviously caught fish that were caught again this year. Thus, depending on how many fish have been caught in the past, th e ASPI index can be either positively or negatively biased.

To determine the implications of using the ASPI approach (equation 2 an d 4), rather than the standard Baranov catch equation to calculate N t , 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, th e 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 100e+00 2e+05 4e+05 6e+05

YearAbundance

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 ra te 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 met hods 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 bo th in absolute numbers and in trend. Simply dividing the total bycatch by the encounter rate ge nerates an unbiased estimate of total numbers, provided that the encounter ra te was calculated correctly.

The method of dividing the total bycatch by the es timated encounter rate was used in the ASPI paper, in Table 14. Although the authors state that th is estimate would be biased by the live release of sturgeons, I see no r eason why it would be. Provided that the tag-recapture model is used to estimate encounter rate (not harvest rate), correctly accoun ting for tag losses, and provided that both the discard estimates and the ta g recaptures come from the exact same fishery, the total discards divided by encounter rate shoul d 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 num ber, 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 deri vation of equations 2 and 4, and explain why they do not just us e the standard Baranov catch equa tion. 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 th e model. Therefore, I suggest that the authors should produce thei r 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-re capture analysis, if (1 ) tag returns from the6 correct fishery components are used, (2) tag loss and reporting rates are co rrectly 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 uncertain ty 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 sens itive 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 tagge d fish that are returned by the fisheries operating in state waters, includi ng rivers and estuaries, includ ed 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 incl ude inshore state fisher ies. Is it possible to use only the recaptures from the fishery ope rating federal waters in this analysis? Alternatively, tag-recapture data from all fish eries could be used, if bycatch numbers and tag reporting rates could be es timated for all fisheries. (4) A sensitivity analysis of the tag-recapture submodel would be useful. Are tags randomly distributed across the populatio n? If not, how is this acc ounted for in the model? (5) A more detailed justification of the model-ba sed 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 th e tagging data and bycatch estimates in an integrated assessment model, or else combin ing 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 th e 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 abundanc e 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 100e+00 2e+05 4e+05 6e+05

YearAbundance

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 est8 #### 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) }