journal of great lakes researchinput distributions to the lake erie lake sturgeon db-sra model and...

Journal of Great Lakes Research xxx (2018) xxx–xxx

JGLR-01296; No. of pages: 8; 4C:

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Journal of Great Lakes Research

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Application of a Depletion-Based Stock Reduction Analysis (DB-SRA) to Lake Sturgeonin Lake Erie

John A. Sweka ⁎, Rachel Neuenhoff, Jonah Withers, Lori DavisU.S. Fish & Wildlife Service, Northeast Fishery Center, P.O. Box 75, Lamar, PA 16848, United States

⁎ Corresponding author.E-mail address: [email protected] (J.A. Sweka).

https://doi.org/10.1016/j.jglr.2018.01.0020380-1330/Published by Elsevier B.V. on behalf of Interna

Please cite this article as: Sweka, J.A., et al., AGreat Lakes Res. (2018), https://doi.org/10.1

a b s t r a c t
a r t i c l e i n f o
Article history:Received 14 November 2017Accepted 9 January 2018Available online xxxx

Communicated by Stephen Charles Riley

Lake Erie supported the greatest yield of lake sturgeonwithin the Laurentian Great Lakes near the end of the 19thcenturywith N2000metric tons caught at the peak of the fishery. The fishery collapsed by the 1920swhen b1% ofthe previous peak catch was removed. Despite closures of the fishery, lake sturgeon remain rare in Lake Erie. Weapplied a depletion-based stock reduction analysis (DB-SRA) to the catch of lake sturgeon from 1879 to 1929 togain estimates of sustainable fishery reference points and the historic carrying capacity of Lake Erie for lake stur-geon.We also simulated population growth of lake sturgeon from 1929 to the present with varying assumptionsof the current carrying capacity of the lake. The estimated historic carrying capacity of lake sturgeon was 22,652metric tons. During the height of the fishery, exploitation was as high as 37% which was more than an order ofmagnitude greater than that required for maximum sustainable yield. Projections of the population from 1929to 2016 suggest sufficient time has passed since the collapse of the fishery that the population should have recov-ered to levels that would support a fishery atmaximum sustainable yield. However, lake sturgeon remain rare inLake Erie indicating that other factors such as habitat availabilitymay be limiting their recovery. Our estimates ofcarrying capacity will be informative when setting recovery targets which consider the amount of habitat loss.

Published by Elsevier B.V. on behalf of International Association for Great Lakes Research.

Keywords:lake sturgeonoverfishingdepletion-based stock reduction analysisstock assessment

Introduction

Lake sturgeon, Acipenser fulvescens, are one of the largest freshwaterspecies endemic toNorth Americawith populations extending through-out the Laurentian Great Lakes, Hudson Bay, andupperMississippi Riverdrainages (Scott and Crossman, 1973). The species is depleted through-out its known range where it is variabily designated as extirpated,threatened, endangered, or of special concern (Pikitch et al., 2005;COSEWIC, 2006). The decline of lake sturgeon throughout its rangehas been attributed to a number of factors, primarily loss of habitatand overharvest (Auer, 1999).

Throughout the Laurentian Great Lakes, Lake Erie had the greatestreported commercial catch of lake sturgeon prior to the end of the19th century (Koelz, 1925). Early descriptions of the lake sturgeon fish-ery in the Great Lakes date back to the early mid 1800s when lake stur-geon were discarded as a nuisance bycatch species (Applegate and VanMeter, 1970). The directed commercial fishery for lake sturgeon in LakeErie began in 1868 when German immigrants established the first ded-icated sturgeon processing facility in Sandusky, Ohio (Bogue, 2000). In1870, lake sturgeon became valued by commercial fisherman for caviar,smoked flesh, the production of oil, and swim bladders used in themanufacturing of isinglass (Koelz, 1925; Applegate and Van Meter,

tional Association for Great Lakes Re

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1970). Catches of lake sturgeon peaked in 1885 at over 2300 metrictons, but declined N90% over the next two decades (Baldwin et al.,2009). Given their latematuration (males 12–15 y, females 17–24 y), ir-regular spawning cycles (males 2–4 y intervals, females 3–7 y intervals)and long life span (80–100 y), lake sturgeon are inherently vulnerable tooverharvest (Scott and Crossman, 1973). Although lake sturgeon har-vest was not under official moratorium until the 1970s, the commercialfishery was functionally extirpated by the turn of the 20th century inLake Erie (Baldwin et al., 2009). After a century of minimal harvest,lake sturgeon in the western basin of Lake Erie have seen only modestpopulation growth in the Detroit River (Casewell et al., 2004). However,lake sturgeon populations in the eastern basin of Lake Erie have notdemonstrated population recovery, and only a small number of matureindividuals have been documented to date in the headwaters of the Ni-agara River (Legard, 2015).

The abrupt decline in commercial catches of lake sturgeon in LakeErie wasmost likely a result of overharvest. Lake Erie underwent signif-icant anthropogenic induced degradation in habitat and water qualityduring the 20th century thatwould be harmful to lake sturgeon survivaland productivity, but these changes largely occurred following the col-lapse of the lake sturgeon fishery. Although the construction of damson Lake Erie tributaries began in the first half of the 19th century, aquery of the National Inventory of Dam (htt://nid.usace.army.mil) re-veals that approximately 80%of the dams constructed in theU.S. portionof the Lake Erie watershed were constructed after 1925 with the

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Table 1Input distributions to the Lake Erie lake sturgeon DB-SRA model and descriptions of sen-sitivity model runs for each distribution.

Parameter Base model distribution Sensitivity perturbation

Age atmaturity

20 years Increase to 25/Decrease to 15

Initial K 10 x maximum catch Decrease to 5 x maximum catchM Lognormal, mean=0.03, CV=

0.3Increase to 0.06

Fmsy/M Lognormal, mean=1.12, CV=0.2

Decrease to 0.8

Bmsy/K Beta, mean = 0.50, CV = 0.1 Decrease to 0.40BT/K Beta, mean = 0.10, CV = 0.20 Decrease to 0.01/Increase to

0.20B1/K Beta, mean = 0.80, CV = .20 Increase to 0.95 - near K

2 J.A. Sweka et al. / Journal of Great Lakes Research xxx (2018) xxx–xxx

greatest period of dam construction occurring in the 1960s and 1970s.Degradation of water quality, eutrophication, and hypoxia reached itsmost extreme conditions during the 1960s which coincided with thedecline of other commercially valuable species such as lake trout(Salvelinus namaychush), cisco (Coregonus artedii), lake whitefish(Coregonus clupeaformis), and walleye (Stizostedion vitreum)(Hartman, 1972; Koonce et al., 1996). Sea lampreys (Petromyzonmarinus) were first reported in Lake Erie in 1921, but it wasn't until1932 when the first spawning run was detected (Lawrie, 1970). If thedecline of lake sturgeon was largely governed by factors other thancommercial harvest, we would not have seen the steep decline in com-mercial catches in such a short period of time in the early 20th century,especially considering the long maturation period of lake sturgeon andthe expected long period of time it would take for factors thatmay com-promise lake sturgeon recruitment to become evident in the size of thespawning stock.

Stock assessment efforts have been hindered by a general lack offisheries-independent surveys and a lack of effort data associated withcommercial fisheries (Koelz, 1925). Lake sturgeon are reported sporad-ically in surveys directed at other species. The rarity of lake sturgeoncatches in these surveys has largely precluded lake-wide or stock spe-cific abundance indices or actionablemanagement advice tomeet resto-ration goals.

Several methods exist for assessing stock status for data-deficientpopulations. These methods are based on trends in commercial catcheswhereby the scale of depletion is assumed based on knowledge of therelationship between current population size relative to the unknownhistoric size (Walters et al., 2006; Dick and MacCall, 2011). Walterset al. (2006) presented an alternative using a stock reduction analysis(SRA) where assumptions about fleet behavior are unnecessary. In-stead, stock reduction draws on estimates of life history parameters(primarily intrinsic population growth rate r, the carrying capacity K,and the natural mortality rateM) and predicts what the historical abun-dance would have been in order to sustain the observed fishery catcheswithout reaching extinction (Walters et al., 2006). Depletion-basedstock reduction analysis (DB-SRA) expands upon this method by intro-ducing a depletion rate (i.e., the ratio of contemporary biomass to carry-ing capacity) as a parameter (Dick and MacCall, 2011). This approachrequires knowledge of basic life history information and does not in-volve assumptions about annual fleet effort. In the present study, weused a DB-SRA to estimate historical abundance of lake sturgeon inLake Erie, andwe calculated reference points from the resulting optimalparameter set. We also used the model to project abundance since thecollapse of the lake sturgeon fishery in the early 20th century to deter-mine what population recovery signals we should expect relative towhat is observed. Our results provide a basis for restoration goals andmanagement metrics for imperiled lake sturgeon populations in LakeErie.

Methods

The DB-SRA model (Dick and MacCall, 2011) uses a surplus produc-tion model of the form

Bt ¼ Bt−1 þ P Bt−að Þ−Ct−1 ð1Þ

where Bt is the biomass at time t, Ct is the catch at time t, and P(Bt−a) isthe production based on parental biomass a years earlier. Theparametera can be considered the median age of maturity. Annual production inthe DB-SRA model follows a Pella-Tomlinson-Fletcher productionmodel (Fletcher, 1978)

P Bt−að Þ ¼ γ �m � Bt−a

K

� �−γ �m � Bt−a

K

� �n

ð2Þ

where n is the shape parameter defining where the maximum

Please cite this article as: Sweka, J.A., et al., Application of a Depletion-BasGreat Lakes Res. (2018), https://doi.org/10.1016/j.jglr.2018.01.002

productivity of the stock occurs relative to the carrying capacity, K.When n=2, the biomass at maximum sustainable yield (Bmsy) equals0.5·K; when n b 2, Bmsy is less than half of K; and when n N 2, Bmsy isgreater than half of K. The parameter γ depends on the value of n

γ ¼ nn

n−1ð Þ= n−1ð Þ ð3Þ

m is themaximum sustainable yield (MSY) of the stock and is a func-tion of n, K¸ and exploitation at maximum sustainable yield (umsy).

m ¼ K � n nn−1ð Þ � umsy ð4Þ

The DB-SRA model proceeds in an iterative manner by first ran-domly drawing values from possible distributions of life history-basedparameters and projects the population forward with the number of re-movals each time step equaling the observed time series of catcheswithsome associated uncertainty in those catches. These life history-basedparameters are the natural mortality rate (M), the ratio of the fishingrate at maximum sustainable yield to natural mortality (Fmsy/M), therelative biomass atmaximum latent productivity (Bmsy/K), and the rela-tive depletion level (BT/K) by some time of interest, T. The model thenanswers the question: if the population sustains some number ofyears of observed catch, what was the virgin population size in orderto sustain those catches without being driven to extinction and end upat some fraction of K by time T? Themodel can start with the populationat K or some proportion of K. Iterations in which a set of parameters re-sults in extinction are rejected and those that allow the population topersist are accepted. Final distributions of K, the intrinsic rate of popula-tion growth (r), Bmsy, and umsy can then be estimated from the distribu-tions of the accepted parameter sets.

Distributions of the input parameters and the age at maturity werebased on values found in the literature for lake sturgeon (Table 1).Male lake sturgeon mature at ages of 12–15 and females mature atages 18–27 (Peterson et al., 2007), and we set the age of maturity at20 in this DB-SRA model application. The maximum age reported forlake sturgeon was approximately 150 years (Rossiter et al., 1995).Using Hoenig's (1983) linear regression estimator of natural moralitybased on maximum age [ln(M) = 1.44− 0.982 · ln (maximum age)],we set the natural mortality of lake sturgeon at 0.03 and assumed alog-normal distribution with a coefficient of variation (CV) of 0.3 andbounded from 0.01 to 0.8. This level of mortality is consistent withother estimates reported in the literature for lake sturgeon(e.g., Schueller andHayes, 2010; Pledger et al., 2013). Bruch (2009) sug-gested annual exploitation of lake sturgeon in the LakeWinnebago Sys-tem, WI should not exceed 5% per year for a sustainable population.Assuming Fmsy=0.05 andM=0.03, we set the Fmsy/M ratio at 1.12 as-suming a log-normal distribution with CV= 0.2 and bounded from 0.4to 3.0. A traditional rule of thumb in stock assessments is that the Fmsy/Mis 1.0, but Walters and Martel (2004) suggest it is 0.8, and we also con-ducted model sensitivity runs with Fmsy/M at 0.8 (see description of



Fig. 1. Estimated total catch of lake sturgeon from Lake Erie (1879–2016). Commercial catch record came from Baldwin et al. (2009).

3J.A. Sweka et al. / Journal of Great Lakes Research xxx (2018) xxx–xxx

sensitivity runs below). Dick andMacCall (2011) assumed a Bmsy/K ratioof 0.40 for Pacific groundfish species and 0.25 for Pacific flatfish speciesdue to their typically high productivity. Given the long age to maturityof lake sturgeon, we assumed lower productivity which would resultin a higher Bmsy/K ratio; and we set Bmsy/K at 0.50 assuming a beta dis-tribution with CV= 0.10 and bounded from 0.10 to 0.90.

Catch records of lake sturgeon from Lake Erie extend back to 1879(Baldwin et al., 2009). In this time series of catch data, there were 25years between 1879 and 1913 in which catch from U.S. waters of LakeErie was not reported, but catch from Canadian waters was reportedfor all years. To estimate the U.S. catch for missing years, we estimatedthe ratio between U.S. catches and Canadian catches for years whereboth were reported and multiplied this ratio by the reported Canadiancatch to estimate the U.S. catch. Canadian and U.S. catches weresummed for the total catch from Lake Erie (Fig. 1).

Fig. 2. Frequency distributions of accepted (black bars) and rejected (grey bars)


Catches of lake sturgeon after the 1920s were minimal compared toprevious years (Fig. 1) and have ceased since the 1980s due to regula-tions closing the fishery. Because the time series of catches of lake stur-geon dates back to 1879 and the fishery collapsed in the early 20thcentury, there is likely a good deal of uncertainty in the total catch oflake sturgeon during this time period. This uncertainty was incorpo-rated into the DB-SRAmodel by drawing catches from a log-normal dis-tribution with a mean equal to the reported values (Fig. 1) and anassumed proportional standard error of 0.30. There were also manychanges to theGreat Lakes ecosystemduring the 20th century includingdegraded habitat, pollution, damming of tributaries, and introductionsof non-native species such as sea lamprey, alewife (Alosapsuedoharengus), Pacific salmon (Onchorhynchus spp.), zebra andquagga mussels (Dreissena spp.), and round goby (Neogobiusmelanostomous) . All these changes likely have changed the carrying

parameter values from the base DB-SRA model for Lake Erie lake sturgeon.



Fig. 3. Bivariate plots of Fmsy and M versus BT/K. Rejected model runs are in gray andaccepted model runs are in black. Ellipses represent the 90th percentiles of distributionsof the points for accepted and rejected runs.


capacity of Lake Erie compared to what it might have been prior to thepeak catches in the late 19th century. Given the great deal of uncertaintyin what present day carrying capacity may be for lake sturgeon, we re-stricted our DB-SRA model to the time period of the great decline incatches (1879–1929).We believed this timeperiodwould give us betterestimates of exploitation during the crash of the fishery – both esti-mated annual exploitation and umsy. This time period also correspondsto a period beforemajor alterations to the Lake Erie ecosystemoccurred,allowing us to bettermeet the inherentmodeling assumption that com-mercial catch was the primary factor driving lake sturgeon populationdynamics. Because lake sturgeon catches decreased by N90% betweenfrom 1885 to 1905, we assumed a high level of depletion by 1929 andset BT/K in 1929 to 0.10 following a beta distribution with CV = 0.20and bounded by 0.01 and 0.90. We also assumed that the populationhad already been reduced to some extent by 1879when the catch seriesbegan and set the biomass relative to K in 1879 (B1/K) equal to 0.80 fol-lowing a beta distribution with CV= 0.2 and bounded by 0.50 and 1.0.

A sensitivity analysis was conducted to evaluate uncertainty in themodel input parameters. The DB-SRA employing the input parameterset described above (Table 1) was considered the “base model”. Wethen altered input parameters one at a time while holding all other pa-rameters equal to those in the base model to examine the effects on es-timated distributions of Bmsy, MSY, umsy,K, and r. These sensitivitymodelruns included: 1) the base model; 2) decreasing the age of maturity to15; 3) increasing the age of maturity to 25; 4) decreasing the initialguess of K to 5× the maximum catch; 5) increasing M to 0.06; 6) de-creasing Fmsy/M to 0.80; 7) decreasing Bmsy/K to 0.40; 8) decreasing BT/K to 0.01; 9) increasing BT/K to 0.20; and 10) increasing B1/K to 0.95.Two sensitivity model runs were conducted for perturbations in BT/K,an increase and a decrease, because Wetzel and Punt (2011) foundDB-SRAmodels to be highly sensitive to the ratio of the current biomassto the starting biomass.

To examine possible population growth of lake sturgeon in Lake Erieafter the collapse of the fishery in the early 20th century, wemade pop-ulation projections beyond 1929. These projections assumed age atma-turity was 20 years, an intrinsic population growth rate (r) from theaccepted iterations of the base model, but allowed K to vary fromwhat was estimated from the base model. Projections were made as-suming K after 1929 was 100%, 75%, 50%, 25%, and 10% of what was es-timated from the base model. Limited catches of lake sturgeon after1929were also subtracted frompredicted annual biomass in these pop-ulation projections.

Results

Overall, 56% of the model runs were accepted (i.e., allowed the pop-ulation to persist over the modeled time period). Distributions of inputparameter values from accepted and rejectedmodel runs overlapped tohigh degree (Fig. 2). Parameter distributions from accepted and rejectedmodel runs diverged for natural mortality (M) and the ratio of Fmsy/Mwith the distribution of accepted runs shifted lower than rejected runsin both cases. Also, bivariate plots of accepted and rejected model runsalong a gradient of BT/K showed that there was a slight trend wherebyhigher values of BT/K allowed higher values of Fmsy without the popula-tion being driven to extinction and the model run rejected (Fig. 3). Theminimum value of Fmsy that resulted in rejection was 0.023, and theminimum value of M that resulted in rejection was 0.016. Valuesbelow these levels never resulted in rejection of a model run no matterwhat level of BT/K.

As expected with the crash of the fishery, the DB-SRA model pre-dicted a steep decline in the biomass of lake sturgeon with overfishingoccurring at the beginning of the catch time series in 1879 (Fig. 4).The population biomass fell below the median estimate of Bmsy of11,287 metric tons (Table 2) as early as 1888 and annual exploitationwas as an order of magnitude greater than the median estimate ofumsy of 0.025 during the period from 1894 to 1906. Themedian estimate


of MSY was 284 metric tons, but this level was exceeded in nearly allyears up until 1905. These model results clearly demonstrate the nega-tive impact the fishery had on the sustainability of the lake sturgeonpopulation in Lake Erie.

Estimated biomass reached a minimum in 1905 with a median esti-mate of 872 metric tons of lake sturgeon remaining in the population.Themedian estimate of carrying capacity (K) of the Lake Erie populationwas 22,652metric tons prior to the commencement of the fishery. Thus,the fishery drove the population down to approximately 4% of the esti-mated unexploited biomass. The assumption of BT/K equaling 0.10 in1929 allowed for some rebuilding of the population by the end of thecatch time series, and this also coincided with a time period in whichannual exploitation fell below umsy.

Model sensitivity runs suggested that the DB-SRAmodel was gener-ally robust to perturbations in input parameter distributions. Box plotsof the distributions of model outputs from each sensitivity run over-lapped the box plots of the base model to a high degree (Fig. 5), al-though in some cases the variation in model outputs could be quitedifferent from the base model. The median estimated carrying capacityfor lake sturgeon was very stable across all sensitivity runs and variedfrom 4 to 8% from that of the base model run. Likewise, median




estimated Bmsy varied only 5–15% from the baselinemodel run across allsensitivity runs. There were two cases where the sensitivity runs dif-fered substantially from the base model. These were when M was in-creased from 0.03 to 0.06 and BT/K was decreased from 0.10 to 0.01(scenarios 5 and 8 in Fig. 5). Both of these cases resulted in greater var-iation in model outputs and in the greatest differences in terms of me-dian estimates compared to the baseline models. Also, these scenarioshad few acceptedmodel runs inwhich the population did not go extinctby 1929 with only 7% of the model runs accepted when increasing Mand only 1% of the model runs accepted when decreasing BT/K.

Population projections of lake sturgeon in Lake Erie based on the in-trinsic rate of population growth (r) estimated from the basemodel var-ied greatly depending on the assumed level of K in more contemporarytimes. If K remained at the level estimated from the time period forwhich the DB-SRA model was run (22,652 metric tons), median bio-mass in 2016 was projected to be 16,930 metric tons (Fig. 6) whichwould exceed the Bmsy reference point, but not yet to the biomass levelsprior to the beginning of the fishery in the 1800s. As expected, when Kwas reduced, population growth decreased and if K was reduced to

Fig. 4. Plots of annual exploitation, catch, and biomass from the lake sturgeon DB-SRAmodel relpercentiles of estimates and the horizontal dashed lines represent reference points of umsy, MS


25% of the historic K or less, the population would have grown to andbe stabilized at this low level by 2016. If present day K was 10% of thehistoric K, the population would be equivalent to what it was in 1929after the fishery had collapsed.

Discussion

Our analysis is not the first to examine the effects of historic exploi-tation on lake sturgeon abundance in Lake Erie. Haxton et al. (2014) alsoexamined historical exploitation for Lake Erie lake sturgeon using a sur-plus production model to gain estimates of MSY and umsy and a deple-tion model to estimate the biomass of lake sturgeon prior to the peakof thefishery. They estimated umsy ranging from0.020–0.047 (95% cred-ible intervals) andour estimates ofumsy (0.014–0.037; 5th and 95th per-centiles of accepted model runs) from our base model overlap thisrange. However, their estimate of r (0.080–0.187) was higher than ourestimate of r (0.028–0.076) and their estimate of K (5,028–11,470 met-ric tons) was lower than our estimate of K (18,469–29,566).

ative to fishery reference points. The dotted lines about the curves represent 25th and 75thY, and Bmsy.



Table 2Percentiles of the distributions of acceptedDB-SRAmodel runs for Lake Erie lake sturgeon.Fifty-six percent of the base model runs were accepted (i.e., the population persistedthrough the modeled time period of 1879–1929).

Percentiles

Parameter 2.5% 25.0% 50.0% 75.0% 97.5%

K 18,469 21,083 22,652 24,400 29,567r 0.0284 0.0421 0.0503 0.0587 0.0760Bmsy 8867 10,378 11,287 12,355 14,929MSY 176 245 284 325 409umsy 0.0143 0.0214 0.0254 0.0293 0.0366


The difference between our estimates and those of Haxton et al.(2014) could be due to the assumption of constant effort and/orcatchability made by Haxton et al. (2014). It is likely that the assump-tion of constant effort is not accurate in the late 19th century as ad-vancements in fishing technology such as the steam net lifter wouldhave increased the number and size of deployed gill nets over whatwas previously possible (Applegate and Van Meter, 1970). Increases incatchability or effort through time can produce an abundance indexthat is insensitive to changing biomass and lead to overly optimistic es-timates of productivity (Wilberg et al., 2010). For example, if effort or

Fig. 5. Boxplots of the distributions of lake sturgeon DB-SRAmodel outputs from sensitivity run3) increasing the age of maturity to 25; 4) decreasing the initial guess of K to 5× the maximum8) decreasing BT/K to 0.01; 9) increasing BT/K to 0.20; and 10) increasing B1/K to 0.95.


catchability were increasing at the same time the population is decreas-ing, the change in total catch through time would not be directly pro-portional to the change in the population over the same time periodand the population would be decreasing at a faster rate than the catchrates indicated. This would lead to the erroneous conclusion that thestock was more productive and could support a greater amount ofcatch than it actually could. These effects would result in an overestima-tion of r and an underestimate of the absolute degree of depletion andsubsequently an underestimate of K, as is in the comparison of Haxtonet al. (2014) results to those presented here. By using a DB-SRAmodel, we did not need to make any assumptions about constant effortor catchability in the lake sturgeonfishery. Nevertheless, our results andthose of Haxton et al. (2014) both demonstrate that harvest of lake stur-geon from Lake Erie greatly exceeded a sustainable level and was re-sponsible for the crash of the fishery.

Sensitivity runs of the DB-SRA model showed the model was robustto changes in the distributions of input parameters except for increasingM from 0.03 to 0.06 and decreasing BT/K from 0.10 to 0.01. In both cases,there was greater variation in the distributions of output parameter es-timates, which was an artifact of the low number of accepted modelruns where the population was not forced to extinction before 1929.This suggests that if the true distribution of of M was much higher

s. The sensitivity scenarios are: 1) the basemodel; 2) decreasing the age ofmaturity to 15;catch; 5) increasingM to 0.06; 6) decreasing Fmsy/M to 0.80; 7) decreasing Bmsy/K to 0.40;




than the baseline run, or the true distribution of BT/K was much lowerthan the baseline run, there would be little chance of the populationpersisting through the period of high harvest. This is also supportedby examining the distributions of model outputs from the base model(Fig. 2). The distribution of accepted values of M was shifted to the leftof rejected values and the distribution of accepted values of BT/K wasshifted to the right of rejected values. Although Wetzel and Punt(2011) found that model output was most sensitive to BT/K, we foundvery little effect of increasing BT/K on final distributions of model outputparameters. This could be due to the fact that we had catch data begin-ning close towhen exploitation began, the catch series showed a contin-uous decline in a relatively short period of time, and the degree ofdepletion over the modeled time period was high as evidenced by thegreat decline in reported catches. This helps to constrain the possiblevalues in model output. For example, if BT/K were much lower, therewould be a high probability that the populationwould have been drivento extinction over the modeled time period. The stability of estimateddistributions of K over all sensitivity runs of the model is desirable formanagers in considering what a fully restored lake sturgeon populationmay look like when developing restoration goals or biomass targets.

Although lake sturgeon take a long time to mature and populationgrowth is expected to be slow, enough time has passed since the col-lapse of the fishery to witness a recovery of the species if the intrinsicrate of population growth and the carrying capacity remained at levels

Fig. 6.Projections of the lake sturgeon population in Lake Erie under different assumptions of con(r) was held constant andKwas allowed to vary from the historical K estimated from theDB-SRSRA model; the dotted lines about the curves represent 25th and 75th percentiles of estimates


equal to those in themid-19th century. Therewas some continued com-mercial catch of lake sturgeon from 1930 to 1983 (b 20 metric tons an-nually; Baldwin et al., 2009) and our population projections includedthis minimal catch. Despite this additional catch, our projections of theLake Erie lake sturgeon population to contemporary times suggestthat the population should have reached approximately 75% of the his-toric carrying capacity by 2016 and exceed Bmsy. However, lake sturgeonstill remain a rare specieswithin Lake Erie. The lack of observable recov-ery suggests the carrying capacity has decreased over the past century.Exactly what the carrying capacity is today is uncertain, but a level of25% or less of the historic carrying capacity seems realistic given the rar-ity of the species in terms of bycatch in commercial fisheries and inother fishery independent surveys targeting other species. Therefore,additional factors other than the historic fishery may be impedimentsto recovery.

Anthropogenic degradation of spawning and nursery habitat andbarriers placed on tributaries appear to be themost probable factors im-peding recovery (Koonce et al., 1996; Haxton et al., 2014). Since exploi-tation peaked in the 1880s several deleterious habitat changes to LakeErie and its tributaries have occurred with increased urban and agricul-tural development in these areas. Furthermore, hydrologic changeswith the implementation of power plants and dams such as that main-tained on the Niagara River can cause large daily fluctuations in flow re-gime degrading suitable habitats at different times. In response to these

temporary carrying capacity (K). In eachprojection, the intrinsic rate of population growthAmodel. The vertical dashed line represents the ending of catch time series used in the DB-.




issues, large-scale habitat restoration efforts have been initiated overthe past several decades. These efforts include the Great Lakes WaterQuality Agreement originally signed by the U.S. and Canada in 1972(IJC, 2012) which has provisions to control pollution, sedimentation,aquatic invasive species, and physical habitat restoration. Also, con-struction of spawning reefs in the Detroit River demonstrated that im-provements to habitat can enhance lake sturgeon reproduction(Roseman et al., 2011). Hatchery supplementation is an attractive man-agement strategy to enhance or reintroduce lake sturgeon populations,and it has been successful in Lake Superior (Schram et al., 1999); but, ifthe impediments to lake sturgeon survival and reproduction have notbeen ameliorated, stocking efforts will not have their intended results(Welsh et al., 2010). Regardless of which recovery strategies are imple-mented, recovery of the lake sturgeon population in Lake Erie will takeconsiderable time given their age at maturity and low populationgrowth rate.

Although we did not simulate changes in the productivity of lakesturgeon in our population projections (decreasing the intrinsic rate ofpopulation growth, r), lake sturgeon inmore recent years may be facingfactors that have reduced their productivity compared to when thecommercial fishery was at its peak. For example, the invasion of non-native fish species such as sea lamprey and round goby may have in-creased lake sturgeon mortality at various life stages. Sea lamprey at-tacks can have physiological effects and acute mortality on juvenilesized lake sturgeon (Sepúlveda et al., 2012), and round gobies areknown to consume the eggs of other fish species (Mychek-Londeret al., 2013). Combined, these species may have reduced the resiliencyof lake sturgeon beyond the obvious effects of habitat limitation.

Output from our DB-SRA model for Lake Erie lake sturgeon can aidfisheries managers in setting recovery goals of this species in LakeErie. It may be unrealistic to expect lake sturgeon in Lake Erie to recoverto the historical carrying capacity estimated by our DB-SRA model(22,652metric tons), but our estimate of the historical carrying capacityprovides a basis for an upper limit from whichmanagers could set con-temporary realistic targets given the degree of habitat destruction thathas taken place within the Lake Erie Basin. For example, Haxton et al.(2014) suggested recovery goals could be tempered by the proportionalloss of spawning and nursery habitat. Also, our estimates of umsy as wellas those from Haxton et al. (2014) and Bruch (2009) demonstrate that,if the population were to reach a level that managers would deem ade-quate to allow harvest, the amount of annual exploitation should below, b3%, in order to sustain the population.

Acknowledgements

Wewould like to thank Jeff Kipp andKatie Drew (Atlantic StatesMa-rine Fisheries Commission) for providing the R script which we modi-fied for our lake sturgeon DB-SRA. We also thank the members of theGLFC Lake Erie Lake Sturgeon work group for the impetus to developthis DB-SRA for Lake Erie. Funding for this work was provided by theU.S. Fish & Wildlife Service and the Great Lakes Restoration Initiative.This manuscript was improved upon with the preliminary review ofDr. Mike Millard (U.S. Fish & Wildlife Service) and two anonymous re-viewers. This work was supported by the U.S. Fish and Wildlife Serviceand the Great Lakes Restoration Initiative. The findings and conclusionsof this article are those of the authors and do not represent the views ofthe U.S. Fish & Wildlife Service.

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https://binational.net/wp-content/uploads/2014/05/1094_-USA-GLWQA-_e.pdf

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