Phil Ganz 1 Terrance Quinn II 1 Peter Hulson 2 1 Juneau Center School of Fisheries and Ocean Sciences University of Alaska Fairbanks 17101 Point Lena Loop Road Juneau AK 99801 USA 2 NOAA ID: 758261
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Time of Death: Modeling Time-varying Natural Mortality in Fish Populations
Phil Ganz1Terrance Quinn II1Peter Hulson2
1Juneau Center, School of Fisheries and Ocean Sciences, University of Alaska Fairbanks, 17101 Point Lena Loop Road, Juneau, AK 99801, USA 2NOAA National Marine Fisheries Service, Alaska Fisheries Science Center, Auke Bay Laboratories, 17109 Point Lena Loop Road, Juneau, AK 99801, USA
Photos: kevskewl.files.wordpress.com, http
://www.fishbase.us/Slide2
Statistical catch-at-age models
Age
Year
2012 2013 2014 2015 2016
1 2 3 4 5
Abundance-at-age
Slide3
Statistical catch-at-age modelsTwo main sources of data:
Commercial fisheries Scientific surveys
http://
www.alaskafishradio.comPhoto credit: Phil GanzSlide4
Statistical catch-at-age modelsTwo main types of data:
Abundance/weight Age composition
http://www.lanierstripedbasscoalition.orghttp://
cdn.lightgalleries.netSlide5
Statistical catch-at-age modelHighly non-linearSome key parameters and values:Natural mortality
(M)Fishing mortality (F)Survey catchability
(q): scales the catch from the survey (I) to that of the total abundance (N)
, so I=qNSelectivity (s): the proportion of fish at a given age that are vulnerable to fishing gearSlide6
Statistical catch-at-age modelsTrying to estimate and predict:Abundance/weightRecruitment, or juvenile fish: sometimes modeled as a function of the number of mature fish, sometimes modeled as random.
Adults (mature and immature)Age compositionUsed with maturity data to estimate reproductive potentialSlide7
Statistical catch-at age modelsBalance between precision and parsimony:Slide8
Baranov catch equation
Catch
Abundance
Proportion that dies
Proportion of deaths that are due to fishingSlide9
Total mortality vs maximum age
Hoenig 1983Slide10
Relating natural mortality to growthVon Bertalanffy growth:
It follows (Beverton, Holt, and others):M related to K via t
maxCan plot M against K, use as predictor Slide11
The problemMisspecifying
M results in bias – Clark 1999“The magnitude of natural mortality relates directly to the
productivity of the stock, the yields that can be obtained, optimal exploitation rates, management quantities, and reference points. Unfortunately, natural mortality is also one of the most difficult quantities to estimate.”
– Brodziak et al. 2011Slide12
Varying natural mortality In general:
This presentation:Parameters are confounded with each other
http://vignette1.wikia.nocookie.net/Slide13
Two Proposed MethodsCovariatesi.e. Use more data
Correlated errori.e. Look at the data in a different way
Jiao et al. 2012Slide14
Covariates to natural mortalityPredation
Herring: Teerlink et al. in revision. Lake trout: Pycha 1980; James
Bence, pers. comm.DiseaseMarty et al. 2010
Marty et al. 2010
www.fhwa.dot.gov
/Slide15
How precise does a covariate need to be?Sablefish
Population constructed from most recent age structured assessment (Hanselman et al. 2014)30 year simulation
Specified scenarios for:Natural moralityPrecision of covariate
www.flickr.com/photos/jikegamiSlide16
Simulated “data”
Two sources:SurveyCommercialTwo types:
AbundanceAgeM covariate
Photos: www.priweb.orgSlide17
Natural mortality scenarios
Error
+
20 iterations Slide18
Covariate scenarios
E
rror
+Slide19
All scenarios
Covariate error
+
M Error
+Slide20
EstimationStatistical catch-at-age model
Same parameterization as operating model Optimized using automatic differentiation:Slide21
ResultsSlide22
Natural MortalityCovariate error
M
Error
+
+Slide23
Total biomass 1985-2014Covariate error
M
Error
+
+Slide24
Total biomass 2014
Covariate error
M Error
+
+Slide25
Comparing Model Structure
v
s.
http://31.media.tumblr.com/ea7e05d71d38c74fc9eb99f268655a4f/tumblr_inline_ne8n0cNpGx1sqh1mk.jpgSlide26
Comparing Model Structure
v
s.
http://31.media.tumblr.com/ea7e05d71d38c74fc9eb99f268655a4f/tumblr_inline_ne8n0cNpGx1sqh1mk.jpgSlide27
Slide28
ConclusionStatistical catch-at-age models in general:
We need to make simplifying assumptionsWhat simplifying assumptions are acceptable?Sablefish simulation: The covariate method is effective, but as errors increase, precision decreases.Not including the covariate can be more precise, if the covariate is very imprecise and natural mortality is constant.Slide29
ConclusionsFuture directionsTrending natural mortality
Correlated error structuresRandom effectsRandom walkSlide30
¿Preguntas?
Thank you!: Terry QuinnPete HulsonDana Hanselman
Pedro GajardoYou for listeningFunding:National Marine Fisheries Service
Stock Assessment Improvement Plangithub.com/michiganzSlide31
ReferencesBrodziak, J., Ianelli
, J., Lorenzen, K., & Methot, R. D. J. 2011. Estimating Natural Mortality in Stock Assessment Applications. st.nmfs.noaa.gov (p. 38). U.S. Dep. Commer. Retrieved from https://www.st.nmfs.noaa.gov/st4/documents/MworkshopReport_final.pdfClark, W. G. 1999. Effects of an erroneous natural mortality rate on a simple age-structured stock assessment.
Canadian Journal of Fisheries and Aquatic Sciences, 56(10), 1721-1731.Hanselman, D.H., C. Lunsford, and C. Rodgveller. 2014. Assessment of the sablefish stock in Alaska, pp. 283-424. In Stock assessment and fishery evaluation report for the groundfish
resources of the GOA and BS/AI for 2015. North Pacific Fishery Management Council, Anchorage, AK.Hoenig, J. M. 1983.
Empirical use of longevity data to estimate mortality-rates. Fishery Bulletin, 81(4), 898-903.Jiao, Y., Smith, E. P., O'Reilly, R., & Orth, D. J. (2012). Modelling non-stationary natural mortality in catch-at-age models. ICES Journal of Marine Science: Journal du
Conseil, 69(1), 105-118.Marty, G.D., Hulson, P.-J.F., Miller, S.E., Quinn, T.J., II, Moffitt, S.D., and
Merizon, R.A. 2010. Failure of population recovery in relation to disease in Pacific herring. Diseases of Aquatic Organisms 90: 1-14.Pycha, R
. L. 1980. Changes in mortality of lake trout (Sulvelinus namaycush) in Michigan waters of Lake Superior in relation to sea lamprey (Petromyzon marinus
) predation, 1968-78. Can. J. Fish. Aquat. Sci. 37: 2063-2073.Teerlink, S.F., Quinn, T.J. II, Straley, J.M., and
Ziegesar, O.V. The ecological importance of humpback whale predation on herring biomass in Prince William Sound as determined by model covariation. In Revision.