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Life histories of Calanus species in the North Atlantic and North Pacific Ocean and responses to climate forcing. Jeffrey Runge and Andrew Pershing, University of Maine David Kimmel and James Pierson University of Maryland Center for Environmental Sciences. Collaborators.

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Life histories of Calanus species in the North Atlantic and North Pacific Ocean and responses to climate forcing

Jeffrey Runge and Andrew Pershing,

University of Maine

David Kimmel and James Pierson

University of Maryland Center for Environmental Sciences


Collaborators
Collaborators

A. Leising, NOAA, SWFSC

C. Johnson, BIO, Fisheries and Oceans, Canada

S. Plourde, IML, Fisheries and Oceans, Canada

R. Harris, PML, England

D. Bonnet, Univ. Montpellier, France

W. Melle, IMR, Norway

A. Gislason, Marine Res. Inst. Iceland

D. Speirs, Univ. Strathclyde, Scotland

D. Mackas, IOS, Fisheries and Oceans, Canada


Overall question
Overall question

How will populations of Calanus species in the North Atlantic and North Pacific Oceans respond to interannual and longer term, climate forced variability in water column temperature and food supply?


Calanus species of interest sibling species in two sympatric species pairs
Calanus species of interest:Sibling species in two sympatric species pairs:

North Atlantic:

C. finmarchicus and C. helgolandicus

North Pacific:

C. marshallaeand C. pacificus

Develop an IBM life cycle model that includes a mechanistic understanding of dormancy and is parameterized for the particular life history traits of each species, tested and refined for regions of interest.


Calanus finmarchicus dormancy dynamics in the nwa

Dormancy vs temperature and Chl-a climatologies

Chl

Temperature

Calanusfinmarchicus dormancy dynamics in the NWA

Johnson et al. 2008


Calanus pacificus dormancy dynamics in the ccs

Dormancy vs temperature and SWIFS-derived Chl-a climatologies

Calanuspacificus dormancy dynamics in the CCS

Leising et al. in prep


Lipid accumulation window hypothesis
Lipid accumulation window hypothesis climatologies

  • Individual Calanus can only enter diapause if their food and temperature history allows them to accumulate sufficient lipid for overwintering and gonad development

  • Decision to proceed to dormancy is initiated when lipid stores reach a preset threshold (30% lipids by weight in stage CIV)

  • Exit from dormancy by endogenous timer or when lipid stores decrease below a preset threshold


The five project objectives
The Five Project Objectives climatologies

  • Compile and analyze data sets across species and regions; publish a series of comparative synthesis papers (Pierson, Runge, collaborators)

    • Demographic data sets

    • Physiological rate characteristics: egg production, growth and development rate


Ag anticosti gyre nw gulf of st lawrence

Abundance (no. m climatologies-2)

Stage Proportion

AG: Anticosti Gyre, NW Gulf of St. Lawrence

OnsetEmergence


Region specific datasets: egg production in NWA climatologies

Runge and Plourde 1996)

Campbell &Head 2000)

(Plourde et al. data)

Runge et al. 2006)


Development rates vs temperature
Development Rates vs. Temperature climatologies

  • C. marshallae is outlier, with very long stage durations, primarily due to extra-long relative C5 stage

  • C. finmarchicus has fastest development-at-temperature

  • C. pacificus and C. helgolandicus lie between these two extremes


The five project objectives1
The Five Project Objectives climatologies

  • Construct synoptic climatologies in selected regions to examine sensitivities of individual species life histories to climate forcing (Kimmel)

    • Example: Western, central and eastern North Atlantic Ocean. Each day SLP (NCAR dataset) from 1950-2007 into a weather pattern modes.

    • Downscaled the patterns to surface winds (u and v) and air temperature from NCEP/NCAR reanalysis data set


Frequency of pattern by month climatologies

Sea Level Pressure

Air temperature anomalies

Vector-diagram of the average winds

D. Kimmel


D. Kimmel climatologies


D. Kimmel climatologies


The five project objectives2
The Five Project Objectives climatologies

  • Investigate mortality: compilation of empirical mortality estimates and comparison with “climatological mortality” estimates based on life history theory. (Runge, Pershing, Leising, student)

    Are there common regional and/or global patterns of mortality? Are the stage-specific, temperature-dependent mortality rates needed to accurately close life cycle models consistent with life history theory?


Optimal life history theory
Optimal life history theory climatologies

Assume populations are at an Evolutionarily Stable Strategy (ESS). A population is at an ESS, with respect to some phenotype or set of phenotypes, if a rare gene introduced into the population, and whose carriers have a different phenotype from the mean of the population, is always eliminated.

The ESS will be the survivorship and reproduction schedule, lx and mx (where x is age in d) which maximizes the intrinsic rate of increase for the population. That is, for any other feasible schedules (lx’ and mx’ ):


The predictions climatologies

of mortality in a population of Acartia clausii in Jakle’s Lagoon (Landry’s data)

Myers and Runge

1983


The five project objectives3
The Five Project Objectives climatologies

4. Refine our stage-structured, IBM life-cycle model with parameterization tuned for each of the sibling species pairs in each region of interest: Leising, F. Maps (post-doc)


Life cycle model with lipid diapause rule comparison with observations

Life cycle model with lipid diapause rule: climatologiescomparison with observations

MODEL

DATA


The five project objectives4
The Five Project Objectives climatologies

  • Using the life cycle model, evaluate the sensitivity of Calanus life histories responses to the climate change scenario climatologies (Leising, Maps, Pershing, all).

    • How do the sibling species between and within the “finmarchicus” and “helgolandicus” groups respond to similar modes of climate forcing?

    • Look for opportunities to link the 1-D life cycle model with 3-D circulation and temperature fields to investigate the role of advection on observed distribution and abundance patterns


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