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.
Jeffrey Runge and Andrew Pershing,
University of Maine
David Kimmel and James Pierson
University of Maryland Center for Environmental Sciences
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
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?
C. finmarchicus and C. helgolandicus
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.
Johnson et al. 2008
Dormancy vs temperature and SWIFS-derived Chl-a climatologies
Leising et al. in prep
Abundance (no. m climatologies-2)
Stage ProportionAG: Anticosti Gyre, NW Gulf of St. Lawrence
Region specific datasets: egg production in NWA climatologies
Runge and Plourde 1996)
Campbell &Head 2000)
(Plourde et al. data)
Runge et al. 2006)
Frequency of pattern by month climatologies
Sea Level Pressure
Air temperature anomalies
Vector-diagram of the average winds
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?
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
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)