Dossier de candidature
HABILITATION A DIRIGER DES RECHERCHES
HDR Sciences
2014
Trophic interactions and environmental variability
Joël Marcel DURANT
Researcher
Centre for Ecological and Evolutionary Synthesis
Department of Biosciences
University of Oslo
P.O. Box 1066 Blindern, NO-0316 Oslo, Norway
Foreword
En 1990, au cours de ma préparation à l’Agrégation, j’ai eu la chance d’avoir été mis
en contact avec Yvon Le Maho qui était à l’époque directeur d’une petite équipe
d’écophysiologistes dans le laboratoire du Professeur Pierre Dejours au CNRS de
Strasbourg. Ce fut pour moi la découverte du monde de la recherche et des questions qui sont
sous-jacentes à celle que je conduis actuellement. Je tiens à remercier mes trois mentors qui
continuent de suivre de plus ou moins près ma carrière : Yvon Le Maho, Yves Handrich et
Nils Christian Stenseth. De même je tiens à remercier tous ceux qui m’ont aidé au cours des
années.
Avec cette synthèse, j’ai essayé de montrer mes changements de méthodologie
(Physiologie, Écophysiologie et Écologie) pour aborder la question : « Comment répondent
les organismes aux changements environnementaux ? »
“It is a capital mistake to theorize before one has data.
Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”
Sherlock Holmes,
A Scandal in Bohemia
Contents
I Curriculum Vitae ................................................................................................................................... 5
I.1 – Current position........................................................................................................................... 5
I.2 – Previous positions........................................................................................................................ 5
I.3 – Research activities ....................................................................................................................... 5
I.3.1 Current research projects ........................................................................................................ 5
I.3.2 Active collaborations: .............................................................................................................. 6
I.4 – Achievements .............................................................................................................................. 6
I.4.1 Educational information .......................................................................................................... 6
I.4.2 Grants and awards .................................................................................................................. 6
I.4.3 Funding .................................................................................................................................... 7
I.5 – Contribution to training, scientific leadership... .......................................................................... 8
I.5.1 Supervision at the PhD level .................................................................................................... 8
I.5.2 Supervision at the Masters level ............................................................................................. 8
I.5.3 Other supervision .................................................................................................................... 8
I.5.4 Scientific leadership an teaching duties .................................................................................. 8
I.5.5 Administrative duties .............................................................................................................. 8
I.5.6 Synergistic activities ................................................................................................................ 8
I.6 – Publications and oral communications ....................................................................................... 9
I.6.1 Complete list of publications: .................................................................................................. 9
I.6.2 Oral presentations ................................................................................................................. 11
II Research activities and project .......................................................................................................... 14
II.1 – Main achievements .................................................................................................................. 14
1. Breeding energetics .................................................................................................. 14
2. Climate change and trophic interactions ................................................................. 14
3. Match-mismatch ....................................................................................................... 14
4. Biodiversity and stock management ......................................................................... 14
II.2 – Introduction .............................................................................................................................. 15
II.3 – Climate change and population dynamics, implications for biodiversity................................. 16
II.4 – Previous work: Energetic constraint during the annual cycle, physiological and
ecophysiological studies on the barn owl ......................................................................................... 17
II.5 – Trophic interaction, Population dynamic and environmental variability on marine ecosystems
........................................................................................................................................................... 21
II.5.1 Background ........................................................................................................................... 21
1. The Norwegian Sea -Barents Sea pelagic ecosystem ............................................... 22
2. The Southern Ocean ................................................................................................. 24
3
3. The Benguela System ................................................................................................ 24
II.5.2 Climate and population abundance ..................................................................................... 25
1. Trophic interaction and Atlantic puffin breeding success variation ........................ 26
2. Climate effect on the puffin reproduction ................................................................ 27
3. Direct or indirect effect of climate ........................................................................... 28
II.5.3 Match-mismatch hypothesis and climate change................................................................ 29
1. Prey abundance and the MMH ................................................................................ 30
2. Spatial distribution and the MMH ........................................................................... 31
3. MMH for predator-controlled systems ..................................................................... 33
4.Further considerations on the MMH ........................................................................ 33
II.5.4. Climate change and biodiversity ......................................................................................... 35
1. Population dynamics and environmental changes ............................................ 37
a. Population growth in fish populations .......................................................... 38
b. Population growth and penguin populations ................................................ 39
2. Competition with other species ................................................................................ 40
a. Competition in a terrestrial system: the tits as example ............................... 41
b. Competition in a seabird colony................................................................... 41
c. Perturbation of the trophic interaction .......................................................... 42
3. Primary production, climate and predators ............................................................. 42
II.6 – Perspectives .............................................................................................................................. 44
II.6.1 Ecosystem studies in the Barents Sea .................................................................................. 45
II.6.2 Match mismatch hypothesis in a changing world ................................................................ 46
II.6.3 Stock recruitment, climate change and human forcing ....................................................... 48
II.6.4 Ecology, Ecophysiology, Climatology, Sociology and Economy and more ─ bridging the gaps
....................................................................................................................................................... 49
1. Unification of ecology and ecophysiology ............................................................... 49
2. Building a new generation of scientists.................................................................... 50
II.6.5 Time series curator work ...................................................................................................... 51
II.7 – References ................................................................................................................................ 52
4
I Curriculum Vitae
Joël Marcel Durant
Centre for Ecological and Evolutionary Synthesis
Department of Biosciences, University of Oslo
P.O. Box 1066 Blindern, NO-0316 Oslo, Norway
Phone:
+47 22 85 47 95
Fax:
+47 22 85 40 01
E-mail:
j.m.durant@ibv.uio.no
I.1 – Current position
- Senior Researcher at the University of Oslo (on a permanent contract position since
September 2013).
- Tenure position as a high school teacher (CAPES) since 1994 (on leave since 2001).
I.2 – Previous positions
2012-2013:
2008-2012:
June 2008:
2005-2008:
2003-2005:
June 2002:
2002:
2001:
June 2000:
Senior Research Fellow at the CEES, project funded by the Research Council of
Norway
Senior Research Fellow at the CEES, project funded by the Research Council of
Norway
Fieldwork (Dassen, South Africa) as help of Dr. Rob Crawford, MCM. Population
ecology of African penguins (NORSA project)
Senior Research Fellow at the CEES, project funded by the Research Council of
Norway
Post-doctoral research fellow at the CEES Olso funded by a Marie Curie Fellowship
Fieldwork (Røst, Lofoten) as help of Dr. Tycho Anker-Nilssen, Norwegian Institute for
Nature Research (NINA). Population ecology of Atlantic puffins at Røst
Post-doctoral research fellow at the CEES, position funded by the Research Council of
Norway
Research fellow, Centre National de la Recherche Scientifique, Strasbourg
Fieldwork for IFRTP-CEPE (Ny Ålesund, Spitsbergen) in collaboration with the
Norwegian Institute for Polar Research. Physiology of the reproduction of the female
Eider duck (Somateria mollissima).
I.3 – Research activities
I.3.1 Current research projects
Competition in variable environment: with NC Stenseth, K-S Chan, A Dhondt, E Matthysen, F
Adriaensen, M Fowler, B Sheldon, M Visser
Explore the climate effect on competition and density-dependence on two tits species (Cyanistes
cæruleus and Parus major)
Adaptive management of living marine resources by integrating different data sources and key
ecological processes (ADMAR): A joint effort by IMR and CEES: with NO Handegard, G
Huse, J H Vølstad, M Skern-Mauritzen, S Subbey, D Dankel, M Pennington, K Enberg, NC
Stenseth, DØ Hjermann, LC Stige, AM Eikeset
Enhance our basic knowledge of ecosystem functioning and to derive a framework for
operational adaptive management.
Norwegian Marine Data Centre: with many partners
5
-
Establishment of a Norwegian Marine Data Centre that will provide seamless access to marine
data and greatly increase the efficiency of marine science in Norway and facilitate the
generation of high quality research
I.3.2 Active collaborations:
Institut Pluridisciplinaire Hubert Curien, Département Ecologie, Physiologie et Ethologie, France :
Y Le Maho, Y Handrich
Centre de Recherche Halieutique Méditerranéenne et Tropicale, IRD - IFREMER & Université
Montpellier II, France : P Cury
Ocean and Coasts, Department of Environmental Affairs, Cape Town, South Africa : R Crawford
Murmansk Marine Institute, Russia: Y Krasnov
Norwegian Institute of Nature Research (NINA), Tromsø, Norway : P Fauchald
Institute of Marine Research , Bergen, Norway : M Mauritzen, U Lindstrøm, EK Stenevik, E
Moksness, E Johannesen, H Sagen
Centre Scientifique de Monaco: C Le Bohec
I.4 – Achievements
I.4.1 Educational information
2001: Qualified to teach Biology at the associate professor level by the French National University
Council.
2001: Qualified to teach Ecology at the associate professor level by the French National University
Council.
2000: Ph-D in Ecophysiology from Université Louis Pasteur of Strasbourg, France. Supervisors
Yves Handrich and Yvon Le Maho. Dissertation: Breeding energetics in Barn Owl
1991: Master in Physiology (Diplôme d’étude approfondie) from Université Claude Bernard Lyon I
and Aix-Marseille II (equivalent to 2 year of a Master). Supervisors Yves Handrich and Yvon
Le Maho. Dissertation: Barn owl winter starvation: successive fasts and refeedings. For this
year I integrated the formation “Survival and Adaptation in extreme environments”.
1990: Qualified for secondary-school and first years of university teaching (CAPES).
1990: Intensive one-year program in biology, geology, and pedagogy known as Classe Préparatoire à
l’Agrégation: Ecole Normale Supérieure-Université Paris Sud.
1989: “Maîtrise” of Natural Sciences (Biology-Geology) at the University Denis Diderot (Paris VII).
1989: “Licence” of Natural Sciences (Biology-Geology) at the University Denis Diderot (Paris VII).
I.4.2 Grants and awards
2004: Press coverage (JF Kocik 2004 TREE 335) for book “Marine Ecosystems and Climate
Variation: The North Atlantic”
2004: Front cover of Ecology Letters of May 2004 Vol 7 (5) for “Durant et al. 2004”
2004: Best poster prize for “Ecosystem approach of the match-mismatch hypothesis” at the 24th
International Ornithological Congress in Hamburg, 13-19 August 2006
2003: Aurora grant “Ecological effects of climate fluctuations”. A mobility programme for research
collaboration between CEES and CEPE (renewed 2004).
2003: Sponsorship by Total-Fina for ClimWork workshop in Climate Change and Biodiversity in
Oslo
2003: Grant Marie Curie Individual Fellowship 2003-2005 “Ecological effects of climate fluctuation
in coupled marine-terrestrial systems”
2001: Research grant from Total-Fina
2000: PhD thesis: Summa cum laude laureate “Très honorable avec félicitations du Jury”
6
I.4.3 Funding
Title
Role: Author (single author), Lead author (Wrote most of it and gave the ideas), and Co-author (participate at
various level to the redaction), RCN= Research Council of Norway, EU =European Union, NMA = Nordic
Marine Academy, NordForsk = Nordic Council of Ministers, ANR = Agence Nationale de la Recherche
Seabirds and Climate, example of the Atlantic puffin
on Røst in the Norwegian Sea
Funding
body
Total
Role
Ecological effects of climatic fluctuations in coupled
marine-terrestrial systems
EU FP5
Ecological effects of climate fluctuations
RCN
Author
Principal
investigator
Author
Principal
investigator
Author
Climate change and biodiversity (a French-Norwegian
workshop)
Economically and ecologically sustainable fisheries
management: optimising fish harvest while conserving
seabird diversity
Structured marine populations: ecology, genetics,
oceanography and statistical modelling - the Skagerrak
cod as a case study: An Advanced Course Organised by
the Nordic Marine Academy
Long-term effects of Oil accidents on the pelagic
ecosystem of the Norwegian and Barents Sea
Dependent species and sustainable development:
seabirds and the South Africa’s purse-seine fishery
Match-mismatching of trophic levels as a structuring
force of ecosystems
Total
Lead author
RCN
Lead author
Principal
investigator
Author
Teaching
NMA
Period /
Budget
2001-2002
250 000 NOK
Collaborators
2003-2005
142 748 EUR
NINA Trondheim Norway
2003-2004
250 000 NOK
DEPE CNRS France
2003
150 000 NOK
2005-2008
2.5 M NOK
NINA Trondheim Norway
Univ. of Tromsø Norway
2006
285 000 NOK
IMR Flødevigen Norway
2006-2008
7 M NOK
2007-2010
600 000 ZAR
2008-2012
6.3 M NOK
Met.no Norway
South
Africa
RCN
Co-author
Investigator
Co-author
Investigator
Author
Principal
investigator
Arctic and sub-Arctic climate system and ecological
response to the early 20th century warming
RCN
Co-author
2008-2011
12 .5 M NOK
Response of trophic relationships to climate change in
Sub-Arctic Seas
RCN
Author
Investigator
2009
250 000 NOK
Response of trophic relationships in Sub‐Arctic Seas to
climate change
RCN
Author
Investigator
2011
220 000 NOK
Adaptive management of living marine resources by
integrating different data sources and key ecological
processes
Nordic Centre for Research on Marine Ecosystems and
Resources under Climate Change (NorMER)
RCN
Co-author
Investigator
2010-2015
30.6 M NOK
NordForsk
Co-author
Investigator
2011-2016
30.5 M NOK
Norwegian Marine Data Centre
RCN
Co-author
Investigator
2011-2016
48 M NOK
Penguins as Indicators of Climate Anomalies in the
Southern Ocean
ANR
Co-author
Investigator
2011-2015
520 000 Euro
Resource-based Green growth under climate change:
Ecological and socio-economic constraints
(GreenMAR)
NordForsk
Co-author
Investigator
2014-2016
27 M NOK
RCN
7
MCM Cape Town South Africa
MCM Cape Town South Africa
MMBI Murmansk, Russia
DEPE CNRS France
WWF Russia
NERSC Bergen Norway
IMR Bergen Norway
Univ. of Aberdeen UK
PINRO Murmansk Russia
IMR Bergen Norway
COAS Univ. of Oregon USA,
IRD Sète France
PINRO Murmansk Russia
IMR Bergen Norway,
COAS Univ. of Oregon USA
NOAA Seattle USA
Bedford Inst. of Oceanogr. Canada
IMR Bergen Norway
Univ. of Bergen Norway
DTU aqua Denmark
Univ. of Island
Stockholm Univ. Sweden
Åbo Akademy Univ.
Univ. of Helsinki Finland
Univ. Faroes
SMHI Sweden
IMR Bergen Norway + 13 other
partners in Norway
DEPE CNRS France
CEFE CNRS France
LOCEAN MNHN France
Stockholm Univ. Sweden
Univ. of Iceland
Princeton University USA
Wageningen Univ. Netherlands
I.5 – Contribution to training, scientific leadership...
I.5.1 Supervision at the PhD level
Philippe Sunil Sabarros (2005- 2010) Spatial statistical modelling in population biology. University
of Oslo
I.5.2 Supervision at the Masters level
Claire Saraux (2008), Environmental variability and effect of banding on king penguin demography at
the Possession Island, Archipelago of Crozet. Master II, ENGREF Paris, Université de Paris
Sud
Naïd Mubalegh (2010), Role of islands and seabird colonies in nutrient cycle processes in oceanic
ecosystems: The case of the King Penguin in the Indian Ocean . Master II, ENS Université
Pierre et Marie Curie
Yoann Ratrimoharinosy (2011), Effect of herring (Clupea harengus) on the recruitment of cod
(Gadus morhua) in the Barents Sea. Master II, Université de Perpignan Via Domitia
I.5.3 Other supervision
- Supervision of two internships (preparation and return of wintering in the T.A.A.F. )
- Referent teacher for six years.
- Claire Saraux (2010), Impact of climate change on seabirds as ecological indicators of Southern
Ocean. On a visiting researcher grant.
- Gabriel Reygondeau (2013) Biogeographical structure of the subarctic and arctic areas of the North
Atlantic Ocean. On a visiting researcher grant.
I.5.4 Scientific leadership an teaching duties
- Principal Investigator in four projects
- Work package leader in three projects
- Organizer and leader for two internationals workshops
- Helped develop a PhD school program (see NorMER project)
- Teaching in different PhD courses
- Tenure teacher in secondary school since 1994 (active for six years)
- Scout leader since 2011
I.5.5 Administrative duties
Since 2005: Leader of the Marine Group of the Centre for Ecological and Evolutionary Synthesis
1995-1997: Elected member of the College F. Dolto board in Marly la Ville
1994-1999: Leader/founder of the biology department of the College F. Dolto (Marly la Ville)
1994-1999 and 2000-2001: Referent teacher
I.5.6 Synergistic activities
Referee for Marine Ecology Progress Series, Journal of Animal Ecology, Climate Research,
Proceedings of the Royal Society, Global Change Biology, Ecosystem, Oikos, Oecologia, Canadian
Journal of Zoology, Canadian Journal of Fisheries and Aquatic Science, Journal of Avian Biology,
PLoS one, Journal of Marine Sciences, US National Science Foundation, The French National
Research Agency, etc...
8
I.6 – Publications and oral communications
Published Items in Each Year
Citations in Each Year
I.6.1 Complete list of publications:
- 37 publications in ISI and 2 book chapters (date 1 Jan 2014)
- 677 cited (without self-citations)
Average Citations per Item: 21.50
h-index: 13
(* indicates the scientist who devised or initiated the study. First author is always the person who contributed
most to the work and wrote the manuscript)
Theses
Durant Joël 2000. Energétique de la reproduction chez la Chouette effraie (Tyto alba). Breeding energetics in
Barn Owl. PhD thesis, University Louis Pasteur, Strasbourg, France.
Durant Joël 1991. Restriction alimentaire hivernale chez la chouette effraie (Tyto alba): Jeûnes successifs et
réalimentations. Barn owl winter starvation: successive fasts and refeedings. DEA thesis, University
Claude Bernard, Lyon, France.
Book chapters
Durant J.M., Stenseth N.C.*, Anker-Nilssen T., Harris M.P., Thompson P. & Wanless S. 2004. Marine birds
and climate fluctuation in North Atlantic. In Stenseth, N.C., Ottersen, G., Hurrell, J.W. & Belgrano, A.
(eds). Marine Ecosystems and Climate Variation: The North Atlantic, Oxford University Press, Oxford.
pp. 95-105.
Le Maho Y.* & Durant J.M. 2011. Impacts of Climate Change on Marine Ecosystems. In: Vidas D, Schei PJ
(eds) The World Ocean in Globalisation: Climate Change, Sustainable Fisheries, Biodiversity,
Shipping, Regional Issues. Brill, Martinus Nijhoff Publishers, Leiden/Boston, p 133-146
Articles in peer-reviewed journals
1.
Stige, L.C.*, Dalpadado, P., Orlova, E., Boulay, A.-C., Durant, J.M., Ottersen, G., Stenseth, N.C. (2014)
Spatiotemporal statistical analyses reveal predator-driven zooplankton fluctuations in the Barents Sea.
Progress in Oceanography 120: 243-253
2.
Chambers, L.E.*, Altwegg, R., Barbraud, C., Barnard, P., Beaumont, L.J., Crawford, R.J.M., Durant, J.M.,
Hughes, L., Keatley, M.R., Low, M., Morellato, P.C., Poloczanska, E.S., Ruoppolo, V., Vanstreels, R.E.T.,
9
Woehler, E.J., Wolfaardt, A.C. (2013) Phenological Changes in the Southern Hemisphere. PLoS ONE
8:e75514
3.
Durant, J.M.*, Hjermann, D.Ø. & Handrich, Y. 2013. Diel feeding strategy during breeding in male Barn
Owls (Tyto alba). Journal of Ornithology 154: 863-869
4.
Durant, J.M.*, Hjermann, D.Ø., Falkenhaug, T., Gifford, D.J., Naustvoll, L.-J., Sullivan, B.K., Beaugrand,
G. & Stenseth, N.C. 2013. Extension of the match-mismatch hypothesis to predator-controlled systems.
Marine Ecology Progress Series 474: 43-52
5.
Durant, J.M.*, Ottersen, G. & Stenseth, N.C. 2013. Impacts of climate and fisheries on sub-Arctic stocks.
Marine Ecology Progress Series 480: 199-203
6.
Ottersen, G.*, Stige, L.C., Durant, J.M., Chan, K.-S., Rouyer, T.A., Drinkwater, K.F. & Stenseth, N.C.
2013. Temporal shifts in recruitment dynamics of North Atlantic fish stocks: effects of spawning stock and
temperature. Marine Ecology Progress Series, 480: 205-225
7.
Durant, J.M.*, Hidalgo, M., Rouyer, T., Hjermann, D.Ø., Ciannelli, L., Eikeset, A.M., Yaragina, N. &
Stenseth, N.C. 2013. Population growth across heterogeneous environments: effects of harvesting and age
structure. Marine Ecology Progress Series, 480: 277-287
8.
Sabarros, P.S, Durant, J.M.*, Grémillet, D., Crawford, R.J.M. & Stenseth, N.C. 2012. Differential
responses of three sympatric seabirds to spatio-temporal variability in shared resources. Marine Ecology
Progress Series 468: 291-301
9.
Durant, J.M.*, Krasnov, Y.V., Nikolaeva, N.G. & Stenseth, N.C. 2012. Within and between species
competition in a seabird community: statistical exploration and modeling of time-series data. Oecologia
169: 685-694
10. Hidalgo, M.*, Rouyer, T.A., Bartolino, V., Cerviño, S., Ciannelli, L., Massuti, E., Jadaud, A., Fran
Saborido-Rey, F., Durant, J.M., Santurtún, M., Piñeiro, C. & Stenseth, N.C. 2012. Context-dependent
interplays between truncated demographies and climate variation shape the population growth rate of a
harvested species. Ecography 7: 637-649
11. Le Maho, Y.*, Saraux, C., Durant, J.M., Viblanc, V.A., Gauthier-Clerc, M., Yoccoz, N.G., Stenseth, N.C.
& Le Bohec, C. 2011. An ethical issue in biodiversity science: The monitoring of penguins with flipper
bands. Comptes Rendus Biologie 334: 378-384
12. Saraux, C., Le Bohec, C.*, Durant, J.M., Viblanc, V.A., Gauthier-Clerc, M., Beaune, D., Park, Y.-H.,
Yoccoz, N.G., Stenseth, N.C. & Le Maho, Y. 2011. Reliability of flipper-banded penguins as indicators of
climate change. Nature 469: 203-206
13. Rouyer, T.*, Ottersen, G., Durant, J.M., Hidalgo, M., Hjermann, D.A.G.Ø., Persson, J., Stige, L.C. &
Stenseth, N.C. 2011. Shifting dynamic forces in fish stock fluctuations triggered by age truncation? Global
Change Biology 17: 3046–3057.
14. Crawford, R.J.M.*, Altwegg, R., Barham, B.J., Barham, P.J., Durant, J.M., Dyer, B.M., Geldenhuys, D.,
Makhado, A.B., Pichegru, L., Ryan, P.G., Underhill, L.G., Upfold, L., Visagie, J., Waller, L.J. &
Whittington, P.A. 2011. Collapse of South Africa’s penguins in the early 21st century. African Journal of
Marine Science 33: 139-156.
15. Durant, J.M.*, Crawford, R.J.M., Wolfaardt, A.C., Agenbag, K., Visagie, J., Upfold, L. & Stenseth, N.C.
2010. Influence of feeding conditions on breeding of African penguins – the importance of adequate local
food supplies. Marine Ecology Progress Series 420: 263-271
16. Durant J.M.*, Hjermann D.Ø., Frederiksen M, Charrassin JB, Le Maho Y, Sabarros P.S., Crawford R.J.M
& Stenseth N.C. 2009. Pros and cons of using seabirds as ecological indicators. Climate Research 39: 115129
17. Durant J.M.*, Gendner J.P. & Handrich Y. 2009. Behavioural and body mass changes before egg laying in
the Barn Owl: cues for clutch size determination? Journal of Ornithology 151: 11-17
18. Durant J.M.*, Hjermann D.Ø., Sabarros P.S. & Stenseth N.C. 2008. Northeast Arctic Cod population
persistence in the Lofoten-Barents Sea system under fishing. Ecological Applications 18(3): 662-669
19. Durant J.M., Landys M.M. & Handrich, Y.* 2008. Composition of the body mass overshoot in European
barn owl nestlings (Tyto alba): insurance against scarcity of energy or water? Journal of Comparative
Physiology B: Biochemical, Systemic, and Environmental Physiology 178: 563–571
10
20. Le Bohec C., Durant J.M., Gauthier-Clerc M., Stenseth N,C., Park Y.-H., Pradel R., Grémillet D., Gendner
J.-P. & Le Maho Y.* 2008. King penguin population threatened by Southern Ocean Warming. Proceedings
of the National Academy of Sciences USA.105(7): 2493 -2497.
21. Cury P.M.*, Shin Y.-J., Planque B., Durant J.M., Fromentin J.-M., Kramer-Schadt S., Stenseth N.C.,
Travers M. & Grimm V. 2008. Ecosystem Oceanography: towards a balanced strategy for global change.
Trends in Ecology and Evolution 26(6): 338-346
22. Hjermann D.Ø., Melsom A., Dingsør G., Durant J.M., Eikeset A.M., Røed L.P., Ottersen G. , Storvik G. &
Stenseth N.C.* 2007. Fish and oil in the Lofoten-Barents Sea system: a synoptic review of what is known
and what is not known. Marine Ecology Progress Series 339: 283-299.
23. Durant J.M.*, Hjermann D.Ø., Ottersen G. & Stenseth N.C. 2007. Climate and the match or mismatch
between predator requirements and resource availability. Climate Research 33(2): 271-283.
24. Durant J.M.*, Anker-Nilssen T. & Stenseth N.C. 2006. Ocean climate prior to breeding affects the duration
of the nesting period in the Atlantic puffin. Biology Letters 2: 628-631.
25. Criscuolo F.*, Bertile, F., Durant J.M., Raclot T., Gabrielsen G.W., Massemin, S. & Chastel, O. 2006.
Body mass and clutch size may modulate prolactin and corticosterone levels in Eiders. Physiological and
Biochemical Zoology 79(3): 514-521.
26. Stenseth, N.C.*, Mysterud, A., Durant J.M., Hjermann, D.Ø. & Ottersen. G. 2005. Uniting ecologists into a
smooth, tasty and potent blend. Marine Ecology Progress Series 304: 289-292.
27. Durant J.M.*, Hjermann, D.Ø., Anker-Nilssen, T., Beaugrand, G., Mysterud, A., Pettorelli, N. & Stenseth,
N.C. 2005. Timing and abundance as key mechanisms affecting trophic interactions in variable
environments. Ecology Letters 8: 952-958.
28. Durant J.M.*, Gendner J.P. & Handrich Y. 2004. Should I brood or should I hunt: a female barn owl’s
dilemma. Canadian Journal of Zoology 82(7):1011-1016.
29. Cherel Y.*, Durant J.M. & Lacroix A. 2004. Plasma thyroid hormone pattern in king penguin chicks: a
semi-altricial bird with an extended posthatching developmental period. General and Comparative
Endocrinology 136: 398-405.
30. Durant J.M.*, Anker-Nilssen T., Hjermann D.Ø. & Stenseth N.C. 2004. Regime shifts in the breeding of an
Atlantic puffin population. Ecology Letters 7: 388-394.
31. Durant J.M., Massemin S. & Handrich Y.* 2004. More eggs the better: Egg formation in captive Barn
Owls. Auk 121(1): 103-109.
32. Durant J.M., Anker-Nilssen T. & Stenseth N.C.* 2003 Trophic interactions under climate fluctuations: the
Atlantic puffin as an example. Proceedings of Royal Society, B, London 270: 1461-1466.
33. Durant J.M.* 2002. The influence of hatching order on the thermoregulatory behaviour of barn owl Tyto
alba nestlings. Avian Science, 2: 167-173.
34. Gauthier-Clerc M., Le Maho Y., Gendner J.-P., Durant J. & Handrich Y.* 2001. State-dependent decisions
in long-term fasting king penguins, Aptenodytes patagonicus, during courtship and incubation. Animal
Behaviour 62: 661-669.
35. Durant J.M., Massemin S., Thouzeau C. & Handrich Y.* 2000. Body reserves and nutritional needs during
laying preparation in barn owls. Journal of Comparative Physiology B. 170: 253-260.
36. Durant J.M. & Handrich Y.* 1998. Growth and food requirement flexibility in captive chicks of the
European Barn Owl (Tyto alba). Journal of Zoology 245: 137-145.
37. Durant J., Gendner J.-P. & Handrich Y.* 1996. A nest automatic weighing device to study the energetics of
breeding barn owls (Tyto alba). In: Proceeding of the 2nd International Conference on Raptors, Urbino,
Journal of Raptor Research (special issue), 34.
I.6.2 Oral presentations
ICES Annual Science Conferences. 18-25 September 2012, Bergen, Norway. Dankel DJ, Dalpadado P, Dingsør
G, Durant JM, Eikeset AM, Enberg K, Hjermann D, Huse G, Korsbrekke K, Nash R, Ottersen G, Stige
LC, Handegard NO, Skern‐Mauritzen M, Pennington M, Subbey S, Vølstad JH, Stenseth NC, Kjesbu
OS. The strategic scientific framework of the ecosystem approach to fisheries in Norway: adaptive
11
management of living marine resources by integrating different data sources and key ecological
processes (ADMAR)
ICES Annual Science Conferences. 18-25 September 2012, Bergen, Norway. Ottersen G, Stige LC,Durant JM,
Rouyer T, Drinkwater K, Stenseth NC. Comparison across 42 North Atlantic fish stocks of temporal
patterns in recruitment dynamics, including the role of spawning stock biomass and temperature
ICES Annual Science Conferences. 18-25 September 2010, Nantes, France. Durant JM; Hidalgo M, Ciannelli
L. How does exploitation of prey fish affect population growth rate in changing seas?
Seabird Group 10th international Conference: 27-30 March 2009, Bruges, Belgium. Durant JM, Le Bohec C,
Hjermann DØ, Stenseth NC, Sabarros, PS. The King Penguin under climate changes: trend and
sensitivity.
ICES Annual Science Conference in Berlin; 20-25 September 2009, Berlin, Germany. Durant JM, Hjermann
DØ, Stenseth NC. Reversing the Match-mismatch relationship: the prey point of view.
A conference on Ecology and Evolution; 03-04 September 2009. Durant JM and Le Maho Y. The effect of
climate variation on seabirds.
6th International Penguin Conference. 03-08 September, Hobart, Tasmania. Le Bohec C, Durant JM, GauthierClerc M, Stenseth NC, Park YH, Pradel R, Grémillet D, Gendner J-P, Le Maho Y. King Penguin
population threatened by the Southern Ocean Warming.
Tropharct; 09-10 November 2009. Durant JM. Response of trophic relationships to climate change in SubArctic Seas.
Seminar at The Zoological Society of London; 24 November 2008, London, UK. Durant JM. Match-Mismatch:
Trophic interactions and climate change.
Eastern Boundary Upwelling Ecosystems Symposium. 02-06 June 2008, Canary Islands, Spain. Sabarros PS,
Durant JM, Crawford RJM, Stenseth NC. Resource sharing between three seabirds in South Africa.
CEES Conference, 27-29 September, Holmen, Norway. Le Bohec C, Durant JM, Gauthier-Clerc M, Stenseth
NC, Park YH, Pradel R, Grémillet D, Gendner J-P, Le Maho Y. King Penguin population threatened by
the Southern Ocean Warming.
PICES XVI annual meeting, 28 October 03 November 2007 Victoria, BC. Durant JM. Match-mismatch, trophic
interactions and climate change. Invited speaker
Ecosystem Dynamics in the Norwegian Sea and the Barents Sea; 12-15 March 2007, Tromsø, Norway. Durant
JM, Hjermann DØ, Sabarros P, Stenseth NC. Food threshold and recruitment
CEES yearly Conference. 27-28 September 2007, Holmen, Norway. Sabarros PS, Durant JM, Crawford RJM,
Stenseth NC. African penguin dynamics in South Africa.
24th International Ornithological Congress in Hamburg, 13-19 August 2006, Hamburg, Germany. Durant JM,
Hjermann DØ & Stenseth NC. Ecosystem approach of the match-mismatch hypothesis. (Poster)
ICES 2005 Annual Science Conference; 20-24 September 2005, Aberdeen, UK. Durant JM, Hjermann DØ,
Stenseth NC. Match-mismatch and Threshold for the North Sea cod recruitment.
Ecological Response to Climate Change; 03-04 November 2005 Helsinki, Finland. Durant JM, Hjermann DØ,
Stenseth NC. Match-Mismatch and Food threshold in recruitment.
EcoClim NoCE workshop; 05 December 2005 Oslo, Norway. Durant JM, Hjermann DØ, Stenseth NC. MatchMismatch: An old new-idea.
The influence of Climate Change on North Atlantic fish Stocks; ICES annual meeting, 11-15 Mai 2004 Bergen,
Norway. Durant JM; Anker-Nilssen T, Hjermann DØ, Stenseth NC. Climate, match-mismatch and
trophic interactions in a marine system: seabird-fish-plankton.
Quantitative Ecosystem Indicators for Fisheries Management International Symposium, 31 March – 3 April
2004, Paris, France. Durant JM, Anker-Nilssen T, Hjermann D & Stenseth NC. Climate,
oceanographic conditions and trophic interactions in a marine system: seabird-fish. (Poster)
Understanding the impact of climate changes on the population dynamics of vertebrates; Chizé, France 29
January 2003. Durant JM, Anker-Nilssen T, Stenseth NC. Marine productivity and climate change Atlantic puffins on Røst in the Norwegian Sea.
Monthly seminar at the French Embassy in Norway; 10 June 2003. Durant JM & Pettorelli N. Changements
climatiques: quelles conséquences écologiques?
ClimWork: Climate change and Biodiversity; 27-28 March 2003 Oslo, Norway. Durant JM, Stenseth NC.
Climate effects on trophic interactions: The Herring-Atlantic puffin relationship as example.
CLIM-POP: Understanding the impact of climate changes on the population dynamics of vertebrates; 27-28
January 2003 Chizé, France. Durant JM, Stenseth NC. Marine productivity and climate change:
Atlantic puffins on Røst in the Norwegian Sea.
The ecological effects of climate fluctuations and change; 12 September 2002 Drøbak, Norway. Durant JM,
Anker-Nilssen T, Stenseth NC. Seabirds and Climate: example of the Atlantic puffin on Røst in the
Norwegian Sea.
12
Ecological effects of climate fluctuations and change on seabirds populations; 26 February 2002 Cromarty, UK.
Anker-Nilssen T, Durant JM, Stenseth NC. Atlantic Puffins on Røst in the Norwegian Sea - indicators
of marine productivity and climate change.
Colloque National Francophone d’Ornithologie, Paris, France, 1998. Handrich Y, Nicolas L & Durant J.
Mortalité hivernale chez la Chouette effraie : Jeûne prolongé et réalimentation en captivité.
Colloque National et Interrégional Francophone d’Ornithologie, Bron, France, 1998. Durant J & Handrich Y.
1998. Comportement au cours de la reproduction chez la Chouette effraie (Tyto alba).
Colloque National et Interrégional Francophone d’Ornithologie, Bron, France, 1998. Michard-Picamelot D,
Durant J & Le Maho Y. 1998. Survie et croissance des poussins de Cigogne Blanche (Ciconia
ciconia).
2nd International Conference on Raptors, Urbino, Italy, 1996. Durant J, Gendner J-P & Handrich, Y. A nest
automatic weighing device to study the energetics of breeding barn owls (Tyto alba). (Poster)
13
II Research activities and project
II.1 – Main achievements
1. Breeding energetics
I demonstrated that the overshoot of mass in both female barn owl Tyto alba during egg
formation and chicks during growth was for the greatest part due to water accumulation in
contradiction to the current hypothesis of energy reserve accumulation. I isolated several
mechanisms explaining the high reproductive capacity of the barn owl (low daily cost for egg
production making it possible to lay egg on daily food intake, indeterminate layer, adjustment
of the clutch size to food conditions, existence of a large plasticity in the food requirement of
the chicks due to the hatching asynchrony and behaviour). Finally, I increased the insight in
the time budget of this species during breeding explaining the interplay between the female,
her mate and the brood needs.
2. Climate change and trophic interactions
Studying the effect of climate on population changes in the population breeding of Atlantic
puffin of Røst. We show that climate is mostly affecting seabirds in an indirect way via sea
temperature and highlights the lower trophic level as an essential component to understand
the effect of climate on the top-level seabirds. We show that climate change is changing
established relationships, provoking regime shifts, possibly via its effect on the prey
availability. Similarly, we show that long-term climate change can generate local differences
in the persistence of spatially structured species assemblages by shifting the equilibrium
point.
3. Match-mismatch
The match-mismatch hypothesis (MMH) has proved to be a useful hypothesis that can be
tested in a range of different settings, and it is a helpful concept for understanding and
predicting complex, natural ecosystems. I have extended the hypothesis to take explicitly into
account prey abundance, not only prey timing and at a later stage to take into account the
effect of predators on prey. We found that the temporal synchrony and the food
abundance/food requirement ratio should be considered conjointly when exploring the MMH
relationship as an increase of food abundance may compensate for the increase of
asynchrony. We illustrate the importance of the spatial distribution in a spatial MMH
affecting both survival and reproduction of central place foraging predators. I expanded the
MMH, making it possible to use the hypothesis to describe both the effect of predators on prey
and the reverse.
4. Biodiversity and stock management
Indicators such as ecological ones are a scientific and societal requirement. Top predators
are often advertised as good ecological indicators. We show that the flipper-banding
14
technique used to collect demography data on penguins introduced bias by reducing both
long-term survival and reproductive output. We also show that the abundance of prey actually
available to the predator (i.e., local abundance), should be used preferable to overall fish
abundance to explain changes in predator breeding. We show that king penguin population
growth rate is most sensitive to changes in the temperature-dependant survival of adults, and
also to changes in juvenile survival, but hardly in breeding success. This puts their
populations at risk due to climate warming.
We show that climate is affecting the presence of seabird colonies due to displacement
of the prey population. We suggested that the stability of the kittiwake and common guillemot
populations at Kharlov, Russia, is due to better feeding conditions than in colonies off the
Norwegian coast, linked to a possible eastward shift of the capelin population with the
temperature increase of the Barents Sea. We show the same effect on the coast of South Africa
where seabirds colonies fluctuated following the displacement of their prey.
We confirmed the importance of managing cod and capelin stocks together by showing
that capelin abundance is the main variable that can be adjusted in order to maintain the cod
population size at a given level of harvesting.
We showed that the removal of the older age classes may reduce the buffering
capacity of the population, thereby making the population growth rate more dependent on
recruitment than on adult survival.
II.2 – Introduction
In 2013 it is scientifically acknowledged that global warming affects all ecosystems albeit at
different strength rates depending on factors such as latitude or altitude... The marine system
is greatly affected by climate warming, even more so at high latitudes 29. For instance, at these
latitudes the increase in temperature leads to the sea ice melting and drastically changes the
environmental conditions for ice-dependent organisms. Hence one of the main scientific
challenges in ecology is to assess the effect of these rapid changes on living organisms, and
whether they will be able to adapt to them or not 30.
A physiological approach is appropriate in order to determine the range of changes, in
our case environmental changes such as ambient temperature, one organism can cope with
under controlled conditions. However, highly reproducible, physiological studies do not
usually take into account the behavioural response that are triggered by such changes. For
this, ecophysiological studies, which measure how organisms respond to environmental
constraints, have a significant part to play. However, organisms exist within webs of
interactions with other organisms, the most important of which involve eating or being eaten
(trophic interactions). To study these interactions one may have to take a broader perspective
than the one of ecophysiological studies: look at the population level.
Following years using first physiological, and later ecophysiological techniques to
study the energy constraints to reproduction, I tried to understand the impacts of
environmental variability on population dynamics with special focus on trophic interactions in
marine populations. For these studies, I started using regression techniques on long-term time
series with a particular focus on seabirds.
15
In 2013, the sustainable exploitation of natural resources and the preservation of the
biodiversity are among the French ecology research priorities. For the first priority, the
superimposition of climate change on overexploitation of resources is increasingly causing
unanticipated changes in ecosystems. For instance, exploited fish species exhibit higher
temporal variability than unexploited species 31. The Reykjavik declaration of 2001 32,
reinforced at the World Summit of Sustainable Development in Johannesburg in 2002,
requires nations to base policy related to marine resource exploitation on an ecosystem
approach. To fulfil this requirement, a strategy based upon innovative science addressing the
complexity of marine ecosystems and interactions between fisheries, the marine ecosystem,
and the environment was required 33,34. Accordingly, there has been an increasing
appreciation of the fact that ecosystem management requires a more holistic approach 35, a
solid understanding of interdependent effects 36 and an ecosystem-based approach which
integrates populations, food webs and fish habitats at different scales 27. The second priority,
similar to the first one, aims at better take into account biodiversity in sustainabledevelopment strategies. Preserving global biodiversity has become a priority in strategic
conservation plans, especially in regard to the current global warming and its effect in
population interactions and migrations. The work presented in this synthesis on Trophic
interactions and environmental variability is in line with these objectives.
To conduct trophic interaction analysis requires more than a good grip on statistical
techniques and good data sets, but also an intimate knowledge of the biology of the species
concerned is essential. In my line of work, this means being simultaneously a fisheries
scientist, an ornithologist and a statistician. While this is not impossible, being all of them at
the same time is quite a challenge. A more practical solution is to collaborate with specialists
in all these fields and learn their vocabulary and scientific jargon (stock vs. population,
recruitment vs. breeding success...) and thus to be at the interface between equally important
fields of science.
After years studying breeding energetics, my research effort is presently aimed at
understanding the impacts of environmental variability on the population dynamics with
special focus on marine populations and seabirds. I am particularly looking into the
relationship between food availability (abundance and accessibility) and reproductive
success/recruitment. For this, I am studying ecosystem functioning using time series analysis
and various theoretical frameworks such e.g. the match-mismatch hypothesis 6,7. I used many
different marine ecosystems (the Southern Ocean, the Barents Sea and the Benguela
Upwelling...), all of which are highly sensitive to climate variability, and, being important
fishing areas, also subject to anthropogenic constraints.
II.3 – Climate change and population dynamics, implications for
biodiversity
Environmental fluctuations have important ecological consequences and influence the
population dynamics of long-lived organisms 36,37. Addressing the issue of the effects of
climate changes on biodiversity and resources is at the heart of the current scientific challenge
and this is especially true when dealing with the marine environment. Oceans, indeed, play a
16
key-role in the regulation of the world’s climate 38 and in biomass and biodiversity production
39
. In this context, the investigation of the effects of climatic perturbations on marine
ecosystems represents a scientific, but also economic and societal, priority 40.
Since 2001, the main objective of my research has been to study studying the effects
of climatic perturbations on population dynamics with a special focus on marine
environments. This certainly took roots in my background as a Physiologist and later
Ecophysiologist with the main focus on the adaptation of organisms to extreme environments.
Indeed, in the context of a global climate warming scenario, a key issue is to understand how
and to what extent organisms are able to cope with climatic variation 30. Organisms are
increasingly confronted with extreme environment and specially in polar/sub-polar regions
where the effects of climate changes are the strongest 29. The most common approach to
investigate the effect of climate change on population dynamics consists in drawing
correlations between observed physical changes in given environments and the dynamics of
the population investigated. However, many statistical tools exist, as do various systems and
question that are related to them and I used a wide array of both.
A post doc depends on short-time contracts usually with very precise objectives
defined in the project submitted to the funding agencies. A post-doc period is thus mix of
opportunities and constraints (pragmatism) and a succession of short-term projects that makes
it difficult to have a coherent work history. I was lucky in two respects : to have remained for
more than a decade in the same institute at the University of Oslo and to have defined and
written the projects that have provided my salary thus determining the direction of my
research. Both were possible only with the continuous support of Prof. Nils Chr. Stenseth to
whom I am deeply indebted. This gave a certain amount of unity to my research that
otherwise would have been much more fragmented.
After briefly presenting the ecophysiology work I conducted during my PhD with Dr.
Yves Handrich at the CNRS in Strasbourg I will present the research I conducted on the
trophic interaction in marine environment and the effect of environmental change.
II.4 – Previous work: Energetic constraint during the annual cycle,
physiological and ecophysiological studies on the barn owl
Main findings
• Using body composition analysis on breeding females and nestlings of barn owl, I
demonstrated that the overshoot of mass was for its greatest part due to water
accumulation in contradiction to the current hypothesis of energy reserve
accumulation.
• I isolated several mechanisms explaining the high reproductive capacity of the barn
owl (low daily cost for egg production making it possible to lay egg on daily food
intake, indeterminate layer, adjustment of the clutch size to food conditions, existence
of a large plasticity in the food requirement of the chicks due to the hatching
asynchrony and behaviour).
17
•
Using telemetric techniques, I increased the insight in the time budget of this species
during breeding explaining the interplay between the female, her mate and the brood’s
needs.
In Alsace, eastern France, the European barn owl population is at the northern limit of its
distribution (see Figure 1). The resulting high mortality of the barn owl in Alsace is attributed
to its low fasting capacity and its foraging ecology (rodent specialist) that lead the bird to
starve to death during long periods of snow cover. The population persistence in this region is
possible because of a high reproductive success despite high winter mortality. The
reproductive success of barn owl could be explained by an optimization of the reproductive
cost. This may be achieved through an important plasticity of the chick food requirement, of
the brood requirement and of the parental investment.
Isotherm 0°C
January
Isotherm 3°C
January
Inf rared video
camera
73 cm
Exit
Population
size
34 cm
Inf rared
photocells
Nest box
Weighing
platf orm
22 cm
Weighing corridor
90 000
pairs
300
0
Control
Acquisition system
Time-lapse
video recorder
Figure 1: Barn owl distribution in Europe (data from 1993 ) to the left and to the right the
description of weighing and video systems used to study the breeding of barn owls in the
wild. The thermoneutral zone of the barn owl is 23-32 °C which makes it a tropical species at the
northern limit of its distribution in Europe. The 3°C isotherm marks the limit between the two
subspecies Tyto alba alba to the west and T. a. guttata to the est. The automatic system to monitor
breeding of barn owls in the wild was used for several studies on: the laying preparation and the
change in the female body mass and behaviour 12; the thermoregulatory behaviour of the nestlings
20
; and the female decision to resume hunting while still brooding young nestlings 26.
The studies realized between 1991-2000 were conducted in part on captive animals
reared in the laboratory on aviary and in part on wild couples breeding in nest boxes in Alsace
26,41
. The barn owl enjoys interesting features in its reproduction biology. Among the diurnal
and nocturnal raptors of Europe, the barn owl is a separate species. Its longevity is low, with a
life expectation after leaving the nest of ca 1.3 years 42, and its productivity is similar to that
of Passeriformes with large broods several times a year. Female barn owls are indeterminate
layers, i.e., they can compensate for egg removal by replacement laying 43,44 and as such have
18
a great flexibility in the number of eggs they develop in order to obtain a predetermined
clutch size 44,45. Analysing 26 eggs laid by “dye-dosed” captive females we found that that the
total duration between initiation of the rapid yolk deposition (RYD) and laying of the
corresponding egg was only 13.6 days, with an interval between yolk completion and
oviposition of 2.4 days 44,46. The dissection of 5 ovaries showed that the total number of
follicles that may have given eggs was ca 25. That high number of follicles and the short
RYD period explain the particularly high reproductive potential of this nocturnal raptor
species. In addition, the high number of nestlings that hatch asynchronously due to an
incubation that starts at the oviposition of the first egg 42 cause significant size/age difference
among siblings with risk of brood reduction 16 when food conditions become unfavourable
with the death and cannibalism of the last-born.
1st
hatching
Body mass, g Laying
1st
female
exit
Chicks peak
of body
mass
Last
Mass, g hatching 1st exit
500
600
400
400
300
Female food
intake
Prey
brought
to nest
Theoretical
brood
requirements
200
200
-40
-20
0
20
40
60
0
80
Days before and after laying of first egg
Prey
brought by
the male
0
10
20
30
Days after first egg hatching
40
Figure 2: Female barn owl body mass change before and after breeding. Merging video and
weighing we were able to associate the behaviour of the brood (nestlings and adults) to the change
of the female body mass (to the left). Merging laboratory studies to behavioural observations in the
field we were able to relate the first exit of the female after incubating the eggs with the
discrepancy between the male food provision to the nest and the brood requirement (to the right).
The first part of my PhD work was to study the period of laying preparation. We have
shown that the body mass of the female owl increases twenty days before laying which is
about 6 days before the first egg starts to develop in the ovary 44. During this time lapse,
breeding females increase their body mass with ca +38.3 g after eggs in formation and
gonadal tractus were removed 47. Analysis of the composition of eggs associated to study of
the female food intake shows that the egg synthesis in the barn owl is provided by food intake
on a daily basis and consequently does not need previously accumulated reserves. The extra
mass deposited by the female during egg formation was found to be mainly of water and was
found positively correlated with clutch size 47. It is likely reflects the metabolic phenomena
associated with protein synthesis (preparation of eggs and moult that takes place after
reproduction). These results raised a general question on the interpretation of the increase in
body mass observed for the income breeders that was usually interpreted as a period of
accumulation of energy reserves. How many of the studies showing such “body reserves”
were in fact a water accumulation?
The second part of this work was to study the chick growth in order to identify the
energy needs and then evaluate the one of the brood. Independent of their sex, chicks raised in
19
ad libitum food conditions by their parents experienced an overshoot in body mass (maximum
of ca. 391 g) when approximately 40 days of age, followed by a decrease (to a mass of ca. 314
g) until fledging at 60 days of age, giving a mass distribution with age in the form of a bellshaped curve. This mass overshoot can be suppressed by a 17% reduction of daily food intake
with no effect on the fledging mass or lengths (bones, feathers...) at 60 days of age 46. This
change in chick body mass is common to many bird species and is usually interpreted as an
accumulation of energy reserves. However, analysis of body composition of growing chicks
showed that the peak of mass was mainly due to an accumulation of water probably
associated with intense protein synthesis (feathers, muscles) that accompanies the rapid
growth of the chick during first 40 days of life 20,48. In addition, a partial food restriction
during growth attenuates the characteristic peak of mass but does not affect other growth
parameters (length and maturation). These observations indicate the existence of a large
plasticity in the food requirement of the chicks. Due to the hatching asynchrony and the
subsequent brood reduction there is also a significant plasticity of the food requirement at the
brood level. We suggested that this plasticity may explain how the barn owl manages to adapt
its reproductive effort to the important variation in year quality (in terms of food availability).
Some years before my involvement on barn owl study, Yves Handrich and Jean-Paul
Gendner have developed a system to automatically measure changes of body mass of
breeding pairs in the field. The systems consisted of nest boxes equipped with an electronic
balance (to obtain the body mass) and an infrared video camera for recording the behaviour 26,41
(see Figure 1). In addition the ambient temperature inside the nesting chamber was monitored 20.
Part of the set-up was developed to estimate the effect of rain, temperature (inside and outside
the nest chamber) on body mass change with control of the behaviour by video recording.
This was not possible, the outside temperature and rain measurements being not reliable.
Whatsoever, I took full advantage of the data collected. Particularly, the system was used to
identified critical phases of parental effort and the strategies adopted by both sexes 12. We
found by using techniques of windows correlation that the clutch size might be determined no
later than few days before laying the first egg as expected for indeterminate layer bird (Figure
2). This result suggested that the female may use the pre-laying period to determine the clutch
size by maybe assessing her male quality. Indeed the role of the male barn owl is crucial to
maximize reproductive success. While brooding, many female raptors rely exclusively on
food provisioning from males. Thus, they may forego hunting until nestlings are about half
grown before exiting the nest to undertake a first foraging trip. This is what we found in barn
owls where the male took charge of the brood and female requirement until the brood needs
exceeded his capacities 26 (Figure 2). The first foraging exit of the female occurred about 15
days after the hatching of the first egg. Indeed, before 15-20 days of age, barn owl nestlings
cannot self-maintain their body temperature (Taylor 1994; Durant and Handrich 1998)
preventing the female from leaving the nest before this date without adverse consequences for
the youngest nestlings. The re-initiation of foraging occurred at about the same time that male
food provisioning no longer matches nestling food requirements — about 17 days after the
hatching of the first egg. Thus the timing of the female’s first hunting trip may be primarily
adjusted to a discrepancy between brood food requirements and available food supply. The
female exit to help foraging has as a consequence an increased risk of mortality for the
20
youngest chick that is not yet fully developed (poor feeding, sibling competition, energy loss
due to thermoregulation…).
Summary. The questions I have addressed studying the barn owl were numerous and very
enlightening to me. In seven papers, we showed the effect of food and environmental
conditions on the fascinating part of the life cycle that is the reproduction. We did much more
than is reported here 49. Invariably my studies led to one direction that was to understand the
relationship of the barn owl with its prey (the common vole Microtus arvalis), i.e., the role of
trophic interactions on population dynamics. However, the barn owl data were not ready to
answer such question in a Post-doc framework.
II.5 – Trophic interaction, Population dynamic and environmental
variability on marine ecosystems
Following advice from Prof. Yvon Le Maho, I made contact with Prof. Nils Chr. Stenseth at
the University of Oslo. In 2001, after finding a private grant (Total), I moved to Oslo and
joined his team. I was then associated with a wide international project studying the effect of
climate on population dynamics (EcoClim The ecological effects of climate fluctuations and
change – a multi-disciplinary and integrated approach) and collaborated with Dr. Tycho
Anker-Nilssen for three years (NINA Trondheim, Norway). EcoClim (2001-2003) aimed at
studying the biological effects of climate fluctuations and change through a series of model
systems involving marine, terrestrial and coupled marine-terrestrial systems. This was done
through the analysis of long-term population data as well as theoretical models. Later on, this
project was prolonged and developed in a Nordic Centre of Excellence – EcoClim (20032005) chaired by the University of Oslo in which I participated.
For EcoClim, I had the task of exploring the impacts of climate fluctuations, from
large to regional scale, and develop improved climatic descriptors for marine-terrestrial
coupled systems. The study was focussed on the population dynamics of fish-eating seabirds
(especially the Atlantic puffin Fratercula arctica L.) and documented to what degree such
impacts were indirectly due to changes in food abundance and availability brought about by
changes at lower trophic levels.
II.5.1 Background
A major issue, when taking into account the present climatic scenarios of the
Intergovernmental Panel on Climate Change 38, is to anticipate the impact of climatic changes
on biodiversity. Marine ecosystems are particularly vulnerable 50. Most of my studies were
conducted on species living in the Lofoten-Barents Sea ecosystem and sensitive to its
characteristics.
21
1. The Norwegian Sea -Barents Sea pelagic ecosystem
The Norwegian Sea-Barents Sea pelagic ecosystem (NS-BS) consists of the Barents Sea, an
open arcto-boreal shelf-sea with an average depth of about 230 m, and the narrow continental
shelf along the Norwegian coast (Figure 3). It connects with the Arctic Ocean to the north.
The ocean circulation is dominated by the Norwegian Coastal Current (NCC) and the
Norwegian Atlantic Current (NAC) 9. The Norwegian Coastal Current passes the southern tip
of Norway as it exits from the Skagerrak, and continues along the Norwegian coastal shelf all
the way to the Barents Sea bringing with it fish larvae and plankton produced in these areas.
The Norwegian Atlantic Current on the other hand is characterized by water masses of
Atlantic origin that flow into the Nordic Seas across the Iceland-Faroe Ridge and through the
Faroe-Shetland Channel 51. At the entrance to the Barents Sea the Norwegian Atlantic Current
divides into two branches, one that enters the Barents Sea, and one that continues northward
toward Spitsbergen (Svalbard). Due to the relatively warm Atlantic water masses of the
Norwegian Atlantic Current, sea temperatures of the NB-BS area are warmer than in other
regions at similar latitudes. As a result the southern part of the Barents Sea stays ice free even
in the most severe winters. The year-to-year variability in temperature south of the oceanic
Polar front is strongly influenced by the inflow of Atlantic water 52. This variability appears to
be mainly wind-driven and is hence linked to the North Atlantic Oscillation (NAO) (Box 14).
The correlation between the NAO and the inflow and temperature of the Barents Sea varies
strongly with time, being most pronounced early in the last century and over the most recent
decades 5,53.
Figure 3: The Norwegian Sea-Barents Sea. Oceanography and ecology knowledge. The left hand figure shows
the main features of water circulation. Red indicates water of Atlantic origin, blue of Arctic waters origin and
green indicates coastal currents 9. The grey line is the Polar front. The right hand figure shows the ecological
controls in the Barents Sea19.
The NS-BS ecosystem is an interesting biological system showing clear bottom-up
effects 54,55, predatory top-down effects 34,56-58 and climate effects 54,59-61. NS-BS can be
characterised by a wasp-waist structure, with the capelin Mallotus villosus as its key species.
This species is strongly interlinked with two economically important fish stocks: the North
East Arctic cod Gadus morhua and the Norwegian spring spawning (NSS) herring Clupea
22
Box 1. The North Atlantic Oscillation and
Climate global indexes
The North Atlantic Oscillation (NAO) refers to a north–
south oscillation in atmospheric mass between the
Icelandic low- and the Azores high-pressure centres 3-5. It
is most clearly identified when time averaged data
(monthly or seasonal) are examined, since time averaging
reduces the ‘noise’ of small scale and transient
meteorological phenomena not related to large-scale
climate variability. It has recently become increasingly
common to use climate proxy such as NAO 18, an
integrated measure linked to many climatic variables such
as precipitation, wind speed and temperature over a large
scale 4. The NAO is globally one of the most robust modes
of recurrent atmospheric behaviour 3,23 and is the
dominant mode of atmospheric behaviour in the North
Atlantic sector throughout the year, but it is most
pronounced during winter and accounts for more than
one-third of the total variance in sea-level pressure 3. In
winter NAO affects the strength of the westerlies and the
movement of air and water masses that modifies sea
temperature 4. When NAO is negative, conditions in the
Norwegian Sea are characterised by low sea temperatures
and dry weather, whereas the opposite conditions are
related to positive NAO. The NAO affects the phenology of
several species of birds and mammals on both sides of the
Atlantic 4.
NAO is one among several global climate indices or
“weather packages” that could be use to understand the
climate effect on population dynamics 28. The main
advantages of using global climate indices are: (i)
biological effects may be related more strongly to global
indices than to any single local climate variable; (ii) it
helps to avoid problems of model selection; (iii) it opens
the possibility for ecologists to make predictions. One
index widely used is the Southern Oscillation Index 18 that
helped me modelling penguins population dynamic in both
South Africa and Crozet islands 14,22.
23
harengus. The BS is linked to the
North Atlantic, where much of the
Calanus finmarchicus, the main
prey of capelin, originates, and
where the herring spends most of
its life. Several fish stocks
(including cod and herring) spawn
on the Norwegian coast south of
the BS and are advected into the
BS by the northwardly flowing
Norwegian coastal current.
The NS-BS is a system
affected by both climate change
and human pressure. For more
than a millennium, the NS-BS has
been one of the main food
chambers for Europe and together
with farmed salmon is the main
reason why Norway is the world’s
third largest exporter of fish,
measured by export value.
Climate warming is expected to
greatly reduce the thickness of the
Arctic ice sheet, resulting in an
increased seasonal ice-melting
and an expansion of the ice-free
62
areas
during
summer
.
Seasonally ice-covered areas will
accordingly retract to the north
and into the polar basin,
fundamentally
altering
the
63
ecosystems in this region . The
area north of the polar front in the
Barents Sea is such an area and is
an important seasonal feeding
area for capelin, cod, seabirds and
marine mammals 64,65. The largescale changes and expansion of
the
northern
Barents
Sea
seasonally ice-covered areas may,
through
alterations
in
productivity, species distributions
and trophic interactions, have
pervasive consequences for fish, marine mammal and seabird populations utilizing this area
for feeding during summer.
2. The Southern Ocean
The Southern Ocean extends from about 40°S to 70°S and connects to the other major oceans
via the Antarctic Circumpolar Current, transporting and mixing salt, heat, and carbon. The
Southern Ocean links cold Antarctic waters with warmer sub-Antarctic and subtropical
waters. It is also a principal source of bottom water for the world’s oceans. Therefore,
significant changes in the Southern Ocean may not only affect regional ecosystems but also
exert a worldwide impact. The Southern Ocean holds some of the major animal biomasses of
our planet, i.e. krill, squid, pelagic fish. While krill is a dominant food-web component further
south, the Antarctic Front (49 to 53° S) contains large quantities of copepod-feeding
mesopelagic and bathypelagic myctophid fish 66,67, which are the main prey of several top
predators e.g., the king penguin Aptenodytes patagonicus. The myctophids are only lightly
exploited in some sectors such as South Georgia. Squid, which has an important role in the
Southern Ocean ecosystem, is also an important prey. Although we have today a much better
understanding of the physical processes involved in the climate variability of the Southern
Circumpolar Ocean 68, little is known about the effects of large-scale environmental
perturbation on the marine productivity of this ocean 69.
Changes in temperature influences marine productivity 70, but also the location of
oceanographic structures such as frontal systems 71. Climate warming is expected to move
southward the Antactic front thus affecting subpolar population of top predator that use it as
feeding ground such as the king penguin population.
3. The Benguela System
The Benguela upwelling system off the west coast of southern Africa is characterized by a
large latitudinal gradient in the dominant time scales for the wind forcing, from seasonal in
the south to more persistently equatorward off Namibia. These wind regimes influence the
ocean area south of Africa from Cape Town to Port Elizabeth, which is characterised by a
very broad shelf where various fish species spawn, including the sardine Sardinops sagax and
the anchovy Engraulis encrasilocus. It is also the region of the annual sardine migration up
the east coast of South Africa in July, the "sardine run". To the south and east, along the shelf
edge, one finds the topographically-steered Agulhas Current. The deep ocean-shelf interaction
in this area and its influence on the environment and ecosystem is still poorly understood.
The Benguela upwelling zone is a rich fishing area. In addition to being the main fish
resources, sardine, anchovy and horse mackerel Trachurus trachurus are also the dominant
zooplankton feeders and thereby pivotal in the system's functioning. The sardine and anchovy
stocks of the Benguela upwelling go through decadal-scale oscillations in abundance and also
large changes in distribution area, with subsequent changes in bird populations 72,73. The
system has been referred to as a wasp-waist ecosystem 74, where the waist (pelagic fish)
control both lower (zooplankton) and higher (top predators, including birds) trophic levels.
Over the past two decades (1987-2007) sardine and anchovy stocks previously
associated with the western coast of South Africa have progressively shifted towards the east
24
75,76
. Environmental fluctuations linked to climate change 77 and spatially unbalanced fishing
pressure 78 may have caused this major distributional shift. In addition to this shift, the overall
sardine biomass and catch that were at a low level in the early 1990’s largely increased (ca 7fold) and reached a peak in 2004 75,76. This was suggested to be one of the reason to the
decrease of top predator population such as the African penguin Spheniscus demersus
population 13,79.
Summary. Following different models provided by IPCC, the temperature of the ocean will
increase. This temperature change will affect eventually affect all level of the trophic chain
through changes in water mass circulation, abundance of prey, trophic relationships, and
fisheries to name only few. In a biodiversity perspective it will become increasingly difficult
to give informative advise without cross-disciplinary studies merging climatology,
oceanography, ecology, physiology, and economy as will be exemplified in the following
parts.
II.5.2 Climate and population abundance
Main findings
• We show that climate is mostly affecting seabirds in an indirect way via sea
temperature effect on lower trophic levels and thus highlights the lower trophic level
as an essential component to understand the effect of climate on the top-level seabirds.
• We show that climate change is changing established relationships, provoking regime
shifts, possibly via its effect on the prey availability.
• We show that long-term climate change can generate local differences in the
persistence of spatially structured species assemblages by shifting the equilibrium
point.
With about 36080 breeding pairs in 2005, Røst was the largest breeding site of Atlantic
puffins and more generally of seabirds in the Norwegian coast. However, when the
monitoring at this colony began in 1979, the number of breeders was more than the double.
The decrease of the breeding population occurred in the 1980s as a consequence of a poor
chick production and blamed on the demise of the overfished NSS herring since the late
1960s. Indeed, the growth and the survival of chicks at Røst have to a great extent been
determined by the supply of first year herring with occasionally alternative prey such as
sandeel Ammodytes marinus.
The puffin’s fledging success, the proportion of nestlings that fledge thus that have
grown enough to fledge, is ranging from 0 to 90% at Røst 80 (Figure 4). This variability is
surprising for a long-lived species which should be able evaluate its environment and skip
breeding when the conditions are too poor in order not to jeopardize its survival. One
hypothesis was that both physical environment and biotic environment were affecting the
reproduction of the Atlantic puffin thus explaining the variations observed.
25
1. Trophic interaction and Atlantic puffin breeding success variation
Due to the location of its breeding site, the population of Atlantic puffins breeding at
Røst is closely linked to the young NSS herring drifting northwards 2 (Figure 3). The NSS
herring spawns along the Norwegian coast from late February to late March. After hatching in
early April, the larvae drift northwards with the coastal current, the majority of those
surviving ending up in the Barents Sea 81. In order to reach a good size, the young herring
need good feeding conditions during this drift 82,83. Their growth and survival depend on the
availability of zooplankton that, in turn depends on the phytoplankton bloom in spring, both
of which are fluctuating with climate 84. Cold temperature conditions may then imply both
reduced growth and increased mortality, resulting in low abundance and poor quality of
young herring reaching the foraging areas of the puffins. Hence climate may influence the
Atlantic puffin through its effect on lower trophic levels.
Figure 4: Atlantic puffin reproduction can be modelled using environmental variables.
Presented to the left, in red the GLM model estimated the fledging success using average sea
temperature in °C for the water column to a depth of 75 m during the period of March to July
and the mean NSS herring length in mm in the chick diet on 1 July R2 = 0.84 2. To the right,
the nestling period duration is predicted using a GAM model using the physical sea
characteristics (average for the water column down to 20 m) in March north-east of Røst
archipelago R2 = 0.87 1.
In order to explore this relationship we have modelled the fledging success using
several environmental descriptors (climate and food abundance indices). Data of fledging
success of puffins breeding at Røst covering a period of 27 years were analysed with parallel
data on sea temperature and the size and abundance of the NSS herring. By fitting statistical
models to the fledging success, we found that one effect of climate on this population of
Atlantic puffins is indirect and mediated by sea temperature that we knew to positively affect
the availability of first-year herring (Figure 4). We also demonstrated that the breeding
success of the Røst puffins may be quantitatively predicted from the size of first-year herring
and sea temperature.
In seasonal environments, the main selection pressure on breeding success seemed to
be the synchrony between offspring requirements and food availability. However, the prey
availability depends on climatic conditions and the reproduction decision and timing in
26
Box 2. Classical representation of the
match-mismatch hypothesis
A high match is represented by a temporal overlap of
the predator and its prey. An increase of the time-lag
(t0) between the two populations leads to a low
match: a small or non-existent overlap.
The match-mismatch hypothesis MMH 6,7 was first
proposed for marine systems and suggests that the
inter-annual variability in fish recruitment is a
function of the timing of the production of their food .
A similar hypothesis was later used for other systems
15,16. The MMH refers to the timing of a predator and
its prey: even if the abundance of prey is plentiful, it
may not be present at the time the predator needs it
24 . For the marine food chain zooplankton-fishseabirds, the spring peak of zooplankton may match
or mismatch with the occurrence of fish larvae (that
depends on small zooplankton for survival).
response to prediction of future food
availability may be deceptive for the
predator introducing the matchmismatch hypothesis (MMH) in my
research 6 (Box 2). This explicative
hypothesis for the relationship
between reproductive success and
prey abundance that is triggered by
climate was developed for fish.
However, such match or mismatch
could influence higher in the trophic
web up to the fish-eating seabirds. The
fledging success of Atlantic puffin
over a period of 25 years could be
explain by the variation of the sea
temperature of the water column
foraged and the size of the main prey
captured, the NSS herring (Figure 4).
Knowing the life cycle of the NSS
herring, the underlying mechanisms
may be also explained by the matchmismatch hypothesis 6,7,85; the food
availability during rearing may or may
not match the chick food requirement
depending on climatic conditions.
It remained to explore how
climate is affecting seabirds. During
the EcoClim project we showed the
importance of the use of weather
5
packages such as the NAO (Box 1). However, the effect of NAO on breeding success shows
a clear geographic pattern 86 introducing the next question for the puffin.
2. Climate effect on the puffin reproduction
During the last decade it has become common to use climate proxy to model
population changes such as the NAO 18 (Box 1). Many studies have shown that climate is
affecting the phenology of reproduction in birds 40. We showed that NAO was affecting the
reproduction of puffins 2 and we wanted to see if it was through timing bearing in mind that if
it was the case it would be a step forward in validating our idea of the MMH explaining our
results. Indeed timing of breeding is a key factor determining the reproductive success in bird
populations and is known to be affected by climate fluctuations. We thus investigated the long
term (1978–2002) relationship between climate and hatching date within the population of
27
Atlantic puffin at Røst 87. The timing of puffin breeding was found to be influenced by the
NAO. However, we isolated two temporal regimes, one where NAO had a significant effect
on hatching date (1978–1986 and 1995–2002) and one where these variables were
independent (1987–1994). In addition to NAO, hatching date depended positively on hatching
date and food abundance in the preceding breeding season (possibly proxies of parental
effort). The difference of NAO between the two regimes suggests that the regime shifts were
induced by climate change, possibly via its effect on the prey availability the preceding year.
The environmental conditions at the continental shelf in central Norway during the drift
northward of NSS herring larvae, i.e., from mid March to mid July, and particularly in MayJune, are believed to be critical for the first year group herring survival. This assumption is
based on the fact that abundance indices of larval herring sampled on the shelf in April do not
correlate very well with the subsequent year-class strength of young herring that reaches the
nursery areas of the Barents Sea, while the quality and quantity of herring in the puffin's diet
at Røst have proven to be robust indicators of herring recruitment to the Barents Sea. We
decided to look further on the effect of climate on the Atlantic puffin reproduction.
3. Direct or indirect effect of climate
From the previous studies we have obtained some new insight on into the biology of
reproduction if of the puffin at Røst. As expected, the chick production was strongly affected
by the food availability and climate. We also showed that NAO influenced the phenology of
reproduction. But it remained to ascertain the indirect effect of climate that was hypothesised
for top-predators 88,89. We then explored the relationship of between local oceanic indices
taken at the time and location where NSS herring eggs are hatching along the coast of
Norway. We fitted statistical models on the duration of the nestling period instead of the
fledging success (Figure 5). The nestling growth depends on the food provided by the parents.
If there is enough food, the puffin chick will fledge at the age of 38-44 days of age 1.
Otherwise, a small food restriction will cause a growth delay and an increase of the nestling
period and a more severe one the death of the chick at a more or less early age 1,90. The
nestling period duration has as an advantage over the fledging to show intermediates
situations that are not visible for the fledging success that only shows a binomial response;
success or not. We found that nestling period duration may be estimated using the average sea
temperature and salinity at 0–20 m depth in March (respectively having a positive and a
negative effect, Figure 3) explaining the food condition of puffin’s chicks 1. We suggested
that when the phytoplankton bloom occurred in early spring, adverse oceanographic
conditions, i.e., low temperature and high salinity in March, had a negative effect on puffin
reproduction by degradation of the prey availability (mainly NSS herring) for chick-feeding
adults three months later.
28
Durant et al. 2003
Present study
Summary. Climate is mostly affecting Atlantic puffin in an indirect way (Figure 4). This is
indeed the case for most seabirds 89 and highlights the lower trophic level as an essential
component to understand the effect of climate on the top-level seabirds. However, our work
on Atlantic puffins left
some questions unresolved.
Among them was the
mean sea temperature °C (0-20 m)
hypothesis
that
the
Local
weather
fluctuation of the puffin
mean salinity °C (0-20 m)
population
could
be
no
87 %
explained by a difference of
Nestling
duration
phenology
between
56 %
breeding date of the seabird
Herring
and the arrival in the
88 %
availability
breeding foraging ground of
70 %
the
NSS
herring.
A
Fledging
84 %
scientific
discussion
success
between Tycho Ankermean sea temperature °C (0-75 m)
Nilssen and myself (each
defending with conviction
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
his side of the debate
Figure 5: Hypothetical chain of mechanisms linking climate and the
between abundance of prey reproduction of the Atlantic puffin population at Røst, Norway,
or synchrony) made me summarizing the results of the articles in the Proceedings of the Royal
1,2
study the hypothesis of Society and Biology Letters . The main relationships documented by
the two papers are presented by arrows.
David Cushing in more
detail 91.
II.5.3 Match-mismatch hypothesis and climate change
Main findings
• We found that the temporal synchrony and the food abundance/food requirement ratio
should be considered together when exploring the MMH relationship as an increase of
food abundance may compensate for the increase of asynchrony.
• We illustrate the importance of the spatial distribution in a spatial MMH affecting
both survival and reproduction of central place foraging predators.
• I expanded the MMH making it possible to use the hypothesis to describe both the
effect of predators on prey and the reverse.
At the beginning of the previous century, Prof. Johan Hjort 24, studying cod, haddock and
herring, suggested that variations in year-class strength of fish mainly resulted from changes
in the availability of planktonic food for fish larvae in a short, critical period (after exhaustion
of the larvae's yolk sack). The first few days after the yolk sack is consumed is critical, as the
fish larvae change from internal to external feeding, with high mortality involved. Johan
Hjort's idea was further developed several decades later by Dr. David Cushing who elaborated
29
what he named the "match-mismatch" hypothesis (MMH). David Cushing’s idea was that
because many fish populations in temperate waters spawn at fixed times while the timing of
the spring bloom varies according to physical environmental conditions, variability in timing
of the peak production of zooplankton prey leads to variability in larval mortality (Box 2).
Variability in mortality may be imposed both because of the vulnerability of first-feeding
larvae to starvation or due to the fact that poorly fed larvae grow slowly and are more
susceptible to predation. Since larval mortality is expected to be high, as compared to that at
later stages, the larval stage may be the principal determinant of year class strength 92. Since
then, the idea have been used for many systems, marine as well as terrestrial, particularly in
relation to climate warming 91. Note that in the 50s’, David Lack 16 was suggesting that birds
time their laying so that hatching coincides with seasonal peaks in food supply 93,94 which is
in essence the MMH 15.
The MMH has been much debated among fisheries biologists. It is not easy to
demonstrate in the field and several authors have criticized it 95,96, arguing that a poor match
yields poor year classes, while a good match may yield both good and poor year classes,
depending on other factors. Even though the MMH has been contested during the last
decades, the importance of trophic/temporal control on larval survival has been supported by
several studies 97-99.
Although a number of alternative hypotheses have been put forward describing
mechanisms linking the environment to fish recruitment through survival and growth during
early life stages 91 the MMH remain a major tool to try to understand the effect of climate
change on trophic interaction. The MMH is hence of great interest today 27,91 when climate is
changing the phenology of many species 100, changes which may be different for different
species within an ecosystem 101,102. To study the MMH, I wrote and led a project funded by
the RCN (Match-mismatching of trophic levels as a structuring force of ecosystems).
1. Prey abundance and the MMH
As we showed earlier the phenology and the food abundance seemed to matter for the
reproduction of puffins at Røst 1,2,87 . This was the starting point for studying the MMH. After
many trials, I developed with Dr. Dag Hjermann (CEES-University of Oslo) a model (Box 3)
later improved in a paper 11. The model incorporated both elements that are the time
synchrony between predator requirement and prey availability, and the prey abundance. This
model following theory was suppose to tell us the relative importance of each element for the
reproductive success. In 1982, David Cushing 103 indicated that the MMH will be modified
with food abundance:
“The production of fish larvae in time should be matched or mismatched to that of
their food. If matched, recruitment would be high within the limits of variation of the
primary production. If mismatched, recruitment will be low- more so if primary
production is low, but less so if it is high.”
30
To test our models we needed
quite specific data. We needed
an abundance of the two
components of the trophic
relationship (i.e., predator prey)
and timing of their abundance.
This task was not easy and
when tested the model didn’t
always perform well enough to
conclude either way. Finally we
Box 3: From theoretical concept to model
found three systems with the
required information. Using our
This box illustrates the thinking process. Between
the time I started thinking of the effect of food
model
that
explicitly
abundance on the match- mismatch hypothesis to
quantifying both the timing and
the writing of the paper published in Ecology Letters
11 several years went by. To the left is displayed one
the abundance component for
of my first attempt to visualize the MMH hypothesis
trophic
relationships,
we
in 2003 while to the left is the model used in the
showed that timing and
2005 paper 11.
abundance of food affect
recruitment differently in a
marine (cod/zooplankton in the North Sea), a marine-terrestrial (puffin/herring in the
Norwegian Sea) and a terrestrial (sheep/vegetation on Hirta in Scotland) ecosystem 11.
In other words, we found that the temporal synchrony and the food abundance/food
requirement ratio should be considered conjointly when exploring the MMH
relationship. While global warming may disrupt the trophic synchrony between
predator and prey creating a mismatch situation sensus the MMH it may at the same
time increase the primary productivity. As we showed 11 this increase of food
abundance may compensate for the increase of asynchrony. This finding was of
importance for the studies on phenology linked to climate change as it added a new
perspective on the otherwise simple pattern of lower synchrony is a lower production.
Later on, I identified three theoretical effects of climate change on phenological
mismatch: a change in the mean relative timing of predators and prey, a change in the
level of prey abundance, or a change in the amplitude of year-to-year variations in
prey timing for regions where inter-annual variability in temperature is expected to
increase. These hypotheses (Figure 14) were reported in a review paper led by Dr.
Philippe Cury (see Box 1in Cury et al. 27) and in a book chapter 104.
2. Spatial distribution and the MMH
During the breeding season, seabirds are typical central place foragers, tied to a
breeding site on land and foraging for marine resources. During their foraging trips many
seabirds regularly cross hundreds or thousands of kilometres within a period of a few days 105108
. A major constraint on breeding for seabirds is thus the distance between the breeding
grounds on land and the feeding zones at sea 109. The distance of foraging is limited by the
31
need to incubate egg(s) or to rear chick(s),
Adult penguin survival
neither of which can usually be left alone
for long periods. It can thus be a similar
problematic to synchrony as addressed by
-2.7 %
the MMH for distance. This can be stated
in the following way: seabird’s recruitment
-3.6 %
will be high if the prey distribution
spatially matches the predators breeding
foraging range, while a mismatch will lead
to poor recruitment. In other words a
+ 0.1 °C
91
spatial MMH .
I tried to find examples to illustrate
Sea surface temperature (°C) at 56°S, 46°-56°E
the spatial MMH. Following my own
experience and the results obtained by
Figure 6: Survival of adult king penguins
other teams 110, I decided to look at this breeding at l’île de la Possession, Crozet
question using king penguins. Indeed, Archipelago, faced with changes in the seaduring breeding the king penguin makes surface temperature around the marginal ice
return trips to the polar front several zone. Our model on adult king penguin
suggested a 9% decline in adult survival for a
hundred of kilometres south of their 0.26°C warming 14. In addition, the increase of
breeding colony at the île de la Possession, sea temperature also led to a decrease in the
Crozet Archipelago. As the polar front breeding success at this colony.
position moves with time, my idea was that
following the spatial MMH, years when the travel distance to the Marginal Ice Zone was the
greatest were also the years of the poorest breeding success for the king penguin. For this I
initiated a collaboration with Dr. Céline Le Bohec, at this time a PhD student from Drs.
Michel Gauthier-Clerc and David Grémillet (DEPE-CNRS Strasbourg). While she was
analysing the adult survival, I used her data to study the breeding success change with time
and environmental variables. For this I used classical time series analysis methods (linear
mixed-effect models). Together we wrote a paper that showed that environmental variables
such as temperature and position of the polar front had indeed a major effect on the king
penguin population at Crozet (Figure 6). In our study we show that warm events negatively
affect both breeding success and adult survival of king penguins 14. Our conclusion for the
article was that the Crozet’s king penguin population was at great risk of extinction in the
context of the current global warming predictions. Comically enough, this conclusion was
criticised by other penguin specialists as being too dramatic; the same groups publishing
studies with exactly the same conclusion some months later. Recently, using longer time
series and population dynamic model, we showed that indeed the population of king penguin
was at risk (see §II.5.4.1.b).
Some time before the study on Crozet’s king penguin population dynamics, I took
contact with South African researchers. I was interested in the trophic relationship in the
Benguela system. These contacts led to a collaboration based around two projects. The first
chaired by Dr. Robert Crawford was on the effect of installation of a marine protected area
around African penguin breeding sites (Dependent species and sustainable development:
seabirds and the South Africa’s purse-seine fishery). The second project, linked to my work at
32
CEES, was the supervision of a PhD student Philippe Sabarros. I combined the two objectives
to study the changes of seabird populations in relation to the change of distribution of the
pelagic fish that compose their diet, the sardine and the anchovy. For this study Philippe
Sabarros used with success available time series of catch data as a proxy of prey abundance
and investigated the local population response of three seabird species (African penguin, the
Cape gannet Morus capensis and the Cape cormorant Phalacrocorax capensis) to the spatiotemporal variability of prey through a 20 years time period 111. We showed that prey
fluctuations influence seabird local responses. Specifically for the African penguins, we found
that the breeding population changes were driven by both a spatial mismatch linked to the
climate-induced eastward shift of pelagic prey species (sardine and anchovy) and a time
mismatch link to the breeding cycle of these fish 111.
3. MMH for predator-controlled systems
The match-mismatch hypothesis tries to
explain the development and survival of a
predator in relation to the synchrony with its
prey. However the MMH tells us nothing of the
effect of synchrony for the prey. With Dag
Hjermann, I expanded on the match-mismatch
hypothesis by considering the simple
statement: “what is bad for the predator should
be good for the prey”. In other words I reversed
Figure 7: The match-mismatch hypothesis
the output of the match-mismatch hypothesis,
expanded for predator and for prey with in
addition to the time component effect the
i.e., the increase of the asynchrony lead to a
effect of the abundance of prey and predator
better survival/recruitment for the prey (Figure
respectively similarly to what was done in
7). Using generalised additive models, I have
the 2005 paper 11.
demonstrated that the MMH can be used to
describe both the effect of predators on prey and prey on predator, with the direction of the
equation varying with the direction of the control of predator on prey or prey on predator 112.
By doing this I expanded the MMH making it possible to use the hypothesis to
describe both the effect of predators on prey and the reverse, with the direction of the equation
varying with the direction of the control of predator on prey or prey on predator 112. Similar to
the abundance MMH, this study required abundance of two components of the trophic
relationship and timing of their abundance. Interestingly we did try our reverse model on the
time series that did not give any results using our model developed for the Ecology Letters
paper 11.
4.Further considerations on the MMH
The match-mismatch relationship and the critical period in particular is a result of
evolution shaping the phenology and spatial distribution of predator and prey. Climate change
will likely change the relationship between cues and the timing of prey, leading (assuming
33
that the predator's use of cues is optimal in the prevailing climatic regime) to increasing
mismatch 113. Species below the top predator level engage in a coevolutionary game: each
species try to match its prey (as well as optimal environmental conditions), but at the same
time "mismatch" its predators 112. The outcome will depend on the relative strength of these
evolutionary forces. Climate change might change the "rules" of this coevolutionary game.
The phenology (and spatial distribution) of species will shift at a different rate from each
other, leading to a change in interspecific interactions 102,113 some predator-prey relationships
may become stronger, while others may become weaker. In general, in food-webs that are
bottom-up controlled, we expect climate change will lead to a decoupling of the food web 27.
This may have severe consequences, including biodiversity loss 113. In other words, as a
consequence of climate change, in the future we must get used to a world where our
knowledge on ecosystem and trophic interactions is no longer accurate, or at least not reliably
so.
Recruitment is an essential concept in ecology. It refers to the number of organisms
that survive until they are large enough to be counted by an observer. In fishery biology,
recruitment is (usually) the number of fish large enough to be fished with ordinary fishing
gear. For species with a short time between birth and recruitment, survival at a larval stage is
expected to have huge effect on recruitment. It is for these species that the effect of a
mismatch with their prey and hence the climate warming de-synchronisation is expected to be
the strongest. This question of the effect of climate warming is not easy to address. For
instance, to test the effect of climate warming one needs to have data (phenology and
abundance) for a bottom-up system that is match-mismatch controlled over a long time
period. One solution could be to build a synthetic species whose recruitment depends on a
prey following a MMH pattern, all the other variables needed coming from published
relationship. I will develop this idea in the Perspective part (§II.6.4).
Predator
breeding ground
Prey Spawning area
Match
Good year
Delayed
hatching
High mortality
Slow growth
Mismatch
Many, large,
schooling
CLIMATE
VARIABILITY
Breeding
success
Poor year
Few, small,
dispersed
Figure 8: Domino effect of the MMH. The zooplankton-herring synchrony affects herring reproduction, which
in turn affects the Atlantic puffin reproduction (to the left). When there is synchrony between zooplanktonherring (degree of mismatch = 0) there is general tendency for the puffin to have good reproduction (to the
right). The blue line is a linear relationship (R2= 0.24, F1,21= 7.876 , p = 0.011). The colour of the dots displays
the fledging success (green=good, orange=poor and red= failed). Assuming that the NSS herring is spawning at
fixed time 6 we used the time of onset of spring sea surface temperature warming, a proxy for the plankton
production, as index of the degree of mismatch. Sea temperature data come from a coastal meteorological station
run by the Norwegian Meteorological Institute near the main NSS herring spawning ground on the Norwegian
coast 21.
34
Summary. The match-mismatch hypothesis has proved to be a useful hypothesis because it
can be tested in a range of different settings if we have the right data, and it is a helpful
concept for understanding and predicting complex, natural ecosystems 114. However, one part
we have not explored yet was the “ecosystem MMH” 91. For this spin off, we considered that
the MMH could have a domino effect. A MMH situation not only affects the predator of
predator-prey pair but also other elements of the trophic chain such as the prey of the prey of
the predator of the predator. We tried to address somehow this on a poster which got noticed
at the IOC in Hamburg 115. In a pre-study we obtained an indication that the puffin
reproduction at Røst was affected by the match-mismatch situation at lower trophic level, i.e.,
between the NSS herring and the plankton (see Figure 8) as suggested by our paper in Biology
Letters 1. Such a study was conducted on a terrestrial system 116.
II.5.4. Climate change and biodiversity
Main findings
• We show that the flipper-banding technique used to collect demography data on
penguins reduces both long-term survival and reproductive output.
• We also show that the abundance of prey actually available to the predator(i.e., local
abundance) should be used preferable to overall fish abundance to explain the
changes in predator breeding.
• We show that king penguin population growth rate is most sensitive to changes in the
temperature-dependant survival of adults, and also to changes in juvenile survival, but
hardly in breeding success. This puts their populations at risk due to climate warming.
• We show that climate is affecting the presence of seabird colony due to displacement
of the prey population.
• We confirmed the importance of managing cod and capelin stocks together by
showing that capelin abundance is the main variable that can be adjusted in order to
maintain the cod population size at a given level of harvesting.
• We showed that the removal of the older age classes may reduce the buffering
capacity of the population, thereby making the population growth rate more
dependent on recruitment than on adult survival.
The superimposition of climate change on overexploitation of resources by human is
increasingly causing for the most unanticipated changes in ecosystems 117. For instance, in
marine systems the exploited fish species exhibit higher temporal variability than unexploited
species 31,118. Still, our understanding of the dynamics of fish abundance in marine ecosystems
is limited because it is based on observations of quite a narrow range of climatic and
ecological conditions. This results in an uncertainty about the ability to predict future
dynamics. This is even more true in the Polar environment where the climate is though to
have a more important effect. Consequently emblematic species of the climate change such as
the polar species (the polar bear and the penguins) are often used a indicators (flagship
indicators 119). Indeed there is a crucial need for such indicators summarising large quantities
35
of information into a few relevant and accessible signals in order to prepare ourselves and
understand the changes linked to the global warming. Seabirds are good candidates for such a
role as indicators being long lived species with a wide foraging range and being relatively
easily accessible while breeding. However, in most studies advocating the use of seabirds as
indicators there is a lack of consideration for the difficulties for e.g., of "inverse inference" 120,
i.e., using the dependent variable to estimate the explanatory variable. For instance, while
prey abundance explains the breeding success of a seabird well, the opposite is not necessarily
true. If the relationship between seabirds and prey has been estimated by ordinary regression,
using seabirds as indicators for prey abundance has a tendency to exaggerate changes in prey
(overestimate prey abundance when it is high and underestimate it when it is low).
Furthermore, while birds may be strongly significantly affected by, for instance climate, they
are at the same time affected by other factors such as anthropogenic threats, e.g., pollution 121.
Hence the observation of a change in the bird population does not necessarily provide
indication on the climatic or environmental change, but may reflect changes in several
possible explanatory variables.
This led to the issue of the quality of the data to use for such inferences. While
individual marking is essential to determine the life-history traits of animals and to track them
in all sorts of ecological, behavioural and physiological studies it should remain neutral for
the species. Claire Saraux, a student I co-supervised, studied this topic for the king penguin
showing that the data collected using flipper-banding technique introduced bias by reducing
both long term survival and reproductive output 122,123. This was a concern I started to have
while banding penguins in Crozet in the early 90s. Is the inference obtained “real” or biased
somehow by the data collection technique? Recently, a paper showed that the data can be bias
positively and negatively by their collection 124. For king penguins, the bias due to data
collection technique was negative and obviously a concern both for the inference and the
species conservation. Indeed, while climatic change is clearly having negative impacts on
some penguin populations 14 developing reliable forecasts requires unbiased estimates of the
relationships between climatic variables and penguin demography.
Finally, we usually devote most of our conclusion to the significant correlations
between two series of data. However, the choice of the right variable is of importance. In a
study I made on African penguins I showed that the local abundance of prey is more
important for breeding than overall fish abundance 22 (Figure 9). This is because, while the
prey stock is well managed the area where the predator forage can be locally overfished.
However, most of the fish data used in correlation analysis are global data coming from
fisheries and not always spatially explicit.
To conclude, indicators such as ecological ones are a scientific and societal requirement
in our time of biodiversity jeopardy and seabirds remain good candidates providing some
caution is exercised 120.
36
Anchovy
Sardine
SOI t-1
Nursery area
-32
Hatching
334
Fledging
-33
Migration late
summer/early autumn
304
D
0d
365
J
SST t
31
N
F
59
Summer
O
M
273
90
S
A
Winter
243
Recruitment Autumn/Winter
A
M
J
-34
Spawning
Spring/late summer
Late summer
SouthWEAST
COAST
212
Hatching
Fledging
Latitude (°)
SOI t
-
African penguin’s breeding cycle in Dassen
WEST COAST
Fishing
J
181
Local fishing
120
151
-
-
+
+
Fish abundance
local
+
-
anchovy
-35
sardine
Adult
Juvenile
Egg
Penguin
population
at Dassen
SOUTH COAST
WESTERN
AGULHAS BANK
18
Central
Agulhas Bank
20
Eastern
Agulhas Bank
24
22
Longitude (°)
Penguin year t-1
Year t-1
Penguin year t
Year t
Figure 9: Relationship between the population of pelagic fish, their harvesting by purseseine fisheries and the African penguin population breeding at Dassen Island off the coast
of South Africa. Dassen (33°25’ S, 18°04’ E) being located on the migratory route of Anchovy
Sardine, both pelagic fish are an important resource for the African penguin reproductive
success 13. In our analysis of the breeding of African penguins at Dassen we suggested that more
than the overall fish abundance it is the local abundance of prey that affects the reproduction of
penguins 22. This conclusion was possible only by merging information available to fish
biologists to the ones available to ornithologists thus illustrating the need to merge disciplines.
1. Population dynamics and environmental changes
A timely topic in ecology is to identify limits/threshold. This is extremely important
for communication with stakeholders such as fisheries who require simple tools from the
scientific community in order to answer their questions. How many fish should be left to
insure the good management of the stock? It is also important related to biodiversity questions
and climate change. When would it be too late for the population? How many fishes should be
left to maintain the seabirds population 125.
In 2005 I became involved in a study trying to identify periods of system change
(turning points) in the southern Benguela pelagic ecosystem and to identify the change in
environmental forcing that may have caused the observed changes (project led by Philippe
Cury). The project was pursued without me and resulted in a publication in Science 125. While
involved, I tried to identify thresholds in various seabird parameters (numerical responses) to
prey abundances. The challenge was to find an objective criterion knowing that the
relationship between food abundance and predator abundance was generally sigmoid 126. Not
being satisfied with the use of inflection point, my first move was to use a technique derived
from the general additive modelling that was developed in close association with the CEES,
TGAM (Threshold Generalized Additive Model; Prof. Kung-Sik Chan from the University of
Iowa) or the equivalent for linear models (Dr. Leif Christian Stige from the CEES). With this
method, it was possible to identify whether the intrinsic processes change as a response to a
set of environmental variables. For instance, density-dependence can be weak when a set of
environmental conditions is fulfilled, and strong if the conditions are different 127. Using this
37
technique I was able to reproduce the results of the article in Ecology Letters 2005 11,128 but I
was not able to reach satisfactory results for the paper led by Philippe Cury125.
However, the disadvantage of the TGAM is that in addition to being a non-parametric
technique that renders the results difficult to interpret it supposes a non linear relationship,
i.e., a change of the intrinsic process. This is not necessary what we were looking for. There
was thus a need to find a technique that did not put any hypothesis on the value of the
threshold or the shape of the relationship and thus ‘put’ the threshold outside the model. The
population growth rate calculated using transition matrix modelling technique was fitting our
needs. A transition matrix that summarizes the vital rates and the annual population's growth
rate is obtained by taking the natural logarithm of its first eigenvalue λ 129. The logarithm of
λ, which is ranging from -1 to +1, gave the threshold we needed. A decreasing population is
characterized by a ln(λ) < 0 while a growing one by a ln(λ) > 0. This technique is used for
population at the equilibrium, the population growth being on average correct. However, such
stable population is not the most common or indeed interesting ones when asking questions
about climate change or overfishing. To use this technique, we made the assumption that
between years a population could be considered to be stable and that a matrix could be built
and hence a λ could be estimated, the value of which fluctuating with the years (note that
some assumptions have to be made that may be not valid. In such cases, the use of transient
dynamic technique is required 130,131). While this technique looked promising to answer our
questions, we needed information on survival at age and reproductive success at age. These
age dependent data were easily available for fish populations.
a. Population growth in fish populations
We used the matrix technique to study several fish stocks. The first study was to elaborate
ecological models for the main fish species of the Barents Sea ecosystem in order to explore
how harvesting and climate affected cod, herring and capelin. Economically, the Northeast
Arctic cod is by far the most important of the 3 stocks. Dried cod originating from the cod’s
spawning grounds at Lofoten has been one of Norway’s largest export items for about 1000
years, France being the first importer of Norwegian cod in 2011. At present, most of the other
large stocks of Atlantic cod have collapsed, with the Northeast Arctic stock the largest
remaining, accounting for about 50% of the total annual cod catch. Using fish stock
abundance derived from annual bottom trawl sampling in the winter feeding area of Northeast
Arctic cod, we showed that cod population growth (ln(λ)) was positively related to the
abundance of capelin, negatively related to the number of cannibalistic cod with a two-year
lag, and marginally positively by the winter NAO. We thus showed that capelin abundance
was the main variable that can be adjusted in order to maintain the population size at a given
level of cod harvesting 8 (Figure 10). This article pointed to the importance of conjointly
managing cod and capelin stocks. The logical sequel of our study was to analyse how
variability in capelin affects the economy of cod fisheries, and given the level of capelin stock
instability to find the optimal management strategy in order to maximize profit while
38
0.2
-0.2
-0.6
Cod instantaneous growth
0.6
maintaining the cod population. We started working on the subject with an economist from
the University of Tromsø (Prof. Claire Armstrong).
After this seminal study,
the matrices technique became
widely used at CEES to relate
Without fishing
environmental change and fish
population. For instance, I was
involved in a study looking at the
With fishing
effect of the age truncation of fish
population by overfishing in the
FISHING
effect of climate variation on
population growth. This was done
-2
-8
-6
-4
for NSS herring and cod in the
ln(Capelin/Cod)
Barents Sea 132 where we found
Figure 10: Instantaneous population growth of the
that the removal of the older age
NEA cod explained by changes in the ratio abundance
classes may reduce the buffering
of spawning capelin (in billions) and cannibalistic cod
capacity of the population, thereby
(in millions). Applying techniques based upon agemaking the population growth rate
structured population matrices we showed that when cod
is fished it requires 24 capelins for each cod to maintain
more dependent on recruitment
the cod population stable while it was only 3 without
than on adult survival and thus
fishing 8. In other words, capelin seemed to be the main
increasing
the
effect
of
variable that to some degree can be adjusted in order to
environmental fluctuation. In an
maintain the population size at a given level of cod
harvesting.
other study, this time on European
hake Merluccius merluccius from
the Atlantic Ocean and Mediterranean Sea, we showed that the interaction between internal
characteristics and external forces changes across geographic locations according to 1) the
importance of demographic truncation, 2) the influence of the climate on the regional
hydrography and 3) the spatiotemporal heterogeneity of the physical environment to which
fish life history is adapted 133. Lately, I led a meta analysis study on 7 gadoids fish stocks
130,134
the objective being to compare the effects of fisheries on population growth rate and the
sensitivity of population growth to climate, and the role of changed population structure in
mediating these effects. We found that there was a general tendency of an increase of the
population growth with the increase of the contribution of recruitment. Note that this paper
was part of a Theme Section published that I edited for Marine Ecology Progress Series 130
following a series of workshops I organized in Oslo between 2010-2012.
b. Population growth and penguin populations
Already at the beginning of the 90’s, the polar researchers at the CNRS of Strasbourg
were looking for an alternative to flipper-banding to monitor the penguin populations. The
solution adopted was to inject a radio frequency ID chip weighing less than a gram under the
skin of each penguin. The penguin identification and data-logging systems was then
automatic. The tag being activated electromagnetically and requiring no battery was designed
39
to provide information over the lifetime of the bird. The first set-up consisting on an enclosed
area in the Crozet’s colony where king penguins naturally bred 135 was followed with one
where antennae were buried on the three paths naturally used by the birds to go back and forth
between the colony and the sea. This set-up provided a permanently installed identification
system for king penguins 136 and expanded for other penguin’s species in the late 2010s. The
penguins were thus not anymore flipper-banded for monitoring purposes. Errors in estimates
of the vital rates due to methodological problems deleterious effects of flipper-band on
penguin reproductive success and survival 123,136-138, that produce inaccuracies in the
estimation of the population growth rate ln(λ) were thus bypassed. This is particularly
important for vital rates such as the adult survival to which ln(λ) is the most sensitive and that
appear to play a key role in a range of species. In addition the strength of the system was also
in its constant recapture effort coupled with a high fidelity of penguins to their natal site,
which provided huge amount of information each year for each age class 139. In short, these
data were of exceptional quality.
Using these demographic parameters on penguins of known age, Céline le Bohec built
annual stage-structured matrices from 1999 to 2008 and reconstructed the past decade
population change in relation to climate variability. Analysis of the matrices told us that king
penguin population growth rate is mostly sensitive to change in temperature dependant
survival of adult but also of juvenile but hardly in breeding success (37% vs. 6% for adults).
Considering several future climate change scenarios 38 penguin population at Crozet is
predicted to go extinct between 90 and 150 years from now confirming our previous
conclusions 14.
2. Competition with other species
At the species level, competition can lead to exclusion if one or both species involved
are limited more by interspecific than intraspecific competition 140-142. However, conclusive
demonstrations of competitive exclusion are rare, in part related to the inadequacy of the
Lotka-Volterra equations to predict the outcomes of interspecific relationships in the presence
of environmental variability. It has recently been hypothesised that climate change may affect
the coexistence of competing species 36 without empirically testing it. In collaboration with
Belgium colleagues we decided to do it. Within a discrete-time setting, and assuming a
Gompertz model framework 143, where competition is assumed to affect the population
density of two species, N 1,t and N 2,t (where t indicates the year), we formulated the LotkaVolterra competition model as follows:
N1,t+1 = N1,texp[r1{K1 - ln(N1,t) - α12ln(N2,t)}/K1]
N2,t+1 = N2,texp[r2{K2 - α21ln(N1,t) - ln(N2,t)}/K2]
where r 1 and r 2 are the maximum specific population growth rates for species 1 and 2, K 1 and
K 2 are the equilibrium values in the habitat for each of the two species in the absence of
competition and α 12 and α 21 represent the per-capita effect of competition of species 2 on
species 1 and vice-versa. Climate might affect the system through any of the coefficients of
the above model, in which case they become functions of the climate covariates – linear or
40
non-linear. We then reformulated the model as a threshold autoregressive model
confronted the model to the data.
144
and
a. Competition in a terrestrial system: the tits as example
4.5
4.0
3.5
3.0
ln(Great tit, Parus major )
5.0
To test the hypothesis of climate effect on competition strength we first used time
series of two small Passeriformes. Blue tits Cyanistes caeruleus and great tits Parus major
compete for food while breeding and for roosting sites during winter; yet coexist across much
of their range. Climate change might thus change the competitive relationships and
coexistence between these two species in the field. Analysing five of the highest quality, longterm data sets available (length of > 15 years) on these species across Europe (2 in Belgium, 1
in Netherland, 1 in UK, and 1 in Germany), we extended the text-book example of
coexistence between competing species to include the dynamic effects of long-term climate
variation. Using threshold time-series statistical modelling, we demonstrated that climate
variation affects species coexistence by influencing density-dependent and densityindependent processes differently. The competitive interaction between blue and great tits has
in one of the studied sites,
shifted creating less stable
coexistence between the two
species (Figure 11).
Our analyses showed
that long-term climate change
can generate local differences
3.5
4.0
2.5
3.0
4.5
in the persistence of spatially
ln(Blue tit, Cyanistes caeruleaus)
Figure 11: Zero-growth isoclines for sympatric populations
structured species assemblages.
of great tits (GT, red line) and blue tits (BT, blue lines).
We demonstrate how longThe two species responded differently to temperature changes
term data from natural
over years in a forest near Antwerp, Belgium. BT population
populations can be used to
shows a threshold interaction with an environmental variable
(mean temperature conditions in spring). For BT, there is a
better understand whether (and
different isocline for temperatures below (blue line) and above
how) climate change (or other
(blue dotted line) the threshold of mean spring temperature of
extrinsic factors) might change
6.3°C. The circles shows the stable equilibrium points, in
the relationships between
black for temperatures below the temperature threshold and in
red above it. The blue arrow indicates the direction of the
currently coexisting species.
change of the BT isocline with an increase of temperature
above the threshold and the light blue dots the data.
b. Competition in a seabird colony
I was curious to look at the interaction between seabird species breeding in the same
colony. I analysed Russian data collected over 27-years by Dr. Yuri V. Krasnov (Murmansk
Marine Biological Institute, Russia) at Kharlov Island in the Barents Sea 145. The question was
to see if the interaction between the seabirds (black-legged kittiwake Rissa tridactyla,
Common guillemot Uria aalge, Brünnich’s guillemot Uria lomvia) was changing with
41
environmental conditions as we found for the tits. We found a competitive effect only for the
kittiwake breeding population size on the common guillemot breeding population size when
kittiwakes were abundant.
The timing of kittiwake breeding negatively affected the number of breeding
Brünnich’s guillemots. The timing of breeding was negatively correlated to biomass of the
main pelagic fish in the Barents Sea, the capelin, which suggests an indirect action. More
interesting from a population perspective was that the kittiwake population did not decrease as
seen in North Norwegian coast populations. Likewise, the common guillemot population,
after a crash in 1985, was recovering at Kharlov while Norwegian populations were
decreasing. Only the Brünnich’s guillemot showed a decrease at Kharlov until 1999. We
suggested that the stability of the kittiwake and common guillemot populations at Kharlov is
due to better feeding conditions than in colonies off the Norwegian coast, linked to a possible
eastward shift of the capelin population with the temperature increase of the Barents Sea, an
idea to link to the work done by Philippe Sabarros on African penguins colony distribution
146
. However, to study this hypothesis we need to conduct a spatial analysis. This will be done
in the near future in a collaboration with Dr. Per Fauchald (Norwegian Institute for Nature
Research (NINA) Tromsø, Norway).
c. Perturbation of the trophic interaction
Regime shifts is a very popular topic to study following the current global warming. A
regime shift is considered to be a sudden shift in the structure and functioning of a marine
ecosystem, affecting several living components and resulting in an alternate state 147.
Classically, a regime shift is suppose to be due to changes in environmental conditions.
However, fishing appears to also play an important role in regime shift processes, e.g., in the
Benguela ecosystems 147. In 2010, Per Fauchald has reported a possible mechanism for
ecosystem hysteresis (past events can influence current dynamics) in the North Sea by a
predator/prey reversal: herring eating cod eggs and larvae 148. In the Barents Sea, while adult
cod is a main predator in the trophic chain the younger life stages may be eaten by the adult
cod's prey, such as herring. For his Master internship, Yoann Ratrimoharinosy investigated
under my supervision the relationship between herring and cod recruitment over the period
1950-1999 using TGAM techniques. We got indications that the effect of herring on cod
recruitment varied depending on the age structure of the cod population, i.e., fishing status. In
a more practical way our results indicate that the herring stock abundance should be
considered when setting cod fishing quotas in the Barents Sea. However, more analyses are
needed to confirm these results.
3. Primary production, climate and predators
In the pelagic ecosystems, phytoplankton constitutes the most important part of the
primary production. The organic matter resulting from this process is then starting a bottom–
up process transferring energy eventually up to the upper level predators, such as marine
42
mammals or seabirds. It is then no surprise that I became involved in studies trying to
characterize the importance of the phytoplankton production and its relationship to climate
change and food web dynamics. Using the opportunity of the research visit of Dr. Jianfeng
Feng (Nankai University, China), we analysed 13-year monthly time series of satellite-derived
chlorophyll (1998-2010), sea surface temperature and zooplankton abundance (copepod taxa
from CPR surveys of http://www.sahfos.ac.uk/) in order to better understand the ecological
processes that regulate the seasonal phytoplankton dynamics in different regions of the North
Atlantic Ocean. The results of the study 149 showed large-scale, seasonally-varying effects of
temperature and zooplankton abundance on chlorophyll concentration and demonstrated that
physical (bottom-up) effects and zooplankton (top-down) effects alternate to shape the
seasonal dynamics of phytoplankton in the North Atlantic.
For another study, Jianfeng
Feng
was
interested
in
understanding the impacts of
climate
forcing
on
marine
phytoplankton at a more global
scale. For this we analysed large
datasets
of
satellite-derived
chlorophyll concentration, for
large areas of the ocean, with
climate data to characterize the
general global patterns in climate
variations on ocean chlorophyll.
Comparing these large areas, we
found that the effect of temperature
tends to be stronger in tropical
oceans than in temperate oceans
Figure 12: The proportion of the variance of chlorophyll a while light contributes more to the
(R2) explained by different climate factors over imposed
variation of chlorophyll in
on a world map of 15 marine regions. R2 was calculated
temperate oceans than in tropical
from GAMs with only the given factor as predictor variable.
The position of the plots corresponds to the respective regions. oceans (Figure 12 upper panel).
However, the use of the areas as
Biogeography of the global ocean (i.e., biogeochemical
17
provinces) calculated by Reygondeau et al. for the
defined by the study were
average period from January 1998 to December 2007.
criticized during the review
process and we are now working to
use the biogeochemical provinces defined by Dr. Gabriel Reygondeau 17, a post-doc who
came to work with me during some months in 2012-2013 (Figure 12 lower panel).
A fundamental source of nutrients for oceans is the output from land. In open ocean
such as the Southern Ocean these are rare and limited to archipelagos such as Crozet, which
represent exclusive breeding places for upper level predators (e.g., ca. 1.5 million king
penguins breed yearly in Crozet). With Céline Le Bohec, we hypothesised that the colonies of
these animals may significantly mediate the transfer of macronutrients from land to the
oceanic system, and thus be crucial elements of nutrient cycles. For her Master internship
with us, Naïd Mubalegh studied this topic using macronutrient concentration (phosphate,
43
silicate, ammonium, nitrite and nitrate) from water samples collected from October 2007 to
January 2010 at different places on the Possession Island, Crozet. More precisely, Naïd
Mubalegh studied whether the presence of the king penguin colonies at several places of the
island could significantly account for variations in nutrient concentrations. The results showed
a general enrichment along the rivers’ path, from inside the island to the sea, which was
related to the presence of king penguin colonies. Then, since the phenology of king penguin
colonies changes according to the breeding cycle, we used data from the colony of La Grande
Manchotière which has been followed for several years (see § II.5.3.1.b) in order to test
whether relationships existed between seasonal variations in the number and activity or stage
of birds present on the island, and those in nutrient concentrations at sea. Our results
suggested that relationships between variations in nutrient concentrations and those of the
phenology of the colony existed and depended on the nutrients. To be complete and have a
better picture of the importance of the nutrients released from the seabird colonies on the
nutrient cycles all breeding colonies of the island (with their specificities in term of size and
phenology) should be considered. Finally, an exiting topic would be to study to which extent
the phenology and the intensity of nutrient release by the colonies can affect the size and
timing of the phytoplankton bloom around the Archipelagos (see match-mismatch topic §
II.5.3).
Summary. In this succession of studies I went deeper into the mechanistic approach of
studying population dynamics by looking at the population growth rate and then trying to
isolate the reasons for its changes. Keeping in touch with the effect of climate and trophic
interactions I went toward community ecology topics. Trophic interactions may be deeply
modified by climate change and direct human influence. It is now well documented that
overfishing is truncating fish stocks with effect both on the stock’s resilience to climate and
competition with other stocks. The increase of temperature is affecting the recruitment of fish
stock both directly by affecting development and indirectly by affecting the lower trophic
level productivity and phenology. For instance temperate marine environments may be
particularly vulnerable to decoupling of phenological relationships because the recruitment
success of higher trophic levels is highly dependent on synchronization with pulsed
planktonic production 150. Likewise climate change may lead to large-scale redistribution of
major exploited marine fishes, with an increase in high-latitude regions and a drop in lower
latitude 151. All these factors will have a strong effect on top predators but also on the lower
trophic levels.
II.6 – Perspectives
Over about ten years, I explored the trophic interactions between species in a changing world
through time series analysis and theoretical modelling. My main focus on the biology of
seabirds had eventually moved down the trophic chain towards the pelagic fishes but
remained anchored to the effect of environmental changes. For this I had to become familiar
with oceanography, statistical tools and economic thinking.
44
If there were only one conclusion to give it would be that the understanding and
management of ecosystems in our times of biodiversity challenges can only be done with a
profound understanding of the trophic interactions (with human species comprised). The use
of indicators may be of great help for management purpose but should not be use without
control. I intend to pursue in this direction in the near future by studying the Barents Sea
system and the potential effect of the climate change on it in collaboration with colleagues
from the Institute of Marine Research (IMR) and the Norwegian Institute for Nature Research
(NINA). In a more theoretical approach I will study how the relationships described by the
MMH are modified by the climate change.
II.6.1 Ecosystem studies in the Barents Sea
To understand the Barents Sea system that in the near future will be of tremendous
economic importance (opening of the north route for maritime transport, gas-petrol
exploitation...) it is necessary to take an ecosystem perspective. For example we can ask how
the predation is balanced between the predators of the system (fishermen counted). Thus the
question is to know if these species can compete for resources and affect each other’s
dynamics. This issue is not simple since we now know that the Barents Sea can be divided in
two, where the northeast part tends to be top-down controlled and the southwest part is
bottom-up controlled (Figure 3) 19. This, and the fact that the trophic interactions seem to
change with space 152, call for a spatial approach.
A first step to study the trophic relationship between top predators is to know to which
extend there is a diet overlap between these predators. I will study this topic using available
data on the fish and zooplankton consumption by cod, minke whales Balaenoptera
acutorostrata, harp seals Pagophilus groenlandicus and seabirds (guillemots and kittiwakes)
to estimate the functional response of predators, i.e. per capita prey consumption as a function
of abundance of possible prey species as well as the spatial distribution of prey on large and
small scales (degree of schooling). We suppose that there is competition between seabirds and
fish for the same resources as shown between cod and kittiwakes 153. However, little is known
for other top-predators pairs 154. I will hence explore the diet overlap between the top predator
pairs for which I have access to data. The statistical method to conduct such analysis needs to
take into account that foraging is a result of choices and processes on several scales (the
predator's large-scale choice of area; finding schools within areas; selecting prey within
schools) and that prey choice may depend on fish physiological state (e.g., sexual maturation)
and past predation (i.e., prey selection may depend on level of stomach fullness).
Fishing is an important component of the Barents Sea system. Hence, I will explore
the competition between fishing and seabirds/fishes. In a recent paper, I showed that there is
competition for resources between different seabird species breeding in the Barents Sea 145.
The hypothesis is that such competition for resources can be modelled between fisheries and
top predators. The logical output will be to develop an economic model based on the
ecological model. A collaboration with economists is needed to fine-tune the societal
45
implication of such relationship. NorMER and GreenMAR networks, project I helped writing,
are perfect place for such collaborations
Predation is not uniformly distributed but concentrated on certain region or location. If
such is the case it may occur local depletion by top predator effect on prey population
dynamics. In the North Sea, Temming et al. 155 found that an aggregation of more than 50
million juvenile cod in an area of only 18 km2 was entirely wiped out in 5 days by predatory
whiting Merlangius merlangus. For example, seabirds are typically feed in the vicinity of the
colony when breeding and otherwise concentrate their foraging effort on oceanographic
features such as fronts 89. I intend to explore how seabirds/sea mammals as top predators may
control the local distribution of their prey and the impact of this control on the prey
dynamics? I will study the effect of spatial/temporal change in prey distribution on seabird
colonies location and/or population dynamics. Changes in the Barents Sea climate is expected
to affect the abundance, size, phenology and distribution of the fish and zooplankton that are
the prey of mammals and seabirds. This might in turn affect the spatial distribution of marine
mammals and birds. I will study such changes, and in particular how they may affect the
dynamical interplay between the species by a meta analysis of seabird colony dynamics
related to spatial distribution of prey species (e.g., capelin, plankton).
All these studies will help us understand the trophic relationship in the Barents Sea.
We already have a good idea of the relationship at the plankton/fish and fish/fish levels and
the spatial part of it is progressing rapidly but we are still lacking the upper trophic levels.
Without understanding this relationship it would not be possible to investigate further the use
of top predators as ecosystem indicator.
Marine mammals and birds are often considered cost-effective and fast-responding
indicators of the state of the marine ecosystem. One example is the mass migration of harp
seals to the Norwegian coast in the end of 1980s, probably linked to ecosystem changes such
as the collapse of the capelin. I will explore to what degree, and how, diet and population
measures (e.g., breeding success) reflect the state of the ecosystem. In particular, I will focus
on the often non-linear relationship between diet of predator and abundance of different fish
species. I intend to explore the dynamics of the diet change in top predators and linked it to
the dynamics of the prey. I will link this to the spatial distribution.
II.6.2 Match mismatch hypothesis in a changing world
The effect of climate and seasonality on marine ecosystems becomes increasingly pronounced
as one moves towards the poles. The life cycles of organisms in Sub-Arctic seas have adapted
to a strong pulse of primary productivity in spring, short summers, and (for some sub-regions)
ice cover in parts of the year. Also, food resources and suitable habitat for young are often
spatially clustered. There is a continuous evolutionary pressure towards developing adaptive
46
strategies such as to optimize the timing of life cycle events (e.g., migration and reproduction,
ageing), using environmental cues (e.g., photoperiod and temperature). Also, there has been a
strong selection pressure to migrate to the right place at the right time, as the extensive
spawning and feeding migrations are energetically costly.
The
production
of
predators, such as fish like cod
Trophic
level
and pollock, as well as top
Ichtyovorous fish
predatory seabirds and marine
Planktivorous
mammals, depends on an
fish
Planktivorous fish
effective energy transfer from
primary
production
Zooplankton bloom
(phytoplankton)
via
zooplankton and zooplanktonPhytoplankton bloom
feeding fish to fish-eating fish.
The effectiveness of this
transfer depends on the
Figure 13. Temporal/spatial match between the
components of the food chain, from phytoplankton
temporal and spatial matching
(bottom) to top predators (top right). In some cases, the
between predator and prey in
temporal pattern is decided by the level below (i.e., the
every food-web link up to the
timing of the phytoplankton bloom is determined by the
top predator (Figure 13). In his
availability of nutrients and light), in other cases, the
predator may be restricted to certain times/space and/or have
match-mismatch
hypothesis,
7
to decide the timing in advance (i.e., breeding birds).
David Cushing focused on the
temporal component of the
match or mismatch between larval fish and its prey. I want to broaden and deepen this theme
by investigating several trophic levels, considering both the spatial and temporal component
of the match-mismatch, and to explore patchiness on several temporal/spatial scales.
Seabirds
Sea mammals
y p
Climate change has already lead to significant changes in the key components of
ecosystems, such as zooplankton 156. One important effect of this is change in the
spatiotemporal distributions. For instance, climatic effects on the North Sea zooplankton has
lead to a temporal mismatch between larval cod and their zooplankton prey 99. In the Bering
Sea, temperature variability on long time scales leads to a change in the timing of the spring
phytoplankton bloom. This, in turn, may change the ecosystem from being bottom-up
controlled (production-controlled) in cold periods to being top-down controlled (predationcontrolled) in warm periods 157. A similar process may be evident in the Barents Sea with
climate moving the border between the different controlled regions (Figure 3). A permanent
shift in temperature may impose a permanent change in ecology. Also, climate change is
likely to rearrange the relationship between the timing of the cues and the prey, it is likely assuming that the predator's use of cues is optimal in the prevailing climatic regime - that
climate change in many cases will lead to increasing mismatch and to weaker predator-prey
relationships. The phenology (and spatial distribution) of species may shift at different rates,
leading to a change in interspecific interactions. Some predator-prey relationships may then
also become stronger. In general, three main effects of climate change can be envisioned for
47
every predator-prey pair (Figure 14). In food-webs that are bottom-up controlled, we expect
that climate change will lead to a decoupling of the food web. This may have severe
consequences, including biodiversity loss and impaired ecosystem services. In order to
develop appropriate management measures ahead of these changes, it is important to
disentangle and quantify the different effects match-mismatch may have on the ecosystems.
Figure 14: Three main effects of climate change can be envisioned: a change in the
mean relative timing of prey Fig 12a 10, a change in the level of prey abundance Fig 12b 11
or a change in the amplitude of year-to-year variations in prey timing (Fig 12c) in regions
where year to year variability in temperature is expected to increase e.g. polar regions 25.
From Cury et al. (2008) 27.
II.6.3 Stock recruitment, climate change and human forcing
Understanding the drivers that determine the productivity of marine ecosystems is an
important issue. For instance, climate and exploitation interact in their effects, such that
climate alterations may cause failure in a fishery management scheme while fisheries may
disrupt the ability of a population to withstand, or adjust to, climate changes. Intense fisheries
often result in loss of the largest individuals. Removal of large (and thus implicitly old and
experienced) individuals, resulting in a juvenated, age-truncated spawning population has
been described by numerous authors to be a result of modern day fisheries patterns 158-160.
This can, for instance, have substantial consequences, through various pathways, for a
population’s capacity to buffer climate variability 161. In particular, it has been suggested that
age-truncation may leave a population with recruitment that follows environmental
fluctuations more closely 60.
In a similar way, non-uniform fishing pressure on population sub-units within
metapopulations may also lead to a reduction in the capacity of populations to withstand
climate variability and change as shown by the collapse of North Sea herring during the
1970s. Furthermore, a full recovery of a depleted population is not successfully accomplished
by merely reaching a biomass threshold: restoring and maintaining spatial diversity might be
equally important, because the latter provides resilience to local changes in exploitation,
environment, and fish behaviour 162,163.
Unlike most terrestrial ecosystems, marine systems may allow for role-reversal between prey
and predators. Small, pelagic, forage fish are often themselves predators on the egg and
48
juvenile stages of their larger fish predators. Consequently, a large population of predator fish
will secure its own recruitment by controlling the population of pelagic fish, thereby
stabilizing the system in a stable predator-dominated state. This is known as the “cultivation
effect” 164. Conversely, it has been suggested that the prey species may increase as a result of
decreased predation pressure and subsequently prevents the recovery of the predator, e.g., by
preying on the predator’s eggs and larvae (the “prey to predator” loop 165,166 ). Thus a large
population of pelagic fish may reduce the recruitment of predators to the extent that the
system switches to an alternate prey-dominated state. Consequently, selective human
harvesting of either the top predators or the pelagic fish resources may cause ecosystem shifts
by ‘‘pushing’’ the system between the respective states 148,165.
I will explore how temperature and spawning stock biomass and age structure affect the
temporal recruitment dynamics of marine fish stocks. I will also look at the question of
predator-prey reversal resulting from the release of the pressure on small pelagic (fishing,
predation, competition). I will also continue exploring the effect of climate and fishing on the
population growth rate.
II.6.4 Ecology, Ecophysiology, Climatology, Sociology and
Economy and more ─ bridging the gaps
1. Unification of ecology and ecophysiology
To predict the future impact of mankind on living organisms we need models for hindcasting
(interpretation of past status of a population), forcasting (prediction of future development)
and to consider the possible outcomes of changed environmental conditions (physical forcing,
climate change). However, models should consider the entire range of possibilities “we need
to know how organisms become adapted to a rapidly changing world and determine the
limitations of adaptive processes.” 30. This triviality is not always taken into account as many
ecologists have only a fuzzy memory of their physiology education when trying to explain
some relationship they observed. At the same time, ecophysiological studies tend to
accumulate data that can and should be analysed by time series techniques. Even if ecological
dynamics and ecophysiology should be intimately linked, scientists specializing within each
of these fields are seldom interacting. Advances in the (re)unification of ecology and
ecophysiology can only be achieved through close collaborative efforts involving both
disciplines, for instance, through data on the intrinsic and extrinsic causes of variation in the
population structures (physiologic, genetic, phenotypic, and demographic) for a range of
species covering multiple spatial and temporal scales. It is what I tried to initiate in 2002
between the CEES (ecology unit) and the DEPE-CNRS (ecophysiology unit) with a mixed
success (only two of us, Céline Le Bohec and myself used the opportunity to work together
14
).
The use of physiological relationships in modelling approach is not novel as it is
classically done in individual-based modelling where mathematical functions of interactions,
e.g. functional response curves, are needed. In the exploration of the effect of climate change
49
on the match-mismatch hypothesis I used the same principle. The limitation to such study is
that it requires a match-mismatch controlled bottom-up system for which we have the data
(phenology and abundance) over a long time period. By developing a synthetic species based
on published relationship between biological process (population abundances, survival,
phenology) and environmental variables (such as sea temperature) it is possible to have the
required data, i.e., phenology and abundance for both predator and prey spanning long time
enough (Figure 15).
The advantage of this kind of
Equation 1 mprey, t
mPredator,t Equation 3
approach is that we have full
constant
constant
sPredator,t
sprey, t
control on the system and it
Equation 2 Nprey, t
NPredator,t
becomes possible to ask the
overlap
following questions:
- What will happen to bottom-up
Equation 4
survival
controlled systems with increased
NPredator, t+1
of temperatures?
- By changing the population Figure 15: Building a MMH dependant fish population
structure (evolutionary change), life cycle. Model used to simulate the change in the fish
species abundance NPredator with the change in degree of
what will be the effect of size synchrony between a prey (e.g. plankton) and a predator
structure
change
for
the (e.g. fish). The three variables needed to build the normal
11
population
in
a
changing distribution curves for the two interacting species (N, m
environment. For instance, will and s) are coming from published relationships at the
exception from NPredator which is simulated.
the size/age structure reduction of
a fish stocks make them more or
less sensible to climate change?
- Will the phenological changes due to climate warming affect the competition between
species?
- How the physiological limits may constraint the potential phenological changes due to
climate warming?
2. Building a new generation of scientists
A major challenge to managers and scientists today is to identify ways that oceans can
provide food and other services in a sustainable way under changing climatic and
socioeconomic conditions. Indeed, massive overfishing, climate change, and loss of
biodiversity are just examples showing us clearly that we are reaching the boundaries of what
our planet can take. At the same time, it is also clear that economic development and higher
food production is a necessity in a world plagued by food shortages. However, predicting the
consequences of climate change is not only an ecological problem as it is complicated by the
fact that the ecological effects of climate and fishing may change the behaviour of fisheries
(individual fishermen or nations). There is indeed a need for combining economic and
ecological approaches to the study of fish and fisheries. In other words, cross-disciplinary
approaches are needed to address the physical, biological and socioeconomic factors needed
to meet this challenge. However, truly cross-disciplinary studies are still rare, even if there is
50
an increasing interest in incorporating detail and realism into effect studies 167. To answer this
challenge the classical approach is to form an interdisciplinary research team to integrate
different essential disciplines – climatology, ecology, sociology and economy. However,
communication between scientific fields is hampered by differences in language and culture.
Scientists of the future need the cross-disciplinary skills to combine physical, biological, and
social/economic science to give appropriate management advice. This is the vision we tried to
implement with the Nordic centre of Excellence I was helping building NorMER (an
international PhD training program).
II.6.5 Time series curator work
Most of the work described in this document is based on time series of various length. Such
long-term data generally cover a much longer period of time than what any single researcher
can achieve (sometimes several times longer than human life-span), and it may require several
data sets to answer the broader questions. While climate worldwide is changing and human
activities are increasing the effects on ecosystems are likely to be profound. To understand
what the ecological response might be to such changes, is a major scientific challenge of
today. For this purpose, long-term ecological data are critically needed. Similarly to the way
climatologists have extensively used past records of climate from ice-cores or sediments to
understand climate variability and build up their models of climate forecast, long-term
ecological data (based upon studies carried out over the past several decades) are of
paramount importance to understand the impact of climate changes on biodiversity.
Long-term data are typically not readily accessible to researchers. In 2012, the
Institute of Marine Research (Bergen, Norway) got funding to develop the Norwegian Marine
Data Centre in order to provide seamless access to marine data and greatly increase the
efficiency of marine science in Norway and facilitate the generation of high quality research.
With Nils Chr. Stenseth, we are representing the University of Oslo in this centre that is
planned to become a permanent institute. I will work on this project in the following years.
Finally, as it was the case for the barn owl, I was confronted when studying the king
penguin by the lack of information on their prey. These data exist but require effort and
funding to make them available to the scientific community. The NMDC and my involvement
in it will help on this important task. For example, very little is known on the dynamics of the
myctophid fish (main prey of the penguins). The access to such data will help better
understanding the Southern Ocean trophic interaction in a similar way to what is done for the
Barents Sea and help fine-tune our ecosystem models.
51
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