Climate change, phenological shifts, eco-evolutionary responses and population viability: toward a unifying predictive approach
被引:22
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作者:
Jenouvrier, Stephanie
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机构:
Woods Hole Oceanog Inst, Woods Hole, MA 02540 USA
216 Univ Colorado, Cooperat Inst Res Environm Sci, Boulder, CO 80309 USA
CNRS CEBC, F-79170 Villiers En Bois, FranceWoods Hole Oceanog Inst, Woods Hole, MA 02540 USA
Jenouvrier, Stephanie
[1
,2
,3
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Visser, Marcel E.
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机构:
Netherlands Inst Ecol NIOO KNAW, NL-6700 AB Wageningen, NetherlandsWoods Hole Oceanog Inst, Woods Hole, MA 02540 USA
Visser, Marcel E.
[4
]
机构:
[1] Woods Hole Oceanog Inst, Woods Hole, MA 02540 USA
[2] 216 Univ Colorado, Cooperat Inst Res Environm Sci, Boulder, CO 80309 USA
[3] CNRS CEBC, F-79170 Villiers En Bois, France
[4] Netherlands Inst Ecol NIOO KNAW, NL-6700 AB Wageningen, Netherlands
The debate on emission targets of greenhouse gasses designed to limit global climate change has to take into account the ecological consequences. One of the clearest ecological consequences is shifts in phenology. Linking these shifts to changes in population viability under various greenhouse gasses emission scenarios requires a unifying framework. We propose a box-in-a-box modeling approach that couples population models to phenological change. This approach unifies population modeling with both ecological responses to climate change as well as evolutionary processes. We advocate a mechanistic embedded correlative approach, where the link from genes to population is established using a periodic matrix population model. This periodic model has several major advantages: (1) it can include complex seasonal behaviors allowing an easy link with phenological shifts; (2) it provides the structure of the population at each phase, including the distribution of genotypes and phenotypes, allowing a link with evolutionary processes; and (3) it can incorporate the effect of climate at different time periods. We believe that the way climatologists have approached the problem, using atmosphere-ocean coupled circulation models in which components are gradually included and linked to each other, can provide a valuable example to ecologists. We hope that ecologists will take up this challenge and that our preliminary modeling framework will stimulate research toward a unifying predictive model of the ecological consequences of climate change.