Bergmann’s Rule and climate change: Disentangling environmental and genetic responses in a wild bird population

Ecological Genetics Research Unit, Department of Biological and Environmental Sciences, PO Box 65, FI-00014 University of Helsinki, Helsinki, Finland.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 10/2008; 105(36):13492-6. DOI: 10.1073/pnas.0800999105
Source: PubMed


Ecological responses to on-going climate change are numerous, diverse, and taxonomically widespread. However, with one exception, the relative roles of phenotypic plasticity and microevolution as mechanisms in explaining these responses are largely unknown. Several recent studies have uncovered evidence for temporal declines in mean body sizes of birds and mammals, and these responses have been interpreted as evidence for microevolution in the context of Bergmann's rule-an ecogeographic rule predicting an inverse correlation between temperature and mean body size in endothermic animals. We used a dataset of individually marked red-billed gulls (Larus novaehollandiae scopulinus) from New Zealand to document phenotypic and genetic changes in mean body mass over a 47-year (1958-2004) period. We found that, whereas the mean body mass had decreased over time as ambient temperatures increased, analyses of breeding values estimated with an "animal model" approach showed no evidence for any genetic change. These results indicate that the frequently observed climate-change-related responses in mean body size of animal populations might be due to phenotypic plasticity, rather than to genetic microevolutionary responses.

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    • "Responses in the mean body size of animal populations to environmental changes have vital implications for fitness traits and might be due to phenotypic plasticity, rather than to a genetic process (Teplitsky et al., 2008). For rodents in general (Patton & Brylski, 1987), and for arvicolines in particular (Pankakoski & Nurmi, 1986; Yoccoz et al., 1993), size shifts follow rapidly even in genetically uniform populations if exposed to varying environmental conditions. "
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    ABSTRACT: Fragmented landscapes entail important consequences for the evolution of the species confined to them. Isolation of population fragments accelerates selection for narrow local conditions and facilitates morphological divergence. Throughout its range, the European snow vole Chionomys nivalis is restricted to fractured, rocky substrate in mountain regions, which is naturally fragmented into ‘continental archipelagos’. Consequently, its extensive morphological and genetic diversity was categorized into about 20 traditional subspecies and at least eight allopatric phylogenetic lineages. In this study, we aimed toward an integrative understanding of cranial variation throughout the European snow vole range. We analyzed seven linear cranial variables on 326 adult skulls from 27 populations belonging to eight phylogenetic lineages. We confirmed significant variation among the fragmented populations but retrieved little meaningful patterning in morphometric variability. Phenetic distances among populations were not related to the phylogenetic architecture of the species, and traditional subspecies were at odds with morphologically diagnosable populations. The lack of an association between morphometric and geographic distances argued against isolation by distance. Furthermore, mean size did not correlate with climatic variables. Morphological principal components 2 and 3 (loaded by the interorbital width and length of neurocranium, respectively) correlated significantly with geographic coordinates and climatic variables. Shape variables discriminated between the abulensis and nivalis phylogroups, and the European and Asiatic populations, but the largest phylogroups (nivalis and malyi) showed high interpopulation heterogeneity and classification accuracy was low. We suggest that cranial shape or size do not incorporate a signal that is strong enough to be of reliable use for subspecific taxonomy. Skull is seemingly prone to vary according to narrow local conditions, which distorts the underlying phylogenetic signal. A small-scale approach, with detailed knowledge of environmental parameters within each habitat fragment, might be more appropriate for a species whose range is actually a continental archipelago.
    Journal of zoology 09/2015; DOI:10.1111/jzo.12274 · 1.95 Impact Factor
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    • "One caveat of this approach is that genetic sources of trait change are not explicitly identified and therefore, physiological and maternal effects (phenotypic plasticity) cannot be rejected as explanations. In some cases, studies that had originally concluded that phenotypic differences had a genetic basis were subsequently credited to plasticity (Charmantier et al., 2008; Teplitsky et al., 2008). Regardless of the overwhelming qualitative support for microevolution to environmental stress, there remain few explicit, quantitative studies that show a genetic basis for phenotypic change to GCC or environmental contaminants (Gienapp et al., 2008; Klerks et al., 2011; Merilä, 2012; Merilä and Hendry, 2013). "
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    ABSTRACT: A fundamental objective within ecotoxicology lies in understanding and predicting effects of contaminants. This objective is made more challenging when global climate change is considered as an environmental stress that co-occurs with contaminant exposure. In this multi-stressor context, evolutionary processes are particularly important. In this paper, we consider several non-" omic " approaches wherein evolutionary responses to stress have been studied and discuss those amenable to a multiple stressor context. Specifically, we discuss common-garden designs, artificial and quasi-natural selection, and the estimation of adaptive potential using quantitative genetics as methods for studying evolutionary responses to contaminants and climate change in the absence of expensive molecular tools. While all approaches shed light on potential evolutionary impacts of stressor exposure, they also have limitations. These include logistical constraints, difficulty extrapolating to real systems, and responses tied strongly to specific taxa, populations, and/or testing conditions. The most effective way to lessen these inherent limitations is likely through inclusion of complementary physiological and molecular tools, when available. We believe that an evolutionary context to the study of contaminants and global climate change is a high priority in ecotoxicology and we outline methods that can be implemented by almost any researcher but will also provide valuable insights [Current Zoology 61 (4): 690–701, 2015].
    Current Zoology 08/2015; 61(4):690-701. · 1.59 Impact Factor
    • "This genetic basis for migration timing was also suggested for snow geese (Bety et al., 2004) and black-tailed godwits (Lourenç o et al., 2011), and may consequently also explain between-individual barnacle geese's variation in migration timing. Moreover, part of the observed repeatability might be phenotype plasticity (i.e. an environmentally based change in the phenotype) that lead to adaptation to the environmental condition (Teplitsky et al., 2008). "
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    ABSTRACT: tAccording to the green wave hypothesis, herbivores follow the flush of spring growth of forage plantsduring their spring migration to northern breeding grounds. In this study we compared two green waveindices for predicting the timing of the spring migration of avian herbivores: the satellite-derived greenwave index (GWI), and an index of the rate of acceleration in temperature (GDDjerk). The GWI was cal-culated from MODIS normalized difference vegetation index (NDVI) satellite imagery and GDDjerk fromgridded temperature data using products from the global land data assimilation system (GLDAS). To pre-dict the timing of arrival at stopover and breeding sites, we used four years (2008–2011) of tracking datafrom 12 GPS-tagged barnacle geese, a long-distance herbivorous migrant, wintering in the Netherlands,breeding in the Russian Arctic. The stopover and breeding sites for these birds were identified and therelations between date of arrival with the date of 50% GWI and date of peak GDDjerk at each site were ana-lyzed using mixed effect linear regression. A cross-validation method was used to compare the predictiveaccuracy of the GWI and GDDjerk indices. Significant relationships were found between the arrival datesat the stopover and breeding sites for the dates of 50% GWI as well as the peak GDDjerk (p < 0.01). The goosearrival dates at both stopover and breeding sites were predicted more accurately using GWI (R2cv= 0.68,RMSDcv= 5.9 and R2cv= 0.71, RMSDcv= 3.9 for stopover and breeding sites, respectively) than GDDjerk.The GDDjerk returned a lower accuracy for prediction of goose arrival dates at stopover ( R2cv= 0.45,RMSDcv= 7.79) and breeding sites (R2cv= 0.55, RMSDcv= 4.93). The positive correlation between the abso-lute residual values of the GDDjerk model and distance to the breeding sites showed that this index ishighly sensitive to latitude. This study demonstrates that the satellite-derived green wave index (GWI)can accurately predict the timing of goose migration, irrespective of latitude and therefore is suggestedas a reliable green wave index for predicting the timing of avian herbivores spring migration.
    Ecological Indicators 06/2015; 58:322-331. DOI:10.1016/j.ecolind.2015.06.005 · 3.44 Impact Factor
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