Florian Hartig

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Intraspecific trait variation across scales: implications for understanding global change responses

Emily V. Moran, Florian Hartig & David M. Bell

Global Change Biology, 22(1), 137–150 (2016)
Cite this
@article{moran2016intraspecific,
  author = {Emily V. Moran and Florian Hartig and David M. Bell},
  title = {Intraspecific trait variation across scales: implications for understanding global change responses},
  journal = {Global Change Biology},
  volume = {22},
  number = {1},
  pages = {137--150},
  year = {2016},
  doi = {10.1111/gcb.13000},
}

DOI: 10.1111/gcb.13000
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Funding

This study was supported through funding by National Science Foundation (1241327).

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Abstract

Recognition of the importance of intraspecific variation in ecological processes has been growing, but empirical studies and models of global change have only begun to address this issue in detail. This review discusses sources and patterns of intraspecific trait variation and their consequences for understanding how ecological processes and patterns will respond to global change. We examine how current ecological models and theories incorporate intraspecific variation, review existing data sources that could help parameterize models that account for intraspecific variation in global change predictions, and discuss new data that may be needed. We provide guidelines on when it is most important to consider intraspecific variation, such as when trait variation is heritable or when nonlinear relationships are involved. We also highlight benefits and limitations of different model types and argue that many common modeling approaches such as matrix population models or global dynamic vegetation models can allow a stronger consideration of intraspecific trait variation if the necessary data are available. We recommend that existing data need to be made more accessible, though in some cases, new experiments are needed to disentangle causes of variation.

What the paper shows and why it matters

(AI-generated)

Many authors have called for more mechanistic models of how species respond to global change, but most current models still describe a species by one set of mean trait values or by broad functional types. Traits, however, differ between populations and between individuals, through inherited differences and through plasticity (one genotype adjusting to its environment). The paper asks when this variation must be included and how far existing models can represent it. It questions the assumption that a species-level mean trait is enough to predict responses to global change.

The paper argues that variation in traits within species is likely to matter for global change predictions in five situations, and that existing model types could represent it far more than they do. It is likely to matter when a trait affects demography (survival, growth or reproduction); when the response is nonlinear in the trait, so that the response at the average trait differs from the average response (Jensen’s inequality); in small populations; when variation is structured, such as individuals that consistently grow faster, so that random noise cannot replace it; and when timescales span several generations under directional selection. Surveying matrix, integral projection, individual-based, species distribution and dynamic global vegetation models, it concludes that none has fully used its capacity to include trait variation, mainly, it suggests, for lack of data and computing capacity. In one example it cites, a lodgepole pine (Pinus contorta) model with population differences projected less than half the range loss of a species-level model under climate change (Oney et al. 2013).

The paper offers these conditions as guidelines for when variation within species is most important to consider. It expects computing limits to ease on their own, but says data limits will not ease without active effort. It therefore recommends that existing individual-level data be made accessible, since individual measurements usually cannot be recovered from the published literature, and that specialised experiments or long-term observations, such as common gardens (genotypes reared in shared conditions) or pedigrees, separate genetic from environmental causes of variation. It closes by arguing that combining theory with such data and modelling techniques will be crucial for robust predictions.

References

  • Oney et al. (2013). Intraspecific variation buffers projected climate change impacts on Pinus contorta. Ecology and Evolution 3, 437–449. doi:10.1002/ece3.426