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Record W1581561170 · doi:10.2172/1043882

Predicting the impacts of climate change on animal distributions: the importance of local adaptation and species' traits

2011· report· en· W1581561170 on OpenAlexaboutno aff
Jonathan Hellmann, Neil F. Lobo

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeRange (aeronautics)EcologyHabitatSpecies distributionGeographyLocal extinctionGlobal warmingPopulationEcosystemGlobal changeEnvironmental scienceBiological dispersalBiology

Abstract

fetched live from OpenAlex

The geographic range limits of many species are strongly affected by climate and are expected to change under global warming. For species that are able to track changing climate over broad geographic areas, we expect to see shifts in species distributions toward the poles and away from the equator. A number of ecological and evolutionary factors, however, could restrict this shifting or redistribution under climate change. These factors include restricted habitat availability, restricted capacity for or barriers to movement, or reduced abundance of colonists due the perturbation effect of climate change. This research project examined the last of these constraints - that climate change could perturb local conditions to which populations are adapted, reducing the likelihood that a species will shift its distribution by diminishing the number of potential colonists. In the most extreme cases, species ranges could collapse over a broad geographic area with no poleward migration and an increased risk of species extinction. Changes in individual species ranges are the processes that drive larger phenomena such as changes in land cover, ecosystem type, and even changes in carbon cycling. For example, consider the poleward range shift and population outbreaks of the mountain pine beetle that has decimated millions of acres of Douglas fir trees in the western US and Canada. Standing dead trees cause forest fires and release vast quantities of carbon to the atmosphere. The beetle likely shifted its range because it is not locally adapted across its range, and it appears to be limited by winter low temperatures that have steadily increased in the last decades. To understand range and abundance changes like the pine beetle, we must reveal the extent of adaptive variation across species ranges - and the physiological basis of that adaptation - to know if other species will change as readily as the pine beetle. Ecologists tend to assume that range shifts are the dominant response of species to climate change, but our experiments suggest that other processes may act in some species that reduce the likelihood of geographic range change. In the first part of our DOE grant (ending 2008) we argued that the process of local adaptation of populations within a species range, followed by climatic changes that occur too quickly for adaptive evolution, is an underappreciated mechanism by which climate change could affect biodiversity. When this process acts, species ranges may not shift readily toward the poles, slowing the rate of species and biome change. To test this claim, we performed an experiment comparing core and peripheral populations in a series of field observations, translocation experiments, and genetic analyses. The papers in Appendix A were generated from 2005-2008 funding. In the second part of the DOE grant (ending 2011) we studied which traits promote population differentiation and local adaptation by building genomic resources for our study species and using these resources to reveal differences in gene expression in peripheral and core populations. The papers in Appendix B were generated from 2008-2011 funding. This work was pursued with two butterfly species that have contrasting life history traits (body size and resource specialization) and occupy a common ecosystem and a latitudinal range. These species enabled us to test the following hypotheses using a single phylogenetic group.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.097
GPT teacher head0.282
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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