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Record W4220968031 · doi:10.1111/gcb.16145

The effect of past defaunation on ranges, niches, and future biodiversity forecasts

2022· article· en· W4220968031 on OpenAlexafffund
Lílian P. Sales, Mauro Galetti, Ana Carolina Carnaval, Sophie Monsarrat, Jens‐Christian Svenning, Mathias M. Pires

Bibliographic record

VenueGlobal Change Biology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsConcordia University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São PauloVillum FondenNational Science Foundation
KeywordsDefaunationBiodiversityEcological nicheEnvironmental scienceNicheEcologyGeographyEnvironmental resource managementBiologyHabitat

Abstract

fetched live from OpenAlex

Humans have reshaped the distribution of biodiversity across the globe, extirpating species from regions otherwise suitable and restricting populations to a subset of their original ranges. Here, we ask if anthropogenic range contractions since the Late Pleistocene led to an under-representation of the realized niches for megafauna, an emblematic group of taxa often targeted for restoration actions. Using reconstructions of past geographic distributions (i.e., natural ranges) for 146 extant terrestrial large-bodied (>44 kg) mammals, we estimate their climatic niches as if they had retained their original distributions and evaluate their observed niche dynamics. We found that range contractions led to a sizeable under-representation of the realized niches of several species (i.e., niche unfilling). For 29 species, more than 10% of the environmental space once seen in their natural ranges has been lost due to anthropogenic activity, with at least 12 species undergoing reductions of more than 50% of their realized niches. Eighteen species may now be confined to low-suitability locations, where fitness and abundance are likely diminished; we consider these taxa 'climatic refugees'. For those species, conservation strategies supported by current ranges risk being misguided if current, suboptimal habitats are considered baseline for future restoration actions. Because most climate-based biodiversity forecasts rely exclusively on current occurrence records, we went on to test the effect of neglecting historical information on estimates of species' potential distribution - as a proxy of sensitivity to climate change. We found that niche unfilling driven by past range contraction leads to an overestimation of sensitivity to future climatic change, resulting in 50% higher rates of global extinction, and underestimating the potential for megafauna conservation and restoration under future climate change. In conclusion, range contractions since the Late Pleistocene have also left imprints on megafauna realized climatic niches. Therefore, niche truncation driven by defaunation can directly affect climate and habitat-based conservation strategies.

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)0.011
metaresearch head score (Gemma)0.048
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 score0.014
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
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 head0.020
GPT teacher head0.238
Teacher spread0.219 · 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

Citations42
Published2022
Admission routes2
Has abstractyes

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