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Record W4403927127 · doi:10.1016/j.biocon.2024.110828

Conserving genetic diversity hotspots under climate change: Are protected areas helpful?

2024· article· en· W4403927127 on OpenAlexaboutno aff
Andrea Chiocchio, Nina Luisa Santostasi, Alice Pezzarossa, Roberta Bisconti, Luigi Maiorano, Daniele Canestrelli

Bibliographic record

VenueBiological Conservation · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsnot available
FundersMinistero dell’Istruzione, dell’Università e della RicercaEuropean Commission
KeywordsClimate changeGeographyGenetic diversityDiversity (politics)EcologyBiologyPopulationPolitical science

Abstract

fetched live from OpenAlex

The conservation of genetic diversity is a major target of the Kunming-Montreal Global Biodiversity Framework and of the EU Nature Restoration Law, as it provides populations with the potential to evolutionary adapt to the ongoing environmental challenges. However, genetic diversity has often been neglected in the conservation planning, and data on the extent to which it is currently preserved by protected areas are scanty. Here, we assessed the efficacy of protected areas in preserving genetic diversity hotspots under global change. Focusing on the Italian peninsula inside the Mediterranean global biodiversity hotspot, we (i) investigated the patterns of genetic diversity of endemic terrestrial vertebrates, (ii) assessed how much genetic diversity is currently covered by protected areas considering both nuclear and mitochondrial DNA, and (iii) estimated the impact of projected range shifts caused by climate changes on the conservation of genetic diversity patterns. We found that protected areas cover <20 % of the areas of high genetic diversity for most of the investigated taxa, and fail to cover almost all multispecies genetic diversity hotspots. Furthermore, our results showed that mitochondrial DNA is not a reliable proxy for nuclear genome diversity, and its use in spatial conservation planning might lead to ineffective initiatives. Finally, we estimated that the extent of genetic diversity covered by protected areas will dramatically decline in the near future (2050). These results identify major gaps in current protection of genetic diversity and provide concrete guidelines to plan area-based conservation initiatives that meet biodiversity conservation targets for 2030. • The Italian network of protected areas covers only a very small amount of genetic diversity of terrestrial vertebrates. • mtDNA variation does not mirror patterns of nuclear DNA variation and its use might be misleading in conservation planning. • The amount of genetic diversity covered by protected areas will decrease in the future, as a consequence of climate changes. • Gaining spatially explicit genomic data is pivotal to increasing the long-term efficacy of area-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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.002

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 head0.125
GPT teacher head0.269
Teacher spread0.144 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations8
Published2024
Admission routes1
Has abstractyes

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