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Record W3167725056 · doi:10.1111/ddi.13358

Predicting range shifts of African apes under global change scenarios

2021· article· en· W3167725056 on OpenAlexaff
Joana S. Carvalho, Bruce Graham, Gaëlle Bocksberger, Fiona Maisels, Elizabeth A. Williamson, Serge A. Wich, Tenekwetche Sop, Bala Amarasekaran, Benjamin Barca, Abdulai Barrie, Richard A. Bergl, Christophe Boesch, H. Boesch, Terry Brncic, Bartelijntje Buys, Rebecca Chancellor, Emmanuel Danquah, Osiris A. Doumbé, Stephane Y. Le‐Duc, Anh Galat‐Luong, Jessica Ganas, Sylvain Gatti, Andrea Ghiurghi, Annemarie Goedmakers, Nicolas Granier, Dismas Hakizimana, Barbara Haurez, Josephine Head, Ilka Herbinger, Annika Hillers, Sorrel Jones, Jessica Junker, Nakedi Maputla, Manasseh Eno‐Nku, Maureen S. McCarthy, Mary Molokwu‐Odozi, Bethan J. Morgan, Yoshihiro Nakashima, Paul K. N’Goran, Stuart Nixon, Louis Nkembi, Emmanuelle Normand, Laurent D.Z. Nzooh, Sarah H. Olson, Leon Payne, Charles‐Albert Petre, A. Piel, Lilian Pintea, Andrew J. Plumptre, Aaron Rundus, Adeline Serckx, Fiona A. Stewart, Jacqueline Sunderland‐Groves, Nikki Tagg, Angelique Todd, Ashley Vosper, José Francisco Carminatti Wenceslau, Erin G. Wessling, Jacob Willie, Hjalmar S. Kühl

Bibliographic record

VenueDiversity and Distributions · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRange (aeronautics)Biological dispersalEcologyClimate changeGeographyTaxonIUCN Red ListHabitatDistribution (mathematics)Species distributionBiologyPopulationDemographyMathematics

Abstract

fetched live from OpenAlex

Abstract Aim Modelling African great ape distribution has until now focused on current or past conditions, while future scenarios remain scarcely explored. Using an ensemble forecasting approach, we predicted changes in taxon‐specific distribution under future scenarios of climate, land use and human populations for (1) areas outside protected areas (PAs) only (assuming complete management effectiveness of PAs), (2) the entire study region and (3) interspecies range overlap. Location Tropical Africa. Methods We compiled occurrence data (n = 5,203) on African apes from the IUCN A.P.E.S. database and extracted relevant climate‐, habitat‐ and human‐related predictors representing current and future (2050) conditions to predict taxon‐specific range change under a best‐ and a worst‐case scenario, using ensemble forecasting. Results The predictive performance of the models varied across taxa. Synergistic interactions between predictors are shaping African ape distribution, particularly human‐related variables. On average across taxa, a range decline of 50% is expected outside PAs under the best scenario if no dispersal occurs (61% in worst scenario). Otherwise, an 85% range reduction is predicted to occur across study regions (94% worst). However, range gains are predicted outside PAs if dispersal occurs (52% best, 21% worst), with a slight increase in gains expected across study regions (66% best, 24% worst). Moreover, more than half of range losses and gains are predicted to occur outside PAs where interspecific ranges overlap. Main Conclusions Massive range decline is expected by 2050, but range gain is uncertain as African apes will not be able to occupy these new areas immediately due to their limited dispersal capacity, migration lag and ecological constraints. Given that most future range changes are predicted outside PAs, Africa's current PA network is likely to be insufficient for preserving suitable habitats and maintaining connected ape populations. Thus, conservation planners urgently need to integrate land use planning and climate change mitigation measures at all decision‐making levels both in range countries and abroad.

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)0.001
metaresearch head score (Gemma)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.239
Teacher spread0.192 · 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

Citations37
Published2021
Admission routes1
Has abstractyes

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