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Record W3037373843 · doi:10.1101/2020.06.25.168815

Predicting range shifts of African apes under global change scenarios

2020· preprint· en· W3037373843 on OpenAlexaff
Joana S. Carvalho, Bruce Graham, Gaëlle Bocksberger, Fiona Maisels, Elizabeth A. Williamson, Serge A. Wich, Tenekwetche Sop, Bala Amarasekaran, Richard A. Bergl, Christophe Boesch, H. Boesch, Terry Brncic, Bartelijntje Buys, Rebecca Chancellor, Emmanuel Danquah, Osiris A. Doumbé, 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, Manasseh Eno‐Nku, Maureen S. McCarthy, Bethan J. Morgan, Stuart Nixon, Louis Nkembi, Emmanuelle Normand, 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

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsUniversity of British Columbia
FundersZoologische Gesellschaft FrankfurtCleveland Zoological SocietyFauna and Flora International
KeywordsBiological dispersalRange (aeronautics)GeographyClimate changePopulationEcologyEconometricsMathematicsBiologyDemography

Abstract

fetched live from OpenAlex

ABSTRACT Aim Modelling African great ape distribution has until now focused on current or past conditions, whilst 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 population changes. Location Sub-Saharan Africa Methods We compiled occurrence data on African ape populations from the IUCN A.P.E.S. database and extracted relevant human-, climate- and habitat-related predictors representing current and future (2050) conditions to predict taxon-specific distribution under a best- and a worst-case scenario, using ensemble forecasting. Given the large effect on model predictions, we further tested algorithm sensitivity by considering default and non-default modelling options. The latter included interactions between predictors and polynomial terms in correlative algorithms. Results The future distributions of gorilla and bonobo populations are likely to be directly determined by climate-related variables. In contrast, future chimpanzee distribution is influenced mostly by anthropogenic variables. Both our modelling approaches produced similar model accuracy, although a slight difference in the magnitude of range change was found for Gorilla beringei beringei, G. gorilla diehli , and Pan troglodytes schweinfurthii . On average, a decline of 50% of the geographic range ( non-default ; or 55% default ) is expected under the best scenario if no dispersal occurs (57% non-default or 58% default in worst scenario). However, new areas of suitable habitat are predicted to become available for most taxa if dispersal occurs (81% or 103% best, 93% or 91% worst, non-default and default , respectively), except for G. b. beringei . Main Conclusions Despite the uncertainty in predicting the precise proportion of suitable habitat by 2050, both modelling approaches predict large range losses for all African apes. Thus, conservation planners urgently need to integrate land-use planning and simultaneously support conservation 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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.036
GPT teacher head0.235
Teacher spread0.198 · 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 teacher head, not a consensus.

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

Citations48
Published2020
Admission routes1
Has abstractyes

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