Predicting range shifts of African apes under global change scenarios
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".