Structural uncertainty in models projecting the consequences of habitat loss and fragmentation on biodiversity
Bibliographic record
Abstract
Ecological theory is essential to predict the effects of global changes such as habitat loss and fragmentation on biodiversity. Species–area relationships (SAR), metapopulation models (MEP) and species distribution models (SDM) are commonly used tools incorporating different ecological processes to explain biodiversity distribution and dynamics. Yet few studies have compared the outcomes of these disparate models and investigated their complementarity. Here we show that the processes underlying SAR (patch area), MEP (patch isolation) and SDM (environmental conditions) models can be compared with a common statistical framework. Our approach allows for species and community‐level predictions under current and future landscape scenarios, facilitates multi‐model comparison and provides the machinery for integrating multiple mechanisms into one model. We apply this framework to the distribution of eight focal vertebrate species in current and future projected landscapes where 10% of the landscape is lost to land‐use change in southwestern, Quebec, Canada. Based on a model selection approach, we found that a model including patch area was the top ranked model for four of our focal species and models including patch isolation and environmental conditions were the top ranked models for two focal species each. Community‐level predictions of models based on patch area, patch isolation and environmental conditions for both current and future landscapes showed high spatial overlap, however, patch area models always predicted a reduction of species richness per patch whereas both the patch isolation and environmental conditions models predicted an increase or decrease in species richness per patch following habitat loss and fragmentation. Our comparative tool will allow ecologists and conservation practitioners to relate structural uncertainty to key mechanisms underlying each model. Ultimately, this approach is one step in the direction of deriving robust predictions for the change and loss of biodiversity under global change, which is key for informing conservation plans.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".