From patches to richness: assessing the potential impact of landscape transformation on biodiversity
Bibliographic record
Abstract
Abstract Natural patchiness and human fragmentation result in habitats that are not continuously distributed. How spatial configuration of patches in fragmented habitats influences biodiversity remains largely controversial. Here, we propose a framework to extend the species–area relationship (SAR) approach to analyze how changes in habitat configuration affect species richness in fragmented habitats. We use hypothetical communities that are characterized by (1) their tolerance to human activities, (2) the dispersal capability of the individuals of any species, (3) the SAR, and (4) the species turnover among patches. Further, the species turnover is a function of (4a) the predictability of species survival and (4b) the species recolonization odds. In our framework, we identify three extreme communities that encompass the richness of all potential different communities, and thus encapsulate the richness of real communities. We propose a graph to visualize the effect of different patch sizes on species richness, an index to quantify those changes, and a second graph using the index to visualize the effect of distance between patches on species richness. After applying our framework and tools to the Tropical Andes, we found strong differences in the impact of natural vs. human‐driven fragmentation on richness between biomes. When projecting future richness values under climate change scenarios, the largest sources of uncertainty in our richness calculation (>90%) were species turnover among patches and species dispersal for most of the biomes rather than future climate or species tolerance to human activities. Habitat loss consistently decreased the species richness; however, fragmentation per se often increased it. The increment was mostly linked to the species turnover rate among patches. Our framework is a new theoretical tool to study the main patterns that underlie regional richness and therefore can provide new insights to face spatial habitat reconfiguration caused by human activities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".