Past and present effects of habitat amount and fragmentation per se on plant species richness, composition and traits in a deforestation hotspot
Bibliographic record
Abstract
Worldwide, human activities are rapidly changing land cover and its spatial configuration. While it is widely acknowledged that habitat loss is a major cause of biodiversity loss, there is less agreement on how biodiversity responds to changes in habitat configuration. We assessed the effects of forest amount and forest fragmentation per se (the number of patches for a given forest amount, an aspect of configuration) on woody species richness, composition, and traits in the Dry Chaco forest, a global deforestation hotspot. We sampled woody plants in 24 forest sites varying in forest amount and fragmentation per se in the surrounding landscapes. Using Generalized Linear Modeling we tested whether a model with just forest amount was at least as able to predict species richness as a model with either patch size or isolation or the combination of both. We also tested whether forest amount and fragmentation per se influenced species richness, composition, and the density of four species traits. Finally, we compared these responses to forest amount and fragmentation per se measured in the past (2009) vs. in the present (2017) to look for time-lagged responses. We found that: 1) in support of the habitat amount hypothesis, species richness was more strongly related to forest amount than to the size and/or isolation of the forest patch containing the sample plot; 2) the positive effect of forest amount on species richness was more important than the effect of fragmentation per se (also positive); 3) fragmentation per se changed species composition such that plots in landscapes with more fragmented forest had species with smaller leaves and seeds, and higher wood density; and 4) species richness showed a time-lagged response to forest amount but not to forest fragmentation per se . Our results suggest that preservation of native Dry Chaco forest should be prioritized regardless of its fragmentation level, for conserving woody plant species diversity .
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) | 0.000 | 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".