Nature vs. Nurture: the Making of the Montado Ecosystem
Bibliographic record
Abstract
The southern Iberian Peninsula is dominated by a savannah-like ecosystem, the montado, which is a typically Mediterranean cultural adaptation to generally poor productive areas. Montados are exploited for three main uses: forestry, agriculture, and extensive grazing, in proportions that vary according to local conditions (more or less productive land) and historical circumstances. Because these ecosystems occur over a large geographic area (they occupy some 6 million ha), biodiversity would be expected to vary among montados. However, differences in management practices may also influence species distribution. In this paper, we investigate differences in plant and bird species diversity among 60 montados distributed all across southern Portugal. The environmental variables studied included geographical coordinates, climatological data, soil type, and altitude. We also investigated agro-economic variables that could describe human activities at each site: animal husbandry (breeds, stock density, grazing rotation, etc.), agriculture (fallow rotation frequency, use of fertilizers, etc.), and forestry (cork harvesting, thinning, etc.). Finally, land-use type and metrics were assessed from rectified aerial photographs. Species richness among these two groups was not correlated, sites with high or low numbers of plant species not necessarily having high or low numbers of bird species. However, both plant and bird communities exhibited a similar pattern of species composition and turnover. This pattern was ecologically based, rather than a result of biological similarities between groups: direct gradient analyses and variance partitioning revealed strong correlations between species distribution and spatial gradients, namely longitude and latitude. In trying to distinguish anthropogenic from biophysical processes, we found that both were equally important as drivers of montado biodiversity. Plants and birds exhibited a similar ecological pattern, although environmental conditions were slightly more important in the case of plants, and human activities were slightly more important in the case of birds.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".