Forest landscape pattern in the KwaZulu–Natal midlands, South Africa: 50 years of change or stasis?
Bibliographic record
Abstract
Abstract Understanding patterns and processes of habitat change is essential for managing and conserving forest fragments in anthropogenically altered landscapes. Digitized aerial photographs from 1944 and 1996 were examined for changes to the indigenous forest landscape in the Karkloof‐Balgowan archipelago in KwaZulu–Natal, South Africa. Attributes relating to proximate land‐use, patch shape, isolation and position in the landscape were used to determine putative causes of forest change. The total change in forest area was −5.7% (forest covered 6739 ha in 1996). This is contrasted with previous reports for the period 1880–1940 that estimated change in total forest area of up to −80%. Attrition was the predominant process of forest transformation between 1944 and 1996. Despite little overall change in forest area, 786 mostly small (<0.5 ha) forest patches were lost from the landscape, leaving 1277 forest patches in 1996. An increase in patch isolation, but no change in patch cohesion accompanied the changes in forest area. Ignoring patches that were eliminated, 514 patches decreased in area. This was partly a function of patch size, but the conversion of natural grassland to commercial plantation forestry in the matrix also influenced forest decline. Their small size and irregular shape caused forest patches in the region to be vulnerable to edge effects. Core area declined in a negative exponential way with increasing edge width and the total area of edge habitat exceeded that of core habitat at an edge width of only 50 m. Nevertheless, total core area decreased by only 2% (65 ha) between 1944 and 1996 because most of the eliminated patches were small and contained no core area. The large Karkloof forest (1649 ha) is a conservation priority for forest interior species, but the ecological role and biodiversity value of small forest patches should not be overlooked.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".