Edge effects on tree dendrometrics, abiotics, and mouse lemur densities in western dry forests in Madagascar
Bibliographic record
Abstract
Introduction Anthropogenic disturbance of forests leads to fragmentation and habitat loss, which strongly influence genetic diversity, extirpations, and extinction of many species (e.g., Fahrig and Merriam, 1994; Gibson et al ., 2013; Radespiel and Bruford, 2014). One of the most significant consequences of fragmentation and habitat loss is an increase in the amount of edge effects and edge habitat (Chen et al ., 1992). Edge effects represent the penetration, to varying depths and intensities, of biotic and abiotic conditions from the surrounding environment (matrix) into the forest interior (Malcolm, 1994). Murcia (1995) described three ecological consequences of edge effects: (1) abiotic effects, (2) direct biological effects, and (3) indirect biological effects. Abiotic effects occur as the result of the penetration of environmental factors, such as temperature and light levels, from the matrix into the forest interior. Direct biological effects are changes in the abundance and distribution of organisms due to physical conditions near the forest edge. For example, Laurance et al . (1997) documented that wind damage led to a drastic drop in tree biomass within 100 m of the forest edge in South America. Indirect biological effects involve changes in species interactions (e.g., herbivory, frugivory), such that species that avoid the edge may be doing so due to the scarcity of preferred food items in these stochastic habitats (Mills, 1995). The combination of these three ecological processes results in dynamic, multidimensional edge habitats that differ from the matrix and forest interior. Although edge effects have been shown to alter forest structure and animal communities (e.g., Lovejoy et al ., 1986; Laurance and Yensen, 1991; Laurance et al ., 1997), they have rarely been studied directly in regards to primate ecology and biogeography. Edge effects are particularly relevant to ecological and biogeographic studies in Madagascar. Madagascar has an unrivaled level of plant endemicity and diversity: of the estimated 12,000 species of plants in Madagascar, 81% are endemic to this country (e.g., Phillipson, 1994, 1996; Du Puy et al ., 1999). This plant diversity is remarkable given that much of the original forest cover has been lost to human perturbations, such as slash-and-burn agriculture, logging, mining, and associated erosion (Green and Sussman, 1990; Du Puy and Moat, 1998).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".