Factors determining<i>Microcebus</i>abundance in a fragmented landscape in Ankarafantsika National Park, Madagascar
Bibliographic record
Abstract
A fundamental issue in conservation biogeography is determining how and why population density (the number of individuals per unit area) and abundance (number of individual animals) vary across the geographic range of plant and animal species (Whittaker et al., 2005). Understanding these relationships is critical because they provide information on population dynamics and extinction probabilities (Wilcox and Murphy, 1985; Lima and Zollner, 1996). For example, Davidson et al. (2009) conducted a meta-analysis of 4500 mammal species and found that population density was one of the main predictors of extinction risk. Consequently, researchers have explored numerous covariates to primate density and abundance, including food quality and amount (Stevenson, 2001; Chapman et al., 2004), temporal distribution of key food resources (Terborgh, 1986), habitat structure such as basal area, stem density, tree height and size (Rovero and Struhsaker, 2007; Pozo-Montuy et al., 2011; Grow et al., 2013), habitat quality such as plant productivity (Janson and Chapman, 1999), rainfall and temperature (Pinto et al., 2009), distribution (Harcourt and Doherty, 2005), fragment area and isolation (Anzures-Dadda and Manson, 2007), and anthropogenic disturbance (Peres, 1990; Ganzhorn and Schmid, 1998; Anzures-Dadda and Manson, 2007; Peres and Palacios, 2007). Despite the wide range of species and habitats studied, few consistent patterns have emerged that explain spatial variations in primate density and abundance. Moreover, those patterns that have found statistical support tend to have been focused on large-bodied, diurnal species, leaving many unanswered questions on the conservation biogeography of small-bodied, nocturnal taxa (McGoogan et al., 2007).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".