Factors determining<i>Microcebus</i>abundance in a fragmented landscape in Ankarafantsika National Park, Madagascar
Bibliographic record
Abstract
Introduction 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). Food availability Food availability typically refers to temporal and spatial variations in food quantity and quality, and has long been considered one of the main drivers of primate abundance and density. Despite extensive investigations into the relationship between food quality and amount and primate density and abundance (Hanya and Chapman, 2012), it is often difficult to determine quantitatively rigorous measures of food availability. This issue arises due to the high plant diversity at many primate research sites in the tropics, as well as stochastic variations in the temporal availability of food resources. Many researchers have employed plant dendrometrics as proxies for food availability, such as diameter at breast height (DBH), basal area, and stem density (e.g., Wieczkowski, 2004; Anzures-Dadda and Manson, 2007; Grow et al ., 2013).
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".