Effects of variation in forest fragment habitat on black howler monkey demography in the unprotected landscape around Palenque National Park, Mexico
Bibliographic record
Abstract
Habitat loss and fragmentation are leading threats to biodiversity today, and primates are particularly vulnerable to anthropogenic habitat disturbance. However, few studies have examined how differential effects of variation in forest fragment characteristics on males and females in a primate population may affect demography and population persistence. We quantified the effects of variation in forest fragment characteristics on the within-fragment demography of black howler monkeys ( Alouatta pigra ) in forest fragments around Palenque National Park, Mexico, and how these effects differed between adult males and females. We quantified forest loss in the landscape between 2000 and 2017, and used a redundancy analysis to examine the effects of 15 variables quantifying fragment dimensions, forest composition and physical structure, and isolation on fragment population size and density, the proportion of adult males and females in the fragment population, and the mean number of adult males and females per group in 34 fragments ( N = 393 monkeys). We hypothesized that (i) population size is positively correlated with fragment area, while population density is negatively correlated, and (ii) the composition of fragment populations results from differential effects of fragment variables on adult males and females. Forest cover decreased by 23.3% from 2000 to 2017. Our results showed a significant effect of fragment variables on population demography in fragments, accounting for 0.69 of the variance in the demographic response variables. Population size increased with fragment area and connectivity, while density decreased. Larger, less isolated fragments with better connectivity, characteristics indicative of abundant secondary growth, and those with more diverse vegetation but lower Simpson’s evenness indices tended to have more adult females per group and a higher proportion of adult females in the population. In contrast, fragments that were largely similar in characteristics of forest composition and structure, but that were more isolated from nearby fragments, had more adult males per group and a higher proportion of adult males. These results may stem from black howler females preferentially remaining in natal groups and fragments when possible, and dispersing shorter distances when they disperse, while males may be more likely to disperse between fragments, traveling longer distances through the matrix to more isolated fragments. These differential effects on males and females have important conservation implications: if females are more abundant in larger, less isolated fragments, while males are more abundant in more isolated fragments, then to effectively conserve this population, both landscape connectivity and fragment areas should be maintained and increased.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".