Promise and prospects in primate pelage research: a comment on Caro et al.
Bibliographic record
Abstract
Caro and coauthors (2021) investigate the drivers of pelage diversity in primates, the most colorful and visually striking order of mammals. This topic has long attracted scientific attention, but most studies have investigated a subset of social or ecological variables, typically within a taxonomic group. In contrast, Caro and coauthors undertake a more ambitious and comprehensive set of analyses, targeting all major hypotheses proposed to date. To do so, the authors amass and code a large set of publicly available images of species spanning all major branches of the Order. The outcome is an idea-rich paper that synthesizes relevant literature, provides support for well-established relationships, and offers new ideas. Investigation of this scale and scope that are reliant on data not originally collected for current application (i.e., an image set drawn from online resources) are often faced with limitations, which is true of the present manuscript. Caro and authors highlight some of these limitations, Here, I focus on a subset of these, and discuss additional topics, with the goal of highlighting areas where future studies might build on and test the ideas presented. While sacrificing depth to allow breadth is expected in a large review, a deeper, more critical evaluation of the material discussed would be useful. Some of the studies referenced are based on more compelling data and analyses than others, but they are given seemingly equal weight. In addition, some recent works that provide major advances are not included. As an example, more comprehensive work on opsin genes for the strepsirrhines is available (e.g., Jacobs et al. 2017). Relying heavily on older papers can limit analyses, and lead to the perpetuation of historical ideas at the expense of acknowledging and contributing to recent and refined advances. While the authors do a commendable job of detailing their protocols and efforts to standardize data collection among observers, some details remain hard to parse. References for some statistical choices are also missing, making it difficult to understand some of the decisions. More generally, the obstacles in scoring the color of unstandardized images can be formidable. While the authors replicate some of the results found by researchers working with standardized images, this does not necessarily indicate their methods are appropriate for all of their treatments and analyses. For example, while analyses of unstandardized images may be sufficient for probing the relationship between body weight and dorsal pigmentary darkening, other questions, including those investigating color vision type and pelage coloration, may not be yet answerable with the resources available. As the authors point out, photographic documentation across the primates is challenging. A critical discussion of this point and reflection on the relative levels of confidence in different results would be useful in guiding future efforts. Turning to the biology of color vision, it is perhaps important to clarify that the genes coding for middle-to-long wavelength sensitive cone opsins are located on the X-chromosome of all primates (Dulai et al. 1999), not just those among the monkeys of the Americas (platyrrhines, “New World”) as implied in the paper. While this should not influence downstream interpretations, in contrast, the decision to code unknown platyrrhine species as dichromatic is confusing, and potentially impactful. Given that no species of platyrrhine monkey is known to be routinely dichromatic, monkeys in the Americas should be coded as polymorphic, with the exception of the routinely trichromatic howler monkeys (genus Alouatta), and the monochromatic owl monkeys (genus Aotus) (Jacobs 2008; Moreira et al. 2019). Furthermore, as the authors also acknowledge, the treatment of all females in polymorphic species as trichromatic, despite the large population of dichromats known to be present, complicates the interpretation of the results. Overall, a more refined approach seems necessary for asking questions about pelage evolution in polymorphic species. Despite the points I have raised, there is no doubt that the authors bring attention, intrigue, enthusiasm, and creative new ideas to a compelling topic of longstanding interest. Such efforts should be celebrated, as discoveries in this area will continue to shed light on primate and human evolution, and make important contributions to understanding pelage variation in primates and other mammalian species.
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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 | 0.024 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.064 | 0.097 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".