Understanding the conservation-genetics gap in Latin America: challenges and opportunities to integrate genetics into conservation practices
Bibliographic record
Abstract
Introduction: Integrating genetic data into conservation management decisions is a challenging task that requires strong partnerships between researchers and managers. Conservation in Latin America is of crucial relevance worldwide given the high biodiversity levels and the presence of hotspots in this region. Methods: We conducted a survey across Latin America to identify gaps and opportunities between genetic researchers and conservation managers. We aimed to better understand conservation managers’ points of view and how genetic research could help conservation practitioners to achieve their goals, by implementing genetic assessments that could effectively inform conservation practices. We distributed an online survey via four regional collaborating organizations and 32 focal points based in 20 Latin American countries. The target respondents were conservation managers of species or areas in Latin America. Results: We collected a total of 468 answered questionnaires from 21 Latin American countries. Most respondents (44%) were from an academic or research institution while non-academics were mainly from non-governmental institutions (30%) and government agencies (25%). Most respondents (65%) have performed or used genetic assessments in their managed area or species, either alone, in partnership, contracting someone else or using published results. For the majority of this group, the genetic results were relevant to their conservation management goals, helping to inform management decisions. Respondents that had not performed genetic assessments (35%) were mainly from the non-academic group, and their main barriers were limited access to funds, genetic lab facilities, and trained personnel to design studies and conduct lab work. Discussion: From the findings, we describe the current situation and provide a general diagnosis of the conservation-genetics gap in Latin America. We describe the gender gap, academic-practitioner co-development of conservation questions and projects, and the nationality and residency of Latin American conservation managers in relation to the countries where they work. We discuss opportunities to co-create research questions and co-develop studies based on conservation practitioners’ needs. We offer recommendations for overcoming barriers to integrate genetic information into conservation actions, and advance agendas that fit the needs and realities of the highly heterogeneous, biodiverse and challenging Latin American region.
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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 | 0.001 | 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".