Multivariate morphometric analysis of <i>Meloidogyne</i> spp. in Tamil Nadu, India: A PCA-based approach to population differentiation in carrot ecosystems
Bibliographic record
Abstract
Root-knot nematodes (Meloidogyne spp.) are major agricultural pests threatening carrot (Daucus carota L.) cultivation in Tamil Nadu, India, causing significant yield losses. This study employed Principal Component Analysis (PCA) to investigate the morphometric traits of second-stage juveniles (J2) and adult females from eight Meloidogyne spp. populations collected from carrot-growing regions across Tamil Nadu. Morphological characterization involved extracting J2 and adult females, preparing whole mounts in glycerin, dissecting female perineal patterns, and recording morphometric measurements using Camera Lucida, phase contrast, and scanning electron microscopy. It revealed variations in key traits, including body and stylet length, with significant differences between nematode populations from hill and plain regions. PCA reduced the high-dimensional data, explaining 93.80% and 93.45% of the total variance in females and juveniles, respectively. Strong positive correlations were observed between body length and median bulb length in juveniles, dorsal gland orifice (DOGO), and body width in females. Negative correlations were also noted for stylet width and DOGO in females. PCA clustering revealed three distinct groups, with juveniles from Shoolagiri and Hosur forming one group and females from the Nilgiris and Kodaikanal clustering together, indicating geographic and environmental influence on morphometric traits. Stylet length emerged as a key distinguishing factor for both juvenile and female populations. These findings clearly explain the morphometric diversity within Meloidogyne populations, offering new insights into nematode management strategies in Tamil Nadu’s carrot ecosystems. This study also underscores the utility of PCA in streamlining morphometric analysis, enhancing the precision and speed of nematode identification.
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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 | 0.001 | 0.008 |
| 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".