Taxonomic error rates affect interpretations of a national‐scale ground beetle monitoring program at National Ecological Observatory Network
Notice bibliographique
Résumé
Abstract Parataxonomists are responsible for taxonomic identifications in large‐scale biodiversity monitoring programs. However, they may lack formal taxonomic training, and thus, quantifying error rate in identification is paramount for evaluating data quality of larger biomonitoring efforts. In large‐scale biomonitoring in particular, parataxonomist error rate could vary among regions with different species richness and composition. Here, we tested whether error rates in identification of ground beetles (Coleoptera: Carabidae) by parataxonomists increased in regions with greater species richness throughout the National Ecological Observatory Network (NEON), a national biomonitoring network spanning the United States. We compared identifications made by both parataxonomist and experts of 33,516 specimens collected between 2013 and 2017 from 18 ecoclimatic regions and analyzed error rates across ecoclimatic regions as a function of total richness identified by taxonomic experts. We then compared the additional level of taxonomic support that would be required to resolve identifications to species‐level identifications. We demonstrated the extent to which parataxonomist error rate can affect interpretation of common objectives of biomonitoring results, such as comparisons of species richness between ecoclimatic regions and capacity to identify target species of interest such as non‐indigenous species. Overall parataxonomist error rate was 11.1% and did not increase in regions with greater species richness. Expert taxonomists were required to resolve parataxonomist identifications to species in an additional 16% of specimens. With an average error rate of 11.1%, species richness estimates based on parataxonomists generally mirrored richness determined by experts. However, parataxonomist error rates as low as 5% were sufficient to misrepresent the gradient of species richness across ecoclimatic regions. Parataxonomist errors also led to false detection/missed detections of non‐indigenous species. As error rates were not influenced by increasing species richness, our study suggests that parataxonomists may be used consistently in large‐scale biomonitoring efforts to amplify the abilities of taxonomists by increasing the quantity and speed in which specimens are processed. However, our study also highlights that due to parataxonomist limitations, a subset of their materials must be regularly verified by professionals to ensure the quality of data collected.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,060 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».