Assessing biological realism of wildlife population estimates in data‐poor systems
Notice bibliographique
Résumé
Summary Large carnivore management is often contentious, particularly in jurisdictions where hunting and conservation efforts collide. Regulated hunting is a common management tool, yet relevant decisions are commonly taken in the absence of reliable population data and are driven by factors other than biological considerations. We used European large carnivore (brown bear Ursus arctos , wolf Canis lupus and Eurasian lynx Lynx lynx ) management to evaluate the biological plausibility of reported population estimates used in hunting decisions. We used Romania as a test case as this region is not only data‐poor, but the public and private game managers are beneficiaries of revenue from hunting activities. We assessed the following: (i) how population growth rates calculated from reported abundances between 2005 and 2012 compared to published growth rates empirically derived from European and North American populations; (ii) whether biological unrealism compounded through time by testing whether reported estimates fell within the bounds of biologically plausible trajectories; and (iii) the relationship between the occurrence of biologically unrealistic estimates and financial incentives (amount of hunting). For U. arctos , which generates high revenue, estimated annual population growth rates were frequently greater than maximum published growth rates (up to 1·5 for reported versus 1·136 in the literature). Reported estimates were greater than maximum simulated populations in 32% of cases, and the difference was positively correlated with hunting ( r s = 0·576). Population growth rates for C. lupus overshot the maximum published growth rate (1·35) less frequently, reported estimates were within the bounds of biologically plausible estimates (91% of cases), and there was a weak correlation between hunting and biologically unrealistic estimates ( r s = 0·182). L. lynx population growth rates derived from reported estimates were lower than minimum simulated populations (60% of cases), and there was a weak correlation between hunting and biologically unrealistic estimates ( r s = 0·164). Synthesis and applications . Our study suggests that comparing population estimates used by management agencies to demographic data obtained through rigorous peer‐reviewed studies is a useful approach for evaluating the biological plausibility of wildlife data in data‐poor systems, especially when management decisions might be influenced by non‐scientific incentives.
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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 | 0,001 | 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) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».