Dispersal strength influences zooplankton co‐occurrence patterns in experimental mesocosms
Notice bibliographique
Résumé
Negative co-occurrence patterns are intriguing because they may reflect the outcome of interspecific interactions and therefore signal how competition shapes communities. However, other factors also contribute to these patterns. For example, theoretical studies as well as two survey-based studies have all suggested that dispersal may also impact these patterns. While natural communities commonly have nonrandom patterns of negative co-occurrence, understanding how different processes drive these patterns requires further research. We tested the influence of dispersal on co-occurrence patterns using a zooplankton mesocosm experiment with four different dispersal treatments varying in the number of dispersers delivered into mesocosms on regular intervals. Our dispersal treatments were intended to adjust the relative importance of dispersal and competition experienced within mesocosms (i.e., high dispersal results in a relatively low influence of competition on species composition and vice versa). Higher dispersal translated into increased zooplankton species richness and inter-mesocosm compositional similarity, and also changed species occupancy patterns such that species occurrences were more even across mesocosms in higher-dispersal treatments. Dispersal treatments also differed markedly in species co-occurrence patterns. Negative co-occurrence patterns were significant for all but the lowest-dispersal treatment, peaked in the intermediate-dispersal treatments, and declined in the highest-dispersal treatment. Stability analyses illustrate that co-occurrence differences are robust to the exclusion of any single mesocosm in null model analyses. Dispersal treatments did not significantly differ with respect to abiotic variation, which has been recognized as a potential driver of negative co-occurrence patterns. These results suggest that not only can dispersal influence patterns of negative co-occurrence via changes to species richness and distribution (occupancy patterns among mesocosms), but the degree to which they do so varies nonlinearly with the strength of dispersal. Critically, because negative co-occurrence patterns were nonsignificant when the contribution of dispersal was lowest, it is possible that dispersal contributes strongly to many observed patterns of negative co-occurrence. Consequently, great care should be taken prior to interpreting significant co-occurrence tests as a product of species interactions.
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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) | 0,002 | 0,001 |
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 ».