Evaluation of Edge Effects and Recreation on Plant Composition and Species Richness and Diversity (Case Study: the Noor Forest Park- Mazandaran Province)
Notice bibliographique
Résumé
Background: It is essential to investigate the depth, strength, and main mechanisms of edge effects and recreational activities to preserve species diversity in forest ecosystems.This study aimed to evaluate edge effects and recreation on woody and herb species composition, richness, and diversity in Hyrcanian broad-leaved forests.Methods: Two treatments (control and recreational regions) were determined in the Noor forest park, Mazandaran Province, to achieve research objectives.Five transects from the edge to the forest interior were established in each region.Measurements of herb and tree layers were collected at -5 (out of the forest stand), 0 (forest edge), 25, 50, 100, 150, 200, and 300 m along each transect.In total, 45 sample points were assigned to each treatment.To collect data on tree and shrub canopy cover, two rectangular sample plots of 200 m 2 (20 × 10 m) were laid out perpendicular to the transect on the left and right sides at each point.The count, diameter at breast height (> 5 cm), height, and canopy cover percentage were the variables measured in the identified tree and shrub species.For sampling herbaceous species, 10 one-m 2 (1×1 m) subplots, with five subplots on each of the right and left sides spaced one m apart, were determined at each sampling point.The type and abundance of herbaceous species were recorded.Using a light sensor device (model Lycor 250), the amount of light entering the forest floor at a height of > 1 m above the ground surface was recorded at each sampling point.The species richness and diversity of tree and herbaceous strata in the sample plots were evaluated using the total number of species present in each sample plot, the rarefaction method, Shannon-Wiener species evenness, and species diversity indices.The SHE method was used to determine the contribution of species richness and evenness to the measurement of species diversity.After calculating species diversity indices, GLM analysis and the Tukey test were used to compare means between treatments.The magnitude of edge influence (MEI) and DEI for all were calculated for species diversity indices and environmental variables.DEI for each variable was calculated using the randomization test of edge influence (RTEI).Data were analyzed with R software version 4.3.1.Results: The light near the edge was more than the interior in the study areas, and the edge positively affected the amount of light.Based on the results of the Rarefaction method and overlap of confidence intervals of the curves related to the study areas, no species richness differentiation was observed between the control and recreational areas.However, the nonoverlap of the tree diagrams reveals the highest and the lowest tree species richness in the recreational and control areas, respectively.DEI values of light were -5, 0, 10, and 50 m in the control forest and -5 and 0 in the recreational area.The number of trees per hectare in distances from 10 to 150 m and the volume and basal area per hectare at a distance of 10 m were higher in the control area than in the recreational area.In general, a positive effect of the edge was observed on the species diversity indices of herb and tree layers.The results for the comparison of species diversity indices between the control and recreational areas showed that species richness and diversity of the herb layer were higher in the distances of 10-300 m of recreational
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».