Evaluation of Edge Effects and Recreation on Plant Composition and Species Richness and Diversity (Case Study: the Noor Forest Park- Mazandaran Province)
Bibliographic record
Abstract
Extended Abstract 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 m2 (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-m2 (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 non-overlap 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 areas than in the control area. In the tree layer, higher species richness was recorded in edges of 10-200 m of the control area than in the recreational area. For the evenness index, DEI values were 10 and 0 m in recreational and control areas, respectively. Moreover, DEI values of herb species richness and diversity were 0 and -5 m in control forests. Conclusion: Based on the results, the forest edge is considered an essential part of the forest landscape structure that significantly affects the structure, functions, and species diversity. Regarding the tourism and recreation industry as one of the largest industries in the world and its increasing importance, it is necessary to understand and improve the knowledge about its effects and the severity of its impact (due to the construction of roads) on different variables for better management and biodiversity conservation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".