Resumo de Effect Of Sampling Intensity In Vegetation Analysis A Forest Patch, Mt.makiling, Laguna, Philippines

The study was conducted in a forest patch in Mt. Makiling. It aimed to determine the effect of varying sampling intensity in vegetation analysis in general. It also aimed to determine the most statistically acceptable sampling intensity as well as the method that determines the minimum sampling intensity that captures the total picture of vegetation. All in all, there were 80 10 x 10m quadrats established in the study area. However, the census of seedlings and saplings were made possible to 77 quadrats, since three of the quadrats are dominated by Paper mulberry (Broussonetia payrifera). The seedlings and saplings encountered were enumerated, counted and mapped its location on the ground in a ruled square paper. Importance values of each species were obtained based on the computed relative density and frequency of each species. Effect of varying sampling intensities to 100% vegetation analysis was tested using a simple random sampling technique with replacement. A total of four replicates for each sampling schedule (i.e., 10%, 20%, 30%, 40% and 50%) were used. The values obtained using Sorensen?s Similarity Index Formula was presented in an M x N matrix table after the pair-wise comparison of each experimental unit. The table was analyzed using the Principal Component Analysis, a data reduction technique. The result was a three-dimensional graph presenting the 21 points arranged into three components. The three-dimensional graph was further simplified to a two-dimensional graph were the inter-plot distances between the sampling intensities were easily determined. The average of each four replicates per sampling intensity was computed and also presented into a two-dimensional graph. The graph clearly showed the relative distance of each sampling intensity with respect to the 100% sample space. Based on the Principal Component Analysis, the 30%, 40% and 50% sampling intensity cluster around the 100% sample space. However, 40% sampling intensity tends to be the most effective sampling schedule that can save money and time when applied on the field. The 30% sampling intensity can also be used when the study needs only a rough estimated of the whole population. The 50% sampling intensity is very much precise but less accurate, thus, will only incur more time and money when applied in the field. It is further recommended to increase the number of replicates for each sampling intensity to acquire greater precision and accuracy of any sampling study.

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