Sampling vegetation is important for managing wildlife. Vegetation provides species

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1 Using Basic Forestry Techniques to Measure Vegetation STACEY PECEN 1, The Pennsylvania State University, 107 Forest Resources Building, University Park, PA 16802, USA ABSTRACT Sampling vegetation is important for managing wildlife. Vegetation provides species with food, cover, and shelter and can give insight into what areas are suitable habitats. It is also possible to study the relationship between anthropogenic effects such as logging and populations of animals. This was seen in a study conducted in Borneo using small mammals. When the habitat didn't include primary and secondary forests, the animals did not thrive (Bernard et al. 2009). The objective of this study was to use basic forestry methods to determine diameter of trees at breast height, average tree heights, and find basal area within a study site outside the Forest Resources Building at the Pennsylvania State University in State College, PA. The mean diameter of the twenty trees studied was found to be / Tree height determinations were calculated for each of the five group members, but the mean was / ft. These show that the trees in our sample were relatively small or young trees. The study site was found to have a basal area of / square meters per hectare. Future implications could involve the use of more technological methods, such as aerial photography or satellite imagery. KEYWORDS: measuring vegetation, DBH, basal area, tree height, wildlife habitat 1: sep5038@psu.edu

2 INTRODUCTION Measuring vegetation is an important tool in wildlife and fisheries management. Wildlife populations need three essential habitat requirements to survive: food, cover, and water (Braun 2005). The spatial arrangement of vegetation is important because if the animal doesn't have all of its requirements nearby, the result could be a poor population or no population of animals at all (Avery and Burkhart 2002). Being able to evaluate and assess vegetation is vital to determine habitat suitability for any species. There are several relevant examples of studies already conducted which use these tactics for sampling vegetation. In a study on 29 temporary ponds in Portugal, researchers found that 168 species were present in 15 plant communities. They discovered that in vernal pools, which are protected as priority habitat, there are two indicator species present (Isoetes and Eryngium corniculatum) (Pinto-Cruz et al. 2009). It is important to know what vegetation is present in these pools because they are declining due to increased agriculture and tourism in the area. In another study, Graham and Knight 2004 compare cliff vegetation in Jefferson County, Colorado. They tested for species richness and diversity at large, medium, and small cliff sites, as well as a non-cliff site. They found that the diversity of a species did not increase with higher cliff areas. They also observed that while most of the same species could be found in cliff and non-cliff sites, 13 species were only observed on cliff faces (Graham and Knight 2004). Similarly, a study conducted on unpolluted watersheds in Chile found that the composition of species changed with an increase in slope. The researchers found that while there was a Fitzroya forest at the base, there was a Pilgerodendron-Tepualia forest in the mid-slope and a community of short trees and mosses at the ridgetop (Battles et al. 2002). These factors would be important to know where habitat types are located and where an optimal habitat could be for a particular species.

3 Assessing vegetation for habitat quality in the midst of disturbances is another implication that is used. An example of this is loss of forest due to logging. In Borneo, small mammals were studied to find the consequences of logging on their habitat. It was found that it was important to "incorporate the size and overall surrounding environment of a forest into forest management concepts, and although not equivalent to areas of primary forest, old regenerating secondary forest needs to be considered as an important component for the preservation of small mammal species diversity" (Bernard et al. 2009). Other factors can affect wildlife and their habitat. For example, the endangered black crested gibbon (Nomascus concolor) is experiencing habitat loss from fragmentation. In order to thrive, they use secondary corridors near their preferred habitat for dispersal (Peng-Fei et al. 2009). It is important not only to survey the vegetation in the study area, but also the surrounding area for any factors that may influence a population. In order to be able to measure vegetation for any of these purposes, some basic field practices are necessary to learn. The objectives of this lab are to gain an understanding of all tools used including clinometers, logger's and diameter tapes, and keyhole prisms. These tools will be used in the field to determine tree heights and basal area. METHODS Tools, including logger's tape and diameter tape were gathered. Each side of the logger's tape was checked in order to become familiarized with the metric and English units of measurement and was found to be five meters in length. Diameter tape was used, and the measurement of the diameter of 20 randomly chosen trees in the study site was obtained. The diameter was found by wrapping the tape fully around the trunk at the Diameter of Breast Height (DBH) of 4.5 ft.

4 The logger technique of pacing was practiced by placing lineal tape on the ground at the distance of 50 m. Each pace was defined as two steps, and five trials were repeated to account for variability. A standard Suunto clinometer was used at a distance of 10 m from each tree (except with large trees where 20 m was used) to determine the percent slope and tree height. The clinometer was held at eye level at the distance of 10 or 20 m. The clinometer was held so the degrees side faced left and the percent slope faced the right. The accuracy was taken into account by making sure the clinometer did not have a reading greater than 100%. A reading was obtained for each tree by each of five group members. Tree height determination was found using DBH, percent slope, and the distance from the tree. It was computed using the formula: Clinometer reading (distance from tree) + height to eye level = tree height A prism was used for the point-centered technique of counting trees based on size. A BAF factor of 5 was chosen because of the smaller trees found in the study site. Ten random points were chosen and the prism was used at breast height by rotating 360 around the prism. Each tree that fit inside the prism at the BAF of 5 was counted. Each of the five group members sampled ten points and the basal area was calculated by multiplying the number of trees taillied and the BAF. RESULTS The DBH for each of twenty trees was measured. The mean of the diameters was found to be / , with a minimum diameter of 3.7 and maximum diameter 67.5 observed within the study site (Table 1).

5 Tree Height (in feet) Table 1. Data of the Diameter at Breast Height of trees observed in Laboratory 2 of WFS 310 on September 2, Min Max Mean +/- St. dev. DBH / The mean pace length was found in Table 2 to be / meters per pace. The same table shows that there was a minimum pace length of meters per pace and a maximum meters per pace. Table 2. Pace lengths determined using five trials over 50 meters. Min Max Mean +/- St dev Pace length / Clinometers, along with the DBH and a determined distance away from each tree gave the average tree heights in feet for each of the five group members (Figure 1). The mean tree height for Dan was calculated to be / ft. Wyatt's average tree height was / ft, while Eric reported a mean of / ft. Nate had an average height of / ft, and Stacey calculated a mean of / ft. Mean Tree Height Determinations of 20 Trees by Each of the Five Group Members Mean Tree Heights 5 0 Dan Wyatt Eric Nate Group Members Figure 1. This graph depicts the mean tree heights reported in feet for each of the group members in Laboratory 2 of WFS310. Stacey

6 Average Basal Area (square m/ha) The study site was found to have a basal area of / square meters per hectare. Each of the group members individual means and standard deviations can be observed in Figure 2 below. It can be seen that Wyatt had the highest basal area of / m²/hectare, while Stacey had the lowest ( / m²/hectare). Average Basal Area (in square meters/hectare) Based on 10 Randomly Selected Points Dan Wyatt Eric Nate Stacey Group Members Basal Area Figure 2. Average basal area in square meters per hectare based each of the five group member's results using ten randomly selected points in the study area. DISCUSSION From measuring the diameters at breast height, the mean of / (as seen in Table 1) showed that an average of the trees are relatively small and there is some variability present. The bigger trees had bigger diameters, as was the case with one of the trees studied. The Acer had a diameter of 67.5, which is much larger than the mean of The large difference in size was accounted for in the standard deviation from the mean. Pace lengths are important tools for foresters. If the pace length is known and practiced, a forester will know the distance that was covered without the aid of an assistant or measuring tape. As can be seen from Table 2, the pace length for each of the five trials stayed relatively constant. This is due to the comfortable, natural gait that was used. The mean of means that for each pace of two steps, (+/ ) meters is covered. Tree heights were determined with the use of DBH and clinometers. For the Acer that

7 had a diameter of 67.5, the distance from the tree had to be increased from 10 meters to 20 meters. This ensured a reading of less than 100% and thus better accuracy with the clinometer. By referring to Figure 1, it can be seen that all five group members had similar averages and standard deviations. The standard deviations of roughly 11 ft show the variability in the sample. All five members attained a mean between 17 and 18 ft. In addition with the DBH that was measured, these results show that the stand consists of smaller individuals. A possible source of error in this method could have been inaccuracies while reading the clinometer. The basal area in Figure 2 showed more variability among the five group members. Wyatt observed a higher area while Stacey observed the lowest. The discrepancies could occur because of where the random sampled points were located. Where one position may have tallied three trees, another position may not have counted any. The basal area factor (BAF) of 5 that was used was justified because of the small trees present in the area. In order to get the targeted trees that should be tallied, a smaller BAF than the typical 10 was necessary. These techniques can be used for further studies and management implications. An example is using vegetation sampling to find suitable habitat for endangered species, as was done with Nomascus concolor in China (Peng-Fei et al. 2009). Potvin and Dutilleul (2009) looked at vegetation plots in Panama and observed that mixed species plots grew more than monocultures. They also observed that the size of neighboring trees proved to be the largest variation with regard to tree diameter and height of an individual. It is noted, however, that the majority of these experiments have been conducted with grassland communities (Potvin and Dutilleul 2009). Therefore, more studies should be conducted with regard to forestry. In addition, vegetation management also can involve satellite imagery and aerial photography. Satellite imagery covers a broad area, while aerial photography is more detailed but works in

8 smaller areas (Glenn and Ripple 2004). Plumb (1991) notes that ground-based classification is not often used, and "most vegetation maps are constructed manually or processed digitally from remotely sensed data." Whether measuring vegetation from the ground or sky it is still necessary to monitor not only for our own use, but for use by wildlife populations. LITERATURE CITED Avery, T. E., and H. E. Burkhart Forest Measurements. Fifth edition. McGraw Hill, New York, New York, USA. Battles, J. J., J. J. Armesto, D. R. Vann, D. J. Zarin, J. C. Aravena, C. Perez, and A. H. Johnson Vegetation composition, structure, and biomass of two unpolluted watersheds in the Cordillera de Piuchue, Chiloe Island, Chile. Plant Ecology 158: Bernard, H., J. Fjeldsa, M. Mohamed A case study on the effects of disturbance and conversion of tropical lowland rainforest on the non-volant small mammals in north Borneo: management implications. Mammal Study 34: Braun, C. E Techniques for Wildlife Investigations and Management. Sixth edition. The Wildlife Society, Bethesda, MD, USA. Glenn, E. M., and W. J. Ripple On using digital maps to assess wildlife habitat. Wildlife Society Bulletin 32: Graham, L., and R. L. Knight Multi-scale comparisons of cliff vegetation in Colorado. Plant Ecology 170: Peng-Fei, F., J. Xue-Long, and T. Chang-Cheng The critically endangered black crested gibbon Nomascus concolor on Wuliang Mountain, Yunnan, China: the role of forest types in the species' conservation. Oryx 43: Pinto-Cruz, C., J. A. Molina, M. Barbour, V. Silva, and M. D. Espirito-Santo Plant communities as a tool in temporary ponds conservation in SW Portugal. Hydrobiologia 634: Plumb, G. A Assessing vegetation types of Big Bend National Park, Texas for imagebased mapping. Vegetatio 94: Potvin, C., and P. Dutilleul Neighborhood effects and size-asymmetric competition in a tree plantation varying in diversity. Ecology 90: