The Concepts of Spatial Patterns of Plants and Abiotic variables using Sida cordifolia as a model organism

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Lab ReportAbstractThe soil solar voltage and soil depth showcased a negative correlation respectively according to the quantitate analysis. Quantitative analysis of solar voltage and soil depth was gathered using solar panels and flagged stakes respectively. S. cordifolia was determined to exhibit clumped distribution (σ2).Also, the types of dispersion pattern S. cordifolia can be exhibited to show whether the abiotic factors tested in this experiment contribute to their dispersion patterns by the statistical analysis (Vaz et al., 31).IntroductionThe report explored the concepts of spatial patterns of plants and abiotic variables using Sida cordifolia as a model organism. Density and dispersion were the spatial parameters measured. Density is the number of individuals per unit (e.g. Number per m2) while dispersion shows how the individuals are arranged in space(Deng et al., 8603).Solar voltage and soil depth are the non-geographical data concerns. Shallow soils tend to host the weakest plants competitors with which density is the function of it. Soil depth influence water transpiration in plants that is how much water is taken in by the plant and then expelled from leaves. Variations in total water available to plants are determined by the variation of in soil depth (Cruz et al., 310).Other studies of flat fields, which are topographically more similar to our habit study show that soil moisture depletion was related to plant density and root depth(Esson, Tanko, Adebola, and Musa, 9)Moss abundance was positively associated with water content, and while the plant studied in our lab is vascular unlike mosses and lichens in arctic conditions. Other studies shows collaborate the correlation across all plants. Photosynthesis is mainly driven by the sun but its variation can directly affect growth through resource availability or indirectly by changing the behaviors or success of herbivory predator (Heil, 43).The main objective of the lab was to learn about the clusters techniques and explicit spatial patterning and random sampling then subsequently using the information to draw conclusions on the abiotic factors that affect spatial patterns for Sida cordifolia. Looking into the solar voltage and soil depth at random samples, an accurate assessment of the effects of the variables on dispersal and density may be attained (Rosell, and Sanz, 132). Learning about the biological and geographical data was the purpose of this lab test.The hypothesis that Sida cordifolia would show a random dispersion and the soil depth would be correlated with density while there would not be any correlation in the case of solar voltage. Our prediction was that soil depth, and thus soil moisture, would be consistent across our samples leading to fewer pockets of resources and consequently a random dispersion pattern with that Gifford Arboretum’s topography was extremely flat. Most competition would be exhibited by higher soils depth but on the other hand, shallow soils would have the lowest resources available and would exhibit low densities of the weakest competitors. Solar voltage correlated with density due to plant vigor hypothesis that states any benefits from the increased sunlight would be negated by high predation (Mitchell et al., 43).DiscussionS.cordifolia had clumped distribution and this suggested limited dispersal abilities or patchily distributed food resources and this leads us to reject our hypothesis that distribution is random (Scalon, 599). Also, a negative correlation between soil depth and the number of individuals was determined by Sida cordifolia. Due to flow in our thinking, we had hypothesized a positive correlation but the results showed otherwise. Soil moisture content and transpiration rates should be less heavily emphasized in the study of S. cordifolia as Coral Gables is a sub-tropical climate with high precipitation and high humidity (Kumar, Rajwar, Sharma, and Soil…, 19).A more individualistic study could measure S. cordifolia size and dispersion patterns within samples. Other factors limiting the experiment are testing the landscape at only one particular time of day and at only one particular time of year. Light levels through effects of light on energy resource availability can directly impact plant growth can affect plant growth (Rejmanek, 66). Light levels can vary depending on the point in time data is being taken.Recording data in the fall, spring, summer or winter will result in different solar voltage data as well as soil depth depending on the climates of each season. In our lab, S. cordifolia abundance showed no correlation with solar voltage, supporting our hypothesis that the positive photosynthetic effects of increased sunlight may be compounded by other variables that we did not measure, i.e. predation (Schulz, and Michael, 634) Our objectives of learning about cluster techniques, implicit and explicit spatial patterning and random sampling were met in the way we gathered our data and the figures that organized them.Work CitedVaz, Aline BM, et al. “Fungal endophyte β-diversity associated with Myrtaceae species in an Andean Patagonian forest (Argentina) and an Atlantic forest (Brazil).” Fungal Ecology 8 (2014): 28-36.Kumar, M., Rajwar, Govind S., Sharma, C.M. 2008. Plant and Soil Diversities in a Sub Tropical Forest of the Garhwal Himalaya. Ghana Journal of Forestry 19A. E. Esson, D. Tanko, M. I. Adebola B. U. Shuaibu, and D. D. Musa. 2015. Density and Diversity of Woody Plants on Kufena Inselberg, Zaria, Nigeria. African Journal of Geo-Science Research 3: 08-11.Heil, Martin. “Indirect defence via tritrophic interactions.” New Phytologist 178.1 (2008): 41-61.Schulz, Kurt E., and Michael S. Adams. “Effect of canopy gap light environment on evaporative load and stomatal conductance in the temperate forest understory herb Aster macrophyllus (Asteraceae).” American journal of botany (1995): 630-637.Mitchell, Cary A., et al. “Light-emitting diodes in horticulture.” Horticultural reviews 43 (2015): 1-87.Deng, Jianming, Zuo, Wenyun, Wang, Zhiqiang, Fan, Zhexuan, Ji, Mingfei, Wang, Genxuan, Ran, Jinzhi, Zhao, Changming, Liu, Jianquan, Niklas, Karl J., Hammond, Sean T., Brown, James H. 2012. Insights into plant size-density relationships from models and agricultural crops. Proceedings of the National Academy of Sciences of the United States, 109(22), 8600-8605.Cruz, Wilton P., Sarmento, Renato A., Neto, Marçal P., Teodoro, Adenir V., Rodrigues, Diego M., Moraes, Gilberto J. 2 014. Population Dynamics of Aceodromus Convolvuli (Acari: Mesostigmata: Blattisociidae) on Spontaneous Plants Associated with Jatropha Curcas in Central Brazil. Experimental and Applied Acarology 64: 309-311Rejmanek, M., Huntley, Brian J., Le Roux, Johannes J., Richardson, David M. 2016. A Rapid Survey of the Invasive Plant Species in Western Angola. African Journal of Ecology 55: 56-69Scalon, João Domingos. “A combined test for randomness of spatial distribution of composite microstructures.” Matéria (Rio de Janeiro) 12.4 (2007): 597-601.Rosell, J. R., and R. Sanz. “A review of methods and applications of the geometric characterization of tree crops in agricultural activities.” Computers and Electronics in Agriculture 81 (2012): 124-141.

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