Author Topic: Estimating the Structure of Population  (Read 616 times)

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Estimating the Structure of Population
« on: August 03, 2008, 10:15:14 AM »
Estimating the Structure of Population

Allegheny College Department of Biology and Environmental Microbiology

Written by: A. Lorenzo Lucino Jr, B.S, B.A


Introduction

   The study of ecology is rather intrinsic in that you have to understand the different aspects of population structure of different plants in different environments. In particular would be is to understand how hard it is to make estimations on population size confidently. There are three components of population structure, which are distribution, density and dispersion. Dispersion answers how each member of a population is spatially located with other members; density will give us how many members of the population are observed per unit area. Distribution will answer where the following organisms are found. This lab will focus on estimating the density and dispersion of a select species of herbaceous and woody plants on the forest floor of Bousson, which includes Red Maples, Partridgeberry and Canada Mayflower. In particular we will examine these differences in two environments, specifically in a conifer plot and a deciduous plot. We will test three hypotheses; the first one is that there are differences in density between populations of the same specie under the different canopy types. The second one is that all species are distributed randomly. The third is that the confidence we have in our population estimates is related to the spatial distribution of the individuals in the population, meaning that estimates with low uncertainty may be easily acquired if a population shows a particular distribution.

Methods
   We will find the estimate of population density of three particular plants, which are Red Maples (Acer Rubrum), Partridgeberry (Mitchella repens), and Canada Mayflowers (Maianthermum canadense) in two areas of Bousson Forest habitats. One plot will be an area that is primarily deciduous and the other will be primarily coniferous. To save time we will mark off 50m x 50m of forest, in which one will be in a different canopy type. This will enable us to find an estimation for population densities, calculation of confidence we have in these estimates and finally finding the dispersion pattern. To supplement this, we will be using plastic quadrats, in each section of the Cartesian XY coordinates; the dimension of a hoola hoop plastic quadrat was 65 Cm in diameter, and the same time our study site was 50m x 50m or precisely 2500 meters. When using quadrats, we will count the number of individual plants within a number of quadrats and when everything is completely tallied, we will be required to obtain the species’ mean, standard deviation, standard error, variance and count for the two canopy plots.  Our confidence in this lab is the ‘best guess’ estimate of the size of the population; we need to establish an interval that bounds the true population size with 95% confidence and to do so we will use the Confidence Interval equation:
CI = +- t Sx and t is given to us as being 1.96.
Another equation which will be quite useful to us would be the Variance equation:
Variance = ( ?X^2 – (?X)^2 / n) / (n-1)

Discussion

   We look back at the three inferred hypotheses, which was that there are differences in density between populations of the same species under the different canopy types. The second stated that all species are distributed randomly and the third stated that the confidence we have in our population estimates is related to the spatial distribution of the individuals in the population of our two plots.
Our first hypothesis was proven wrong in that there really wasn’t that much differences in population densities in the particular species population in the two plots; as figure 1 provides that the error bars even point out overlaps between all three species ranging from red maples, partridgeberry and Canada mayflower. The second hypothesis was addressed by both figures 2 and 3 in that it showed little evidences of randomness as there indeed was a linear relationship for both figures. Figure 3 showed some kind of randomness between variance in mean, however in figure 2, the individual samples showed ‘on the dot’ precision. The third hypothesis is answered by the graph on figure 4, which showed a linear composite of the confidence interval vs. clumpyness (variance/mean); which illustrates how confidence we have in the population estimates is related to the spatial distribution of the individuals in the population; meaning that estimates with low uncertainty are better obtained if the population shows distribution.
The field lab was successful in that it allowed us to entirely study, albeit using a rather unreliable method, and identify density, distribution and dispersion.


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