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Ecology Poster Presentation Template

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Ecology Poster Presentation

Transcript: by: Lauren and Sarah Population Immigration & Emigration Immigration and Emigration Immigration and Emigration are opposites. To immigrate is when individuals come into a new place from a different place. Emigration is when an individual leaves one place and goes to another. If a large number of individuals emigrate, they needs more individuals to immigrate to keep the population thriving in that specific area. These gazelles are immigrating to a new place These gazelles are immigrating to a new area Bob and friends are emigrating! They are leaving the flock. Bob Birth Rate and Death Rate Birth and Death Rate Birth and Death Rate are also opposites. Both are rates but one is about birth and the other about death. They both relate to population because if the death rate of a species is higher than the birth rate than the population will decrease. While when the birth rate is higher than the death rate the population will increase. Logistic and Exponential Population Growth and Carrying Capacity Logistic and Exponential Population Growth and Carrying Capacity Logistic Population Growth is when there's not enough resources for the population to thrive. In other words, it reaches its carrying capacity. Carrying capacity is the largest population an environment can handle. Exponential Population Growth is when a species grows greatly because of more than needed resources. Exponential and Logistic Population Growth are opposites. Graphs See carrying capacity on Logistic Growth Density-Independent Factor and Density-Dependent Factor Density-Independent and Density-Dependent factors are opposites of each other. The population size does not matter to density-independent factors. But it does matter when there are density-dependent factors. Density-Independent Factor and Density-Dependent Factor A tornado is an example of an independent factor Independent Example Disease is an example of density-dependent factor. Dependent Example

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Transcript: Survivorship Curve Before and After Influenza Vaccine Cady Houghton Procedure Background Experimental Design Methods Research Question 1) A data base of cemeteries was looked up to find death dates and information. 2) From the data base information was put into a google drive document. 3) 40 people dying before the 1945 influenza vaccine were put into one chart showing the ages of death. 4) 40 people dying after the 1945 influenza vaccine were put into another chart also showing ages of death. 5) Both charts of groups of people were converted into a life history table. 6) From each life history table the data was put into another chart to make two survivorship curve charts. 7) The survivorship curve charts were put into one survivorship curve chart to show the difference in curves. A laptop was used for this lab. If the influenza vaccine works then if we compare the survivorship curve of before and after the vaccine it will show people living longer because the vaccine will be decreasing the death rate. The independent variable in this experiment is the influenza vaccine. The dependent variable is the death rate in this experiment. The exponential variables in this experiment are the other disease epidemics during the time period the data was chosen from. The control is the survivorship curve before the influenza vaccine and the experimental is the survivorship curve after the influenza vaccine. The survivorship curves of before and after the influenza vaccine was created are both Type I survivorship curves. They also have the caracteristics for K-selected species. While they are both Type I and K-selected species, the survivorship curve after the influenza vaccine was created had a decrease in death rates between 20 and 70 years of age of about .5 #/1,000 surviving. Then, towards the 75 and 90 years range, the #/1000 surviving leveled out and became extremely similar in curves. Did people die younger before the influenza vaccine was created? In comparing survivorship curves a Log10 scale is used for its ability to show you greater numbers in a more readable way and to show same mortality with the same curve when two scales are put together. In a Log10 scale there are three Types of information. The first, Type 1, are mainly humans. Type I survivorship curves are characterized by high survival in early and middle life, followed by a rapid decline in survivorship in later life. Type 2 is mainly birds and Type 3 is mainly reptiles. Within these Types are two categories of species, K-Selected, or Equilibrium, species and R-Selected, or Opportunist, species. K-Selected species usually have stable resources and a stable environment, and this is usually what humans are categorized as. R-Selected species are usually whose population size tends to fluctuate greatly in reaction to variations in the environment. Comparing the survivorship curves, defined as a graph showing the number or proportion of individuals surviving at each age for a given species or group cohort, before and after the influenza vaccine was created will show if the vaccine helped increase the population. Before the 1940s when the influenza vaccine was created, the epidemic was particularly deadly in US Army training camps, where the death rate was as high as 80% in some camps. Death rate is usually calculated as the number of deaths per one thousand people per year. usually calculated as the number of deaths per one thousand people per year. The US military was getting hit with influenza the most forcing them to developed the first approved vaccine for influenza, which was used in the Second World War. Studying this specific survivorship curve will also help to tell if their were more survival rates in the US military during the time. Results With the original research question in mind, before the influenza vaccine was created people were dying younger. This question was answered by the survivorship curve graph comparing the curves before and after the influenza vaccine. As the graph shows between 20 and 70 years of age there is a significant difference in curves, meaning that the death rate before the vaccine in that time period was increased more than after the vaccine was created. From that specific area of the graph it can be concluded that people were dying younger before the influenza vaccine was created, thus the hypothesis was correct. For future study it would be interesting to see the survivorship curve in the US military before and after the vaccine not just the population in general. Because the US military was the one who invented it because they were the one being impacted with the death rate from the influenza epidemic the most, the survivorship curve would have a chance of being more predominant. Graph Clear Digital Media, Inc. (1997-2011). Cemetery Records Online. October 1, 2013, www.interment.net Genealogical Gleanings. (1997-2004). Plagues and Diseases. October 3, 2013, genealogical-gleanings.com/Plauges.htm Introduction

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