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BIS 2B Notes, Lecture #7

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by: India Rangel

BIS 2B Notes, Lecture #7 BIS 2B

India Rangel
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About this Document

These notes cover the last lecture before Midterm #1.
General Biology
Dr. M. Schwartz
Class Notes




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1 review
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"If India isn't already a tutor, they should be. Haven't had any of this stuff explained to me as clearly as this was. I appreciate the help!"
Jamir Durgan

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This 1 page Class Notes was uploaded by India Rangel on Monday January 18, 2016. The Class Notes belongs to BIS 2B at University of California - Davis taught by Dr. M. Schwartz in Fall 2015. Since its upload, it has received 46 views. For similar materials see General Biology in Biological Sciences at University of California - Davis.

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If India isn't already a tutor, they should be. Haven't had any of this stuff explained to me as clearly as this was. I appreciate the help!

-Jamir Durgan


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Date Created: 01/18/16
Lecture #7: Human Population (55)   Logistic growth model seen left): population growth equals the amount expected under  exponential growth multiplied by what fraction of the carrying capacity is left.  If N is very close to  K, then population growth will be slow, while if N is very small, growth will be nearly exponential.   These population models can be used to predict maximum sustained yield (fisheries), survivorship curves (game management),  reproductive value (breeding, game management), and population viability (conservation biology).  For example, fisheries  determine the maximum growth rate of a population by finding carrying capacity and dividing it by two.  Growth rate  decelerates to zero as N=K and 1-(N/K) approaches zero, but accelerates nearly exponentially as 1-(N-K) is small (density  dependence is weak).     Demography a​ study of the vital statistics (birth and death) within a population and how they vary with age.  A life table  organizes these statistics,x​representing survivorship ax​age-specific fecundity.  This information can also be shown  by survivorship curves.  There are three different curves: Type I, where most individuals survive to old age, Type II, which shows  a constant probability of dying, and Type III, where most newly born individuals die, but the survival rate of adults is high.  R0​is the product of survival and fecundity, summed over all age x​x​.  When 0​> 1, a population is growing, when  R0​< 1, a population is shrinking, and0​= 1, the size is constant (births = deaths).    Main Points:  1. A logistic growth model is limited by carrying capacity, while an exponential one is not.  2. Demography a ​ study of the vital statistics within a population and how they vary with age.    3. There are three types of survivorship curves: I, II, and III.   a. Type I shows a group that has low infant mortality, but survivorship rates gradually decline.   b. In Type II, mortality is constant.   c. Type III demonstrates the opposite of Type I, where there is high infant mortality, but survivorship rates  increase with age.      


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