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What is a Population A collection of potentially interacting organisms of one species within a defined geographic area. Figure 14.1. Estimates of Population Size Recall the Lincoln index from recitation: M/N = m/n M is the total number marked; m is the number marked in the sample;
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What is a Population A collection of potentially interacting organisms of one species within a defined geographic area.
Estimates of Population Size Recall the Lincoln index from recitation: M/N = m/n M is the total number marked; m is the number marked in the sample; n is the sample size.
Elementary Postulates 1. Every living organism has arisen from at least one parent of the same kind. 2. In a finite space there is an upper limit to the number of finite beings that can occupy or utilize that space.
Populations grow by multiplication. A population increases in proportion to its size, in a manner analogous to a savings account earning interest on principal: at a 10% annual rate of increase: a population of 100 adds 10 individuals in 1 year a population of 1000 adds 100 individuals in 1 year allowed to grow unchecked, a population growing at a constant rate would rapidly climb toward infinity
Geometric population growth Usually think of animals increasing by distinct generations. N(1) = N(0) + B – D + I – E N(1) = N(0)R N(2) = N(0)RR N(t) = N(0)Rt
Critical parameter is = net replacement rate (Ro). N(t+1) = N(t) where:N(t + 1) = number of individuals after 1 time unit N(t) = initial population size = ratio of population at any time to that 1 time unit earlier, such that λ = N(t + 1)/N(t)
To calculate the growth of a population over many time intervals, we multiply the original population size by the geometric growth rate for the appropriate number of intervals t: N(t) = N(0) t For a population growing at a geometric rate of 50% per year ( = 1.50), an initial population of N(0) = 100 would grow to N(10) = N(0) 10 = 5,767 in 10 years.
Given No = 5000; N1 = 6000; What is N2 ? N2 = (6/5)2 x 5000 = 7200
Exponential Growth Generations overlap, usually not discrete generations. For convenience, most of our models are continuous.
Think about a complex model approximated by may term in a potentially infinite series. Then consider how many of these terms are needed for the simplest acceptable model. dN/dt = a + bN + cN2 + dN3 + .... From parenthood postulate, N = 0 ==> dN/dt = 0, therefore a = 0. Simplest model ===> dN/dt = bN, (or rN, where r is the intrinsic rate of increase.)
Solve equation: N = No e rt Alternative form: dN/dt = bN - dN = (b-d)N Rarely do b and d remain constant, but if well below what environment can support, then OK assumption. Each species has optimum environment with r = max
Human lice; r=.111/day How fast will a population that starts at 100 lice increase? (i.e., what is rate of increase of 100 lice?) dN/dt = rN = .111 x 100 = 11.1 lice/day
Human population in 1993 = 5,600,000,000 b = 26/1000, d = 9/1000 How fast was the population growing? dN/dt = rN = (.017)(5,600,000,000) = 95,200,000 (i.e., in excess of 1/3 US population per year)
Humans currently have b and d of 26 and 9 per 1000. How many years to double the population? N = No e rt = Nox2 2 = ert ln2/.017 = 40.77 yrs
1700-1800 Human population from 600,000,000 ==> 900,000,000. Calculate r. r = ln (N/No) / t = ln(9/6)/100 = .0040547
Logistic Growth There has to be a limit. Postulate 2. Therefore add a second parameter to equation. dN/dt = rN + cN2 call c = -r/K dN/dt = rN ((K-N)/K) Nt = K/[1+((K-No)/No)e-rt]
Optimal yield problem. dN/dt = rN - rN2/K d2N/dt2 = r - 2rN/K set = 0 N = K/2 If want maximum yield, should exercise continual cropping around N = K/2
Further Refinements of the Theory Third term to equation? More realism? Symmetry; No reason why the curve has to be a symmetric curve with maximal growth at N = K/2.
What if the population is too small? Is r still high under these conditions? Need to find each other to mate Need to keep up genetic diversity Need for various social systems to work
Examples of small population problems • Whales, Heath hens, Bachmann's warbler • dN/dt = rN[(K-N)/K][(N-m)/N]
Instantaneous response is not realistic. Need to introduce time lags into the system dN/dt = rNt[(K-Nt-T)/K]
Three time lag types Monotonic increase of decrease: 0 < rT < e-1 Oscillations damped: e-1 < rT < /2 Limit cycle: rT > /2
Finite difference equations and Chaos Nt+1 = aNt(1-Nt) Models populations with discrete, nonoverlapping generations, like many temperate zone insects. if 1<a<3, population settles to a steady state.if 3<a<3.57.., population settles into a stable cycle.if 3.57..<a<4, population apparently random or chaotic.if 4< a, N runs away to minus infinity.
This weird range of behaviors is generic to most difference equations that describe a population with a propensity to increase at low values and to decrease at high values. Similar behavior arises if there are many discrete but overlapping generations.