Introduction to Population Biology – BDC222 Prof Mark J Gibbons: Rm 4.102, Core 2, Life Sciences...

Introduction to Population Biology – BDC222

Prof Mark J Gibbons: Rm 4.102, Core 2, Life Sciences Building, UWCTel 021 959 2475: Email [email protected]

The Basics

Populations rarely have a constant size

Intrinsic Factors

BIRTH

IMMIGRATION

DEATH

EMIGRATION

Extrinsic factors

Predation

Weather

Nt+1 = Nt + B + D + E + I

Populations grow IF (B + I) > (D + E)

Populations shrink IF (D + E) > (B + I)

Diagrammatic Life-Tables….

What is a population?

Assume E = I

AdultsNt

AdultsNt+1

SeedsNt.f

SeedlingsNt.f.g

f

g

e

p

BIR

TH

SU

RV

IVA

L

Nt+1 = (Nt.p) + (Nt.f.g.e)

AdultsM F2.3 2.3

AdultsM F

2.5 2.5

Pods18.25

Eggs200.75

Instar I15.86

Instar II11.42

Instar III8.91

Instar IV6.77

P=0

7.3

11

0.079

0.72

0.78

0.76

0.69

t = 0

t = 1

t = 0

t = 1

AdultsM F5 5

AdultsM F4 4

Eggs50

1 mo Nestlings42

3 mo Fledglings29.8

10

0.71

0.1

0.5

0.84

Overlapping Generations: Discrete Breeding

a0 a1 a2 a3 an t1

a0 a1 a2 a3 an t3

a0 a1 a2 a3 an t2

p01 p12

p23

Birth

NB: Different age groups have different probabilities of surviving from one time interval to the next, and different

age groups produce different numbers of offspring

t1

t2

p01 p12

p23

Birth

Birth

NB – ALL Adults or Females?

Conventional Life-Tables Best studied from Cohort – Define

Stage (x) ax lx dx qx px log10ax log10lx log10ax - log10ax+1 Fx mx lxmx

Eggs (0) 44000 1.0000 0.9201 0.9201 0.0799 4.6435 0 1.0975 0 0 0Instar I (1) 3515 0.0799 0.0224 0.2805 0.7195 3.5459 -1.098 0.1430 0 0 0Instar II (2) 2529 0.0575 0.0138 0.2400 0.7600 3.4029 -1.241 0.1192 0 0 0Instar III (3) 1922 0.0437 0.0105 0.2399 0.7601 3.2838 -1.36 0.1191 0 0 0Instar IV (4) 1461 0.0332 0.0037 0.1102 0.8898 3.1647 -1.479 0.0507 0 0 0Adults V (5) 1300 0.0295 -- -- -- 3.1139 -1.53 -- 22617 17.3977 0.51

Subscript x refers to age/stage class

a refers to actual numbers counted – case specific



l refers to proportions wrt t0 – allows comparisons between cases: lx = ax / a0











d refers to standardised mortality, calculated as lx – lx+1: data can be summed














q age specific mortality, calculated as dx / lx: data cannot be summed















p age specific survivorship, calculated as 1 - qx (or ax+1 / ax): cannot be summed




















k killing power – reflects stage specific mortality and can be summed



K





















K

F Total number offspring per age/stage class























K




m mean number offspring per individual a, Fx / ax























K




m mean number offspring per individual a, Fx / ax





lm number of offspring per original individual

REAL DATA

Σ lxmx = R0 = ΣFx / a0 = Basic Reproductive rate

R0 = mean number of offspring produced per original individual by the end of the

cohort

It indicates the mean number of offspring produced (on average) by an individual

over the course of its life, AND, in the case of species with non-overlapping

generations, it is also the multiplication factor that converts an original

population size into a new population size – ONE GENERATION later



Σ lxmx = R0 = 0.51

N0 . R0 = 44000 . 0.51 = 22400 = NT

Generation time

Fundamental Reproductive Rate (R) = Nt+1 / Nt

IF Nt = 10, Nt+1 = 20: R = 20 / 10 = 2

Populations will increase in size if R >1Populations will decrease in size if R < 1

Populations will remain the same size if R = 1

R combines birth of new individuals with the survival of existing individuals

Population size at t+1 = Nt.RPopulation size at t+2 = Nt.R.RPopulation size at t+3 = Nt.R.R.R

Nt = N0.Rt

R0 cannot be used to predict population sizes at one time interval to another, if

the populations have overlapping generations – to do that we need to calculate R

Nt = N0.Rt Overlapping generations

NT = N0.R0Non-overlapping generations

NT = N0.RTIF t = T, then

R0 = RT

lnR0 = T.lnR

Can now link R0 and R:

T = Σxlxmx / R0

T can be calculated from the cohort life tables – already know R0

X = age class

How do you calculate R from a life-table? - Indirectly

lnR = r = lnR0 / T = intrinsic rate of natural increase

ln = natural log – Calculated in MSExcel as =LN(cell address)

T = Σxlxmx / R0

T can be calculated from the cohort life tables – already know R0

X = age class

lnR = r = lnR0 / T = intrinsic rate of natural increase

ln = natural log – Calculated in MSExcel as =LN(cell address)

x a l d q p F m lm xlm0.00 1000000.00 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.001.00 62.00 0.00 0.00 0.45 0.55 285200.00 4600.00 0.29 0.292.00 34.00 0.00 0.00 0.41 0.59 295800.00 8700.00 0.30 0.593.00 20.00 0.00 0.00 0.23 0.78 232000.00 11600.00 0.23 0.704.00 15.50 0.00 0.00 0.29 0.71 196850.00 12700.00 0.20 0.795.00 11.00 0.00 0.00 0.41 0.59 139700.00 12700.00 0.14 0.706.00 6.50 0.00 0.00 0.69 0.31 82550.00 12700.00 0.08 0.507.00 2.00 0.00 0.00 0.00 1.00 25400.00 12700.00 0.03 0.188.00 2.00 0.00 0.00 1.00 0.00 25400.00 12700.00 0.03 0.209.00 0.00 0.00 -- -- -- -- -- -- --

Totals 1.2829 3.935

T 3.06726947

R calculated from r by raising e (base of natural logs) to power r:

=exp(r)

Other statistics that you can calculate from basic life tables

Life Expectancy – average length of time that an individual of age x can expect to live

L average number of surviving individuals in consecutive stage/age classes: (ax + ax+1) / 2

x a l d q p F m lm xlm L T e0 8760 1.000 0.760 0.760 0.240 0 0 0 0 5431.0 6991.0 0.7981 2102 0.240 0.182 0.758 0.242 42040 20 4.799 4.799 1305.5 1560.0 0.7422 509 0.058 0.058 1.000 0.000 12216 24 1.395 2.789 254.5 254.5 0.5003 0 0.000

R0 6.194T 1.225r 1.488R 4.430


R0 6.194T 1.225r 1.488R 4.430

T cumulative L: Σ Lxi

n


R0 6.194T 1.225r 1.488R 4.430

e life expectancy: Tx / ax

NB. Units of e must be the same as those of x

Thus if x is measured in intervals of 3 months, then e must be multiplied by 3 to give life expectancy in terms of months

A note on finite and instantaneous rates

The values of p, q hitherto collected are FINITE rates: units of time those of x expressed in the life-tables (months, days, three-months etc)

They have limited value in comparisons unless same units used

[Adjusted FINITE] = [Observed FINITE] ts/to

Where ts = Standardised time interval (e.g. 30 days, 1 day, 365 days, 12 months etc)to = Observed time interval

To convert FINITE rates at one scale to (adjusted) finite rates at another:

e.g. convert annual survival (p) = 0.5, to monthly survival

Adjusted = Observed ts/to = 0.5 1/12 = 0.5 0.083 = 0.944

e.g. convert daily survival (p) = 0.99, to annual survival

Adjusted = Observed ts/to = 0.99 365/1 = 0.99 365 = 0.0255

INSTANTANEOUS MORTALITY rates = Loge (FINITE SURVIVAL rates)

ALWAYS negative

Finite Mortality Rate = 1 – Finite Survival rate: (q = 1 – p)

Finite Mortality Rate = 1.0 – e Instantaneous Mortality Rate

MUST SPECIFY TIME UNITS

E.G.IF FINITE SURVIVAL (p) = 0.35, then INSTANEAOUS MORTALITY (Z) = - 1.05

STATIC LIFE-TABLES

x (yrs) a l d q p1.00 129 1 0.12 0.12 0.882.00 114 0.88 0.01 0.01 0.993.00 113 0.88 0.25 0.28 0.724.00 81 0.63 0.02 0.04 0.965.00 78 0.60 0.15 0.24 0.766.00 59 0.46 -0.05 -0.10 1.107.00 65 0.50 0.08 0.15 0.858.00 55 0.43 0.23 0.55 0.459.00 25 0.19 0.12 0.64 0.3610.00 9 0.07 0.01 0.11 0.8911.00 8 0.06 0.01 0.13 0.8812.00 7 0.05 0.04 0.71 0.2913.00 2 0.02 0.01 0.50 0.5014.00 1 0.01 -0.02 -3.00 4.0015.00 4 0.03 0.02 0.50 0.5016.00 2 0.02 0.02 1 017.00 -- 0 0 -- --

RAW Data

x (yrs) a l d q p1 122 1 0.02 0.02 0.982 119 0.98 0.13 0.13 0.873 103 0.84 0.10 0.12 0.884 91 0.75 0.15 0.20 0.805 73 0.60 0.04 0.07 0.936 67 0.55 0.06 0.11 0.897 60 0.49 0.09 0.19 0.818 48 0.40 0.15 0.39 0.619 30 0.24 0.13 0.53 0.4710 14 0.12 0.05 0.43 0.5711 8 0.07 0.02 0.29 0.7112 6 0.05 0.02 0.41 0.5913 3 0.03 0.01 0.30 0.7014 2 0.02 0 0.00 1.0015 2 0.02 -0.01 -0.29 1.2916 3 0.02 0.01 0.33 0.6717 2 0.02 0.02 -- --

General Smoothing

x (yrs) a l d q p1 129 1 0.12 0.12 0.882 114 0.88 0.01 0.01 0.993 113 0.88 0.25 0.28 0.724 81 0.63 0.02 0.04 0.965 78 0.60 0.08 0.14 0.866 67 0.52 0.06 0.11 0.897 60 0.46 0.09 0.19 0.818 48 0.37 0.18 0.48 0.529 25 0.19 0.09 0.44 0.5610 14 0.11 0.05 0.43 0.5711 8 0.06 0.02 0.29 0.7112 6 0.04 0.02 0.41 0.5913 3 0.03 0.01 0.30 0.7014 2 0.02 0.00 0.00 1.0015 2 0.02 -0.01 -0.29 1.2916 3 0.02 0.02 1.00 0.0017 0 0.00 0.00 -- --

Selected Smoothing

WHAT DO LIFE TABLES TELL US?

Allow us to make generalisations - Survivorship

Allow us to make generalisations - Fecundity

Allow us to build models of populations…..

Projecting Populations into the future: Basic Model Building

KEY PIECES of INFORMATION: p and m

Rearrange Life Table

WHY?

m 0.000 20.000 24.000 0p 0.24 0.242 0 0

0 1 2 30 8760 2102 509 0123456

Age Classtime

x a l d q p F m0 8760 1.000 0.760 0.760 0.240 0 01 2102 0.240 0.182 0.758 0.242 42040 202 509 0.058 0.058 1.000 0.000 12216 243 0 0.000

Dealing first with survivorship

m 0.000 20.000 24.000 0p 0.24 0.242 0 0

0 1 2 30 8760 2102 509 01 2102.423456

Age Classtime

m 0.000 20.000 24.000 0p 0.24 0.242 0 0

0 1 2 30 8760 2102 509 01 2102.4 508.684 02 0 508.7808 03 0 0 04 0 0 05 0 0 06 0 0 0

Age Classtime

Copy Formula Down and Across

Table quickly fills up with 0s

m 0.000 20.000 24.000 0p 0.24 0.242 0 0

0 1 2 30 8760 2102 509 01 2102.4 508.684 02 0 508.7808 03 0 0 04 0 0 05 0 0 06 0 0 0

Age Classtime

m 0.000 20.000 24.000 0p 0.24 0.242 0 0

0 1 2 30 8760 2102 509 01 54256.42 2102.4 508.684 02 13021.54 508.7808 03 0 3151.212641 04 0 0 05 0 0 06 0 0 0

Age Classtime

54256.42

Adding Fecundity

Copy Down

m 0.000 20.000 24.000 0p 0.24 0.242 0 0

0 1 2 30 8760 2102 509 01 54256.416 2102.4 508.684 02 272641.536 13021.54 508.7808 03 1384308.48 65433.969 3151.212641 04 7024721.18 332234.03 15835.02041 05 35648276.9 1685933.1 80400.6363 06 180903629 8555586.5 407995.8059 0

Age Classtime

m 0.000 20.000 24.000 0p 0.24 0.242 0 0

0 1 2 3 Total R0 8760 2102 509 0 11371.00 5.001 54256.416 2102.4 508.684 0 56867.50 5.032 272641.536 13021.5398 508.7808 0 286171.86 5.083 1384308.476 65433.9686 3151.212641 0 1452893.66 5.074 7024721.176 332234.034 15835.02041 0 7372790.23 5.075 35648276.91 1685933.08 80400.6363 0 37414610.63 5.076 180903628.5 8555586.46 407995.8059 0 189867210.79 5.077 918028263.1 43416870.8 2070451.923 0 963515585.86 5.078 4658700849 220326783 10506882.74 0 4889534514.59 5.079 23641422030 1118088204 53319081.52 0 24812829315.48 5.07

10 1.19973E+11 5673941287 270577345.3 0 125917200664.89 5.0711 6.08823E+11 2.8793E+10 1373093792 0 638989662230.89

Age Classtime

NB – R eventually stabilises

R = (Nt+1) / Nt

Converting NUMBERS of each age class to PROPORTIONS (of the TOTAL) generates the age-structure of the population. NOTE, when R stabilises, so too does the age-structure, and this is known as the stable-age distribution of the population, and proportions represent TERMS (cx)

time0 1 2 3

0 0.7704 0.1849 0.0448 01 0.9541 0.0370 0.0089 02 0.9527 0.0455 0.0018 03 0.9528 0.0450 0.0022 04 0.9528 0.0451 0.0021 05 0.9528 0.0451 0.0021 06 0.9528 0.0451 0.0021 07 0.9528 0.0451 0.0021 08 0.9528 0.0451 0.0021 09 0.9528 0.0451 0.0021 010 0.9528 0.0451 0.0021 011 0.9528 0.0451 0.0021 0

Age Class

Because the terms of the stable age distribution are fixed at constant R, we can partition r (lnR) into birth and death per individual

Nt+1 = Nt.(Survival Rate) + Nt.(Survival Rate).(Birth Rate)

Nt+1 = Nt.(Survival Rate).(1 + Birth Rate)

m 0.000 20.000 24.000 0p 0.24 0.242 0 0

0 1 2 3 Total R Births Birth Rate Survivors Survival Rate0 8760 2102 509 0 11371.00 5.00 8760 3.3550 26111 54256.416 2102.4 508.684 0 56867.50 5.03 54256 20.7793 2611 0.22962 272641.536 13021.53984 508.7808 0 286171.86 5.08 272642 20.1504 13530 0.23793 1384308.476 65433.96864 3151.21264 0 1452893.66 5.07 1384308 20.1838 68585 0.23974 7024721.176 332234.0343 15835.0204 0 7372790.23 5.07 7024721 20.1820 348069 0.23965 35648276.91 1685933.082 80400.6363 0 37414610.63 5.07 35648277 20.1821 1766334 0.23966 180903628.5 8555586.459 407995.806 0 189867210.79 5.07 180903629 20.1821 8963582 0.23967 918028263.1 43416870.85 2070451.92 0 963515585.86 5.07 918028263 20.1821 45487323 0.23968 4658700849 220326783.1 10506882.7 0 4889534514.59 5.07 4658700849 20.1821 230833666 0.23969 23641422030 1118088204 53319081.5 0 24812829315.48 5.07 23641422030 20.1821 1171407285 0.2396

10 1.19973E+11 5673941287 270577345 0 125917200664.89 5.07 119972682032 20.1821 5944518633 0.239611 6.08823E+11 28793443688 1373093792 0 638989662230.89 608823124752 20.1821 30166537479 0.2396

timeAge Class

No Births = No a0

Calculating Birth Rate First

m 0.000 20.000 24.000 0p 0.24 0.242 0 0


10 1.19973E+11 5673941287 270577345 0 125917200664.89 5.07 119972682032 20.1821 5944518633 0.239611 6.08823E+11 28793443688 1373093792 0 638989662230.89 608823124752 20.1821 30166537479 0.2396

timeAge Class

Divide by No Individuals producing them: Σax1

n

e.g. B = 35648277 / (1685933 + 80401 + 0) = 20.1821

m 0.000 20.000 24.000 0p 0.24 0.242 0 0


10 1.19973E+11 5673941287 270577345 0 125917200664.89 5.07 119972682032 20.1821 5944518633 0.239611 6.08823E+11 28793443688 1373093792 0 638989662230.89 608823124752 20.1821 30166537479 0.2396

timeAge Class

Calculating Survival Rate

Σax1

n

Survivors: Total number of individuals at time t, older than 0:

m 0.000 20.000 24.000 0p 0.24 0.242 0 0


10 1.19973E+11 5673941287 270577345 0 125917200664.89 5.07 119972682032 20.1821 5944518633 0.239611 6.08823E+11 28793443688 1373093792 0 638989662230.89 608823124752 20.1821 30166537479 0.2396

timeAge Class

Survival Rate: No Survivors at time t, divided by total population size at time t-1

e.g. Survival Rate (t4) = No survivors (t4) / total population size (t3)

S = 348069 / 1452894 = 0.2396

Nt+1 = Nt.(Survival Rate).(1 + Birth Rate)

Nt+1 / Nt = R = er = (Survival Rate).(1 + Birth Rate)

m 0.000 20.000 24.000 0p 0.24 0.242 0 0


10 1.19973E+11 5673941287 270577345 0 125917200664.89 5.07 119972682032 20.1821 5944518633 0.239611 6.08823E+11 28793443688 1373093792 0 638989662230.89 608823124752 20.1821 30166537479 0.2396

timeAge Class

B = 20.1821S = 0.2396

At Stable-Age

R = 0.2396 x (20.1821 + 1) = 5.07

Annual Survival Rate for an individual in the population is in the range p0, p1, p2, but NOT the average

Annual Birth Rate for an individual in the population is between m1 and m2, but NOT the average

NOTE

Reproductive Value (vx) – a measure of present and future contributions by the

different age classes of a population to R

vx is calculated as the number of offspring produced by an individual age x and older, divided by the number of individuals age x right now

vx* = [(vx+1.lx+1) / (lx.R)]

vx* = residual reproductive value

vx = mx + vx*This expression can

ONLY be used to calculate vx* IF the time

intervals used in the life-table are equal.

To calculate vx* work backwards in the life-table, because vx* = 0 in the last year of life

x a l m v* v0 8760 1.000 01 2102 0.240 202 509 0.058 24 0.000 24.0003 0 0.000

x a l m v* v0 8760 1.000 01 2102 0.240 20 1.145 21.1452 509 0.058 24 0.000 24.0003 0 0.000

x a l m v* v0 8760 1.000 0 1.000 1.0001 2102 0.240 20 1.145 21.1452 509 0.058 24 0.000 24.0003 0 0.000

Copy upwards

All the calculations that we have hitherto done concern populations displaying, pulsed births - where reproduction is concentrated at a single point when individuals leave an age class.

In birth flow populations, there is a constant addition of individuals to an age class and constant leaving. Furthermore, reproduction is spread across an age class, so that individuals at the end of age class may produce a different number of offspring to those at the start of an age class.

Birth Flow vs Birth Pulse

0

5

10

15

20

25

30

0 1 2

Age

m

Age l d q p m0 1 0.76 0.76 0.24 01 0.24 0.182 0.758333 0.241667 202 0.058 0.058 1 0 24

First of all we must assume that all reproduction occurs at the mid-point

of an age-class. The mx values are appropriate for the end of an x class - not at the middle - need get average.

To get at average of mx and mx+1 = (mx-

1 + mx)/2

NEXT - need to consider survival from period from x-1 through x to x+1

i.e. px = [(lx + lx+1)/2] / [(lx + lx-1)/2] = [(lx + lx+1)] / [(lx + lx-1)]

and so we then adjust mx * as: mx * = ((mx-1 + pxmx)/2

Age l d q p m0 1 0.76 0.76 0.24 01 0.24 0.182 0.758333 0.241667 202 0.058 0.058 1 0 24

0 1 2 3 Total R0 1.0000 0.2400 0.0580 0 1 5.00151 6.1939 0.2400 0.0581 0 6 5.03222 31.1247 1.4865 0.0581 0 33 5.07703 158.0326 7.4699 0.3597 0 166 5.07464 801.9417 37.9278 1.8077 0 842 5.07475 4069.6049 192.4660 9.1785 0 4271 5.07476 20651.9459 976.7052 46.5768 0 21675 5.07477 104802.0437 4956.4670 236.3626 0 109995 5.07478 531836.9701 25152.4905 1199.4650 0 558189 0.0000

agetime

Age 0 1 2 3l 1 0.24 0.058 0

m 0 20 24 0p 0.24 0.242 0 0

This is the basic life table (birth pulse) that we have constructed so far: projections are based on m. MUST ADJUST m

Age 0 1 2 3l 1 0.24 0.058 0

m 0 20 24 0p 0.24 0.242 0 0

Adj p 0.24 0.2403 0.1946Adj m 0 2.4032 22.3356

px = [(lx + lx+1)/2] / [(lx + lx-1)/2] = [(l1 + l2)] / [(l1 + l0)] = p1

mx * as: mx * = ((mx-1 + pxmx)/2) = m1*

These values of mx now get used in projections of your population

NOTE - px values used in the life tables, and calculations therein, do not change. I.e. px (birth-flow) = px (birth-pulse) and the revised px values above

are only used to calculate mx values.

Age 0 1 2 3l 1 0.24 0.058 0

m 0 20 24 0p 0.24 0.242 0 0

Adj p 0.24 0.2403 0.1946Adj m 0 2.4032 22.3356

The difference between these results and those calculated using pulse-flow models may appear inconsequential, but it is not!

0 1 2 3 Total R0 1.0000 0.2400 0.0580 0 1 5.00151 6.1939 0.2400 0.0581 0 6 5.03222 31.1247 1.4865 0.0581 0 33 5.07703 158.0326 7.4699 0.3597 0 166 5.07464 801.9417 37.9278 1.8077 0 842 5.07475 4069.6049 192.4660 9.1785 0 4271 5.07476 20651.9459 976.7052 46.5768 0 21675 5.07477 104802.0437 4956.4670 236.3626 0 109995 5.07478 531836.9701 25152.4905 1199.4650 0 558189 0.0000

agetime

Age 0 1 2 3l 1 0.24 0.058 0

m 0 20 24 0p 0.24 0.242 0 0

Pulse

0 1 2 3 Total R0 1.0000 0.2400 0.0580 0 1 1.67341 1.8740 0.2400 0.0581 0 2 1.32872 2.3781 0.4498 0.0581 0 3 1.55313 3.8027 0.5708 0.1088 0 4 1.41204 5.2784 0.9127 0.1381 0 6 1.49555 7.9775 1.2668 0.2209 0 9 1.44426 11.4486 1.9146 0.3066 0 14 1.47507 16.9521 2.7477 0.4633 0 20 1.45638 24.6292 4.0685 0.6649 0 29 1.46769 36.1965 5.9110 0.9846 0 43 1.4607

10 52.8274 8.6872 1.4305 0 63 1.464911 77.4254 12.6786 2.1023 0 92 1.462312 113.1873 18.5821 3.0682 0 135 1.463913 165.7236 27.1650 4.4969 0 197 1.462914 242.4173 39.7737 6.5739 0 289 1.463515 354.8050 58.1802 9.6252 0 423 1.463216 519.1182 85.1532 14.0796 0 618 1.463417 759.6848 124.5884 20.6071 0 905 1.463318 1111.5927 182.3243 30.1504 0 1324 1.4633

agetime

Age 0 1 2 3l 1 0.24 0.058 0

m 0 20 24 0p 0.24 0.242 0 0

Adj p 0.24 0.2403 0.1946Adj m 0 2.4032 22.3356

Flow

Stable R

Stable R

Unitary or Modular Organisms….

I Module Type I = genet

Population = genets and ramets

Modular organisms often branched..

Predation does not lead to death..

Cloning…

IIII

II

Module II = ramet

Introduction to Population Biology – BDC222 Prof Mark J Gibbons: Rm 4.102, Core 2, Life Sciences...

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Transcript of Introduction to Population Biology – BDC222 Prof Mark J Gibbons: Rm 4.102, Core 2, Life Sciences...