macroecology & uneven distributions of wealth
DESCRIPTION
Macroecology & uneven distributions of wealth. Ken Locey. http://tchester.org/srp/plants/communities/figures/global_biodiversity_by_area.gif. 183,913,348 records of birds in the Global Biodiversity Information Facility database. Macroecology. …study of ecological relationships - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/1.jpg)
Macroecology & uneven distributions of wealth
Ken Locey
![Page 2: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/2.jpg)
http://tchester.org/srp/plants/communities/figures/global_biodiversity_by_area.gif
![Page 3: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/3.jpg)
183,913,348 records of birds in theGlobal Biodiversity Information Facility database
![Page 4: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/4.jpg)
Abundance: ni/N
Distribution: f(k;λ) = λke-λ/k!
Diversity: H’ = -Σpi*ln(pi)
…study of ecological relationshipsthat involves characterizing and explaining statistical patterns of…
Macroecology
![Page 5: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/5.jpg)
Land birds
Landmammals
Geographic range patternsN
orth
-Sou
th (k
m)
East-West (km) 100 1,000 10,000
![Page 6: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/6.jpg)
Metabolic Theory of Ecology (MTE)Ecological phenomenon ∝ M3/4e-E/kt
Temp. corrected max. rate of whole organism biomass production
Slope = 0.76R2 = 0.99
![Page 7: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/7.jpg)
1 3 6 10 14 19 33 38 69 93150
0123456789
10
Species Abundance Distribution
Abundance Class
freq
uenc
y(frequency distribution)
![Page 8: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/8.jpg)
http://encyclopediaurantia.org/images/ROM11.JPG
![Page 9: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/9.jpg)
![Page 10: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/10.jpg)
DATA
![Page 11: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/11.jpg)
Computing
GISMath & Stats
Metabolic rate ∝ M3/4e-E/kt
Information:
Tools
![Page 12: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/12.jpg)
COLLABORATION & SHARING
Code
dev
elop
men
t Sharing
Source networks
![Page 13: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/13.jpg)
GISProgrammingPublished ResearchData managementMath & StatsCollaboration
Undergraduate &Graduate research
Skills
Jobs
Grad School
![Page 14: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/14.jpg)
![Page 15: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/15.jpg)
Center for Macroecology, Evolution, & Climate www.macroecology.ca
macroecology.ku.dk
whitelab.weecology.org
![Page 16: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/16.jpg)
![Page 17: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/17.jpg)
![Page 18: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/18.jpg)
Species Abundance Distribution
Abundance Class
freq
uenc
y(frequency distribution)
1 3 6 10 14 19 33 38 69 93150
0123456789
10
![Page 19: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/19.jpg)
1 6 11 16 21 26 31 361
10
100
Species Abundance Distribution
Rank in Abundance
Abun
danc
e(Rank-abundance distribution)
![Page 20: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/20.jpg)
Wheat Production (tons)
tons62.9 104588178.7
![Page 21: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/21.jpg)
Poverty in Rural America, 2008
Percent in Poverty54.5 - 25 25 - 20 20 – 14.3 14.2 – 12.2 12.1 - 10 10 – 3.1
![Page 22: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/22.jpg)
![Page 23: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/23.jpg)
Distributions of Wealth (DOW)Supreme importance attaches to one economic problem, that of the distribution of wealth. Is there a natural law according to which the wealth of society is divided? – John Bates Clark
Wealth: sources of human welfare which are material, transferable, and limited in quantity.
![Page 24: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/24.jpg)
Total quantity (Q)Community abundanceGlobal Oil ConsumptionGDP, GNP
Number of entities (N)SpeciesNationsEconomic classes
Distributions of Wealth (DOW)
![Page 25: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/25.jpg)
If Q = 10 and N = 3, then:8 unordered ways to sum Npositive integers to obtain Q 8+1+1 7+2+1 6+3+1 6+2+2 5+4+1 5+3+2 4+4+2 4+3+3
Distributions of Wealth (DOW)
![Page 26: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/26.jpg)
Do we observe the average of possible DOWs?
![Page 27: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/27.jpg)
The feasible set(all possible shapes of the DOW)
16,958 shapes forQ = 50 & N = 10
Rank
Wea
lth
![Page 28: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/28.jpg)
Combinatorial Explosion
Q N Size of feasible set
50 10 16,928
500 10 2.013 × 1012
5000 10 1.531 × 1021
![Page 29: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/29.jpg)
Heat mapping the feasible set(or a random sample)
ln(w
ealth
)
Rank
Q=1,000N=80
![Page 30: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/30.jpg)
Heat mapping the feasible set(or a random sample)
ln(a
bund
ance
)
Rank in abundance
ca. 4.02x1029 possible shapes for N=1000 & S=80
![Page 31: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/31.jpg)
Ecological DOWs(species-abundance distributions)
• North American Breeding Bird Survey (1,583 sites)• Forest Inventory and Analysis (7,403 sites)• Mammal Community Database (42 sites)• North American Butterfly Association (306 sites)• Aquatic prokaryotes (92 metagenomes)
– Arctic surface waters, Deep-sea Hydrothermal vents
• Terrestrial prokaryotes (48 metagenomes) – Arctic soils, agricultural soils
• Indoor fungi (124 metagenomes)– All continents except Antarctica
Total: 9,598 different sites of diverse communities
![Page 32: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/32.jpg)
Q = Total community abundance (i.e. number of individuals)
N = Species richness (i.e. number of species)
Ecological DOWs(species-abundance distributions)
![Page 33: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/33.jpg)
Obs
erve
d w
ealth
100 101 102
Predicted wealth
102
101
100
R2 per site
OBSERVED: [1, 2, 10, 12, 20, 30, 40, 60, 110]PREDICTED: [1, 2, 11, 11, 22, 28, 43, 50, 117]
R2 = 0.99
![Page 34: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/34.jpg)
Obs
erve
d ab
unda
nce
100 101 102
Predicted abundance
102
101
100
R2 per siteR2 = 0.99R2 = 0.89R2 = 0.80R2 = 0.75
![Page 35: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/35.jpg)
Obs
erve
d ab
unda
nce
100 101 102
Predicted abundance
102
101
100
R2 per siteR2 = 0.99R2 = 0.89R2 = 0.80R2 = 0.75
R2 per site
0.0 1.0
![Page 36: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/36.jpg)
Obs
erve
d ab
unda
nce
100 101 102
Predicted abundance
102
101
100
R2 per site0.0 1.0
R2 = 0.93
![Page 37: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/37.jpg)
Obs
erve
d ab
unda
nce
R2 = 0.931,583 sites in the Breeding Bird Survey
100 101 102
102
101
100
R2 per site
Predicted abundance
0.0 1.0
![Page 38: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/38.jpg)
R2 = 0.84
7,403 treecommunities
R2 = 0.80
306 butterflycommunities
R2 = 0.78
42 mammal communities
Observed abundance vs. Abundance at the center of the feasible set
Count data
![Page 39: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/39.jpg)
R2 = 0.58
48 terrestrial prokaryote
Observed abundance vs. Abundance at the center of the feasible set
R2 = 0.83
92 aquatic prokaryote
R2 = 0.76
124 indoor fungi
Metagenomes
![Page 40: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/40.jpg)
Food & Agriculture Organization of the UN
US Dept of Energy, Energy Information Admin.
![Page 41: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/41.jpg)
0.83 0.91 0.93
Predicted supply
Obs
erve
d su
pply
Food supply among nations(1960-2010)
grams/capita/day * 0.1tons * 0.0001grams/capita/day
![Page 42: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/42.jpg)
0.69 0.77 0.91
Predicted pop. size
Obs
erve
d po
p. si
zePopulation sizes among nations
(1960-2009, millions of people)
![Page 43: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/43.jpg)
0.88 0.92 0.92
Predicted
Obs
erve
d
Oil use among nations(1980-2009, barrels per day * 0.01)
![Page 44: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/44.jpg)
Predicted home runs
Obs
erve
d ho
me
runs
0.93 0.88 0.90
0.91 0.91 0.89
0.94 0.93
(2002-2010)http://mlb.mlb.com
![Page 45: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/45.jpg)
Are DOWs similar to the average of possible shapes? …very often
Do Q and N constrain the DOW more than ever realized? …Yup
Is the feasible set good for more than predictions? …Absolutely
Is combinatorial explosion a pain in the *expletive*? …Not for long…?
![Page 46: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/46.jpg)
Funding
• USU College of Science– Willard L. Eccles Fellowship
• NSF CAREER award to Ethan White
• Research grant from Amazon Web Services
![Page 47: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/47.jpg)
Acknowledgments• Individuals, agencies, organizations responsible for the collection
and management of the:– Breeding Bird Survey, Christmas Bird Count, Forest Inventory and Analysis,
Mammal Community Database, North American Butterfly Association, Argonne National Laboratory’s MG-RAST metagenomic server
• Colleagues & Collaborators– USU: Ethan White, Xiao Xiao, Dan McGlinn– Berkeley Harte Lab: Justin Kitzes– SESYNC: Bill Burnside
• UCO college of Math and Science
![Page 48: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/48.jpg)
The feasible set as a framework
Understanding
Comparing
Inequality
![Page 49: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/49.jpg)
Perc
entil
e of
the
feas
ible
set
Gini’s coefficient of inequality
![Page 50: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/50.jpg)
The feasible set(all possible values of species evenness)Sp
ecie
s eve
nnes
s
Species richness, S
Total abundance, N = 60
![Page 51: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/51.jpg)
pdf10,173 sites
R2 (obs vs. random sample)
![Page 52: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/52.jpg)
Combinatorial Explosion
Q N Size of feasible set
1000 10 886,745,696,653,253
1000 100 302,194,941,264,401,427,042,462,944,147
1000 900 190,569,292
![Page 53: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/53.jpg)
1,583 sites in the Breeding Bird Survey
0.2 0.4 0.6 0.8
0.8
0.6
0.4
0.2
R2 (obs vs. random sample)
![Page 54: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/54.jpg)
![Page 55: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/55.jpg)
Feasible sets are dominatedby hollow-curves
Evar
Prob
abili
ty d
ensit
y fu
nctio
nN = 50, S = 205,507 macrostates
N = 50, S = 1016,928 macrostates
Q=50, N=20
Q=50, N=10
Evenness(Smith & Wilson, 1996)
![Page 56: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/56.jpg)
MTE prediction: species richness decreases with temperature (S ∝ Ae-Ea/kT )
Computer Science Student:Biology student:
Chemistry Studentmodels based on chemical kinetics/activation energy
microbe data,reasons why MTEshould (not) workfor microbes
model developmentdata scraping & management
Temperature-richness predictions of MTE do not hold for diverse microbe communities as tested using several models of chemical kinetics. This may be explained by microbial dormancy and dispersal.
+Conclusion(?)
![Page 57: Macroecology & uneven distributions of wealth](https://reader036.vdocuments.site/reader036/viewer/2022062520/568164d3550346895dd7048f/html5/thumbnails/57.jpg)
Body mass (g)
Land birds Land mammals
Body-size distributions
Num
ber o
f spe
cies continental
regional
patch