Deciphering Universal Patterns

of

Biodiversity

Dan McGlinn http://mcglinn.web.unc.edu

Weecology Lab @danmcglinn

Deciphering

STRANGE

Patterns

Dan McGlinn http://mcglinn.web.unc.edu

Weecology Lab @danmcglinn

What Determines Mate Choice

1st Law of Geography

• Everything is related to everything else…

• But near things are more related than distant

things

• As we move further way the more

different things become

“the nice Watson girl next door”

Aunt May

Things are Patchy

Ecological Communities are Patchy

Ecological Communities are Patchy

Ecological Communities are Patchy

Ecological Communities are Patchy

Distribution of Energy

Preston 1950

Temperature (i.e., Energy)

Num

ber

of

Mole

cule

s

Galactus the Devourer

Preston 1950

Distribution of Energy

Temperature (i.e., Energy)

Num

ber

of

Mole

cule

s

Preston 1950

Distribution of Energy

Temperature (i.e., Energy)

Num

ber

of

Mole

cule

s

Preston 1950

Distribution of Energy

Temperature (i.e., Energy)

Num

ber

of

Mole

cule

s

Household Income

Distribution of Wealth in the US

$50,000 $100,000 $150,000 $200,000 $250,000 $0

Num

be

r of H

ou

se

ho

lds = 500,000 Households

Most Poor

Few Wealthy

Distribution of Abundance

Among Species

Preston 1948

Most species Rare Few Species Common

Two Universal STRANGE

Patterns

Species are Patchy Species are Rare

Universal Explanation?

• Maximum Entropy Theory of Ecology (METE) Harte et al. (2008), Harte (2011)

• Predicts many distributions Abundance and Patchiness

• Prior Information Total number of species (S0)

Total number of individuals (N0)

Total area of a community (A0)

Total energy of community (E0)

• Assumes that communities are in a ‘most likely’ state given constraints

Show me the Data • 16 communities

– 15 tree

– 1 herb

• 6 habitat types – Tropical forest

– Oak-hickory

– Old field, pine forest

– Oak woodland

– Mixed-evergreen forest

– Serpentine grassland

• 1611 species

• 350,000 individuals

Example Results

Species Rank Area (m2)

Abundance

Num

be

r o

f S

pe

cie

s

100000

Total Number of Species = 124

Total Number of Individuals = 32,320

1

10

100

Observ

ed A

bundance

1000

10000

1 10 100 1000 10000

R2 = 0.85

Predicted Abundance

Crosstimbers

1

10

100

Observ

ed A

bundance

1000

10000

1 10 100 1000 10000

Predicted Abundance

R2 = 0.85

1 10 100

1

10

100

Predicted Number of Species

Observ

ed N

um

ber

of S

pecie

s

R2 = 0.99

Implications

• We can predict local scale

– patterns of abundance across species

– patterns of diversity across areas

• With only prior knowledge of

– S: total number of species in the community

– N: total number of individuals in the community

• Approach likely can be applied to other

disciplines

Why is this Important?

How many species are quite rare?

Why is this Important?

How many species are likely to go locally

extinct?

Thank you!

• The volunteer data collectors

• The data providers

• NSF Career Award to E.P. White

• Utah State Ecology Center

• Weecology Lab

Questions