Spent 22 Months Collecting Spent 22 Months Collecting Fine Scale Data on the Fine Scale Data on the Composition & Abundance of Composition & Abundance of Bat Species in Caatinga & Bat Species in Caatinga & Edaphic Cerrado Biomes of Edaphic Cerrado Biomes of Northeastern BrazilNortheastern Brazil

COMMUNITY ECOLOGISTCOMMUNITY ECOLOGIST

Time ConsumingTime Consuming

Narrow SpecificityNarrow Specificity

Insufficient for Addressing Insufficient for Addressing Broad QuestionsBroad Questions

Unclear Comparative ContextUnclear Comparative Context

LIMITATIONSLIMITATIONS

RANGE MAPS:RANGE MAPS:

Wealth of Biogeographic,Wealth of Biogeographic,

Ecological, andEcological, and

Evolutionary InformationEvolutionary Information

BAT RANGE MAPS:BAT RANGE MAPS:

Hall for North AmericaHall for North America

Koopman for South Koopman for South AmericaAmerica

Supplemented by “Others”Supplemented by “Others”

RANGE MAPS:RANGE MAPS:

Expert OpinionExpert Opinion

Metadata ProblemsMetadata Problems

Heterogeneous QualityHeterogeneous Quality

GRADIENTS OF RICHNESSGRADIENTS OF RICHNESSAND RANGE AND RANGE SIZE:SIZE:

BATS ANDBATS ANDMARSUPIALS MARSUPIALS IN THE NEW WORLDIN THE NEW WORLD

LATITUDINALLATITUDINALGRADIENTGRADIENT

OFOFSPECIES SPECIES

RICHNESSRICHNESS

CAUSESCAUSES

• Competition• Population Size• Growth Rates• Epiphyte Load• Harshness• Predation

• Heterogeneity• Niche Width• Patchiness• Host Diversity• Mutualism• Epidemics

CAUSESCAUSES

• Stability• Productivity• Heterogeneity• Aridity• Habitat Number

• Predictability• Rarefaction• Area• Seasonality• Range Size

• Evolutionary Speed

LIMITATIONSLIMITATIONS• Qualitative PredictionsQualitative Predictions

• Non Mutually ExclusiveNon Mutually Exclusive

• Unspecified FormUnspecified Form

• No Expected ValuesNo Expected Values

Hemispheric PatternsHemispheric Patterns

CLASSICAL APPROACHCLASSICAL APPROACH

RICHNESS

L A T I T U D E

HO

HA1

HA2

CHANCE

STOCHASTICSTOCHASTIC

PROCESSES PROCESSES

ANDAND

NULL MODELSNULL MODELS

SIMULATION NULL MODELSIMULATION NULL MODEL

LATITUDE

0

1

3

4

5

7

5

3

2

2

RICHNESS

SIMULATION APPROACHSIMULATION APPROACH

•Randomly generate N & S termini for a Randomly generate N & S termini for a speciesspecies

•Repeat until S = richness of species poolsRepeat until S = richness of species pools

•Calculate richness at each latitudeCalculate richness at each latitude

•Repeat 1,000 timesRepeat 1,000 times

•Calculate mean and variance of richness Calculate mean and variance of richness per latitudeper latitude

EFFECT OF SPECIES POOL SIZEEFFECT OF SPECIES POOL SIZESIMULATION RESULTSSIMULATION RESULTS

1q

p

P

0

PROBABALISTIC APPROACHPROBABALISTIC APPROACH

BINOMIAL NULL MODELBINOMIAL NULL MODEL

p + q = 1( p + q ) 2 = 1

p 2 + 2 pq + q 2 = 1

2 pq S = Richness at “P”

1

0

q

p

P

SPECIES RICHNESS

GRAPHIC REPRESENTATIONGRAPHIC REPRESENTATION

2pqS

Domain

NULL MODELNULL MODEL

• Predicts Form of Predicts Form of RelationRelation

• Quantitative PredictionsQuantitative Predictions

• FalsifiableFalsifiable

NEW WORLDNEW WORLDBATSBATSANDAND

MARSUPIALSMARSUPIALS

Chrotopterus auritus

Neoplatymopsmattogrossensis

BATSBATS

Species richSpecies rich

Trophically richTrophically rich

Abundant in tropicsAbundant in tropics

BATS – ENTIRE CONTINENTBATS – ENTIRE CONTINENT

BATS – TAXON EXTENTBATS – TAXON EXTENT

BATS – 95% OF EXTENTBATS – 95% OF EXTENT

Didelphis virginiana

Marmosa cinerea

MARSUPIALSMARSUPIALS

Ancient group of mammalsAncient group of mammals

Moderate species richnessModerate species richness

Trophically diverse in pastTrophically diverse in past

MARSUPIALS – ENTIRE CONTINENTMARSUPIALS – ENTIRE CONTINENT

MARSUPIALS – TAXON EXTENTMARSUPIALS – TAXON EXTENT

MARSUPIALS – 95% OF EXTENTMARSUPIALS – 95% OF EXTENT

MODEL UTILITYMODEL UTILITY

• Deviations from the model Deviations from the model differ between bats and differ between bats and marsupialsmarsupials

• Deviations are not related to Deviations are not related to the area of latitudinal bandsthe area of latitudinal bands

RANDOM SUBSETSRANDOM SUBSETS

• 20 Ranges20 Ranges

• 2020oo Latitude Latitude

• 20 Species20 Species

RANDOM SUBSETSRANDOM SUBSETS

SPECIES RICHNESS1

0

q

p

BATS20 ***r = 0.77

MARSUPIALS19 ***r = 0.73

P

ASSESSMENTASSESSMENT

Although stochastic mechanisms Although stochastic mechanisms may not be the only factors may not be the only factors affecting gradients, they play an affecting gradients, they play an appreciable role throughout the appreciable role throughout the distribution of a biotadistribution of a biota

MODEL UTILITYMODEL UTILITY

• Deviations from the model Deviations from the model differ between bats and differ between bats and marsupialsmarsupials

• Deviations are not related to Deviations are not related to the area of latitudinal bandsthe area of latitudinal bands

MULTIFACTORIALMULTIFACTORIAL

• Many FactorsMany Factors

• Species-Specific LimitsSpecies-Specific Limits

• Factor-Specific N and S Factor-Specific N and S Limits Limits

EXTRAPOLATIONSEXTRAPOLATIONS

• Disturbance GradientsDisturbance Gradients

• Productivity GradientsProductivity Gradients

• Abiotic GradientsAbiotic Gradients

LATITUDINAL LATITUDINAL

GRADIENT OF GRADIENT OF

SPECIES RANGE SIZESPECIES RANGE SIZE

RAPOPORT’S RAPOPORT’S RULE RULE

METHODOLOGICAL BIASESMETHODOLOGICAL BIASES

TropicalSpecies

TemperateSpecies

0o

SIMULATION APPROACHSIMULATION APPROACH

•Randomly generate N & S termini for a Randomly generate N & S termini for a speciesspecies

•Repeat until S = richness of species poolsRepeat until S = richness of species pools

•Calculate correlation between latitudinal Calculate correlation between latitudinal range size and mid-latituderange size and mid-latitude

•Repeat 1,000 timesRepeat 1,000 times

•Calculate mean and variance of correlationsCalculate mean and variance of correlations

SOUTH AMERICA NORTH AMERICAL

AT

ITU

DIN

AL

RA

NG

E

0

150

100

50

MID-LATITUDE-70 -50 -30 -10 10 30 50 70

BATSBATS

SOUTH AMERICA NORTH AMERICAL

AT

ITU

DIN

AL

RA

NG

E

0

150

100

50

MID-LATITUDE-70 -50 -30 -10 10 30 50 70

MARSUPIALSMARSUPIALS

BATS

0

100

200

FR

EQ

UE

NC

Y

-0.56 -0.53 -0.49 -0.45 -0.42 -0.38 -0.35 -0.31

CORRELATION COEFFICIENT

MARSUPIALS

-0.63 -0.56 -0.49 -0.42 -0.35 -0.29 -0.22 -0.150

100

200

MID-LATITUDE RESULTSMID-LATITUDE RESULTSLess

Negative

LessNegative

LATITUDE

RA

NG

E S

IZE

MID-LATITUDE RESULTSMID-LATITUDE RESULTS

Rapoport’s Rule

Empirical Pattern

Empirical Pattern

Stochastic Pattern

Comparisons of Gradients Comparisons of Gradients of Diversity of Diversity at Two at Two Scales:Scales:

Communities Versus Communities Versus Regional Species PoolsRegional Species Pools

SCALE

Regional Patterns

Local Patterns

LATITUDINAL LATITUDINAL GRADIENTS OFGRADIENTS OF

COMMUNITYCOMMUNITYORGANIZATIONORGANIZATION

DESIGN DESIGN

• Geographical Constraints (50 km)Geographical Constraints (50 km)

• Ecological Constraints (biome)Ecological Constraints (biome)

• Sampling Constraints (asymptote)Sampling Constraints (asymptote)

• Temporally Constrained (1-5 yr)Temporally Constrained (1-5 yr)

32 Sites32 Sites

TemperateTemperate

SubtropicalSubtropical

TropicalTropical

SubtropicalSubtropical

TemperateTemperate

DIVERSE HABITATSDIVERSE HABITATS

Riparian Temperate Forest (1)Riparian Temperate Forest (1)Desert (4)Desert (4)Montane Tropical Forest (6)Montane Tropical Forest (6)Wet Tropical Forest (13)Wet Tropical Forest (13)Dry Tropical Forest (2)Dry Tropical Forest (2)Tropical Woodland-Savanna (1)Tropical Woodland-Savanna (1)Wet Semi-Tropical Forest (4)Wet Semi-Tropical Forest (4)Dry Semi-Tropical Forest (1)Dry Semi-Tropical Forest (1)

FAUNAL POOL - SPECIFIC DATAFAUNAL POOL - SPECIFIC DATA

• Number of species whose Number of species whose geographic range overlaps a geographic range overlaps a communitycommunity

• Identities of species whose range Identities of species whose range overlaps a community overlaps a community

COMMUNITY - SPECIFIC DATACOMMUNITY - SPECIFIC DATA

• Species identities &Species identities & abundances in each abundances in each communitycommunity

• Indexes of diversity that are sensitive to Indexes of diversity that are sensitive to richness (3), evenness (4), dominance (3), richness (3), evenness (4), dominance (3), diversity (4)diversity (4)

BIODIVERSITY INDICIESBIODIVERSITY INDICIESRICHNESSRICHNESSCommunity RichnessCommunity RichnessMargalef IndexMargalef IndexMenhinick IndexMenhinick Index

EVENNESSEVENNESSShannon IndexShannon IndexPIE IndexPIE IndexCamargo’s IndexCamargo’s IndexShoener’s IndexShoener’s Index

DIVERSITYDIVERSITYCamargo IndexCamargo IndexLog Series AlphaLog Series AlphaBrillouin IndexBrillouin IndexShannon IndexShannon Index

DOMINANCEDOMINANCESimpson’s IndexSimpson’s IndexBerger-Parker IndexBerger-Parker IndexMcIntosh IndexMcIntosh Index

3

-3 3-3

0

0

FACTOR 1

FA

CT

OR

2

TropicalSubtropicalTemperate

Evenness DominanceDiversityRichness

Factor AnalysisFactor Analysis

CE O

BP PIESI

MD

SHDB

CD

AMAR

R

SHE

MER

Latitudinal GradientsLatitudinal Gradients

-3

0

3

0 15 30 45-3

0

3

0 15 30 45

TropicalSubtropicalTemperate

TropicalSubtropicalTemperate

Latitude Latitude

Fac

tor

1

Fac

tor

2

RichnessRichness EvennessEvennessBB1 1 = 0.0002; r= 0.0002; r22 < 0.01; P = 0.999 < 0.01; P = 0.999BB1 1 = -0.055; r= -0.055; r22 = 0.37; P < 0.001 = 0.37; P < 0.001

REGIONAL & LOCAL GRADIENTSREGIONAL & LOCAL GRADIENTS

Latitude

Ric

hn

ess

RegionalRegionalLocalLocal

12O

90

60

90

10 20 30 40

LATITUDINAL GRADIENTSLATITUDINAL GRADIENTS