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
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
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
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”
NULL MODELNULL MODEL
• Predicts Form of Predicts Form of RelationRelation
• Quantitative PredictionsQuantitative Predictions
• FalsifiableFalsifiable
BATSBATS
Species richSpecies rich
Trophically richTrophically rich
Abundant in tropicsAbundant in tropics
MARSUPIALSMARSUPIALS
Ancient group of mammalsAncient group of mammals
Moderate species richnessModerate species richness
Trophically diverse in pastTrophically diverse in past
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
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
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
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