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Effects of livestock grazing and environmental parameters on butterfly species richness and
community composition in an East African catena
Xingli Giam and Ann Thomas
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Introduction• Anthropogenic land-use change is a major threat to
species• Increasing humans and livestock in the Acacia-
Commiphora bushlands and thickets of Africa– Environmental degradation owing to heavy grazing (WWF
2001)
• In Mpala Conservancy, livestock herding is actively managed to prevent overgrazing and to minimize impacts on the natural habitat
• No study has assessed the efficacy of this low intensity and highly-managed form of ranching towards species conservation
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1
54
23
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Research questions
1. Does grazing affect butterfly species composition and result in species turnover between plots?
2. Does grazing affect butterfly species richness?
3. If not, can we identify the transect-level predictors of butterfly species richness?
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Boma 2OldCattleTransition Soil
Field Sites
Boma 1NewCattleRed soil
Boma 3OldCattleTransition Soil
Boma 4NewSheepTransition Soil
Boma 5OldCattleRed Soil
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Methods
BomaH DG C BF AE
1000 m
500 m
250 m
100 m
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Methods
25m
10m
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Methods
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Estimating percent cover
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Confounding Factors
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Results: Species Richness Estimation through Incidence Rarefaction Curves
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Estimating Species Richness1 1 1 1
1 1 1
1 1 1
1 1 1
0
1 1 1 1
1 1
0
0
0
0
0
1 1
1 1 1
0
0
0
0
0
1 1 1 1
0
0
1 1 1
0
1 1
0
0
0
0
1 1
1 1
0
Belenois spp.
Catopsilia florella
Colotis antevippe
Colotis aurigineus
Colotis eucharis
Colotis euippe
Colotis evagore
Colotis hetaera
Dixea orbona
Dixea pigea
Eronia leda
Eurema brigitta
Pinacopteryx eriphia
Pontia helice
Acraea alicia
Byblia sp.
Charaxes kirkii
Danaus chrysippus
Hypolimnus missippus
Junonia hierta
Junonia oenone
Neocoenyra gregorii
Vanessa cardui
Anthene amara
Azanus jesous
Chilades kedonga
Eicochrysops hippocrates
Lampides boeticus
Leptotus pirithous
Zizeeria knysa
Zizinia antanossa
Papilio demodocus
Day
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Success of Rarefaction Curves
• High degree of variation in saturation levels, even between adjacent plots (right)
• Rare species estimator• Criteria for Saturation
somewhat arbitrary, but:• Percent change from
Day 1Day 2 > D2 D3
• RSE ≤ 20% of Day 3 species number
• Half the plots fail these criteria (mostly the 2nd)
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Effects of Pastoral Practices on Community Composition
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Diversity at the Community Level
If livestock grazing affects biodiversity, we would expect the community composition of grazed and ungrazed land to differ
Hypothesis: Changes in community composition correlate positively with distance from boma to create a “grazing gradient”
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Diversity at the Community Level
β diversity– Comparison of community composition between two
sites– β diversity = ((unique A) + (unique B))/(shared species)
Site A Site B
6 unique
10 shared
4 unique
Example to the right:β diversity = 1.0
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β diversity as a proxy for community homogeneity around bomas
Boma 1B Boma 1C1 1 1 1
1 1 1
1 1 1
1 1 1
0
1 1 1 1
1 1
0
0
0
0
0
1 1
1 1 1
0
0
0
0
0
1 1 1 1
0
0
1 1 1
0
1 1
0
0
0
0
1 1
1 1
0
Belenois spp.
Catopsilia florella
Colotis antevippe
Colotis aurigineus
Colotis eucharis
Colotis euippe
Colotis evagore
Colotis hetaera
Dixea orbona
Dixea pigea
Eronia leda
Eurema brigitta
Pinacopteryx eriphia
Pontia helice
Acraea alicia
Byblia sp.
Charaxes kirkii
Danaus chrysippus
Hypolimnus missippus
Junonia hierta
Junonia oenone
Neocoenyra gregorii
Vanessa cardui
Anthene amara
Azanus jesous
Chilades kedonga
Eicochrysops hippocrates
Lampides boeticus
Leptotus pirithous
Zizeeria knysa
Zizinia antanossa
Papilio demodocus
1 0 0 1
1 1 1 1
1 1 1
0
0
1 1 1 1
0
0
0
0
0
0
1 1
1 1 1
0
0
0
0
0
1 1 1
1 1
0
1 1 1 1
0
0
0
0
0
0
1 1 1 1
1 1 1 1
0
Unique to Site 1B: 3 species
Unique to Site 1C: 1 species
Common to both: 10 species
β diversity / shared = 0.4
1. Perform for all combinations of sites within a boma
2. Plot (β diversity / shared) against distance for each side and across entire boma
3. Small values and small slope indicates homogeneity between sites
BomaH DG C BF AE
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Regression statisticsBoma Side F P-value strength of association (r^2)
1 A 12.59 0.07 0.861 B 3.62 0.19 0.6441 Across 4.02 0.18 0.6672 A 1.09 0.4 0.352 B 6.68 0.12 0.762 Across 0.4 0.58 0.173 A 0.49 0.55 0.193 B 0.81 0.46 0.293 Across 15 0.06 0.884 A 1.07 0.41 0.354 B 0.77 0.47 0.274 Across 0.006 0.94 0.0035 A 0.44 0.63 0.315 B 0.15 0.76 0.135 Across 0.05 0.84 0.025
After Bonferroni correction, no evidence of significant relationship between distance and β diversity
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Effect of Soil Type and Boma Age on β diversity
soil age
B diversity analysis Shared Adiff Bdiff β
β/shared
diff diff B1-B2 21 3 3 60.28571
4
diff diff B1-B3 19 5 7 120.63157
9
diff diff B4-B5 17 3 1 40.23529
4
diff same B1-B4 19 5 1 60.31578
9
diff same B2-B5 17 7 1 80.47058
8diff same B3-B5 16 10 2 12 0.75
same diff B1-B5 17 7 1 80.47058
8same diff B2-B4 20 4 0 4 0.2
same diff B3-B4 19 7 1 80.42105
3
same same B2-B3 21 3 5 80.38095
2
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Conclusions from β diversity trends
• Lack of relationship between β diversity and distance suggests that ranch pastoral methods do not create a “grazing gradient” on the scale of 100s of meters from the boma
• The natural heterogeneity of the environment overshadows any effects of pastoral practices on biodiversity
• Alternately, grazing is highly heterogeneous and unrelated to distance from boma; not sufficient data to rule this out
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2. Effect of grazing on butterfly species richness
Assumption: Grazing intensity decreases as a function of increasing distance from boma
Hypothesis: Species richness will increase with distance from boma as grazing intensity decreases
Candidate models:
Sij ~ Pois(μij), a ~ N(0, σa2)
1. μij = exp(β0 + β1 Ageij + ai)
2. μij = exp(β0 + ai), for the jth transect in ith boma
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S/N Model n k AICc dAICc wAICc %DE
Null μ = exp(2.54) 16 2 8.91 0 0.78
1 μ = exp(0.048*Dist + 2.42) 16 3 11.41 2.51 0.22 14.32
• Distance from boma does not predict butterfly species richness across 16 current transects
• Suggests that the impact on grazing on butterfly species richness is minimal
Results of Distance Analysis
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2. Effect of grazing on butterfly species richness
Assumption: The effect of grazing is more pronounced in current bomas
Hypothesis: Species richness is higher in old bomas compared to current bomas
Candidate GLMMs:
Sij ~ Pois(μij), a ~ N(0, σa2)
1. μij = exp(β0 + β1 Ageij + ai)
2. μij = exp(β0 + ai), for the jth transect in ith boma
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S/N Model n k AICc dAICc wAICc %DE
Null μ = exp(2.48) 38 3 18.93 0 0.67
1 μ = exp(-0.09*Status + 2.54) 38 4 20.33 1.39 0.33 6.68%
• Status of boma does not predict butterfly species richness across 38 transects
• Similar conclusions as Distance analysis – grazing does not seem to affect butterfly species richness
Results of age analysis
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• Data suggests that the impact of grazing on butterfly species richness is minimal– Are there any plot-level environmental factors that
predict species richness?• Fitted candidate GLMMs based on a priori
hypotheses– Multimodel selection and model using Akaike
weights (Burnham & Anderson 2002)– Information-theoretic measure of the likelihood of
model i being the best model in the set
3. Environmental correlates of butterfly species richness
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Model specification• Species richness is modeled as a poisson count– Non negative integers
• Random intercept model– The intercept is allowed to vary among bomas– Transects are nested within bomas– Account for some of the spatial dependence
between transects of the same boma• Candidate GLMMs:
Sij ~ Pois(μij), a ~ N(0, σa2)
Sample model: μij = exp(β0 + β1Coverij + β1Shrubij + ai)
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Candidate modelsNo. Model No. Model
1 ~ Cover 11 ~ log(GrassFl) + log(NonGrassFl)
2 ~ Cover2 + Cover 12 ~log(GrassFl) + Cover + Shrubs
3 ~ Shrubs 13 ~ log(GrassFl) + Cover2 + Cover + Shrubs
4 ~ log(GrassFl) 14~ log(GrassFl) + log(NonGrassFl) + Cover + Shrubs
5 ~ log(NonGrassFl) 15
~ log(GrassFl) + log(NonGrassFl) + Cover2 + Cover + Shrubs
6 ~ Cover + Shrubs Null ~ 1
7 ~ Cover2 + Cover + Shrubs
8 ~ Cover + log(GrassFl)
9 ~ Cover2 + Cover + log(GrassFl)
10 ~ Shrub + log(GrassFl)
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No. Model n k AICc dAICc wAICc %DENull μ = exp(2.48) 38 2 18.94 0.00 0.23 0.00
4 μ = exp(0.14*log(GrassFl) + 2.05) 38 3 19.44 0.50 0.18 12.78
2 μ = exp(-0.95*Cover2 + 1.03*Cover + 2.28) 38 4 20.57 1.63 0.10 22.20
3 μ = exp(0.006*Shrubs + 2.41) 38 3 20.73 1.80 0.09 3.88
5 μ = exp(0.024*NonGrassFl + 2.45) 38 3 21.04 2.11 0.08 1.77
• Null model is the best (prob ~23%)• Some evidence that species richness
increases with the abundance of flowering grasses
Environmental correlates of species richness
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2.0 2.5 3.0 3.5
810
12
14
16
log(GrassFl)
Species
2.0 2.5 3.0 3.58
10
12
14
16
log(GrassFl)
Species
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Conclusions• Livestock grazing at Mpala does not appear to affect
butterfly species diversity– Management regime is effective
• Butterfly species richness is largely stochastic at the plot level
• Very weak evidence that species richness increases with abundance of flowering grasses– Species observed were not grass feeders– Camouflage, grass flowers, habitat heterogeneity
• Species richness might be better characterized at a landscape scale or region scale (e.g., Kerr et al 2001)
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Natural History: Time to Meet the Butterflies!
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All day generalists
Belenois spp.Colotis spp.Catopsilia florellaPontia heliceJunonia hiertaVanessa cardui*Zizeeria knysa
Pieridae
Lycaenidae
Nymphalidae
* Entirely absent from Boma 5
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All day specialists/rare
Pinacopteryx eriphia (Pieridae)
Azanus jesous (Lycaenidae)Zizinia antanossa
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Time-sensitive generalists
Eurema brigitta
Junonia oenoneDanaus chrysippusHypolimnus missippus
Chilades kedonga
Pieridae
Nymphalidae
Lycaenidae
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Rare species
Dixea spp.Eronia leda
Acraea aliciaCharaxes kirkiiNeocoenyra gregorii
Papilio demodocus
Pieridae
Nymphalidae
Papilionidae
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Thank you!