Effects of livestock grazing and environmental parameters on butterfly species richness and

community composition in an East African catena

Xingli Giam and Ann Thomas

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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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?

Boma 2OldCattleTransition Soil

Field Sites

Boma 1NewCattleRed soil

Boma 3OldCattleTransition Soil

Boma 4NewSheepTransition Soil

Boma 5OldCattleRed Soil

Methods

BomaH DG C BF AE

1000 m

500 m

250 m

100 m

Methods

25m

10m

Methods

Estimating percent cover

Confounding Factors

Results: Species Richness Estimation through Incidence Rarefaction Curves

Estimating Species Richness1 1 1 1

1 1 1

1 1 1

1 1 1

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1 1 1 1

1 1

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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

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)

Effects of Pastoral Practices on Community Composition

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”

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

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

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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

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1 1 1 1

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1 1 1

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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

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

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

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diff diff B4-B5 17 3 1 40.23529

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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

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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

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

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

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

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

• 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

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)

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)

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

2.0 2.5 3.0 3.5

810

12

14

16

log(GrassFl)

Species

2.0 2.5 3.0 3.58

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12

14

16

log(GrassFl)

Species

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)

Natural History: Time to Meet the Butterflies!

All day generalists

Belenois spp.Colotis spp.Catopsilia florellaPontia heliceJunonia hiertaVanessa cardui*Zizeeria knysa

Pieridae

Lycaenidae

Nymphalidae

* Entirely absent from Boma 5

All day specialists/rare

Pinacopteryx eriphia (Pieridae)

Azanus jesous (Lycaenidae)Zizinia antanossa

Time-sensitive generalists

Eurema brigitta

Junonia oenoneDanaus chrysippusHypolimnus missippus

Chilades kedonga

Pieridae

Nymphalidae

Lycaenidae

Rare species

Dixea spp.Eronia leda

Acraea aliciaCharaxes kirkiiNeocoenyra gregorii

Papilio demodocus

Pieridae

Nymphalidae

Papilionidae

Thank you!