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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.

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Presentation on theme: "Effects of livestock grazing and environmental parameters on butterfly species richness and community composition in an East African catena Xingli Giam."— Presentation transcript:

1 Effects of livestock grazing and environmental parameters on butterfly species richness and community composition in an East African catena Xingli Giam and Ann Thomas

2 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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4 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?

5 Boma 2 Old Cattle Transition Soil Field Sites Boma 1 New Cattle Red soil Boma 3 Old Cattle Transition Soil Boma 4 New Sheep Transition Soil Boma 5 Old Cattle Red Soil

6 Methods Boma HDGCBFAE 1000 m 500 m 250 m 100 m

7 Methods 25m 10m

8 Methods

9 Estimating percent cover

10 Confounding Factors

11 Results: Species Richness Estimation through Incidence Rarefaction Curves

12 Estimating Species Richness Day

13 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 1 Day 2 > D2 D3 RSE 20% of Day 3 species number Half the plots fail these criteria (mostly the 2 nd )

14 Effects of Pastoral Practices on Community Composition

15 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

16 Diversity at the Community Level β diversity – Comparison of community composition between two sites – β diversity = ((unique A) + (unique B))/(shared species) 6 unique 10 shared 4 unique Example to the right: β diversity = 1.0

17 β diversity as a proxy for community homogeneity around bomas Boma 1BBoma 1C Unique to Site 1B: 3 species Unique to Site 1C: 1 species Common to both: 10 species β diversity / shared = 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 Boma HDGCBFAE

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19 After Bonferroni correction, no evidence of significant relationship between distance and β diversity

20 Effect of Soil Type and Boma Age on β diversity soilage B diversity analysisSharedAdiffBdiffββ/shared diff B1-B diff B1-B diff B4-B diffsameB1-B diffsameB2-B diffsameB3-B samediffB1-B samediffB2-B samediffB3-B same B2-B

21 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

22 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: S ij ~ Pois(μ ij ), a ~ N(0, σ a 2 ) 1.μ ij = exp(β 0 + β 1 Age ij + a i ) 2.μ ij = exp(β 0 + a i ), for the jth transect in ith boma

23 S/NModelnkAIC c dAIC c wAIC c %DE Nullμ = exp(2.54) μ = exp(0.048*Dist ) 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

24 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: S ij ~ Pois(μ ij ), a ~ N(0, σ a 2 ) 1.μ ij = exp(β 0 + β 1 Age ij + a i ) 2.μ ij = exp(β 0 + a i ), for the jth transect in ith boma

25 S/NModelnkAIC c dAIC c wAIC c %DE Nullμ = exp(2.48) μ = exp(-0.09*Status ) % 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

26 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

27 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: S ij ~ Pois(μ ij ), a ~ N(0, σ a 2 ) Sample model: μ ij = exp(β 0 + β 1 Cover ij + β 1 Shrub ij + a i )

28 Candidate models No.ModelNo.Model 1~ Cover11~ log(GrassFl) + log(NonGrassFl) 2~ Cover 2 + Cover12~log(GrassFl) + Cover + Shrubs 3~ Shrubs13~ log(GrassFl) + Cover 2 + Cover + Shrubs 4~ log(GrassFl)14 ~ log(GrassFl) + log(NonGrassFl) + Cover + Shrubs 5~ log(NonGrassFl)15 ~ log(GrassFl) + log(NonGrassFl) + Cover 2 + Cover + Shrubs 6~ Cover + ShrubsNull~ 1 7~ Cover 2 + Cover + Shrubs 8~ Cover + log(GrassFl) 9~ Cover 2 + Cover + log(GrassFl) 10~ Shrub + log(GrassFl)

29 No.ModelnkAIC c dAIC c wAIC c %DE Nullμ = exp(2.48) μ = exp(0.14*log(GrassFl) ) μ = exp(-0.95*Cover *Cover ) μ = exp(0.006*Shrubs ) μ = exp(0.024*NonGrassFl ) 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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31 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)

32 Natural History: Time to Meet the Butterflies!

33 All day generalists Belenois spp. Colotis spp. Catopsilia florella Pontia helice Junonia hierta Vanessa cardui* Zizeeria knysa Pieridae Lycaenidae Nymphalidae * Entirely absent from Boma 5

34 All day specialists/rare Pinacopteryx eriphia (Pieridae) Azanus jesous (Lycaenidae) Zizinia antanossa

35 Time-sensitive generalists Eurema brigitta Junonia oenone Danaus chrysippus Hypolimnus missippus Chilades kedonga Pieridae Nymphalidae Lycaenidae

36 Rare species Dixea spp. Eronia leda Acraea alicia Charaxes kirkii Neocoenyra gregorii Papilio demodocus Pieridae Nymphalidae Papilionidae

37 Thank you!


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