# 107 chapter 5 structural equation models sem

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107Chapter 5Structural Equation Models (SEM) Predicting Species Recurrence-indexSEMs were used to predict each species’ recurrence-index, a continuous responsevariable that measures both how many times a species was observed in an occupied cell and ameasure of its abundance in the cell. In general, the SEMs explain more of the observedvariation in the spatial distribution of smaller grazers than larger grazers (Fig. 5).The SEM forThomson’s gazelle explains 39% of the overall observed variance in their recurrence-index,whereas the SEM for buffalo explains only 3%.The correlations between exogenous environmental variables (i.e. variables on the farleft of Fig. 5) are less than 0.35 for all SEM’s, with the exception of rainfall, woody cover, anddistance to drainage beds for the Thomson’s gazelle SEM and the Grant’s gazelle SEM only.Thesmall correlation between the exogenous variables suggests that the unique variation of thevariables is high and therefore the conceptual nature of the composite variable is notconfounded by other variables. The correlations between exogenous variables change betweenthe SEM’s for each species because they occupy different areas.The SEM reflects the real corre-lation among the variables in the habitats the species occupies.Previous SEM’s included proximity to humans and water as additional variablesaccounting for the variation in the recurrence-index, however these models were not supportedby the data.This suggests that the factors that predict a species recurrence-index are a subsetof those predicting presence/absence.The chi-square associated with the SEM describing the buffalo recurrence-indexsuggests the data supports oura prioriinterpretation (χ2=5.99, df=7, p=0.540), however themodel explains very little of the overall observed variance (3%) (Fig. 5a).Only the abundanceof food (as estimated by rainfall and the topographic wetting index) is significant in explainingthe variation in the buffalo recurrence-index (standardized path strength = 0.14).The exposureto predation risk (as estimated by the amount of woody cover available to ambush predators,the distance to riverbeds, and the landscape curvature) is not significant in explaining thebuffalo recurrence-index.The amount of nitrogen available in the grass (i.e. forage quality) onlyexplains 0.02 of the variation in the buffalo recurrence-index and was also not significant.The data on the recurrence of Coke’ hartebeest support the structural equation model2=6.52, df=7, p=0.480).The SEM describes 8% of the variation in Coke’s hartebeest recur-rence-index, which is negatively related to food quality (standardized path strength = -0.13),food abundance (-0.19) and risk of predation (-0.17) approximately equally (Fig. 5b).The recurrence-index of topi is positively related to the abundance of food (0.36),while the risk of predation and food quality had non-significant negative trends.The SEMdescribing the topi recurrence-index is supported by thedata (χ2=9.57, df=6, p=0.144), andaccounts for 14% of the overall observed variance (Fig. 5c).

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Term
Spring
Professor
Drefert
Tags
Tanzania, Masai Mara, Serengeti, Lion, Zebra, Ngorongoro Conservation Area