ecology of phasmids - KLUEDO - Universität Kaiserslautern
ecology of phasmids - KLUEDO - Universität Kaiserslautern
ecology of phasmids - KLUEDO - Universität Kaiserslautern
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Community structure & host range 13<br />
phasmid community structure (e.g., niche breadth or niche overlap) for three reasons: 1) these data were<br />
biased towards collection on specific host plants and hence towards focal phasmid species; 2) rare<br />
phasmid species were underrepresented in these field records; and 3) species abundances would be<br />
underestimated because <strong>of</strong> the collection (i.e., removal) <strong>of</strong> specimens from the community. To calculate<br />
relative abundances <strong>of</strong> phasmid species data from line-transects in the understory and at forest edges<br />
were pooled (for each habitat per monitor date) due to too many zero counts, especially in the<br />
understory.<br />
This data also built the pool for the calculation <strong>of</strong> diversity, eveness and similarity <strong>of</strong> phasmid<br />
communities for the specific habitat. All measures <strong>of</strong> community structure were based on methods<br />
described by Krebs (1989). To facilitate comparison among studies, I decided to use the most common<br />
measures (applicability to the dataset presupposed).<br />
Similarity in species composition between habitats was calculated with the index <strong>of</strong> Morisita Cλ:<br />
Equation 2-1<br />
C<br />
λ<br />
2<br />
= ∑<br />
n<br />
X<br />
ij<br />
X<br />
ik<br />
( λ1<br />
+ λ2<br />
) N j N k<br />
where Cλ = Morisita’s index <strong>of</strong> similarity between samples j and k<br />
Xij, Xik = Number <strong>of</strong> individuals <strong>of</strong> species i in samples j and k<br />
Nj, Nk = ∑Xij, ∑Xik = Total number <strong>of</strong> individuals in sample j and in sample k respectively<br />
[ X ij ( X ij −1)<br />
]<br />
( N −1)<br />
1 = ∑<br />
n<br />
λ and<br />
N<br />
j<br />
j<br />
2 = ∑<br />
n<br />
λ<br />
N<br />
[ X ik ( X ik −1)<br />
]<br />
( N −1)<br />
The Morisita index <strong>of</strong> similarity is a quantitative measure for similarity and ranges from 0 for no<br />
similarity in species composition to 1 for identical species composition.<br />
Species diversity and eveness were calculated on the base <strong>of</strong> the Shannon-Wiener Function:<br />
Equation 2-2<br />
H<br />
'<br />
=<br />
s<br />
∑<br />
i=<br />
1<br />
( p )(log<br />
i<br />
2<br />
where H’ = Index <strong>of</strong> species diversity measured by the Shannon–Wiener function<br />
s = Number <strong>of</strong> species<br />
pi = Proportion <strong>of</strong> total sample belonging to the species i<br />
The Shannon–Wiener index <strong>of</strong> species diversity expresses the uncertainty <strong>of</strong> predicting the species <strong>of</strong><br />
the next individual sampled. Thus if H’ = 0 then all individuals in a sample belong to one species. H’ is<br />
increasing with the number <strong>of</strong> species in a sample and with increasing eveness <strong>of</strong> these species. The<br />
most common approach to measure eveness is to scale the observed index <strong>of</strong> species diversity relative to<br />
its maximum value when each species in the sample is represented by the same number <strong>of</strong> individuals.<br />
In case <strong>of</strong> the Shannon Wiener Function maximum diversity occurs when p = 1/S and can be calculated<br />
as:<br />
Equation 2-3<br />
'<br />
H MAX =<br />
log 2<br />
S<br />
p )<br />
i<br />
k<br />
k