Lab 2 Submission

Part B

Qualitative Impressions Table
S Simpson (1/D) Simpson Evenness Shannon (H’) Shannon Evenness (J’)
Possible range of values 0 to Positive Infinity 1 to S (Positive Infinity) 0 to 1 0 to ln(S) (Positive Infinity 0 to 1
My community #9 20 4.65730771011776 0.232865385505888 2.05333394261707 0.685419708811659
Most similiar #20 20 4.76915168175062 0.238457584087531 2.07339150412543 0.692115087329101
Least similar #19 14 1.72253742529115 0.123038387520796 0.785099661982811 0.297492461862232

Part C

Comparing the different indices of evenness, my qualitative assessments of similarity were relatively accurate. Dissimilarity was harder to determine as patterns were not as apparent. According to the indices, community 14 was the least similar and community 20 was the most similar (to community 9).

Part D

Ordering communities based on similarity is difficult as there are many indices that all represent different properties of the community.

Part E

E1

Communities A and B are most similar. Communities B and C are most dissimilar. If only using the Sorenson index, A and C would be the most similar. However, Sorenson only measures absence and presence; so we determined it not very robust overall. Bray-Curtis index accounts for abundance, which we thought was more appropriate for this context. Across most indices, B and C were the most dissimilar; albeit the Bray-Curtis index was a few points higher (likely statistically insignificant) over A and C.

Similiarity Indices of Plant Communities
B C
Correlation A 0.9943766 0.1596325
Sorenson 0.666666666666667 0.733333333333333
Bray-Curtis 0.368728121353559 0.159817351598174
Correlation B – 0.1381449
Sorenson – 0.454545454545455
Bray-Curtis – 0.197014925373134

E2

Minimum is 0 and maximum is 1 for the correlation coefficient.

E3

Minimum is 0 and maximum is 1 for the Sorenson index of similarity. Looking from the Sorenson formula, if no species are present in both sites, the numerator is 0. Thus lowest is 0. If all species are present in both sites, numerator and denominator are equal. Thus highest is 1.

E4

Minimum is 0 and maximum is 1 for the Bray-Curtis index. To get the maximum, communities must have all the same species at the same abundance. To get the minimum, communities must have no same species.

E5

If a species is absent in the comparison communities, this species cannot not affect the indices. However, if a species was removed from the comparison communities (and before/after indices were measured), the Sorenson index would generally be more affected than Bray-Curtis.

E6

In this plant context, an ecological reason why a species could be absent from a sample may be seasonal. Some plants have no/low biomass during specific seasons, leading to absence/undersampling.

Sampling related reasons may be due to chance (huamn error) or non random sampling.

E7

If the abundance of native species has not changed, and my goal is conservation of native species, I would use the Bray-Curtis index as it takes into account the abundance of the exotic species.

I would also use a ranked percentage abundance graph to show the abundance of exotic species. Below is hypothetical data for a grass community with uneven species abundance distribution. If we expect the 2000 unit abundance specie as the exotic species, the high percent abundance is highlighted in the graph.

A regular log-base 10 rank abundance curve is also provided. The shape of the curve can be compared with other communities to determine rate of exotic species proliferation.

hyp.raw <- data.frame(c(2000, 400, 340, 154, 45, 32, 20, 10, 7, 2, 1))

ranks <- data.frame(c(1:11))

hyp.data <- data.frame(c(hyp.raw, (hyp.raw/sum(hyp.raw)*100), ranks))

colnames(hyp.data) <- c("Abundance", "Percentage", "Rank")

ggplot(data = hyp.data, aes(y = Percentage, x = Rank, fill = Percentage)) +
  geom_bar(stat = 'identity') +
  ylab("Percentage Abundance") +
  xlab("Species Rank") +
  labs(title = "Ranked Percent Abundance")
ggplot(data = hyp.data, aes(y = Abundance, x = Rank)) +
  geom_point() +
  geom_smooth(se = FALSE, method = "loess", formula = y ~ x) +
  scale_y_continuous(trans='log10') +
  labs(title = "Rank Abundance Curve") +
  ylab("Logged Abundance")
Hypothetical Exotic Species Data
Abundance Percentage Rank
2000 66.4231152 1
400 13.2846230 2
340 11.2919296 3
154 5.1145799 4
45 1.4945201 5
32 1.0627698 6
20 0.6642312 7
10 0.3321156 8
7 0.2324809 9
2 0.0664231 10
1 0.0332116 11