Supplementary Material: Biswas SR, MacDonald RL, Chen HYH. Disturbance increases negative spatial autocorrelation in species diversity. Landscape Ecology

Appendix A.List of species identified in this study*

Abiesbalsamea / Erysimumcheiranthoides / Populustremuloides
Achilleamillefolium / Fragariavesca / Ptilium crista-castrensis
Actaearubra / Galium spp. / Pyrolaasarifolia
Agrostisscabra / Gaultheria hispida / Pyrolasecunda
Alnuscrispa / Geummacrophyllum / Ribesoxyacanthoides
Alnusrugosa / Goodyeraoblongifolia / Ribes triste
Anemone quinquefolia / Gymnocarpiumdryopteris / Rosa acicularis
Aralia nudicaulis / Habenariaobtusata / Rubusidaeus
Arctostaphylosuva-ursi / Heracleumlanatum / Geocaulonlividum
Arnica cordifolia / Hylocomniumsplendens / Rumex spp.
Aster ciliolatus / Impatiens capensis / Salix spp.
Athyriumfilix-femina / Lathyrusochroleucus / Saniculamarilandica
Aulocomiumpalustre / Ledumgroenlandicum / Saxifrage spp.
Betulapapyrifera / Linnea borealis / Schizachnepurpurascens
Brachythecium spp. / Lonicerainvolucrata / Sheperdiacanadensis
Calamagrostiscanadensis / Luzulaparviflora / Smilacinatrifolia
Calthapalustris / Lycopodium annotinum / Sonchusarvensis
Cardaminepensylvanica / Lycopodium obscurum / Sphagnum spp.
Carex spp. / Maianthemumcanadense / Stellariacalycantha
Castillejaminiata / Mertensiapaniculata / Streptopusamplexifolius
Cinna latifolia / Mitellanuda / Rubuspedatus
Circaeaalpina / Oryzopsisasperifolia / Taraxacumofficinale
Cladina spp. / Osmorhizachilensis / Thalictrumdasycarpum
Cladonia spp. / Pedicularislabradorica / Tiarellatrifoliata
Cllimaciumdendroides / Oxycoccusmicrocarpus / Urticadiocia
Conocephalumconicum / Peltigera spp. / Usnea spp.
Coptistrifolia / Petasitespalmatus / Vaccinium spp.
Cornuscandensis / Phleumpratense / Vacciniumvitis-ideae
Danthonia intermedia / Piceaglauca / Veronica americanus
Delphinium glaucum / Piceamariana / Vibernumedule
Dicranum spp. / Pinuscontorta / Viceaamericana
Drepanocladus spp. / Plagiomnium spp. / Viola spp.
Dryopteris spp. / Pleuroziumschreberi
Epilobiumangustifolium / Polytrichium juniper
Equisetum spp. / Populusbalsamifera

…….*see the excel file (a separate file) for treatment and year wise species cover value for each site (raw data)

Appendix B

R-scripts for quantifying overall, positive and negative components of spatial patterns (Moran’s coefficient) in species richness and evenness and their significance testing

Source: Biswas, S. R., Mallik, A. U., Braithwaite, N. T. and Wagner, H. H. 2016. A conceptual framework for the spatial analysis of functional trait diversity. – Oikos125 (2): 192-200.

require(vegan)

require(spdep)

require(spacemakeR)

# Import data and prepare spatial weight

data <- read.csv("becky.csv", header=TRUE)

sp.cord=matrix(cbind(data$x.cord,data$y.cord), ncol=2) # spatial coordinates

#Assign distance-based neighbours and create spatial weight matrix (W)

nb <- dnearneigh(sp.cord, 0.1,5.1) # distance-based neighbour[threshold 5.1m ]

listw <- nb2listw(nb,style="W") # row standardized weight

S0 = sum(unlist(weights(listw)))

n = length(weights(listw))

## Positive and negative spatial patterns

sc.tri <- scores.listw(listw)

MEM=sc.tri

n.pos <- sum(MEM$values > 0) ## summing all the positive eigenvalues

y=data$richness ## here we are calculating for species richness, for example

MCk <-cor(y,MEM$vectors)^2 * MEM$values

Subset <- 1:n.pos

S.tot.obs <- sum(MCk) ## overall spatial pattern (eqn. 1)

S.pos.obs <- sum(MCk[Subset]) ## positive spatial pattern (eqn. 2)

S.neg.obs <- sum(MCk[-Subset]) ## negative spatial pattern (eqn. 3)

S.obs <- c(S.tot.obs, S.pos.obs, S.neg.obs)

S.obs

## Significance testing

R = 999

MC.sim <- matrix(NA, R, 3, dimnames=list(NULL, c("S.tot","S.pos","S.neg")))

for(r in 1:R)

{

MCk.sim <-cor(sample(y),MEM$vectors)^2 * MEM$values ##randomization of y

Subset <- 1:n.pos

MC.sim[r,1] <- sum(MCk.sim)

MC.sim[r,2] <- sum(MCk.sim[Subset])

MC.sim[r,3] <- sum(MCk.sim[-Subset])

}

MC.sim <- data.frame(MC.sim)

p.value.pos <- (sum(MC.sim$S.pos >= S.pos.obs) + 1) / (R+1)

p.value.neg <- (sum(MC.sim$S.neg <= S.neg.obs) + 1) / (R+1)

p.value.tot <- (sum(abs(MC.sim$S.tot - mean(MC.sim$S.tot)) >= abs(S.tot.obs

- mean(MC.sim$S.tot))) + 1) / (R+1)

p.value <- c(p.value.tot, p.value.pos, p.value.neg)

p.value

Appendix C

Mean values of species richness and evenness in riparian plant communities for disturbed versus control sites, and for pre- and 1, 5 and 7 years of post-disturbance conditions. Repeated measures analyses of variance (model syntax same as equation 3 in main text) showed that the mean values of richness and evenness varied significantly with sampling year and disturbance × sampling year interactions (P <0.05). Bars containing the same letter did not differ significantly at α = 0.05, as identified by post-hoc analyses (Tukey). See MacDonald et al. 2014 for further detail.

MacDonald RL, Chen HYH, Palik BJ, Prepas EE (2014) Influence of harvesting on understory vegetation along a boreal riparian-upland gradient. Forest Ecol Manag 312:138-147

Appendix D

Results of repeated measures analysis of variance showing changes in spatial patterns in species richness and evenness as a result of disturbance

Response (St-So) / Df / Mean Square / F-value / P
(a) Overall spatial autocorrelation in richness
Between subjects
Disturbance / 2 / 0.526 / 3.536 / 0.074
Error-1 / 9 / 0.148
Within subjects
Year / 2 / 0.168 / 1.916 / 0.176
Disturbance × Year / 4 / 0.079 / 0.904 / 0.482
Error-2 / 18 / 0.087
(b) Negative spatial autocorrelation in richness
Between subjects
Disturbance / 2 / 0.202 / 3.719 / 0.067
Error-1 / 9 / 0.054
Within subjects
Year / 2 / 0.028 / 1.144 / 0.341
Disturbance × Year / 4 / 0.013 / 0.528 / 0.717
Error-2 / 18 / 0.024
(c) Positive spatial autocorrelationin richness
Between subjects
Disturbance / 2 / 0.084 / 1.974 / 0.195
Error-1 / 9 / 0.043
Within subjects
Year / 2 / 0.059 / 2.249 / 0.134
Disturbance × Year / 4 / 0.030 / 1.121 / 0.377
Error-2 / 18 / 0.026
(d) Overall spatial autocorrelation in evenness
Between subjects
Disturbance / 2 / 0.579 / 2.512 / 0.136
Error-1 / 9 / 0.230
Within subjects
Year / 2 / 0.078 / 0.664 / 0.527
Disturbance × Year / 4 / 0.078 / 0.667 / 0.623
Error-2 / 18 / 0.117
(e) Negativespatial autocorrelation in evenness
Between subjects
Disturbance / 2 / 0.338 / 4.526 / 0.044
Error-1 / 9 / 0.075
Within subjects
Year / 2 / 0.025 / 0.806 / 0.462
Disturbance × Year / 4 / 0.007 / 0.234 / 0.382
Error-2 / 18 / 0.030
(f) Positivespatial autocorrelation in evenness
Between subjects
Disturbance / 2 / 0.001 / 0.006 / 0.940
Error-1 / 9 / 0.108
Within subjects
Year / 2 / 0.019 / 0.0535 / 0.595
Disturbance × Year / 4 / 0.040 / 1.110 / 0.382
Error-2 / 18 / 0.036