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1 Landscape effects on mallard habitat selection at multiple spatial scales during the nonbreeding period William S. Beatty, Elisabeth B. Webb, Dylan C. Kesler, Andrew H. Raedeke, Luke W. Naylor, Dale D. Humburg Supplementary Material Appendix 1 Mean season dates according to year assigned to birds (n =14) that did not meet criteria for inclusion in migration analyses (Beatty et al. 2013): Autumn migration 2010: 5 November – 24 November Winter 2010-2011: 25 November – 6 March Spring migration 2011: 7 March – 24 April Autumn migration 2011: 25 October – 4 December Winter 2011-2012: 5 December – 26 February Spring migration 2012: 27 February – 15 April Appendix 2 Multinomial logit hierarchical model in WinBUGS to examine habitat selection of midcontinent models called using R2WinBUGS. Although this program has been used by the U.S. Geological Survey (USGS), no warranty, expressed or implied, is made by the USGS or the U.S. Government as to the accuracy and functioning of the program and related program material nor shall the fact of distribution constitute any such warranty, and no responsibility is assumed by the USGS in connection therewith. Data were initially in long format where one row represents one alternative. Thus, each choice set contains as many rows as there are alternatives. Variables: T = the total number of rows in the data sheet, equal to the number of alternatives within each choice set × the number of choice sets. chsets = indexes choice sets in long format, ranges from 1 to the total sample size. alts = indexes alternatives in long format within a choice set, ranges from 1 to 20 for local scale, 1 to 25 for relocation scale. use = use in long format, 0 for available resource units, 1 for used resource units. nchsets = the number of choice sets, equal to the total sample size. nalts = the number of alternatives within a choice set, equal to 20 at the local scale, 25 at relocation scale. ninds = the number of unique individuals. water.dist = distance to the nearest water feature, centered and standardized. water.ha = water area in 3.46 km buffer, centered and standardized. X = a matrix of habitat covariates in long format. npred = the number of predictors, equal to the number of columns for matrix X. DuckID = indexes individual ducks in long format, ranges from 1 to ninds. 2 duckid2 = indexes individual ducks in wide format, ranges from 1 to ninds. R script to import data to WinBUGS: data <- as.data.frame(read.csv("data.csv", header=T)) T <- nrow(data) chsets <- data$ChoiceSet alts <- data$Alt use <- data$Use nchsets <- max(data$ChoiceSet) nalts <- max(data$Alt) ninds <- max(data$DuckID) water.dist <- data$WaterDist water.ha <- data$WaterHa X = cbind(water.dist, water.ha) npred <- ncol(X) duckid2 <- subset(data, Use==1)$DuckID win.data <- list(npred=as.integer(npred), duckid2=as.integer(duckid2), ninds=as.integer(ninds), chsets=as.integer(chsets), nchsets=as.integer(nchsets), alts=as.integer(alts), nalts=as.integer(nalts), use=as.numeric(use), T=as.integer(T), X=X) WinBUGS model: model { ## Loops to transcribe Y and X matrices to wide format. for (i in 1:T) { y[chsets[i],alts[i]] <- use[i] for (j in 1:npred) {Z[j,chsets[i],alts[i]] <- X[i,j]} } ## Priors for (a in 1:ninds){ for (j in 1:npred) { beta[a,j] ~ dnorm(mu[j], tau[j]) } } 3 ## Hyperparameters for (j in 1:npred){ mu[j] ~ dnorm (0,0.01) tau[j] <- 1/(sig[j]*sig[j]) sig[j] ~ dunif(0,100) } ## Likelihood for (i in 1:nchsets) { y[i,1:nalts] ~ dmulti(p[i,1:nalts], 1) for (k in 1:nalts) { log(phi[i,k]) <- inprod2(beta[duckid[i],],Z[,i,k]) p[i,k] <- phi[i,k] / sum(phi[i,1:nalts]) } } } Appendix 3 GPS fixes were obtained at specific hours of the day (CST): 00:00 (midnight), 01:00, 09:00, 10:00, 15:00, 16:00, 19:00, 20:00. We classified locations obtained at 00:00, 01:00, 19:00, and 20:00 as nocturnal locations whereas locations obtained at 09:00, 10:00, 15:00, and 16:00 were classified as diurnal locations. Appendix 4 Set of candidate models to evaluate alternative hypotheses regarding mallard habitat selection: 1. A null model that assumed the probability of use for all alternatives was equal to random expectations (1/20 for the local scale, 1/25 for the relocation scale). 2. A general model that included proximity to wetland habitat (WetPrx). This model would be appropriate if ducks did not exhibit selection for specific types of wetland habitats and landscape composition did not play a prominent role in habitat selection. Additionally, if model 1 or model 2 were consistently topranked, this would indicate that geospatial data (NLCD 2006) were not adequate to predict habitat selection. 3. A general landscape composition model that included proximity to wetland habitat (WetPrx) and wetland habitat area (WetAr). This model would be appropriate if ducks did not exhibit selection for specific types of wetlands yet landscape composition influenced habitat selection. 4. A local model that included proximity to cultivated crops (CrpPrx), proximity to emergent herbaceous wetland (EmgPrx), proximity to open water (OwtrPrx) 4 5. and proximity to woody wetlands (WodyPrx). This model would be appropriate if ducks selected resource units based on local variables. A full model that included CrpPrx, EmgPrx, OwtrPrx, WodyPrx, cultivated crop area (CrpAr), emergent herbaceous wetlands area (EmgAr), open water area (OwtrAr), and woody wetlands area (WodyAr). The full model would be appropriate if ducks selected resources units based on local variables and potential to provide opportunities for foraging and roosting at relatively broad spatial scales. Appendix 5 Table S1. Candidate models and associated Markov Chain Monte Carlo (MCMC) settings to examine midcontinent mallard habitat selection during autumn migration, winter, and spring migration from 2010–2012. Model 1 Iterations Burnin 100,000 100,000 100,000 Thinning 1,000 1,000 4,000 Chains 3 3 3 Null WetPrx 1,100,000 WetPrx + WetAr 1,100,000 CrpPrx + EmgPrx + OwtrPrx + WodyPrx 4,100,000 CrpPrx + EmgPrx + OwtrPrx + WodyPrx + CrpAr + EmgAr + OwtrAr + WodyAr2 4,100,000 100,000 4,000 3 1 Null model assumed probability of use equal to random expectations. 2 Local scale, spring day iterations = 8,000,000; burnin = 4,000,000; thinning = 4,000; chains = 3. 5 Table S2. Delta deviance information criterion (DIC) for models that examined habitat selection of midcontinent mallards from 2010–2012 at the local scale (0.25–30.00 km movements) during the non-breeding period of the life cycle. Model CrpPrx + EmgPrx + OwtrPrx + WodyPrx + CrpAr + EmgAr + OwtrAr + WodyAr CrpPrx + EmgPrx + OwtrPrx + WodyPrx WetPrx + WetAr WetPrx Null Fall Migration Winter Diurnal Nocturnal Diurnal 0.00 55.57 208.73 221.03 604.67 0.00 74.20 182.80 184.80 486.92 0.00 1215.78 1879.10 2354.50 3606.16 Spring Migration Nocturnal Diurnal Nocturnal 0.00 940.22 1689.23 1934.13 3096.03 0.00 516.62 803.21 1093.01 1758.01 0.00 501.98 854.35 1141.35 2062.25 6 Table S3. Delta deviance information criterion (DIC) for models that examined habitat selection of midcontinent mallards from 2010–2012 at the relocation scale (>30.00 km movements) during the non-breeding period of the life cycle. Model Relocation WetPrx + WetAr 0.00 CrpPrx + EmgPrx + OwtrPrx + WodyPrx + CrpAr + EmgAr + OwtrAr + WodyAr 6.52 WetPrx 12.20 CrpPrx + EmgPrx + OwtrPrx + WodyPrx 12.94 Null 128.20