sanderlings: Sanderling Moult Data

Description Usage Format Details Source References Examples

Description

This data set gives moult indices for 164 Sanderlings trapped on 11 days.

Usage

1

Format

A data frame with 164 observations on the following 2 variables.

Day

a numeric vector of day bird was measured, 1 = 1 July

MIndex

a numeric vector of moult indices, 0 = bird has not started moult, 1 = bird has completed moult

Details

This data set gives moult indices for 164 Sanderlings trapped on 11 days in the southwestern Cape, South Africa, between October 1978 and April 1979. Day 1 = 1 July). Moult indices are a transformation of moult scores so that moult index increases linearly with time. See Underhill and Zucchini (1988) for details.

Source

Underhill and Zucchini (1998)

References

Underhill, L. G. and Zucchini, W. (1988) A model for avian primary moult. Ibis 130, 358–372.

Examples

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data(sanderlings)

## fit model of type 1 to data
m1 <- moult(MIndex ~ Day, data = sanderlings, type = 1)               
summary(m1)

## model of type 2 (default)
m2 <- moult(MIndex ~ Day, data = sanderlings)                     
summary(m2)

## model of type 3
m3 <- moult(MIndex ~ Day, data = sanderlings, type = 3)              
summary(m3)

## find intercept and slope of mean moult trajectory line
uza <- - coef(m2, "mean") / coef(m2, "duration")    
uzb <- 1 / coef(m2, "duration")

## extract how many birds observed on each of the days
nn <- as.numeric(table(sanderlings$Day))        
## extract days of observations
day <- unique(sanderlings$Day)                                            

## probabilities of moult stages
## Table 6 in Underhill and Zucchini 1988
p1 <- predict(m2, newdata = data.frame(day))       
p1$M * nn

## Table 7 in Underhill and Zucchini 1988
days2 <- seq(70, 310, by = 10)
p2 <- predict(m2, newdata = data.frame(days2))
p2$M * 100                                  

p3 <- predict(m3, newdata = data.frame(day))      
p3

## Comparison with regression models
MInd <- sanderlings$MIndex[sanderlings$MIndex > 0 &
                           sanderlings$MIndex < 1]
MTime <- sanderlings$Day[sanderlings$MIndex > 0 &
                         sanderlings$MIndex < 1]

lm1 <- lm(MTime ~ MInd)                           
lm1.int <- coef(lm1)[1]
lm1.slope <- coef(lm1)[2]

lm2 <- lm(MInd ~ MTime)

## regression of Index on Time
plot(MTime, MInd, pch = 19, cex=0.7)

## regression of Time on Index: gives better estimates 
## for mean start day and duration of moult	    
abline(lm2, col = "blue", lwd = 2) 
abline(-lm1.int / lm1.slope, 1 / lm1.slope, col = "orange", lwd = 2) 
abline(uza, uzb, col = "red", lty = 2, lwd = 2)

Example output

Call:
moult(formula = MIndex ~ Day, data = sanderlings, type = 1)


Duration coefficients:
            Estimate Std. Error
intercept.1    96.59      10.53

Mean start date coefficients:
          Estimate Std. Error
intercept    133.3      3.254

Coefficients for standard deviation in start date:
            Estimate Std. Error
(Intercept)    26.56      13.65

Log-likelihood: -102.3 on 3 Df

Call:
moult(formula = MIndex ~ Day, data = sanderlings)


Duration coefficients:
            Estimate Std. Error
intercept.1    96.08      6.902

Mean start date coefficients:
          Estimate Std. Error
intercept    131.4      2.096

Coefficients for standard deviation in start date:
            Estimate Std. Error
(Intercept)    19.18      6.169

Log-likelihood:  -255 on 3 Df

Call:
moult(formula = MIndex ~ Day, data = sanderlings, type = 3)


Duration coefficients:
            Estimate Std. Error
intercept.1    99.44      18.53

Mean start date coefficients:
          Estimate Std. Error
intercept    126.4      8.817

Coefficients for standard deviation in start date:
            Estimate Std. Error
(Intercept)    17.46      6.187

Log-likelihood: -151.6 on 3 Df
         0 0 - 0.1 0.1 - 0.2 0.2 - 0.3 0.3 - 0.4 0.4 - 0.5 0.5 - 0.6 0.6 - 0.7
117  4.638   0.726     0.396     0.168     0.054     0.012     0.000     0.000
127 79.650  23.895    16.740     9.180     3.915     1.350     0.405     0.135
152  0.141   0.142     0.188     0.195     0.158     0.100     0.050     0.019
166  0.036   0.061     0.115     0.170     0.197     0.179     0.127     0.071
184  0.003   0.009     0.028     0.067     0.122     0.176     0.198     0.174
207  0.000   0.000     0.008     0.048     0.152     0.392     0.792     1.256
238  0.000   0.000     0.000     0.000     0.000     0.001     0.004     0.015
264  0.000   0.000     0.000     0.000     0.000     0.000     0.000     0.000
269  0.000   0.000     0.000     0.000     0.000     0.000     0.000     0.000
294  0.000   0.000     0.000     0.000     0.000     0.000     0.000     0.000
302  0.000   0.000     0.000     0.000     0.000     0.000     0.000     0.000
    0.7 - 0.8 0.8 - 0.9 0.9 - 1     1
117     0.000     0.000   0.000 0.000
127     0.000     0.000   0.000 0.000
152     0.006     0.001   0.000 0.000
166     0.031     0.010   0.003 0.001
184     0.120     0.064   0.027 0.012
207     1.552     1.504   1.144 1.144
238     0.040     0.086   0.145 0.709
264     0.001     0.006   0.020 0.972
269     0.003     0.009   0.033 2.955
294     0.000     0.000   0.000 5.000
302     0.000     0.000   0.000 2.000
       0 0 - 0.1 0.1 - 0.2 0.2 - 0.3 0.3 - 0.4 0.4 - 0.5 0.5 - 0.6 0.6 - 0.7
70  99.9     0.1       0.0       0.0       0.0       0.0       0.0       0.0
80  99.6     0.3       0.1       0.0       0.0       0.0       0.0       0.0
90  98.5     1.2       0.3       0.1       0.0       0.0       0.0       0.0
100 94.9     3.5       1.2       0.3       0.1       0.0       0.0       0.0
110 86.7     7.9       3.6       1.3       0.4       0.1       0.0       0.0
120 72.4    14.0       8.2       3.7       1.3       0.4       0.1       0.0
130 52.9    18.8      14.2       8.4       3.9       1.4       0.4       0.1
140 32.7    19.4      18.9      14.4       8.6       4.0       1.5       0.4
150 16.6    15.3      19.3      19.0      14.7       8.8       4.2       1.5
160  6.8     9.3      15.1      19.2      19.1      14.9       9.1       4.3
170  2.2     4.3       9.1      14.9      19.1      19.2      15.1       9.3
180  0.6     1.5       4.2       8.8      14.7      19.0      19.3      15.3
190  0.1     0.4       1.5       4.0       8.6      14.4      18.9      19.4
200  0.0     0.1       0.4       1.4       3.9       8.4      14.2      18.8
210  0.0     0.0       0.1       0.4       1.3       3.7       8.2      14.0
220  0.0     0.0       0.0       0.1       0.4       1.3       3.6       8.0
230  0.0     0.0       0.0       0.0       0.1       0.3       1.2       3.5
240  0.0     0.0       0.0       0.0       0.0       0.1       0.3       1.2
250  0.0     0.0       0.0       0.0       0.0       0.0       0.1       0.3
260  0.0     0.0       0.0       0.0       0.0       0.0       0.0       0.1
270  0.0     0.0       0.0       0.0       0.0       0.0       0.0       0.0
280  0.0     0.0       0.0       0.0       0.0       0.0       0.0       0.0
290  0.0     0.0       0.0       0.0       0.0       0.0       0.0       0.0
300  0.0     0.0       0.0       0.0       0.0       0.0       0.0       0.0
310  0.0     0.0       0.0       0.0       0.0       0.0       0.0       0.0
    0.7 - 0.8 0.8 - 0.9 0.9 - 1     1
70        0.0       0.0     0.0   0.0
80        0.0       0.0     0.0   0.0
90        0.0       0.0     0.0   0.0
100       0.0       0.0     0.0   0.0
110       0.0       0.0     0.0   0.0
120       0.0       0.0     0.0   0.0
130       0.0       0.0     0.0   0.0
140       0.1       0.0     0.0   0.0
150       0.4       0.1     0.0   0.0
160       1.6       0.5     0.1   0.0
170       4.5       1.7     0.5   0.1
180       9.5       4.6     1.8   0.7
190      15.5       9.8     4.8   2.5
200      19.5      15.8    10.0   7.6
210      18.7      19.5    16.0  18.1
220      13.7      18.5    19.6  34.9
230       7.7      13.5    18.4  55.3
240       3.3       7.5    13.3  74.3
250       1.1       3.2     7.3  88.0
260       0.3       1.1     3.1  95.5
270       0.1       0.3     1.0  98.7
280       0.0       0.1     0.2  99.7
290       0.0       0.0     0.0  99.9
300       0.0       0.0     0.0 100.0
310       0.0       0.0     0.0 100.0
$M
        0 0 - 0.1 0.1 - 0.2 0.2 - 0.3 0.3 - 0.4 0.4 - 0.5 0.5 - 0.6 0.6 - 0.7
117 0.705   0.547     0.295     0.116     0.033     0.007     0.001     0.000
127 0.487   0.424     0.315     0.170     0.067     0.019     0.004     0.001
152 0.072   0.123     0.201     0.241     0.211     0.134     0.062     0.021
166 0.012   0.034     0.086     0.161     0.219     0.217     0.158     0.083
184 0.000   0.003     0.012     0.041     0.099     0.174     0.223     0.209
207 0.000   0.000     0.000     0.002     0.009     0.034     0.089     0.174
238 0.000   0.000     0.000     0.000     0.000     0.001     0.005     0.027
264 0.000   0.000     0.000     0.000     0.000     0.000     0.000     0.003
269 0.000   0.000     0.000     0.000     0.000     0.000     0.000     0.002
294 0.000   0.000     0.000     0.000     0.000     0.000     0.000     0.000
302 0.000   0.000     0.000     0.000     0.000     0.000     0.000     0.000
    0.7 - 0.8 0.8 - 0.9 0.9 - 1     1
117     0.000     0.000   0.000 0.000
127     0.000     0.000   0.000 0.000
152     0.005     0.001   0.000 0.000
166     0.032     0.009   0.002 0.000
184     0.143     0.071   0.026 0.008
207     0.246     0.254   0.192 0.140
238     0.103     0.286   0.577 0.756
264     0.027     0.173   0.796 0.986
269     0.021     0.153   0.825 0.993
294     0.005     0.077   0.919 1.000
302     0.003     0.060   0.937 1.000

moult documentation built on Jan. 12, 2018, 5:03 p.m.