Bright moonlight triggers natal dispersal departures
Behavioral Ecology and Sociobiology
Vincenzo Penteriania,e, María del Mar Delgadob, Anna Kuparinenc,d, Pertti Saurolae, Jari Valkamae, Eino Salof, Jere Toivolag, Adrian Aebischerh and RaphaëlArlettazh
a Department of Conservation Biology, Estación Biológica de Doñana, C.S.I.C., c/ Americo Vespucio s/n, 41092 Seville, Spain (email: )
bMetapopulation Research Group, Department of Biosciences, University of FI-00014 Helsinki, Finland
c Department of Environmental Sciences, d Department of Biosciences, University of Helsinki, FI-00014 Helsinki, Finland
eFinnish Museum of Natural History, Zoology Unit, University of Helsinki, FI-00014 Helsinki, Finland
f Paloniityntie 90, Forssa, Finland
g Taatilantie 74, Tarttila, Finland
hDivision of Conservation Biology, Institute of Ecology and Evolution, University of Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
Supplementary Material
Here we give details of linear mixed-effects model showing the effect of moon phase, age and sex of individuals on the decision to start dispersal:maximum models before simplification and steps of the optimal model selection are described.
Linear mixed-effects model fitted by maximum likelihood
Date of dispersal (juldisp)
Selection of random factor
reg1=gls(juldisp~z.age+sex+I(cos(z.rad))+I(sin(z.rad))+I(cos(2*z.rad))+I(sin(2*z.rad))+I(cos(z.rad)):z.age+I(sin(z.rad)):z.age + I(cos(2*z.rad)):z.age+I(sin(2*z.rad)):z.age, data=owls, method="REML")
reg2=lme(juldisp~z.age+sex+I(cos(z.rad))+I(sin(z.rad))+I(cos(2*z.rad))+I(sin(2*z.rad))+I(cos(z.rad)):z.age+I(sin(z.rad)):z.age + I(cos(2*z.rad)):z.age+I(sin(2*z.rad)):z.age, random=~1|year,data=owls, method="REML")
reg3=lme(juldisp~z.age+sex+I(cos(z.rad))+I(sin(z.rad))+I(cos(2*z.rad))+I(sin(2*z.rad))+I(cos(z.rad)):z.age+I(sin(z.rad)):z.age + I(cos(2*z.rad)):z.age+I(sin(2*z.rad)):z.age, random=~1|country,data=owls, method="REML")
reg4=lme(juldisp~z.age+sex+I(cos(z.rad))+I(sin(z.rad))+I(cos(2*z.rad))+I(sin(2*z.rad))+I(cos(z.rad)):z.age+I(sin(z.rad)):z.age + I(cos(2*z.rad)):z.age+I(sin(2*z.rad)):z.age, random=~1|nest,data=owls, method="REML")
reg5=lme(juldisp~z.age+sex+I(cos(z.rad))+I(sin(z.rad))+I(cos(2*z.rad))+I(sin(2*z.rad))+I(cos(z.rad)):z.age+I(sin(z.rad)):z.age + I(cos(2*z.rad)):z.age+I(sin(2*z.rad)):z.age, random=~1|year/country,data=owls, method="REML")
reg6=lme(juldisp~z.age+sex+I(cos(z.rad))+I(sin(z.rad))+I(cos(2*z.rad))+I(sin(2*z.rad))+I(cos(z.rad)):z.age+I(sin(z.rad)):z.age + I(cos(2*z.rad)):z.age+I(sin(2*z.rad)):z.age, random=~1|country/nest,data=owls, method="REML")
reg7=lme(juldisp~z.age+sex+I(cos(z.rad))+I(sin(z.rad))+I(cos(2*z.rad))+I(sin(2*z.rad))+I(cos(z.rad)):z.age+I(sin(z.rad)):z.age + I(cos(2*z.rad)):z.age+I(sin(2*z.rad)):z.age, random=~1|year/nest,data=owls, method="REML")
reg8=lme(juldisp~z.age+sex+I(cos(z.rad))+I(sin(z.rad))+I(cos(2*z.rad))+I(sin(2*z.rad))+I(cos(z.rad)):z.age+I(sin(z.rad)):z.age + I(cos(2*z.rad)):z.age+I(sin(2*z.rad)):z.age,random=~1|year/country/nest, data=owls, method="REML")
AIC(reg1,reg2,reg3,reg4,reg5,reg6,reg7,reg8)
df AIC
reg1 12 1351.641
reg2 13 1307.194
reg3 13 1222.852
reg4 13 1266.246
reg5 14 1249.273
reg6 14 1197.585
reg7 14 1095.048
reg8 15 1072.457
Full model
AIC BIC logLik
1100.168 1114.611 -535.084
Random effects:
Formula: ~1 | year
(Intercept)
SD: 0.00445135
Formula: ~1 | country inyear
(Intercept)
SD: 25.25119
Formula: ~1 | nest in country in year
(Intercept) Residual
SD: 20.14765 2.772645
Fixed effects: juldisp ~ z.age + sex + I(cos(z.rad)) + I(sin(z.rad)) + I(cos(2 * z.rad)) + I(sin(2 * z.rad)) + I(cos(z.rad)):z.age + I(sin(z.rad)):z.age + I(cos(2*z.rad)):z.age + I(sin(2*z.rad)):z.age
Value SE df t P
(Intercept) 266.15542 9.410758 65 28.282037 <0.001
z.age 21.29896 4.528525 56 4.703289 0.001
sex2 -1.88626 0.794091 56 -2.375369 0.0210
I(cos(z.rad)) -4.84824 7.820690 56 -0.619925 0.5378
I(sin(z.rad)) -1.74149 1.103271 56 -1.578475 0.1201
I(cos(2*z.rad)) 1.47196 3.456771 56 0.425820 0.6719
I(sin(2*z.rad)) 1.97532 1.699030 56 1.162614 0.2499
z.age:I(cos(z.rad))-4.23522 7.268408 56 -0.582689 0.5624
z.age:I(sin(z.rad))-0.78899 1.180648 56 -0.668266 0.5067
z.age:I(cos(2*z.rad))1.14209 3.147873 56 0.362812 0.7181
z.age:I(sin(2*z.rad))-0.93386 1.685271 56 -0.554129 0.5817
Model reduction steps
reg1=update(reg,~.-I(sin(2 * z.rad)))
anova(reg,reg1)
Model df AIC BIC logLik test Likelihood ratio P
reg 1 15 1100.168 1144.611 -535.0840
reg1 2 14 1099.613 1141.093 -535.8066 1 vs 2 1.445222 0.2293
reg2=update(reg1,~.-I(sin(2 * z.rad)):z.age)
anova(reg1,reg2)
Model df AIC BIC logLik test Likelihood ratio P
reg1 1 14 1099.613 1141.093 -535.8066
reg2 2 13 1097.615 1136.132 -535.8074 1 vs 2 0.001652219 0.9676
reg3=update(reg2,~.-I(cos(2 * z.rad)):z.age)
anova(reg2,reg3)
Model df AIC BIC logLik test Likelihood ratio P
reg2 1 13 1097.615 1136.132 -535.8074
reg3 2 12 1095.616 1131.170 -535.8078 1 vs 2 0.000758724 0.978
reg4=update(reg3,~.-I(cos(2 *z.rad)))
anova(reg3,reg4)
Model df AIC BIC logLik test Likelihood ratio P
reg3 1 12 1095.616 1131.170 -535.8078
reg4 2 11 1094.176 1126.768 -536.0882 1 vs 2 0.5607486 0.454
reg5=update(reg4,~.-z.age:I(sin(z.rad)))
anova(reg4,reg5)
Model df AIC BIC logLik test Likelihood ratio P
reg4 1 11 1094.176 1126.768 -536.0882
reg5 2 10 1096.568 1126.196 -538.2839 1 vs 2 4.391497 0.0361
reg6=update(reg5,~.-I(sin(z.rad)))
anova(reg5,reg6)
Model df AIC BIC logLik test Likelihood ratio P
reg5 1 10 1096.568 1126.196 -538.2839
reg6 2 9 1096.870 1123.536 -539.4350 1 vs 2 2.302215 0.1292
Final model
AIC BIC logLik
1084.187 1110.533 -533.0937
Random effects:
Formula: ~1 | year
(Intercept)
SD: 0.004192047
Formula: ~1 | country inyear
(Intercept)
SD: 26.85128
Formula: ~1 | nest in country in year
(Intercept) Residual
SD: 19.96445 3.073115
Fixed effects: juldisp ~ z.age + sex + I(cos(z.rad)) + I(cos(z.rad)):z.age
Value SE df t P
(Intercept) 264.62801 8.280659 65 31.95736 0.001
z.age 20.64759 0.993385 62 20.78508 <0.001
sex2 -2.05409 0.809511 62 -2.53745 0.0137
I(cos(z.rad)) -2.22890 1.226151 62 -1.81780 0.0739
age:I(cos(z.rad)) -3.01319 1.132227 62 -2.66129 0.0099