Brief overview of model analysis results, dynamically rendered as
website output using the open data. The full files are presented in the
analysis
folder.
# read the data
df <- readRDS(here::here("data/curated/data_MDL.rds"))
# fit the model
fm_lagged <- glmmTMB::glmmTMB(
altbin ~ moon_illuminance + lag_altitude + species + (1|species/tag),
data = df,
family = binomial
)
# report statistics of Table 1
print(summary(fm_lagged))
## Family: binomial ( logit )
## Formula:
## altbin ~ moon_illuminance + lag_altitude + species + (1 | species/tag)
## Data: df
##
## AIC BIC logLik deviance df.resid
## 35953.3 36016.4 -17969.6 35939.3 60881
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## tag:species (Intercept) 9.549e-01 0.9771959
## species (Intercept) 1.613e-07 0.0004016
## Number of obs: 60888, groups: tag:species, 23; species, 3
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.51356 0.36989 -1.39 0.16500
## moon_illuminance 8.90868 0.26969 33.03 < 2e-16 ***
## lag_altitude 1.97752 0.02047 96.59 < 2e-16 ***
## speciesapus_pallidus -1.77630 0.52346 -3.39 0.00069 ***
## speciestachymarptis_melba -4.27890 0.49564 -8.63 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# read the data
df <- readRDS(here::here("data/curated/data_GPS.rds"))
# fit the model
fm <- glmmTMB::glmmTMB(
altbin ~ moon_illuminance + species + (1|species/tag),
data = df,
family = binomial
)
# report the statistics of Table2
print(summary(fm))
## Family: binomial ( logit )
## Formula: altbin ~ moon_illuminance + species + (1 | species/tag)
## Data: df
##
## AIC BIC logLik deviance df.resid
## 1631.0 1658.4 -810.5 1621.0 1775
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## tag:species (Intercept) 3.357e-01 0.5793967
## species (Intercept) 8.335e-09 0.0000913
## Number of obs: 1780, groups: tag:species, 11; species, 2
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.1526 0.3992 -5.392 6.96e-08 ***
## moon_illuminance 15.3049 1.0566 14.485 < 2e-16 ***
## speciesapus_pallidus 0.2165 0.4509 0.480 0.631
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The above model responses are well captured visually in Figure 4 of the main manuscript, as shown below.
# read the data
df <- readRDS(here::here("data/curated/data_twilight.rds"))
fm <- lme4::lmer(
twilight_timing ~ moon_illuminance + (1|species/tag),
data = df
)
# return twilight statistics
print(summary(fm))
## Linear mixed model fit by REML ['lmerMod']
## Formula: twilight_timing ~ moon_illuminance + (1 | species/tag)
## Data: df
##
## REML criterion at convergence: 51826
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0770 -0.6582 -0.0225 0.6765 4.0411
##
## Random effects:
## Groups Name Variance Std.Dev.
## tag:species (Intercept) 13.576 3.685
## species (Intercept) 2.056 1.434
## Residual 188.241 13.720
## Number of obs: 6410, groups: tag:species, 23; species, 3
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 8.558 1.150 7.44
## moon_illuminance 129.888 8.039 16.16
##
## Correlation of Fixed Effects:
## (Intr)
## moon_llmnnc -0.094