Table 1. A description of the Netherlands bird avoidance model workflow (Fig. 1) presented for a single species, the common buzzard (Buteo buteo). GAM stands for general additive regression model, and BAM for bird avoidance model.


Step 1 GAM 
The relationship between the counts on samples sites and environmental
variables is described using a GAM. A separate GAM is developed for each survey
and applied at a resolution of 1 km², covering the entire
Netherlands. For the buzzard, counts are available from five surveys: one
breeding bird survey and four point counts of nonbreeding birds throughout the
year (Fig. 2A), resulting in five different GAMs and five different
maps.

Step 2 Kriging 
To account for spatial correlation in the residuals, the residuals for each
GAM are spatially interpolated using universal kriging.

Step 3 Regressionkriging 
The spatially interpolated residuals are added to the respective GAMs
creating regressionkriging distribution maps (Fig. 2B). In this step, a
breeding factor is used to convert counts of breeding pairs to numbers of
individuals.

Step 4 Combined maps 
This step combines the different regressionkriging models to estimate the
spatial distribution of each species twice a month. For different groups of
birds, different count data are available. For example, for some species, only
the breeding bird counts and the nonbreeding bird counts are available; for
other species, monthly waterbird counts are available as well. Each
regressionkriging map is assigned a weight (data set weight) based on proximity
of the survey period to the BAM period (weight diminishes with time).
Subsequently, linear interpolation is applied to produce predictive maps for
those BAM periods for which no surveys are assigned.

Step 5 Normalized maps 
To prevent abrupt changes from one BAM period to another because of
differences between the various data sets, the combined maps were smoothed with
known seasonal trends. The smoothing procedure was as follows. Total abundance
over the Netherlands, on the basis of the combined maps for each species and
each BAM period, was normalized to range between 0 and 1; we call this the
“model_trend.” We used the known seasonal trends (the expert_trend) to
correct the model trend via the following equation: NM_{i,t}
= CM_{i,t} * ( model_trend + ( model_trend 
expert_trend )* expert_weight ) where CM_{i,t } stands
for Combined Map for bird i in BAM period t, NM_{i,t} stands for
Normalized Map for bird species i in BAM period t, and expert_weight is
the degree to which expert_trend can correct model_trend (for most
birds, expert_weight is set to 0.5).

Step 6 Inflated maps 
For each bird species, the altitude distributions and activity patterns were
derived for the four periods of day (morning, afternoon, dusk, dawn). Maps of the
number of birds in the air are created for each combination of BAM period, time
of day, and altitude, producing 480 predictive maps/species (24 periods a year
x 4 times a day x 5 flight altitude layers).

Step 7 
Classified maps: Each map was classified into eight equal interval classes of
birds/km². Composite maps: Maps of all species were combined to
create maps of total number of birds/km² and total
mass/km². Summary table: The sum of birds was calculated for three
regions/time of year, time of day, and altitude layer, for the top 10 most
abundant species. 


