DOI: 10.5061/dryad.s4mw6m93d
title: Data from: Breeding habitat and nest site selection by an obligatory “nest-cleptoparasite”, the Amur Falcon Falco amurensis

description: The selection of a nest site is crucial for successful reproduction of
 birds. Animals which re-use or occupy nest sites constructed by other
 species often have limited choice. Little is known about the criteria of
 nest-stealing species to choose suitable nesting sites and habitats. Here,
 we analyze breeding site selection of an obligatory “nest-cleptoparasite”,
 the Amur Falcon Falco amurensis. We collected data on nest sites at
 Muraviovka Park in the Russian Far East, where the species breeds
 exclusively in nests of the Eurasian Magpie Pica pica. We sampled 117
 Eurasian Magpie nests, 38 of which were occupied by Amur Falcons.
 Nest-specific variables were assessed and a recently developed habitat
 classification map was used to derive landscape metrics. We found that
 Amur Falcons chose a wide range of nesting sites, but significantly
 preferred nests with a domed roof. Breeding pairs of Eurasian Hobby Falco
 subbuteo and Eurasian Magpie were often found to breed near the nest in
 about the same distance as neighboring Amur Falcon pairs. Additionally,
 the occurrence of the species was positively associated with bare soil
 cover, forest cover and shrub patches within their home range and
 negatively with the distance to wetlands. Areas of wetlands and fallow
 land might be used for foraging since Amur Falcons mostly depend on an
 insect diet. Additionally, we found that rarely burned habitats were
 preferred. Overall, the effect of landscape variables on the choice of
 actual nest sites appeared to be rather small. We used different
 classification methods to predict the probability of occurrence, of which
 the Random forest method showed the highest accuracy. The areas determined
 as suitable habitat showed a high concordance with the actual nest
 locations. We conclude that Amur Falcons prefer to occupy newly built
 (domed) nests to ensure high nest quality, as well as nests surrounded by
 available feeding habitats.
description: Study area Our study area, the Muraviovka
 Park for Sustainable Land Use and its surroundings, is situated at the
 southern end of the Zeya-Bureya plain on the middle section of the Amur
 River in the Russian Far East (Fig. 2). The area stretches about 16 km
 from south to north and 11.5 km from east to west, covering an area of
 about 13289 ha. The valley of the Amur River and its first terraces ranges
 in altitudes from 105 to 348 m above sea level. The
 landscape is dominated by wetlands (6346 ha) with <i>Carex
 meyeriana</i>, <i>Carex lasiocarpa</i>, <i>Iris
 laevigata</i> and <i>Menyanthes trifoliata</i>
 (Akhtymaov et al., 2002). Other land cover types include agricultural
 fields (480 ha) with changing crops from year to year such as soy and
 buckwheat and fallow fields (1392 ha). The forest islands (475 ha) contain
 species of <i>Quercus mongolica</i>, <i>Betula
 dahurica</i> and <i>Lespedeza bicolor</i>, reed (323 ha)
 with a dominant vegetation of <i>Phragmites australis</i> and
 shrubs (268 ha) comprising <i>Corylus heterophylla</i>,
 <i>Salix bebbiana</i> and <i>Lespedeza bicolor</i>
 (Akhtymaov et al., 2002). One year after its
 establishment in 1994, the Park and its adjacent territories became part
 of the Ramsar List of Wetlands of International Importance. The Amur Bird
 Project has been investigating the threatened avifauna together with the
 staff of Muraviovka Park since 2011 (Heim & Smirenski, 2013;
 2017). Data collection We searched
 the complete study area for nests of magpies, other corvids and raptors
 during April-July 2013. Nests were easily located due to the limited
 number of trees in the area (Figure 1). We collected data on their
 location using a handheld GPS (Garmin eTrex 10) and assessed the following
 nest-specific variables: tree genera, nest height, status of the roof (old
 magpie nests often lose their roof) and nest content (breeding species,
 number of eggs or chicks). For the latter variable, the trees were climbed
 or a prolonged stick with an integrated camera was used to correctly
 identify the status of the nest. Data
 analysis Data were checked for consistency and nest
 locations were intersected with the habitat classification map (Heim,
 2018) using ArcGIS (version 10.4). Around each nest location, a buffer
 with a radius of 2500 m was created that approximately represents the home
 range of the individual breeding pair. The decision about the size of the
 home range (1963.5 ha) was based on references regarding home range
 estimations for the closely related Red-footed Falcon <i>Falco
 vespertinus</i> (38 to 3467 ha) (Fehérvári et al., 2009; Palatitz et
 al., 2015). Subsequently, buffers were intersected with the habitat
 classification map to obtain the particular set of habitat patches around
 each nest. With the help of a fire frequency map (Heim et al., 2019) we
 tested for an influence of fire on the species occurrence.
 The created surroundings of the nests were used for a further
 calculation of different landscape metrics using the software
 <i>Fragstats</i> (version 4.2.1). The environmental variables
 on a landscape level included proximity, area-edge, shape, aggregation and
 diversity metrics. The splitting index refers to the fragmentation of the
 landscape (McGarigal, 2017; Schindler et al., 2008). High values represent
 a mosaic-like structure with a higher diversity of different habitat
 patches. The landscape metric perimeter-area fractal dimension expresses
 the complexity of the perimeter-area ratio and accounts for the rise or
 decrease of environmental gradients between patches of a landscape
 (McGarigal, 2017; Schindler et al., 2013; Wang and Malanson,
 2007). Descriptive statistics were carried out and
 distributions were tested for the following statistical applications with
 the statistical software R (version 3.3.3). In order to start the
 statistical analyses, the categorical variables such as roof (unknown, no,
 yes), nesting habitat (willow shrubs, wetland, steppe, shrubs, forest,
 water, reed, field, bare soil) and tree genera (<i>Betula</i>,
 <i>Quercus</i>, <i>Prunus</i>,
 <i>Ulmus</i>, <i>Salix</i>,
 <i>Populus</i>, <i>Tilia</i>,
 <i>Crataegus</i>, dead unidentified tree) were transformed
 into factor variables. Results accounting for the
 differences among the nest occupants are presented by using the median and
 the median absolute deviation (MAD). The MAD is an alternative to the
 standard deviation or the interquartile range and considered as a robust
 scale measure, especially in the presence of outliers. It is calculated by
 finding the median of absolute deviations from the median (Leys et al.,
 2013; Rousseeuw & Croux, 1993). The dataset was
 divided into a training (70 %), test (15 %) and validation (15 %) set.
 With the help of the R package <i>Rattle</i> (Williams, 2009),
 the classification methods <i>decision trees</i> and
 <i>random forest</i> were tested. Decision trees are built by
 the creation of binary splits of the training data on every predictor
 variable and the structure of the algorithm allows classifying every new
 observation into one of two groups. The aim is to construct most
 homogeneous subsets of the data. The classification threshold can be taken
 from the pictorial graph of the decision tree (Kabacoff, 2015). Random
 forest combines many classification trees to produce more accurate
 classifications (Cutler et al., 2007). First, all
 variables were incorporated into the machine learning algorithms. The
 splitting variables of the <i>decision trees</i>, the variable
 importance measure of the <i>random forest</i> application and
 the p-values of the <i>Chi-square</i> (χ²) test from the
 <i>logistic regression</i> served as indicators for
 influential variables. Decision trees as well as random forests
 overestimate variables with many categories. Those variables are divided
 in many auxiliary variables and therefore are more likely to be chosen.
 Due to this bias towards variables with many classes, nesting habitat and
 tree genera had to be excluded. Minimum buckets in the
 <i>classification trees</i> were put to seven and the numbers
 of trees for the <i>random forest</i> application were
 manually changed to 5000 to obtain better statistical results. The model
 run started with 63 variables and subsequent underperformers were removed.
 Depending on the lowest Akaike information criterion (AIC) values, a set
 of variables was chosen and incorporated into the classification procedure
 (Fig. 3). An implied variable importance measure of the
 random forest application is called Mean Decrease Accuracy (MDA). The MDA
 shows the decrease in accuracy of the model performance by an error rate
 calculated with and without the variable. The error rate is calculated for
 every predictor and then averaged over all constructed trees, which used
 this specific variable. Predictors with high MDA values are seen as
 important in the classification of the data, as the predictive accuracy of
 the model would decrease, if those variables would be left out during
 calculation (Breiman, 2001). The performances of the
 models, each of which includes different sets of predictor variables were
 compared using the area under the curve (AUC) values and the overall
 error. An AUC value of 1 resembles a perfect fit and the overall error
 validates the accuracy of the classification algorithms by accounting for
 all the misclassified cases (Williams, 2009).