1. Introduction
The growing demand for green energy has resulted in the rapid development of wind energy production, yielding an increasing number of wind turbines globally. This has in turn led to a green-on-green predicament, due to the adverse effects of turbines on many avian species [
1,
2,
3,
4,
5,
6,
7,
8,
9]. These adverse effects of wind turbines on birds result primarily from direct fatalities due to collisions and secondarily through habitat alteration and loss [
9]. The precise number of avian collisions with wind turbines is uncertain, but even relatively few fatalities can have detrimental effects on slow maturing species with low reproduction rates, particularly for species of conservation concern when considering regional and national populations [
1,
7]. Collision-related mortality is unevenly spread among species, with a few species often accounting for a large proportion of collision victims [
10]. Large soaring raptors are known to be specifically vulnerable to collision with turbines [
2,
3,
9,
11]. Other species groups prone to collision fatalities at wind farms include herons, geese, and gulls [
10,
11].
Differences between species in regard to their susceptibility to turbine collisions are suggested to be associated with species-specific foraging behavior, flight behavior, and morphology [
1,
3,
12]. In some species, e.g., raptors and cranes, particularly vulnerable to collisions, their visual field is such that even a small (25–35°) forward pitch head movement will leave them blind directly ahead [
13]. Thus, the foraging behavior of many raptors may explain why these species are more prone to turbine collisions. Many raptor species forage from the air, by bending their heads and looking downwards to search for prey or carrion on the ground [
14]. Raptors that hunt terrestrial prey have a large blind spot above their head, which functions to avoid sun dazzling, thus, allowing for better prey detection. However, this blind spot also renders these raptors blind in their travel direction whilst foraging [
13,
14].
Morphological differences between species have also been proposed to attribute to species-specific differences in collision fatalities, where birds with high wing loading, i.e., the ratio between a bird’s total mass and wing area, and relatively large bodies, e.g., cranes (
Grus spp.), are suggested to be more vulnerable to turbine collision due to reduced maneuverability and rapid flight speeds [
15,
16,
17]. Morphological differences between species may also impact how different species are affected by various weather conditions. It has been suggested that herons (
Ardea spp.) and other species with a slow and ponderous flight can more easily be buffeted off course during strong headwinds, thus, making them vulnerable to turbine collision when flying between roosts and foraging sites [
18]. Furthermore, many large raptors utilize thermal soaring, i.e., slow circle-soaring flight on thermals. This flight strategy is highly dependent on weather conditions, e.g., temperature and cloud coverage, and under less favorable conditions for gaining altitude thermal soaring may impact turbine collision risk [
12,
19,
20,
21]. Turbine collision risk has also been found to differ throughout the day and throughout the year [
20,
22].
Species-specific differences in flight behavior and collision risk may also be correlated with phylogenetic relatedness of species. This correlation can be quantified by the phylogenetic signal of each trait. The phylogenetic signal can be defined as the tendency for closely related species to resemble one another more than species drawn at random from the phylogenetic tree [
23]. A phylogenetic signal will occur if traits evolve in a Brownian motion-like manner, i.e., relatively small changes occur randomly in any given direction over time independent of the former state of the trait. This pattern of evolution can arise from genetic drift or natural selection [
24,
25]. Hence, the phenotypic difference can be expected to be smaller between species who shared a common ancestor more recently [
24,
26]. For a trait that changes gradually over time, the covariance between species’ trait values is expected to be proportional to the shared evolutionary history between species, i.e., the sum of the species’ shared branch length in a phylogenetic tree. Furthermore, the variance of a trait value for a given species is expected to be proportional to the total length of the tree for that species, i.e., the summed branch length from the root to the tip [
25].
Herrera-Alsina et al. [
27] used a similar phylogenetic approach to study species-specific collision risk in relation to the species’ morphological differences and phylogenetic relationship, finding a correlation between wing loading and the likelihood of flight in the risk zone. However, it is more difficult to assess species-specific differences in flight behavior associated with collision risk, as flight behavior can be difficult to observe and quantify, particularly in relation to flight in close proximity to wind turbines (within a 100 m radius from the nearest turbine’s rotor tip), i.e., in collision risk zones [
28]. The relationship between species-specific behavioral traits and turbine collision risk is, therefore, often based on assumptions. However, the development of automated camera-based monitoring systems, e.g., IdentiFlight, has effectuated the collection of large amounts of behavioral data within wind farms [
29,
30]. The IdentiFlight system was developed to mitigate the impact of wind turbines on species of conservation concern, by detecting birds in flight and curtailing turbines if a protected species is at collision risk [
29,
30]. The utilization of bird images and flight trajectories collected by the IdentiFlight system, to assess flight behavior, has recently been demonstrated and the quantitative flight behavioral traits head position, active flight, track tortuosity, and track symmetry described by Linder et al. [
28] can possibly be used to model collision risk.
The aim of this study was, therefore, to investigate how the flight behavioral traits; head position, active flight, track tortuosity, and track symmetry can be used to model collision risk along with other influencing factors, i.e., the weather variables; temperature, wind speed, and cloud coverage, and the temporal variables; time of day, and time of year. This was achieved through a case study investigating the behavior of birds from 11 different genera (Anser, Ardea, Aquila, Buteo, Corvus, Grus, Haliaeetus, Milvus, Larus, Pandion, and Phalacrocorax) at a wind farm located on the Swedish island Gotland. The flight behavior of these species was assessed using data collected by the IdentiFlight system, e.g., flight trajectories and images of the birds throughout their track. It was expected that collision risk could be predicted by species, flight behavior, weather variables, and temporal variables. More specifically, collision risk was expected to increase with time spent looking down and an increase in tortuosity. Moreover, increased utilization of active flight was expected to decrease collision risk. This study will also investigate whether the four flight behavioral traits and thereby collision risk are species-specific, and if such species-specificity is due to random effects (lack of evolutionary signal) or shared evolutionary history (significant evolutionary signal). This was achieved by assessing the phylogenetic signal, i.e., the tendency of related species to resemble each other more than species randomly drawn from the same phylogenetic tree, for the four behavioral traits (head position, active flight, track symmetry, and track tortuosity). The behavioral trait head position was expected to be species-specific in relation to the species’ foraging ecology and the active flight was expected to be species-specific in relation to the species’ morphological differences. Track symmetry and tortuosity were expected to be species-specific as a result of both foraging ecology and morphology. These expected species-specific differences in behavioral traits were expected to be correlated with phylogenetic relatedness.