Bird Behavior Towards Approaching Trains
A more detailed scale of bird interactions with the HSR is the behavior of individual
birds facing an approaching train. Note that if birds are able to react before the
arrival of a train, even high rates of crossing the risk area would be harmless.
However, it is methodologically complex to analyze this issue. Data presented in
the previous sections were in all cases collected with traditional direct observation
methodologies, which involve time-space limitations (e.g., length of sections under
scrutiny, difficulty or impossibility of accessing the infrastructure, and frequency of
trains) that limit data quantity and quality (Wells et al. 1999). Such limitations are
especially noticeable for relatively rare animals and exacerbated by the difficulty of
observing the process in real time, given the velocity of HSR trains (Rodríguez
et al. 2008).
To overcome these limitations, data in this section were obtained in the
framework of a project currently under development (LIFE+ Impacto 0 2016) that
uses high-speed video recording from the cockpits of running trains. This study is
the first time that this method was applied in a railway study, previous studies used
direct observations by train conductors (SCV 1996; SEO/BirdLife 1997; Wells
et al. 1999), and video recordings have begun to be used to assess the interactions of
wildlife with powerlines (Carlton and Harness 2001), wind turbines (Desholm et al.
2006; Cryan et al. 2014), airplanes (Doppler et al. 2015), and cars (Legagneux and
Ducatez 2013; DeVault et al. 2015). Cockpit video data provide accurate information on location, time, conditions (i.e., speed of the train, type of stretch), and the
behavior (i.e., distance of flight, type of reaction) of animals detected in front of the
vehicle in events of crossing, infrastructure use, and collision. Also, observations
can be made continuously along the train path for tens or even hundreds of kilometers, and include sections inaccessible to walking personnel due to viaducts and
tunnels. Hence the cockpit video approach increases the number and types of
observations that can be made.
Video recordings included in the present analysis were made with
forward-facing, high-speed cameras (120–240 frames per second, GoPro Hero 3+
Black Edition) through the cockpit windshield. A technician located in the cabin
operated the recording equipment together with GPS register systems and recorded
all bird observations (perching, crossings, collisions) on standardized forms.
A series of basic data were thus used to simplify later analysis (e.g., recording time,
relative size or bird species, position in the image, type of observation). These
forms served as a reference for the systematic review of recordings, focusing
attention on direct observations. Technicians’ observations, species, numbers of
individuals, and behaviors were confirmed in the videos. In parallel, data from the
navigation system, such as train speed, location (geographic coordinates and mile
mark), stretch configuration, and surrounding habitat were extracted. High-speed
recording allowed repeated and frame-to-frame review of each event to facilitate the
analysis of the observations.
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A more detailed scale of bird interactions with the HSR is the behavior of individual
birds facing an approaching train. Note that if birds are able to react before the
arrival of a train, even high rates of crossing the risk area would be harmless.
However, it is methodologically complex to analyze this issue. Data presented in
the previous sections were in all cases collected with traditional direct observation
methodologies, which involve time-space limitations (e.g., length of sections under
scrutiny, difficulty or impossibility of accessing the infrastructure, and frequency of
trains) that limit data quantity and quality (Wells et al. 1999). Such limitations are
especially noticeable for relatively rare animals and exacerbated by the difficulty of
observing the process in real time, given the velocity of HSR trains (Rodríguez
et al. 2008).
To overcome these limitations, data in this section were obtained in the
framework of a project currently under development (LIFE+ Impacto 0 2016) that
uses high-speed video recording from the cockpits of running trains. This study is
the first time that this method was applied in a railway study, previous studies used
direct observations by train conductors (SCV 1996; SEO/BirdLife 1997; Wells
et al. 1999), and video recordings have begun to be used to assess the interactions of
wildlife with powerlines (Carlton and Harness 2001), wind turbines (Desholm et al.
2006; Cryan et al. 2014), airplanes (Doppler et al. 2015), and cars (Legagneux and
Ducatez 2013; DeVault et al. 2015). Cockpit video data provide accurate information on location, time, conditions (i.e., speed of the train, type of stretch), and the
behavior (i.e., distance of flight, type of reaction) of animals detected in front of the
vehicle in events of crossing, infrastructure use, and collision. Also, observations
can be made continuously along the train path for tens or even hundreds of kilometers, and include sections inaccessible to walking personnel due to viaducts and
tunnels. Hence the cockpit video approach increases the number and types of
observations that can be made.
Video recordings included in the present analysis were made with
forward-facing, high-speed cameras (120–240 frames per second, GoPro Hero 3+
Black Edition) through the cockpit windshield. A technician located in the cabin
operated the recording equipment together with GPS register systems and recorded
all bird observations (perching, crossings, collisions) on standardized forms.
A series of basic data were thus used to simplify later analysis (e.g., recording time,
relative size or bird species, position in the image, type of observation). These
forms served as a reference for the systematic review of recordings, focusing
attention on direct observations. Technicians’ observations, species, numbers of
individuals, and behaviors were confirmed in the videos. In parallel, data from the
navigation system, such as train speed, location (geographic coordinates and mile
mark), stretch configuration, and surrounding habitat were extracted. High-speed
recording allowed repeated and frame-to-frame review of each event to facilitate the
analysis of the observations.
128
J.E. Malo et al.