Introduction
Camera trapping has become a powerful research tool for collecting data
on wildlife because it can be carried out at a relatively low cost
compared to some other survey or monitoring methods that would require
extended human presence in the study area. Moreover, its non-invasive
nature in data collection enables the monitoring of elusive species,
also in remote locations (Burton et al. 2015, Caravaggi et al. 2017).
This approach has traditionally been used to gather information on
various aspects of large terrestrial mammals such as occurrence
(Salvatori et al. 2023), abundance (Taylor et al. 2022, Santini et al.
2022) and behaviour patterns (Li et al. 2020, Gracanin and Mikac 2022),
but nowadays it is used increasingly also for birds (Caravaggi et al.
2017). It is especially useful in the study of ground dwelling birds,
where game cameras have been recently used to describe, for example,
activity patterns (Nykänen et al. 2021), foraging (Sperry et al. 2021),
habitat use (Firth et al. 2020, Puffer et al. 2021), abundance (Kanka et
al. 2023) and predation pressure (Laux et al. 2022).
Despite the great potential of game cameras as an effective research
tool in a range of applications (Wearn and Glover-Kapfer 2019), they may
also have several limitations associated to them, such as variability in
camera performance or challenges in sampling design (Rovero et al. 2013,
Jacobs and Ausband 2018, Palencia et al. 2022, Santini et al. 2022). One
key aspect in camera performance is the trigger mode: in motion
sensitive triggering a passive infrared (PIR) sensor triggers the
camera to capture an image, whereas in time lapse triggering the
camera is programmed to take images at a predefined time interval.
Problems may occur, if the camera produces false triggers leading to
vast amounts of blank or empty images and therefore drains batteries and
fills memory card space. On the other hand, detections of target species
can be missed, if the camera is not triggered appropriately. This all
causes extra work for researchers and may bias the results of studies.
Here, we compare the performance of motion sensitive and time lapse
camera settings in gathering occurrence and relative abundance data of
the bean goose (Anser fabalis ) in its breeding areas in Finland.
The bean goose is breeding sporadically in remote and inaccessible
habitats in the arctic and boreal zones from Fennoscandia to Western and
Eastern Siberia (Scott and Rose 1996, Kear 2005). The Western Palearctic
population of the species, consisting mainly of individuals belonging to
the subspecies taiga bean goose (A. f. fabalis ), has declined in
recent decades (Fox et al. 2010, CAFF 2018), the conservation status of
the subspecies being considered Vulnerable in Finland (Lehikoinen et al.
2019). While efforts have been put into increasing the accuracy of
methods used to estimate taiga been goose numbers in the nonbreeding
season (Piironen et al., 2023), monitoring its numbers in the breeding
season remains a challenging obstacle to efficient conservation of the
subspecies. During the breeding season taiga bean goose is highly
elusive, and any survey method involving disturbance caused by the
presence of human observers will further reduce the detectability of the
species (Pirkola and Kalinainen 1984).
Hence, the main objective of this study was to gain a better
understanding of game camera trap performance for reaching more reliable
results with the most cost-effective data collection procedure for
ground dwelling avian studies, bean goose serving as a research species.
A specific aim of the study was to find out which trigger type, motion
sensor or time lapse, captures greater goose numbers or is associated
with higher daily capture probability, the latter being critical in
providing occurrence data.