Discussion
Time lapse cameras may allow the collation of more standardized data than motion triggered cameras and potentially reduce the number of empty images produced by false triggering. Our study shows that the overall daily capture probability of taiga bean geese was higher with cameras set to time lapse compared to motion sensitive cameras. However, there was no significant difference in the daily number of geese captured with the two trigger types. This difference in the results of the two models, albeit quite subtle, could at least partly be explained by the motion triggered cameras failing to capture the more distant animals, whereas the time lapse method captures both near-by and distant animals. This makes positioning of the camera traps on time lapse setting less critical as animals can be detected even if they do not use their exact assumed route or habitat. Indeed, in order for the animal to be captured with the motion trigger mode, the PIR sensor must detect motion in the trigger area while the animal is within the camera’s viewable area (Moeller et al. 2023).
Our study on taiga bean geese in their breeding areas shows that time lapse cameras are more useful than motion triggered cameras in collecting data on the habitat use of animals because of the higher capture probability of the former. In addition, required memory card space and battery needs are predictable. This is an important finding, because the use of time lapse cameras reduces the need to visit the camera sites for changing batteries and memory cards, hence reducing the disturbance during the sensitive breeding period of the geese. An important advantage also is that time lapse cameras will produce more reliable data of sites that are used by taiga bean geese in the breeding areas, an information crucially important for protection of the species. This information is difficult to obtain using human observers as the species is highly elusive and occupies very remote breeding areas.
Choosing the triggering mode depends on the research objectives and characteristics of the target species and its habitat. As our study shows, time-lapse may be more suitable, for example, in studies involving habitat use and the designation of protected areas where it is important to distinguish between used and unused sites. Motion trigger mode, on the other hand, may be more useful in studies on movement or behaviour, or in cases where the density of the animals is low, as it is possible that the time lapse method may fail to detect the target species altogether (Moeller et al. 2023). Moreover, motion triggered cameras have been shown to capture a higher proportion of animals than cameras operating on time lapse, such as in a study monitoring wildlife underpass usage (Pomezanski and Bennett 2018).
Camera trapping has gained popularity in wildlife studies over the recent years due to its efficiency to collect opportunistic image data without the need to dedicate numerous hours of researchers’ time present on the study site. At the same time, large volumes of images collected with this method continue to be one of the challenges in the subsequent data management and analysis. For example, a single camera operating on time lapse with a 15-minute interval, a setting used in this study, outputs 96 photos in a 24-hour period, and accumulates a dataset containing more than a thousand images over a study period longer than ten weeks. On the other hand, motion sensitive triggering may produce thousands of “empty” images in a short time period that still need to be checked for the presence of the target species. In total, our two-year study period produced nearly 350,000 images, which all were gone through manually, forming the most labour intensive and costly part of the study. In order to reduce the time allocated to this painstaking process, machine learning techniques (automatic identification algorithms) together with citizen science approach (e.g., Wei et al. 2020, Hilton et al. 2022, Bjerge et al. 2023) have the potential to become some of the most important innovations in the cost-effective identification and counting of animals from large camera trap datasets in future studies.
We found a difference in the number of geese and in their capture probability (i.e., presence) between the two study years. Unfortunately, because standardized monitoring data of the annual numbers of the taiga bean goose in the breeding areas are not available, it is not possible to say if the between-year difference reflects a real difference of breeding numbers or just random variation. Nevertheless, the findings of this study and those of Nykänen et al. (2021), together covering data from four successive breeding seasons (2018‒2021), suggest that wildlife cameras are a feasible method for monitoring taiga bean goose during the breeding season. First, they can reveal between-year differences both in the numbers and occurrence of the species. Second, wildlife cameras set at fixed sites are a cost-effective method to gather highly comparable long-term data from remote breeding areas that are difficult to access and cover using other survey methods. And finally, non-expert citizens could be engaged in the monitoring based on wildlife cameras, making it possible to cover a large number of potentially important breeding sites (peatlands).