AbstractEcologists increasingly rely on camera trap data to estimate a wide range of biological parameters such as occupancy, population abundance or activity patterns. Because of the huge amount of data collected, the assistance of non-scientists is often sought after, but an assessment of the data quality is a prerequisite to their use.We tested whether citizen science data from one of the largest citizen science projects - Snapshot Serengeti - could be used to study breeding phenology, an important life-history trait. In particular, we tested whether the presence of juveniles (less than one or 12 months old) of three ungulate species in the Serengeti: topi Damaliscus jimela, kongoni Alcelaphus buselaphus and Grant’s gazelle Nanger granti could be reliably detected by the “naive” volunteers vs. trained observers. We expected a positive correlation between the proportion of volunteers identifying juveniles and their effective presence within photographs, assessed by the trained observers.We first checked the agreement between the trained observers for age classes and species and found a good agreement between them (Fleiss’ κ > 0.61 for juveniles of less than one and 12 month(s) old), suggesting that morphological criteria can be used successfully to determine age. The relationship between the proportion of volunteers detecting juveniles less than a month old and their actual presence plateaued at 0.45 for Grant’s gazelle and reached 0.70 for topi and 0.56 for kongoni. The same relationships were however much stronger for juveniles younger than 12 months, to the point that their presence was perfectly detected by volunteers for topi and kongoni.Volunteers’ classification allows a rough, moderately accurate, but quick, sorting of photograph sequences with/without juveniles. Obtaining accurate data however appears more difficult. We discuss the limitations of using citizen science camera traps data to study breeding phenology, and the options to improve the detection of juveniles, such as the addition of aging criteria on the online citizen science platforms, or the use of machine learning.
Camera settings and habitat type influence the accuracy of citizen science approaches to camera trap image classification.
