Abstract
Aim Morphological abnormalities may reduce fitness in wild animals, making them ecologically relevant for conservation efforts. Anomalies, such as a bifurcation or a trifurcation of a regenerated gecko tail, can change locomotion, social signalling, and predator evasion, with potential effects on survival and reproductive success. Even though rare, these abnormalities raise important questions about their potential associations with environmental conditions and global distribution. We investigate the prevalence and potential correlates of abnormally regenerated tails in geckos. Location Global. Time Period 1964–2023. Major Taxa Studied Lizards. Methods We analysed spatio‐temporal patterns of anomaly prevalence based on 136,069 iNaturalist observations of 122 gecko genera. We overlaid the location of each observation with climate variables from WorldClim and PM2.5 air pollution data from a global monitoring database and explored whether ecological traits, i.e., activity period (diurnal, nocturnal, or cathemeral) and habitat use (fossorial, arboreal, terrestrial, or saxicolous) change the likelihood of abnormal tail regeneration. Results We identified 89 cases of abnormal tail regrowth in 21 genera. While the number of all observations increased over time, particularly since 2018, the annual rate of abnormalities peaked in 2006. No spatial autocorrelation was detected, and ecological traits, such as activity period and habitat use, did not affect abnormality rates. Fossorial genera exhibited no caudal abnormalities. Solar radiation showed a marginally positive effect ( β = −0.23, p = 0.063) on the likelihood of multiple tails. Other factors, such as thermal amplitude, PM2.5, and precipitation, showed non‐significant and weaker effects. Main Conclusions Our findings indicate that exposure to solar radiation is potentially associated with an increased rate of abnormal tail regeneration in geckos alongside subtle or unmeasured environmental drivers. Our results underscore the value of citizen (or community) science data for detecting rare morphological phenomena across broad spatial and temporal scales. Such insights are essential for anticipating how environmental stressors may affect wildlife health and resilience.