Abstract
Carnivora occupy many ecological niches fundamental to ecosystem functioning. Within this diverse order, carnivore species compete to establish dominance, ensure survival and maintain fitness. Subordinate carnivores must, therefore, adapt their behaviour to coexist with dominant species. One such strategy is the partitioning of temporal activity patterns. We aim to determine interspecific avoidance patterns among sympatric carnivores by examining coexistence along a temporal axis. We compared the temporal activity patterns of 13 carnivore species using multi-seasonal camera trapping data from four protected areas across South Africa: Associated Private Nature Reserves, Madikwe Game Reserve, Mountain Zebra National Park and Tswalu Kalahari Reserve. Interspecific coefficients of overlap in diel and core activity periods were calculated over the study period and during the wet and dry seasons. Furthermore, interspecific spatiotemporal behaviour was examined using time-to-event analyses. Our results showed that complete avoidance of diel activity patterns was rare among South African carnivore species. Most species were predominantly nocturnal and, therefore, diel activity overlap was high, whereas core activity overlap was significantly lower (p < .001). Diel activity overlap was significantly lower during the dry than wet seasons (p = .045). Lastly, evidence of spatiotemporal aggregation revolved around scavenging species. We show the importance of seasonality in the temporal avoidance behaviours of South African carnivores while highlighting the need for fine-scaled behavioural analyses. Overall, we show that the daily activity patterns of most subordinate South African carnivore species are not influenced by top-down forces in the form of competitional suppression and risk exerted by dominant species. If avoidance is required, it is more likely to manifest as fine-scaled avoidance of core activity periods. We suggest that the focus on core activity periods might be a more suitable tool for interspecific temporal partitioning research.