Estimating animal abundance is essential for research, management and conservation purposes. Although reliable methods exist to estimate absolute density for populations with individually marked animals, robust relative abundance indices (RAIs) may allow to track changes in population size when individual identification is not possible. Their performance, however, needs be thoroughly evaluated. We investigated the relative performance of several common faeces-based and camera-based RAIs for estimating small-scale variation in red fox abundance, a mesopredator of high relevance for management, in two different study areas. We compared precision, cost and performance of the methods in capturing relationships with covariates of local abundance. Random transect-based RAIs had a low mean, a comparatively high coefficient of variation and a high proportion of zeros, prohibiting or impeding analysis in relation to environmental predictors. Rectangular scat plots and transects along linear landscape features had an intermediate amount of zeros while retaining a high precision, but were less sensitive to local variation in abundance related to environmental predictors and required a large field effort. Camera trap-based RAIs yielded low to intermediate precision, but were more sensitive to small-scale variation in relative abundance than faeces-based methods. Camera traps were the most expensive methods for an initial monitoring session, but required the lowest field effort, were cheapest in the long run and were the least susceptible to observer bias and detection error under a robust sampling protocol. Generally, faeces count-based RAIs appear more suitable for studies that aim to compare local abundance between several study sites of equal landscape composition under constant detection probability. Camera traps provide more flexible data for studies that require accounting for influences of landscape composition on local abundance and are more cost-effective for long-term or continuous monitoring and more suitable to achieve high replication. Accordingly, the choice of the most suitable method and plot design is context-dependent.