Dryland ecosystems are highly vulnerable to global warming and increasingly prone to desertification, yet their resilience often hinges on complex biotic interactions. At the center of these interactions are dung beetles, acting as potential ecosystem engineers. Local experiments have demonstrated their powerful ability to buffer warming impacts—significantly reducing warming-induced shrub growth loss and mortality, while also reversing density-dependent plant competition. Crucially, this plant-facilitation mechanism is strictly dependent on the presence of ungulates, which provide the essential resource (dung) that fuels beetle populations. Forecasting how this multi-trophic cascade (ungulate-beetle-shrub) will perform under higher risks of extreme weather events remains a critical challenge for landscape management.

Here, we present a predictive framework that integrates this complex ecological cascade with Bayesian modeling to forecast ecosystem resilience in two dryland systems. To power these forecasts, we synthesize a multi-layered dataset integrating biotic interactions alongside key abiotic drivers: spatial distribution and abundance monitoring of ungulates (resource availability), constant-effort pitfall trapping of dung beetles (population dynamics monitoring), and field experiments of shrub growth under dung-beetle activity. Crucially, our experiments show that dung beetles completely reverse density-dependent growth suppression, shifting a 9.6% competitive penalty into a 3.1% net facilitation, and strongly buffer plant mortality, sustaining high survival rates between 88.5% and 91.8%. By integrating this robust empirical data into our Bayesian models, we can effectively scale individual-level processes to landscape-level dynamics.

Keywords: Dryland ecosystems; Dung beetles; Bayesian modeling; Ecosystem resilience; Global warming