Bayesian networks elucidate interactions between fire and other drivers of terrestrial fauna distributions

Abstract

Fire is a major driver of community composition and habitat structure and is extensively used as an ecological management tool in flammable landscapes. Interactions between fire and other processes that affect animal distributions, however, cause variation in faunal responses to fire and limit our ability to identify appropriate fire management regimes for biodiversity conservation. Bayesian networks (BNs) have not previously been used to examine terrestrial faunal distributions in relation to fire, but offer an alternative statistical framework for modeling complex environmental relationships as they explicitly capture interactions between predictor variables. We developed a conceptual model of the interactions between drivers of faunal distributions in fire-affected landscapes, and then used a non-parametric BN modeling approach to describe and quantify these relationships for a suite of terrestrial native mammal species. We also tested whether BNs could be used to predict these species' distributions using only remote-sensed or mapped variables. Data were collected at 113 sites across 47,000 ha of continuous eucalypt forest in the Otway Ranges, southeastern Australia; time-since-fire (TSF) ranged from six months to 74 yr. Habitat complexity increased with TSF and forest wetness. Critical-weight-range (35-5500 g) marsupials and rodents were generally more likely to occur at long unburnt sites with high habitat complexity, and in wetter forest types. In contrast, large grazers and browsers preferred less complex habitats and younger or drier forest. Species occurrences were more strongly affected by habitat complexity than TSF, coarse woody debris cover, or invasive predator (Vulpes vulpes or Felis catus) occurrence. Bayesian network models effectively discriminated between the presence and absence of most native mammal species, even when only provided with data on remote-sensed or mapped variables (i.e., without field-assessed data such as habitat complexity). Non-parametric BNs are an effective technique for explicitly modeling the complex and context- dependent influence of fire history on faunal distributions, and may reduce the need to collect extensive field data on habitat structure and other proximate drivers.