Detecting state changes for ecosystem conservation with long-term monitoring of species composition

Effective conservation requires an understanding not only of contemporary vegetation distributions in the landscape, but also cognizance of vegetation transitions over time with the goal of maintaining persistence of all states within the landscape. Using a state and transition model framework, we investigated temporal transitions over 31 years in species composition among five upland swamp vegetation communities in southeastern Australia. We applied fuzzy clustering to document transitions across communities; evaluated the resilience and resistance of communities to change; and explored the relationship between ecosystem states and major environmental factors posited to structure the system. We also evaluated the predictive ability of an established vegetation dynamics model. We found that community composition remained stable or underwent reversible or directional transitions depending on the vegetation type. Wetter communities (Ti-tree thicket and Cyperoid heath) were more stable (i.e., resistant) while drier communities showed a greater propensity to transition (i.e., had lower resistance) under the observed disturbance regime (low variance fire intervals). The resilience of drier communities differed under this regime, with Banksia thicket showing reversible compositional change, while Restioid heath and Sedgeland showed directional change. In accord with an established conceptual model, we found that communities were distributed along a hydrological gradient. In addition, vegetation structure, along with light penetration to ground level, differentiated communities. However, internal dynamics of drier communities were complex: differences in fire regime (penultimate fire interval in 2014 and number of fires since 1965) were unable to predict differences in community membership among sites. Aspects of the fire regime are expected to be more important predictors if fire intervals vary more strongly among sites in the future. Fuzzy clustering of compositional data allows managers to track community transitions over time and facilitates planned interventions for conservation purposes.