Applying the IUCN Global Ecosystem Typology to classify, describe, and map ecosystems based on regional data and Indigenous knowledge

Effective ecosystem conservation for biodiversity and human well-being relies on accurate information. Consistent approaches to classifying, describing, and assessing ecosystems can improve understanding of ecological processes, threats, and management. We explored how the International Union for Conservation of Nature (IUCN) Global Ecosystem Typology—a global classification framework based on ecosystem function—could support the development of a classification of ecosystems for the Tiwi Islands, Australia, by incorporating scientific information and Indigenous Tiwi knowledge to facilitate environmental management and conservation. We synthesized ecosystem information from previous research, field data, reports, and Tiwi knowledge authorities to develop a classification, descriptions, and a map of 14 terrestrial ecosystem types. These ecosystem types were defined and described based on ecological processes and were broader yet largely congruent with existing vegetation classifications. Including functional properties accounted for variation in the vegetation physiognomy exhibited by dynamic and disturbance-prone ecosystems, such as savannas. Because we considered Tiwi knowledge authorities and the IUCN Global Ecosystem Typology, our inventory included ecosystem types that were typically omitted from previous classifications, which should allow for more comprehensive assessments and management. Relating the new ecosystem typology to the IUCN Global Ecosystem Typology will facilitate comparisons among similar ecosystems, regarding, for example, effective threat abatement options. Describing the biota and processes opens new avenues for monitoring. More collaborative work is needed to explore how Western scientific ecosystem inventories operate alongside and in connection with management of Tiwi murrakupuni enacted by Tiwi people. Given the ongoing loss of biodiversity, ecosystem management must draw on information across domains, scales, and knowledge systems. We demonstrated an approach to this task and provided socioecologically relevant ecosystem information.