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Covert rewilding is the secret and illegal translocation of species in the pursuit of conservation objectives. Recent history contains multiple covert rewilding events, frequently occurring after official permission was denied. In order to better understand the phenomenon, I formulate covert rewilding as an optimisation problem, with the goal of creating a population that is too large to be eradicated once it is detected. I then consider a hypothetical covert rewilding of Tasmanian devils Sarcophilus harrisii to the Australian mainland. Three different release locations would allow a covert devil population to remain undetected for years, by which time its size and distribution may preclude eradication. Optimal release locations also represent optimal locations for official surveillance, but a more effective approach to halting covert rewilding could be a more permissive stance towards legal rewilding. [ABSTRACT FROM AUTHOR]

Reintroduction projects, which are an important tool in threatened species conservation, are becoming more complex, often involving the translocation of multiple species. Ecological theory predicts that the sequence and timing of reintroductions will play an important role in their success or failure. Following the removal of sheep, goats and feral cats, the Western Australian government is sequentially reintroducing 13 native fauna species to restore the globally important natural and cultural values of Dirk Hartog Island (DHI).We use ensembles of ecosystem models to compare 23 alternative reintroduction strategies on DHI, in Western Australia. The reintroduction strategies differ in the order, timing and location of releases on the island. Expert elicitation informed the model structure, allowing for use of different presumed species interaction networks which explicitly incorporated uncertainty in ecosystem dynamics.Our model ensembles predict that almost all of the species (~12.5 of 13, on average) will successfully establish in the ecosystem studied, regardless of which reintroduction strategy is undertaken. The project can therefore proceed with greater confidence and flexibility regarding the reintroduction strategy. However, the identity of the at‐risk species varies between strategies, and depends on the structure of the species interaction network, which is quite uncertain. The model ensembles also offer insights into why some species fail to establish on DHI, predicting that most unsuccessful reintroductions will be the result of competitive interactions with extant species.Synthesis and applications. Our model ensembles allow for the comparison of outcomes between reintroduction strategies and between different species interaction networks. This framework allows for inclusion of high uncertainty in dynamics. Finally, an ensemble modelling approach also creates a foundation for formal adaptive management as reintroduction projects proceed. [ABSTRACT FROM AUTHOR]