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Accurate information on patterns of introduction and spread of non-nativespecies is essential for making predictions and management decisions. In manycases, estimating unknown rates of introduction and spread from observed datarequires evaluating intractable variable-dimensional integrals. In general,inference on the large class of models containing latent variables of large orvariable dimension precludes exact sampling techniques. Approximate Bayesiancomputation (ABC) methods provide an alternative to exact sampling but rely oninefficient conditional simulation of the latent variables. To accomplish thistask efficiently, a new transdimensional Monte Carlo sampler is developed forapproximate Bayesian model inference and used to estimate rates of introductionand spread for the non-native earthworm species Dendrobaena octaedra (Savigny)along roads in the boreal forest of northern Alberta. Using low and highestimates of introduction and spread rates, the extent of earthworm invasionsin northeastern Alberta was simulated to project the proportion of suitablehabitat invaded in the year following data collection.

Transdimensional Approximate Bayesian Computation for Inference on Invasive Species Models with Latent Variables of Unknown Dimension