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Aim Use of local-scale non-native plant species ( NNS) distribution models has the potential to decrease survey effort and improve population prioritization for management. We developed and evaluated data collection methods and minimum sampling requirements to inform local-scale models of NNS distribution. We also evaluated overall model predictive performance for 16 species at two sites and determined how classes of variables contributed to model performance and suggest invasion drivers. Location Wyoming and Idaho, USA Methods A simulation study was used to test the efficiency of different sampling methods to predict imposed species distributions. Empirical distribution models of species occurrence data from two environmentally disparate sites were cross-validated at increasing sample sizes, and the asymptotic maximum predictive performance and relative contribution of classes of variables were determined for 16 NNS. Results Transect sampling was the most efficient method for maximizing model performance after accounting for logistics. Minimum sample sizes to reach model maximum predictive performance were similar for the simulation (< 0.5% of study area) and empirical studies (mean of 0.13% using transects). Maximum predictive performance tended to be greater at the site with steeper environmental gradients, and topo-climatic/biotic variables were most important to model improvement. Main conclusions Local-scale SDMs can be useful to NNS managers. Using transect methodology, enough data can be collected (ca. 0.13% of the management area) to fit models within logistical/budgetary constraints. These models are most predictive for well-established species as opposed to new invaders and in areas with steeper environmental gradients. Finally, topo-climatic/biotic predictors are the most important variables for predicting more established species, but disturbance and dispersal limitation should be considered and quantified to ensure variables associated with dominant processes are included in the SDM. [ABSTRACT FROM AUTHOR]

Occupancy models are widely applied to estimate species distributions, but few methods exist for model checking. Thorough model assessments can uncover inadequacies and allow for deeper ecological insight by exploring structure in the observed data not accounted for by a model. We introduce occupancy model residual definitions that utilize the posterior distribution of the partially latent occupancy states. Residual‐based assessments are valuable because they can target specific assumptions and identify ways to improve a model, such as adding spatial correlation or meaningful covariates. Our approach defines separate residuals for occupancy and detection, and we use simulation to examine whether missing structure for modeling detection probabilities can be distinguished from that for occupancy probabilities. In many scenarios, our residual diagnostics were able to separate inadequacies at the different model levels successfully, but we describe other situations when this may not be the case. Applying Moran's I residual diagnostics to assess models for silver‐haired (Lasionycteris noctivagans) and little brown (Myotis lucifugus) bats only provided evidence of residual spatial correlation among detections. Targeting specific model assumptions using carefully chosen residual diagnostics is valuable for any analysis, and we remove previous barriers for occupancy analyses—lack of examples and practical advice. [ABSTRACT FROM AUTHOR]