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Aim Presence-only datasets represent an important source of information on species' distributions. Collections of presence-only data, however, are often spatially biased, particularly along roads and near urban populations. These biases can lead to inaccurate inferences and predicted distributions. We demonstrate a new approach of accounting for effort bias in presence-only data by explicitly incorporating sample biases in species distribution modelling. Location Alberta, Canada. Methods First, we used logistic regression to model sampling effort of recorded rare vascular plants, bryophytes and butterflies in Alberta. Second, we simulated presence/absence data for nine 'virtual' species based on three relative occurrence thresholds – common, rare and very rare – for each taxonomic group. We sampled these virtual species using our bias model to represent typical sampling effort characteristic of presence-only datasets. We then modelled the distributions of these virtual species using logistic regression and attempted to recover their original simulated distributions using a sample weighting term (prior weight) estimated as the inverse of probability of sampling. Bias-adjusted model estimates were compared to those obtained from random samples and biased samples without adjustment. We also compared prior-weight adjustment to bias-file and target-group background approaches in Maxent. Results Sample weighting recovered regression coefficients and mapped predictions estimated from unbiased presence-only data and improved model predictive accuracy as evaluated by regression and correlation coefficients, sensitivity and specificity. Similar model improvements were achieved using the Maxent bias-file method, but results were inconsistent for the target-group background approach. Main conclusions These results suggest that sample weighting can be used to account for spatially biased presence-only datasets in species distribution modelling. The framework presented is potentially widely applicable due to availability of online biodiversity databases and the flexibility of the approach.

Aim Although data collected by citizen scientists have received a great deal of attention for assessing species distributions over large extents, their sampling efforts are usually spatially biased. We assessed whether the bias of spatially varied sampling effort for opportunistic citizen data can be corrected using occupancy models that incorporate observation processes. Location Hokkaido Island, northern Japan. Methods We applied occupancy models for citizen data with spatially biased sampling effort to model and map large-scale distributions of 52 forest and 23 grassland/wetland bird species. We used estimated species richness (summed occupancy probabilities among the species) as the aggregated distributional patterns of each species group and compared them among two occupancy models (i.e. single-species and multispecies occupancy models), two conventional logistic regression models and Maxlike, which do not explicitly deal with observation processes. Results Conventional logistic regression models and Maxlike predicted inappropriate patterns, such as forest species preferring lowland non-forested areas where most of the data were collected. Occupancy models, however, showed more appropriate results, indicating that forest species preferred lowland forested areas. The prediction by logistic models was somewhat improved by the use of spatially biased non-detection data as the absence data; however, estimates of species richness were still much lower than those of occupancy models. Differences in model outputs were evident for the forest species but not for grassland/wetland species because citizen data covered virtually all environmental niches for grassland/wetland species. Results of the single-species and multispecies occupancy models were nearly identical, but in some cases, estimates from the single-species models were not converged or deviated notably from those of other species compared with estimates by the multispecies model. Main conclusions We found that citizen data with spatially biased sampling effort can be appropriately utilized for large-scale biodiversity distribution modelling with the use of occupancy models, which encourages data collection by citizen scientists.