Pollen analysis is an important tool in many fields, including pollination ecology, paleoclimatology, paleoecology, honey quality control, and even medicine and forensics. However, labour‐intensive manual pollen analysis often constrains the number of samples processed or the number of pollen analysed per sample. Thus, there is a desire to develop reliable, high‐throughput, automated systems. We present an automated method for pollen analysis, based on deep learning convolutional neural networks (CNN). We scanned microscope slides with fuchsine stained, fresh pollen and automatically extracted images of all individual pollen grains. CNN models were trained on reference samples (122,000 pollen grains, from 347 flowers of 83 species of 17 families). The models were used to classify images of different pollen grains in a series of experiments. We also propose an adjustment to reduce overestimation of sample diversity in cases where samples are likely to contain few species. Accuracy of a model for 83 species was 0.98 when all samples of each species were first pooled, and then split into a training and a validation set (splitting experiment). However, accuracy was much lower (0.41) when individual reference samples from different flowers were kept separate, and one such sample was used for validation of models trained on remaining samples of the species (leave‐one‐out experiment). We therefore combined species into 28 pollen types where a new leave‐one‐out experiment revealed an overall accuracy of 0.68, and recall rates >0.90 in most pollen types. When validating against 63,650 manually identified pollen grains from 370 bumblebee samples, we obtained an accuracy of 0.79, but our adjustment procedure increased this to 0.85. Validation through splitting experiments may overestimate robustness of CNN pollen analysis in new contexts (samples). Nevertheless, our method has the potential to allow large quantities of real pollen data to be analysed with reasonable accuracy. Although compiling pollen reference libraries is time‐consuming, this is simplified by our method, and can lead to widely accessible and shareable resources for pollen analysis.

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Predictive models of the spatial distribution and abundance of species based on habitat characteristics are finding increasing use in management and conservation. The European badger attracts interest as a model species both for conservation reasons and because of the important role the species is playing in understanding carnivore sociality. We developed a statistical habitat model based on presence/absence data on badger setts. To maximise the utility of the model in management, we limited the choice of model variables to those that had a clear basis in badger ecology and that could be obtained on a nation-wide digital format. We extrapolated the habitat model to a region in Denmark and developed a threshold-independent sett distribution algorithm to estimate sett densities. The habitat model was simpler than previously published models of badger sett habitat selection, but nevertheless had a predictive ability in excess of 80% judged against independent data. The sett distribution algorithm was able to simultaneously reproduce several observed patterns of sett density and distribution over the probability gradient. It thus represents a significant improvement over threshold-dependent methods used to discriminate between suitable and unsuitable habitat predicted by presence/absence regression models. Our approach demonstrates that a model of badger sett habitat suitability with high predictive power can be obtained using easily accessible map-variables and presence/absence data. This is a prerequisite for using habitat models as predictive tools over large areas. The use of a simple sett distribution algorithm circumvents the common problem of subjectively fixing a threshold to discriminate between suitable and unsuitable habitat. In conjunction the models presented here constitute an important contribution to the management of the badger in Denmark and, upon further validation, possibly to similar regions in Northern Europe.

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