Increasing evidence suggests that degradation of biodiversity in human populated areas is a threat for the ecosystem processes that are relevant for human well-being. Fungi are a megadiverse kingdom that plays a key role in ecosystem processes and affects human well-being. How urbanization influences fungi has remained poorly understood, partially due to the methodological difficulties in comprehensively surveying fungi. Here we show that both aerial and soil fungal communities are greatly poorer in urban than in natural areas. Strikingly, a fivefold reduction in fungal DNA abundance took place in both air and soil samples already at 1 km scale when crossing the edge from natural to urban habitats. Furthermore, in the air, fungal diversity decreased with urbanization even more than in the soil. This result is counterintuitive as fungal spores are known to disperse over large distances. A large proportion of the fungi detectable in the air are specialized to natural habitats, whereas soil fungal communities comprise a large proportion of habitat generalists. The sensitivity of the aerial fungal community to anthropogenic disturbance makes this method a reliable and efficient bioindicator of ecosystem health in urban areas.

Motivation: When targeted to a barcoding region, high-throughput sequencing can be used to identify species or operational taxonomical units from environmental samples, and thus to study the diversity and structure of species communities. Although there are many methods which provide confidence scores for assigning taxonomic affiliations, it is not straightforward to translate these values to unbiased probabilities. We present a probabilistic method for taxonomical classification (PROTAX) of DNA sequences. Given a pre-defined taxonomical tree structure that is partially populated by reference sequences, PROTAX decomposes the probability of one to the set of all possible outcomes. PROTAX accounts for species that are present in the taxonomy but that do not have reference sequences, the possibility of unknown taxonomical units, as well as mislabeled reference sequences. PROTAX is based on a statistical multinomial regression model, and it can utilize any kind of sequence similarity measures or the outputs of other classifiers as predictors.

Fungi are among the most diverse and ecologically important kingdoms in life. However, the distributional ranges of fungi remain largely unknown as do the ecological mechanisms that shape their distributions^1,2. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardized aerial sampling of fungal spores³. The vast majority of operational taxonomic units were detected within only one climatic zone, and the spatiotemporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating for only a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with a microbial lifestyle follow the global biodiversity paradigms known for macroorganisms^4,5.

An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades, progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge. In this article, we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentation and a combination of global and local data can be used to train a convolutional neural network to classify vocalizations of 101 bird species. We construct a model and train it with a global data set to obtain a base model. The base model is then fine‐tuned with local data from Southern Finland in order to adapt it to the sound environment of a specific location and tested with two data sets: one originating from the same Southern Finnish region and another originating from a different region in German Alps. Our results suggest that fine‐tuning with local data significantly improves the network performance. Classification accuracy was improved for test recordings from the same area as the local training data (Southern Finland) but not for recordings from a different region (German Alps). Data augmentation enables training with a limited number of training data and even with few local data samples significant improvement over the base model can be achieved. Our model outperforms the current state‐of‐the‐art tool for automatic bird sound classification. Using local data to adjust the recognition model for the target domain leads to improvement over general non‐tailored solutions. The process introduced in this article can be applied to build a fine‐tuned bird sound classification model for a specific environment.

Current high-throughput sequencing platforms provide capacity to sequence multiple samples in parallel. Different samples are labeled by attaching a short sample specific nucleotide sequence, barcode, to each DNA molecule prior pooling them into a mix containing a number of libraries to be sequenced simultaneously. After sequencing, the samples are binned by identifying the barcode sequence within each sequence read. In order to tolerate sequencing errors, barcodes should be sufficiently apart from each other in sequence space. An additional constraint due to both nucleotide usage and basecalling accuracy is that the proportion of different nucleotides should be in balance in each barcode position. The number of samples to be mixed in each sequencing run may vary and this introduces a problem how to select the best subset of available barcodes at sequencing core facility for each sequencing run. There are plenty of tools available for de novo barcode design, but they are not suitable for subset selection. We have developed a tool which can be used for three different tasks: 1) selecting an optimal barcode set from a larger set of candidates, 2) checking the compatibility of user-defined set of barcodes, e.g. whether two or more libraries with existing barcodes can be combined in a single sequencing pool, and 3) augmenting an existing set of barcodes. In our approach the selection process is formulated as a minimization problem. We define the cost function and a set of constraints and use integer programming to solve the resulting combinatorial problem. Based on the desired number of barcodes to be selected and the set of candidate sequences given by user, the necessary constraints are automatically generated and the optimal solution can be found. The method is implemented in C programming language and web interface is available at http://ekhidna2.biocenter.helsinki.fi/barcosel . Increasing capacity of sequencing platforms raises the challenge of mixing barcodes. Our method allows the user to select a given number of barcodes among the larger existing barcode set so that both sequencing errors are tolerated and the nucleotide balance is optimized. The tool is easy to access via web browser.