Give me a sample of air and I will tell which species are found from your region : Molecular identification of fungi from airborne spore samples

Fungi are a megadiverse group of organisms, they play major roles in ecosystem functioning and are important for human health, food production and nature conservation. Our knowledge on fungal diversity and fungal ecology is however still very limited, in part because surveying and identifying fungi is time demanding and requires expert knowledge. We present a method that allows anyone to generate a list of fungal species likely to occur in a region of interest, with minimal effort and without requiring taxonomical expertise. The method consists of using a cyclone sampler to acquire fungal spores directly from the air to an Eppendorf tube, and applying DNA barcoding with probabilistic species identification to generate a list of species from the sample. We tested the feasibility of the method by acquiring replicate air samples from different geographical regions within Finland. Our results show that air sampling is adequate for regional-level surveys, with samples collected >100km apart varying but samples collectedPeer reviewe

Data from: Give me a sample of air and I will tell which species are found from your region – molecular identification of fungi from airborne spore samples

Fungi are a megadiverse group of organisms, they play major roles in ecosystem functioning, and are important for human health, food production, and nature conservation. Our knowledge on fungal diversity and fungal ecology is however still very limited, in part because surveying and identifying fungi is time demanding and requires expert knowledge. We present a method that allows anyone to generate a list of fungal species likely to occur in a region of interest, with minimal effort and without requiring taxonomical expertise. The method consists of using a cyclone sampler to acquire fungal spores directly from the air to an Eppendorf tube, and applying DNA barcoding with probabilistic species identification to generate a list of species from the sample. We tested the feasibility of the method by acquiring replicate air samples from different geographical regions within Finland. Our results show that air sampling is adequate for regional-level surveys, with samples collected >100 km apart varying but samples collected <10 km apart not varying in their species composition. The data show marked phenology, and thus that obtaining a representative species list requires aerial sampling that covers the entire fruiting season. In sum, aerial sampling combined with probabilistic molecular species identification offers a highly effective method for generating a species list of airborne dispersed fungi. The method presented here has the potential to revolutionize fungal surveys, as it provides a highly cost-efficient way to include fungi as a part of large-scale biodiversity assessments and monitoring programs

Fungi identified from airborne spore samples in Finland

The zip file contains two excel files which correspond to the species x samples and covariates x samples data matrices, respectively. In the species dataset, the columns are the species and the rows are the samples. In the covariates dataset, the columns are the covariates and the rows are the samples. The sheets in the species dataset correspond to (1) species-level identifications with 0.5 reliability threshold (denoted as Species 0.5), (2) species-level identifications with 0.9 reliability threshold (denoted as Species 0.9), (3)genus-level identifications with 0.5 reliability threshold (denoted as Genus 0.5), (4) genus-level identifications with 0.9 reliability threshold (denoted as Genus 0.9), (5) wood-inhabiting fungal species-level identifications with 0.5 reliability threshold (denoted as Wood-inhabiting fungi 0.5), (6) wood-inhabiting fungal species-level identifications with 0.9 reliability threshold (denoted as Wood-inhabiting fungi 0.9)

Sequence data of the spore samples

This file contains the sequence data of the 134 spore samples. The sequence data is provided in the fasta format, the file name being coded as sample_n.seq, where n = 1...134 is the sample id (see Table S1 in the Supplemental information)