
doi: 10.1111/cobi.13240
pmid: 30350883
Abstract Next‐generation sequencing (NGS) has significantly increased knowledge of microbial communities and their distribution. However, it is still not common to apply NGS technology to microbial conservation. We sought to use NGS technologies to evaluate conservation strategies for wood‐inhabiting fungi. Evaluating a deadwood experiment 3 years after it was established, we specifically examined which tree species combinations promoted the highest richness of wood‐inhabiting fungi. Deadwood enrichment was an effective strategy and logs of 6 tree species, either those with the highest wood‐inhabiting fungal α and γ diversity or those with the highest β diversity, maintained >1,000 operational taxonomic units (OTUs) spread over a wide range of taxonomic groups. In comparison, a conservation strategy based only on the results of sporocarp surveys yielded 591 OTUs. This result highlights the need to use NGS approaches to inform microbial conservation strategies. We also determined that 5 tree species with the highest saproxylic beetle γ diversity simultaneously conserved wood‐inhabiting fungi. Apart from deadwood volume, we suggest data on deadwood quality and species also be included as indicators, especially for wood‐inhabiting fungal diversity, and incorporated quickly in forest assessment and monitoring systems in Central Europe.
Europe, Conservation of Natural Resources, Fungi, Animals, High-Throughput Nucleotide Sequencing, Biodiversity, Wood, Trees
Europe, Conservation of Natural Resources, Fungi, Animals, High-Throughput Nucleotide Sequencing, Biodiversity, Wood, Trees
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 14 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
