@article{KortmannRothBuseetal.2022,
  author    = {Kortmann, Mareike and Roth, Nicolas and Buse, J{\"o}rn and Hilszczański, Jacek and Jaworski, Tomasz and Morini{\`e}re, J{\´e}r{\^o}me and Seidl, Rupert and Thorn, Simon and M{\"u}ller, J{\"o}rg C.},
  title     = {Arthropod dark taxa provide new insights into diversity responses to bark beetle infestations},
  series = {Ecological Applications},
  volume    = {32},
  journal   = {Ecological Applications},
  number    = {2},
  doi       = {10.1002/eap.2516},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-276392},
  year      = {2022},
  abstract  = {Natural disturbances are increasing around the globe, also impacting protected areas. Although previous studies have indicated that natural disturbances result in mainly positive effects on biodiversity, these analyses mostly focused on a few well established taxonomic groups, and thus uncertainty remains regarding the comprehensive impact of natural disturbances on biodiversity. Using Malaise traps and meta-barcoding, we studied a broad range of arthropod taxa, including dark and cryptic taxa, along a gradient of bark beetle disturbance severities in five European national parks. We identified order-level community thresholds of disturbance severity and classified barcode index numbers (BINs; a cluster system for DNA sequences, where each cluster corresponds to a species) as negative or positive disturbance indicators. Negative indicator BINs decreased above thresholds of low to medium disturbance severity (20\%-30\% of trees killed), whereas positive indicator BINs benefited from high disturbance severity (76\%-98\%). BINs allocated to a species name contained nearly as many positive as negative disturbance indicators, but dark and cryptic taxa, particularly Diptera and Hymenoptera in our data, contained higher numbers of negative disturbance indicator BINs. Analyses of changes in the richness of BINs showed variable responses of arthropods to disturbance severity at lower taxonomic levels, whereas no significant signal was detected at the order level due to the compensatory responses of the underlying taxa. We conclude that the analyses of dark taxa can offer new insights into biodiversity responses to disturbances. Our results suggest considerable potential for forest management to foster arthropod diversity, for example by maintaining both closed-canopy forests (>70\% cover) and open forests (<30\% cover) on the landscape.},
  language  = {en}
}
@article{HardulakMoriniereHausmannetal.2020,
  author    = {Hardulak, Laura A. and Morini{\`e}re, J{\´e}r{\^o}me and Hausmann, Axel and Hendrich, Lars and Schmidt, Stefan and Doczkal, Dieter and M{\"u}ller, J{\"o}rg and Hebert, Paul D. N. and Haszprunar, Gerhard},
  title     = {DNA metabarcoding for biodiversity monitoring in a national park: Screening for invasive and pest species},
  series = {Molecular Ecology Resources},
  volume    = {20},
  journal   = {Molecular Ecology Resources},
  number    = {6},
  doi       = {10.1111/1755-0998.13212},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-217812},
  pages     = {1542 -- 1557},
  year      = {2020},
  abstract  = {DNA metabarcoding was utilized for a large-scale, multiyear assessment of biodiversity in Malaise trap collections from the Bavarian Forest National Park (Germany, Bavaria). Principal component analysis of read count-based biodiversities revealed clustering in concordance with whether collection sites were located inside or outside of the National Park. Jaccard distance matrices of the presences of barcode index numbers (BINs) at collection sites in the two survey years (2016 and 2018) were significantly correlated. Overall similar patterns in the presence of total arthropod BINs, as well as BINs belonging to four major arthropod orders across the study area, were observed in both survey years, and are also comparable with results of a previous study based on DNA barcoding of Sanger-sequenced specimens. A custom reference sequence library was assembled from publicly available data to screen for pest or invasive arthropods among the specimens or from the preservative ethanol. A single 98.6\% match to the invasive bark beetle Ips duplicatus was detected in an ethanol sample. This species has not previously been detected in the National Park.},
  language  = {en}
}
@article{MuellerMitesserSchaeferetal.2023,
  author    = {M{\"u}ller, J{\"o}rg and Mitesser, Oliver and Schaefer, H. Martin and Seibold, Sebastian and Busse, Annika and Kriegel, Peter and Rabl, Dominik and Gelis, Rudy and Arteaga, Alejandro and Freile, Juan and Leite, Gabriel Augusto and de Melo, Tomaz Nascimento and LeBien, Jack and Campos-Cerqueira, Marconi and Bl{\"u}thgen, Nico and Tremlett, Constance J. and B{\"o}ttger, Dennis and Feldhaar, Heike and Grella, Nina and Falcon{\´i}-L{\´o}pez, Ana and Donoso, David A. and Moriniere, Jerome and Buřivalov{\´a}, Zuzana},
  title     = {Soundscapes and deep learning enable tracking biodiversity recovery in tropical forests},
  series = {Nature Communications},
  volume    = {14},
  journal   = {Nature Communications},
  doi       = {10.1038/s41467-023-41693-w},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-358130},
  year      = {2023},
  abstract  = {Tropical forest recovery is fundamental to addressing the intertwined climate and biodiversity loss crises. While regenerating trees sequester carbon relatively quickly, the pace of biodiversity recovery remains contentious. Here, we use bioacoustics and metabarcoding to measure forest recovery post-agriculture in a global biodiversity hotspot in Ecuador. We show that the community composition, and not species richness, of vocalizing vertebrates identified by experts reflects the restoration gradient. Two automated measures - an acoustic index model and a bird community composition derived from an independently developed Convolutional Neural Network - correlated well with restoration (adj-R² = 0.62 and 0.69, respectively). Importantly, both measures reflected composition of non-vocalizing nocturnal insects identified via metabarcoding. We show that such automated monitoring tools, based on new technologies, can effectively monitor the success of forest recovery, using robust and reproducible data.},
  language  = {en}
}