@article{RablAlonsoRodriguezBrehmetal.2020,
  author    = {Rabl, Dominik and Alonso-Rodr{\´i}guez, Aura M. and Brehm, Gunnar and Fiedler, Konrad},
  title     = {Trait variation in moths mirrors small-scaled ecological gradients in a tropical forest landscape},
  series = {Insects},
  volume    = {11},
  journal   = {Insects},
  number    = {9},
  issn      = {2075-4450},
  doi       = {10.3390/insects11090612},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213016},
  year      = {2020},
  abstract  = {Along environmental gradients, communities are expected to be filtered from the regional species pool by physical constraints, resource availability, and biotic interactions. This should be reflected in species trait composition. Using data on species-rich moth assemblages sampled by light traps in a lowland rainforest landscape in Costa Rica, we show that moths in two unrelated clades (Erebidae-Arctiinae; Geometridae) are much smaller-sized in oil palm plantations than in nearby old-growth forest, with intermediate values at disturbed forest sites. In old-growth forest, Arctiinae predominantly show aposematic coloration as a means of anti-predator defense, whereas this trait is much reduced in the prevalence in plantations. Similarly, participation in M{\"u}llerian mimicry rings with Hymenoptera and Lycidae beetles, respectively, is rare in plantations. Across three topographic types of old-growth forests, community-weighted means of moth traits showed little variation, but in creek forest, both types of mimicry were surprisingly rare. Our results emphasize that despite their mobility, moth assemblages are strongly shaped by local environmental conditions through the interplay of bottom-up and top-down processes. Assemblages in oil palm plantations are highly degraded not only in their biodiversity, but also in terms of trait expression.},
  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}
}