TY  - JOUR
A1  - Ziegler, Alice
A1  - Meyer, Hanna
A1  - Otte, Insa
A1  - Peters, Marcell K.
A1  - Appelhans, Tim
A1  - Behler, Christina
A1  - Böhning-Gaese, Katrin
A1  - Classen, Alice
A1  - Detsch, Florian
A1  - Deckert, Jürgen
A1  - Eardley, Connal D.
A1  - Ferger, Stefan W.
A1  - Fischer, Markus
A1  - Gebert, Friederike
A1  - Haas, Michael
A1  - Helbig-Bonitz, Maria
A1  - Hemp, Andreas
A1  - Hemp, Claudia
A1  - Kakengi, Victor
A1  - Mayr, Antonia V.
A1  - Ngereza, Christine
A1  - Reudenbach, Christoph
A1  - Röder, Juliane
A1  - Rutten, Gemma
A1  - Schellenberger Costa, David
A1  - Schleuning, Matthias
A1  - Ssymank, Axel
A1  - Steffan-Dewenter, Ingolf
A1  - Tardanico, Joseph
A1  - Tschapka, Marco
A1  - Vollstädt, Maximilian G. R.
A1  - Wöllauer, Stephan
A1  - Zhang, Jie
A1  - Brandl, Roland
A1  - Nauss, Thomas
T1  - Potential of airborne LiDAR derived vegetation structure for the prediction of animal species richness at Mount Kilimanjaro
JF  - Remote Sensing
N2  - The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results.
KW  - biodiversity
KW  - species richness
KW  - LiDAR
KW  - elevation
KW  - partial least square regression
KW  - arthropods
KW  - birds
KW  - bats
KW  - predictive modeling
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262251
SN  - 2072-4292
VL  - 14
IS  - 3
ER  - 
TY  - JOUR
A1  - Kortmann, Mareike
A1  - Roth, Nicolas
A1  - Buse, Jörn
A1  - Hilszczański, Jacek
A1  - Jaworski, Tomasz
A1  - Morinière, Jérôme
A1  - Seidl, Rupert
A1  - Thorn, Simon
A1  - Müller, Jörg C.
T1  - Arthropod dark taxa provide new insights into diversity responses to bark beetle infestations
JF  - Ecological Applications
N2  - 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.
KW  - arthropods
KW  - biodiversity
KW  - conservation
KW  - metabarcoding
KW  - national park
KW  - natural disturbance
KW  - threshold indicator taxa analysis
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-276392
VL  - 32
IS  - 2
ER  -