TY  - JOUR
A1  - Seibold, Sebastian
A1  - Hothorn, Torsten
A1  - Gossner, Martin M.
A1  - Simons, Nadja K.
A1  - Blüthgen, Nico
A1  - Müller, Jörg
A1  - Ambarlı, Didem
A1  - Ammer, Christian
A1  - Bauhus, Jürgen
A1  - Fischer, Markus
A1  - Habel, Jan C.
A1  - Penone, Caterina
A1  - Schall, Peter
A1  - Schulze, Ernst‐Detlef
A1  - Weisser, Wolfgang W.
T1  - Insights from regional and short‐term biodiversity monitoring datasets are valuable: a reply to Daskalova et al. 2021
JF  - Insect Conservation and Diversity
N2  - Reports of major losses in insect biodiversity have stimulated an increasing interest in temporal population changes. Existing datasets are often limited to a small number of study sites, few points in time, a narrow range of land‐use intensities and only some taxonomic groups, or they lack standardised sampling. While new monitoring programs have been initiated, they still cover rather short time periods.
Daskalova et al. 2021 (Insect Conservation and Diversity, 14, 1‐18) argue that temporal trends of insect populations derived from short time series are biased towards extreme trends, while their own analysis of an assembly of shorter‐ and longer‐term time series does not support an overall insect decline. With respect to the results of Seibold et al. 2019 (Nature, 574, 671–674) based on a 10‐year multi‐site time series, they claim that the analysis suffers from not accounting for temporal pseudoreplication.
Here, we explain why the criticism of missing statistical rigour in the analysis of Seibold et al. (2019) is not warranted. Models that include ‘year’ as random effect, as suggested by Daskalova et al. (2021), fail to detect non‐linear trends and assume that consecutive years are independent samples which is questionable for insect time‐series data.
We agree with Daskalova et al. (2021) that the assembly and analysis of larger datasets is urgently needed, but it will take time until such datasets are available. Thus, short‐term datasets are highly valuable, should be extended and analysed continually to provide a more detailed understanding of insect population changes under the influence of global change, and to trigger immediate conservation actions.
KW  - Arthropod
KW  - biodiversity
KW  - insect decline
KW  - land use
KW  - time series
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-228309
VL  - 14
IS  - 1
SP  - 144
EP  - 148
ER  - 
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  - Peters, Marcell K.
A1  - Hemp, Andreas
A1  - Appelhans, Tim
A1  - Behler, Christina
A1  - Classen, Alice
A1  - Detsch, Florian
A1  - Ensslin, Andreas
A1  - Ferger, Stefan W.
A1  - Frederiksen, Sara B.
A1  - Gebert, Frederike
A1  - Haas, Michael
A1  - Helbig-Bonitz, Maria
A1  - Hemp, Claudia
A1  - Kindeketa, William J.
A1  - Mwangomo, Ephraim
A1  - Ngereza, Christine
A1  - Otte, Insa
A1  - Röder, Juliane
A1  - Rutten, Gemma
A1  - Costa, David Schellenberger
A1  - Tardanico, Joseph
A1  - Zancolli, Giulia
A1  - Deckert, Jürgen
A1  - Eardley, Connal D.
A1  - Peters, Ralph S.
A1  - Rödel, Mark-Oliver
A1  - Schleuning, Matthias
A1  - Ssymank, Axel
A1  - Kakengi, Victor
A1  - Zhang, Jie
A1  - Böhning-Gaese, Katrin
A1  - Brandl, Roland
A1  - Kalko, Elisabeth K.V.
A1  - Kleyer, Michael
A1  - Nauss, Thomas
A1  - Tschapka, Marco
A1  - Fischer, Markus
A1  - Steffan-Dewenter, Ingolf
T1  - Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level
JF  - Nature Communications
N2  - The factors determining gradients of biodiversity are a fundamental yet unresolved topic in ecology. While diversity gradients have been analysed for numerous single taxa, progress towards general explanatory models has been hampered by limitations in the phylogenetic coverage of past studies. By parallel sampling of 25 major plant and animal taxa along a 3.7 km elevational gradient on Mt. Kilimanjaro, we quantify cross-taxon consensus in diversity gradients and evaluate predictors of diversity from single taxa to a multi-taxa community level. While single taxa show complex distribution patterns and respond to different environmental factors, scaling up diversity to the community level leads to an unambiguous support for temperature as the main predictor of species richness in both plants and animals. Our findings illuminate the influence of taxonomic coverage for models of diversity gradients and point to the importance of temperature for diversification and species coexistence in plant and animal communities.
KW  - community ecology
KW  - macroecology
KW  - tropical ecology
KW  - biodiversity
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-169374
VL  - 7
ER  -