@inproceedings{WeissLinde2023, author = {Weiss, Fabio and Linde, Andreas}, title = {How precise are size-weight equations for estimating carabid biomass and how could they be improved?}, series = {22. Tagung der Deutschen Gesellschaft f{\"u}r allgemeine und angewandte Entomologie e.V. (DGaaE) 2023 in Bozen, Italien}, booktitle = {22. Tagung der Deutschen Gesellschaft f{\"u}r allgemeine und angewandte Entomologie e.V. (DGaaE) 2023 in Bozen, Italien}, doi = {10.57741/opus4-396}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-3966}, pages = {1}, year = {2023}, abstract = {Insect biomass has been used as an ecological indicator in the past, but in recent years has become a key metric in the study of insect population trends, especially since first reports about the so-called insect decline (e.g. "Krefeld study" of 2017). However, measuring insect biomass can be methodologically challenging, and very labour intensive. For many datasets, weight measurements are not available and the original samples may have already been lost. Size-weight equations provide a straightforward method for estimating insect biomass when the insect's body length is known. If insects have been determined to species level one can also use the average body length given in literature. Based on the correlation between the body length and the weight of an insect, there is a variety of size-weight equations for different groups of insects. They are widely used in insect research, but have rarely been tested with independent data. We evaluated two size-weight models for carabid beetles, by Szyszko (1983) and Booij et al. (1994), drawing on previously published independent data by comparing model predictions with actual measurements of biomass, using relative deviation graphs and observed versus predicted from regression. Moreover, we also tested if the inclusion of additional taxonomic parameters, in this case subfamily, can improve results. We found that both models produced systematically biased results: Szyszko´s model gave more accurate results for larger species, while the model of Booij et al. did so for smaller species. This bias is most likely caused by the different origin of the respective training data. This underlines the restricted applicability of such models. We demonstrate that additional taxonomic parameters have the potential to increase the accuracy of size-weight equations and represent a potential solution to the issue of restricted applicability. As actual biomass measurements for carabids are scarce, data availability is limited to only few subfamilies. Until data on more subfamilies become available, we recommend a combined use of both evaluated models: Szyszko's model for carabids ≥ 11.8 mm, and the model of Booij et al. for carabids < 11.8 mm, respectively.}, language = {en} } @article{WeissvonWehrdenLinde2023, author = {Weiss, Fabio and von Wehrden, Henrik and Linde, Andreas}, title = {Random year intercepts in mixed models help to assess uncertainties in insect population trends}, series = {Insect Conservation and Diversity}, volume = {16}, journal = {Insect Conservation and Diversity}, number = {4}, publisher = {Wiley}, issn = {1752-4598}, doi = {10.1111/icad.12644}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-6496}, pages = {531 -- 537}, year = {2023}, abstract = {1. An increasing number of studies is investigating insect population trends based on time series data. However, the available data is often subject to temporal pseudoreplication. Inter-annual variability of environmental conditions and strong fluctuations in insect abundances can impede reliable trend estimation. Temporal random effect structures in regression models have been proposed as solution for this issue, but remain controversial. 2. We investigated trends in ground beetle abundance across 24 years using generalised linear mixed models. We fitted four models: A base model, a model featuring a random year intercept, a model featuring basic weather parameters, and a model featuring both random year intercept and weather parameters. We then performed a simple sensitivity analysis to assess the robustness of the four models with respect to influential years, also testing for possible spurious baseline and snapshot effects. 3. The model structure had a significant impact on the overall magnitude of the estimated trends. However, we found almost no difference among the models in how the removal of single years (sensitivity analysis) relatively affected trend coefficients. The two models with a random year intercept yielded significantly larger confidence intervals and their p-values were more sensitive during sensitivity analysis. Significant differences of the model with random year intercept and weather parameters to all other models suggest that the random year effects and specific weather effects are rather additive than interchangeable. 4. We conclude that random year intercepts help to produce more reliable and cautious uncertainty measures for insect population trends. Moreover, they might help to identify influential years in sensitivity analyses more easily. We recommend random year intercepts in addition to any variables representing temporally variable environmental conditions, such as weather variables.}, language = {en} }