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Data from: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range

Cite this dataset

Moens, Merijn et al. (2024). Data from: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range [Dataset]. Dryad. https://doi.org/10.5061/dryad.cvdncjtc2

Abstract

Studies have found that biotic information can play an important role in shaping the distribution of species even at large scales. However, results from species distribution models are not always consistent among studies, and the underlying factors that influence the importance of biotic information to distribution models, are unclear. 2. We studied wild bees and plants, and cleptoparasite bees and their hosts in the Netherlands to evaluate how the inclusion of their biotic interactions affects the performance of species distribution models. We assessed model performance through spatial block cross-validation and by comparing models with interactions to models where the interacting species were randomized. Finally, we evaluated how, (i) spatial resolution, (ii) taxonomic rank (genus or species), (iii) degree of specialization, (iv) distribution of the biotic factor, (v) bee body size and (vi) type of biotic interaction, affect the importance of biotic interactions in shaping the distribution of wild bee species using generalized linear models. 3. We found that the models of wild bees improved when the biotic factor was included. The model performance improved the most for parasitic bees. Spatial resolution, taxonomic rank, distribution range of the biotic factor, and degree of specialization of the modelled species all influenced the importance of the biotic interaction to the models. 4. We encourage researchers to include biotic interactions in species distribution models, especially for specialized species and when the biotic factor has a limited distribution range. However, before adding the biotic factor we suggest considering different spatial resolutions and taxonomic ranks of the biotic factor. We recommend using single species or genus data as a biotic factor in the models of specialist species and for the generalist species, we recommend using an approximate measure of interactions, such as flower richness.

README: The importance of biotic interactions in distribution models of wild bees depends on the type of ecological relations, spatial scale and range.

https://doi.org/10.5061/dryad.cvdncjtc2

These files contain supplementary material, including additional text (with or without figures), tables, figures, scripts, and raw data.

On Zenodo:

  • Appendix text 1: Model optimization: regularization multiplier.
  • Appendix text 2: Details RI-SDMs.
  • Appendix text 3: Details of the generalized linear models
  • Appendix text 4: Detailed results evaluation measures
  • Appendix text 5: Comparing the contribution of the biotic factor with climate, land use and soil variables
  • Appendix text 6: Detailed results resolution and taxonomic rank
  • Appendix text 7: References of supporting information
  • Appendix figure 1: The effect of the resolution and taxonomic rank on the permutation importance of the biotic factor to the model, expressed as the ranking of the biotic factor contribution per species (from high to low: 1-5; 1A) and the difference in variable contribution between the species that the modelled species interacts with (biotic factor) added at species and genus taxonomic rank per species (1B).
  • Appendix figure 2: The results of the Generalized Linear Models (GLMs) show the relation between flower specialization (the inverse of the Shannon-Wiener index of number of plants genera interacted with) and the permutation importance of the biotic factor to the models of the oligolectic and polylectic bees (fig. 2A). Fig. 2B shows effect of distribution of the most visited genus on the permutation importance of the biotic factor to the model. Fig. 2C shows the relation between the distribution of the host bees and the permutation importance of the biotic factor to the models of the cleptoparasitic bees.
  • Appendix script 1: Functions that were used for the known interaction SDMs (KI-SDMs) and randomized interaction SDMs (RI-SDMs).
  • Appendix script 2: Comparing evaluation metrics with and without the biotic factor.
  • Appendix script 3: Analyzing the results of the randomized interactions species distribution models (RI-SDMs).
  • Appendix script 4: Analysing the influence of spatial resolution and taxonomic level on the evaluation measures of the KI-SDMs.
  • Appendix script 5: The influence of specialization, body size and distribution range on the variable importance of the biotic factor.

On Dryad:

  • Appendix table 1: Overview of the different land use classes used.
  • Appendix table 2: Overview of the different soil categories used.
  • Appendix table 3: Comparison of plant pollen dependencies and most visited plants.
  • Appendix table 4: Cleptoparasitic bees and their hosts used from the literature (Peeters et al. 2012).
  • Appendix table 5: The standardized ODMAP protocol for reporting species distribution models from Zurell et al. 2020.
  • Supplementary data 1: The correlation (Spearman’s ρ) between all the different biotic variables and the abiotic variables.
  • Supplementary data 2: The variable importance for different resolutions of the biotic factor. This data is analyzed in script 4 and used as an input for the generalized linear models (GLMs) in script 5.
  • Supplementary data 3: The values of the input variables and the percentage contribution of the biotic factor for the GLMs in script 5 for the oligolectic and polylectic bees.
  • Supplementary data 4: The values of the input variables and the permutation importance of the biotic factor for the GLMs in script 5 for the oligolectic and polylectic bees.
  • Supplementary data 5: The values of the input variables and the percentage contribution of the biotic factor for the GLMs in script 5 for the cleptoparasitic bees.
  • Supplementary data 6: The values of the input variables and the permutation importance of the biotic factor for the GLMs in script 5 for the cleptoparasitic bees.
  • Supplementary data 7: The cumulative variable importance for climate, land use, soil and biotic variables. The data is analyzed in script 4.
  • Supplementary data 8: The ranking of the percentage contribution of the biotic factor for the different resolutions. This data is analyzed in script 4.
  • Supplementary data 9: The ranking of the permutation importance of the biotic factor for the different resolutions. This data is analyzed in script 4.
  • Supplementary data 10: The evaluation measures of the different models including or excluding biotic factors at different taxonimic levels. This data is analyzed in script 2.
  • Supplementary data 11: The rank of the biotic factor among randomized interactions. This data is analyzed in script 3.

Description of the data and file structure

The various supplementary files referenced in the manuscript (DOI: 10.1111/oik.10578) provide additional information accessible in this repository. The text files offer detailed descriptions of methods and results. Figure and table captions are included above and the figures and tables are cited in the manuscript. The scripts contain all the necessary functions for modeling and analyzing the results presented in the study. The supplementary data consists of model outputs in different formats for use with the supplementary scripts.

Sharing/Access information

The supplementary data files are exclusively accessible through this Dryad repository.

References

Peeters, T., Nieuwenhuijsen, H., Smit, J., van de Meer, F., Raemakers, I. P., & Heitmans, W. R. (2012). De Nederlandse bijen. Naturalis Biodiversity Center & European Invertebrate Survey.

Zurell, D., Franklin, J., König, C., Bouchet, P. J., Dormann, C. F., Elith, J., Fandos, G., Feng, X., Guillera‐Arroita, G., & Guisan, A. (2020). A standard protocol for reporting species distribution models. Ecography, 43(9), 1261–1277.

Funding

Dutch Research Council, Award: 776608