The drivers of dark diversity in the Scandinavian mountains are metric-dependent. Hostens, L., Van Meerbeek, K., Wiegmans, D., Larson, K., Lenoir, J., Clavel, J., Wedegärtner, R., Pirée, A., Nijs, I., & Lembrechts, J. J. Journal of Vegetation Science, 34(6):e13212, 2023. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jvs.13212![The drivers of dark diversity in the Scandinavian mountains are metric-dependent [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Question Dark diversity refers to the set of species that are not observed in an area but could potentially occur based on suitable local environmental conditions. In this paper, we applied both niche-based and co-occurrence-based methods to estimate the dark diversity of vascular plant species in the subarctic mountains. We then aimed to unravel the drivers explaining (a) why some locations were missing relatively more suitable species than others, and (b) why certain plant species were more often absent from suitable locations than others. Location The Scandinavian mountains around Abisko, northern Sweden. Methods We calculated the dark diversity in 107 plots spread out across four mountain trails using four different methods: two co-occurrence-based (Beals’ index and the hypergeometric method) and two niche-based (the climatic niche model and climatic niche model followed by species-specific threshold). We then applied multiple Generalized Linear Mixed-Effects Models and General Linear Models to determine which habitat characteristics and species traits contributed the most to dark diversity. Results The study showed a notable divergence in the predicted drivers of dark diversity depending on the method used. Nevertheless, we can conclude that plot-level dark diversity was generally 17% higher in areas at low elevations and 31% higher in areas with a low species richness. Conclusion Our findings call for caution when interpreting statistical findings of dark-diversity estimates. Even so, all analyses point toward an important role for natural processes such as competitive dominance as the main driver of the spatial patterns found in dark diversity in the northern Scandes.
@article{hostens_drivers_2023,
title = {The drivers of dark diversity in the {Scandinavian} mountains are metric-dependent},
volume = {34},
copyright = {© 2023 International Association for Vegetation Science.},
issn = {1654-1103},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jvs.13212},
doi = {10.1111/jvs.13212},
abstract = {Question Dark diversity refers to the set of species that are not observed in an area but could potentially occur based on suitable local environmental conditions. In this paper, we applied both niche-based and co-occurrence-based methods to estimate the dark diversity of vascular plant species in the subarctic mountains. We then aimed to unravel the drivers explaining (a) why some locations were missing relatively more suitable species than others, and (b) why certain plant species were more often absent from suitable locations than others. Location The Scandinavian mountains around Abisko, northern Sweden. Methods We calculated the dark diversity in 107 plots spread out across four mountain trails using four different methods: two co-occurrence-based (Beals’ index and the hypergeometric method) and two niche-based (the climatic niche model and climatic niche model followed by species-specific threshold). We then applied multiple Generalized Linear Mixed-Effects Models and General Linear Models to determine which habitat characteristics and species traits contributed the most to dark diversity. Results The study showed a notable divergence in the predicted drivers of dark diversity depending on the method used. Nevertheless, we can conclude that plot-level dark diversity was generally 17\% higher in areas at low elevations and 31\% higher in areas with a low species richness. Conclusion Our findings call for caution when interpreting statistical findings of dark-diversity estimates. Even so, all analyses point toward an important role for natural processes such as competitive dominance as the main driver of the spatial patterns found in dark diversity in the northern Scandes.},
language = {en},
number = {6},
urldate = {2024-03-26},
journal = {Journal of Vegetation Science},
author = {Hostens, Lore and Van Meerbeek, Koenraad and Wiegmans, Dymphna and Larson, Keith and Lenoir, Jonathan and Clavel, Jan and Wedegärtner, Ronja and Pirée, Amber and Nijs, Ivan and Lembrechts, Jonas J.},
year = {2023},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jvs.13212},
keywords = {Beals’ index, co-occurrence model, habitat characteristics, method comparison, niche model, plant diversity, plant ecology, plant traits, regional species pool},
pages = {e13212},
}
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In this paper, we applied both niche-based and co-occurrence-based methods to estimate the dark diversity of vascular plant species in the subarctic mountains. We then aimed to unravel the drivers explaining (a) why some locations were missing relatively more suitable species than others, and (b) why certain plant species were more often absent from suitable locations than others. Location The Scandinavian mountains around Abisko, northern Sweden. Methods We calculated the dark diversity in 107 plots spread out across four mountain trails using four different methods: two co-occurrence-based (Beals’ index and the hypergeometric method) and two niche-based (the climatic niche model and climatic niche model followed by species-specific threshold). We then applied multiple Generalized Linear Mixed-Effects Models and General Linear Models to determine which habitat characteristics and species traits contributed the most to dark diversity. Results The study showed a notable divergence in the predicted drivers of dark diversity depending on the method used. Nevertheless, we can conclude that plot-level dark diversity was generally 17% higher in areas at low elevations and 31% higher in areas with a low species richness. Conclusion Our findings call for caution when interpreting statistical findings of dark-diversity estimates. Even so, all analyses point toward an important role for natural processes such as competitive dominance as the main driver of the spatial patterns found in dark diversity in the northern Scandes.","language":"en","number":"6","urldate":"2024-03-26","journal":"Journal of Vegetation Science","author":[{"propositions":[],"lastnames":["Hostens"],"firstnames":["Lore"],"suffixes":[]},{"propositions":[],"lastnames":["Van","Meerbeek"],"firstnames":["Koenraad"],"suffixes":[]},{"propositions":[],"lastnames":["Wiegmans"],"firstnames":["Dymphna"],"suffixes":[]},{"propositions":[],"lastnames":["Larson"],"firstnames":["Keith"],"suffixes":[]},{"propositions":[],"lastnames":["Lenoir"],"firstnames":["Jonathan"],"suffixes":[]},{"propositions":[],"lastnames":["Clavel"],"firstnames":["Jan"],"suffixes":[]},{"propositions":[],"lastnames":["Wedegärtner"],"firstnames":["Ronja"],"suffixes":[]},{"propositions":[],"lastnames":["Pirée"],"firstnames":["Amber"],"suffixes":[]},{"propositions":[],"lastnames":["Nijs"],"firstnames":["Ivan"],"suffixes":[]},{"propositions":[],"lastnames":["Lembrechts"],"firstnames":["Jonas","J."],"suffixes":[]}],"year":"2023","note":"_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jvs.13212","keywords":"Beals’ index, co-occurrence model, habitat characteristics, method comparison, niche model, plant diversity, plant ecology, plant traits, regional species pool","pages":"e13212","bibtex":"@article{hostens_drivers_2023,\n\ttitle = {The drivers of dark diversity in the {Scandinavian} mountains are metric-dependent},\n\tvolume = {34},\n\tcopyright = {© 2023 International Association for Vegetation Science.},\n\tissn = {1654-1103},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jvs.13212},\n\tdoi = {10.1111/jvs.13212},\n\tabstract = {Question Dark diversity refers to the set of species that are not observed in an area but could potentially occur based on suitable local environmental conditions. In this paper, we applied both niche-based and co-occurrence-based methods to estimate the dark diversity of vascular plant species in the subarctic mountains. We then aimed to unravel the drivers explaining (a) why some locations were missing relatively more suitable species than others, and (b) why certain plant species were more often absent from suitable locations than others. Location The Scandinavian mountains around Abisko, northern Sweden. Methods We calculated the dark diversity in 107 plots spread out across four mountain trails using four different methods: two co-occurrence-based (Beals’ index and the hypergeometric method) and two niche-based (the climatic niche model and climatic niche model followed by species-specific threshold). We then applied multiple Generalized Linear Mixed-Effects Models and General Linear Models to determine which habitat characteristics and species traits contributed the most to dark diversity. Results The study showed a notable divergence in the predicted drivers of dark diversity depending on the method used. Nevertheless, we can conclude that plot-level dark diversity was generally 17\\% higher in areas at low elevations and 31\\% higher in areas with a low species richness. Conclusion Our findings call for caution when interpreting statistical findings of dark-diversity estimates. 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