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Heterogeneity–diversity relationships differ between and within trophic levels in temperate forests

Nature Ecology & Evolution volume 4, pages 1204–1212 (2020)Cite this article

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An Author Correction to this article was published on 30 July 2020

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Abstract

The habitat heterogeneity hypothesis predicts that biodiversity increases with increasing habitat heterogeneity due to greater niche dimensionality. However, recent studies have reported that richness can decrease with high heterogeneity due to stochastic extinctions, creating trade-offs between area and heterogeneity. This suggests that greater complexity in heterogeneity–diversity relationships (HDRs) may exist, with potential for group-specific responses to different facets of heterogeneity that may only be partitioned out by a simultaneous test of HDRs of several species groups and several facets of heterogeneity. Here, we systematically decompose habitat heterogeneity into six major facets on ~500 temperate forest plots across Germany and quantify biodiversity of 12 different species groups, including bats, birds, arthropods, fungi, lichens and plants, representing 2,600 species. Heterogeneity in horizontal and vertical forest structure underpinned most HDRs, followed by plant diversity, deadwood and topographic heterogeneity, but the relative importance varied even within the same trophic level. Among substantial HDRs, 53% increased monotonically, consistent with the classical habitat heterogeneity hypothesis but 21% were hump-shaped, 25% had a monotonically decreasing slope and 1% showed no clear pattern. Overall, we found no evidence of a single generalizable mechanism determining HDR patterns.

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Fig. 1: Conceptional framework for the relationship between habitat heterogeneity and species richness.
Fig. 2: Summary of the relationships between the single facets of heterogeneity and species richness and mean populations size as estimated by mean abundances per species.
Fig. 3: The level of heterogeneity which maximizes species richness summarized over all six facets of heterogeneity and ordered along trophic position and dispersal ability of the species groups.

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Data availability

Data reported in this paper can be accessed from the Biodiversity Exploratories Information System (https://www.bexis.uni-jena.de), DataSetID 25126. All data used in this manuscript is publicly available at https://doi.org/10.25829/bexis.25126-1.

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Acknowledgements

We dedicate this work to the memory of Kwesi Abbey Afful and Emmanuel Lartey-Williams, who were part of the moth sampling campaign. We sincerely thank the local management teams and student helpers for their assistance in the assessment of the species data, H. Hacker for moth determination, A. Ostrowski for managing the central database and M. Fischer, E. Linsenmair, D. Hessenmöller, D. Prati, I. Schöning, F. Buscot, E.-D. Schulze, W. W. Weisser and the late E. Kalko, for their role in setting up the Biodiversity Exploratories project. This work was (partly) funded by the DFG Priority Program 1374 ‘Infrastructure-Biodiversity-Exploratories’ grant nos MU3621/2-1, KR 3292/2-1 and LE3316/2-1. Field work permits were issued by the responsible state environmental offices of Baden-Württemberg, Thüringen and Brandenburg.

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Authors and Affiliations

  1. Department of Animal Ecology and Tropical Biology, University of Würzburg, Würzburg, Germany

    Lea Heidrich, Soyeon Bae, Sebastian Seibold, Simon Thorn & Jörg Müller

  2. CSIRO Land and Water, Winnellie, Northern Territory, Australia

    Shaun Levick

  3. Terrestrial Ecology Research Group, Technical University of Munich, Freising, Germany

    Sebastian Seibold, Wolfgang Weisser & Inken Doerfler

  4. Department of Geoinformatics, Munich University of Applied Sciences, München, Germany

    Peter Krzystek & Alla Serebryanyk

  5. Forest Inventory and Remote Sensing, Faculty of Forest Sciences and Forest Ecology, University of Göttingen, Göttingen, Germany

    Paul Magdon

  6. Faculty of Geography, Philipps-University Marburg, Marburg, Germany

    Thomas Nauss & Stephan Wöllauer

  7. Silviculture and Forest Ecology of the Temperate Zones, Faculty of Forest Sciences and Forest Ecology, University of Göttingen, Göttingen, Germany

    Peter Schall & Christian Ammer

  8. Bavarian Forest National Park, Grafenau, Germany

    Claus Bässler, Marco Heurich & Jörg Müller

  9. Institute for Ecology, Evolution and Diversity, Faculty of Biological Sciences, Goethe University Frankfurt, Frankfurt, Germany

    Claus Bässler

  10. Institute of Biology and Environmental Science, Vegetation Science & Nature Conservation, University of Oldenburg, Oldenburg, Germany

    Inken Doerfler

  11. Institute of Plant Sciences, University of Bern, Bern, Switzerland

    Markus Fischer

  12. Forest Entomology, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland

    Martin M. Gossner

  13. Chair of Wildlife Ecology and Wildlife Management, University of Freiburg, Freiburg, Germany

    Marco Heurich

  14. Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland

    Torsten Hothorn

  15. Evolutionary Ecology and Conservation Genomics, University Ulm, Ulm, Germany

    Kirsten Jung

  16. Biodiversity, Macroecology & Biogeography, University of Goettingen, Göttingen, Germany

    Holger Kreft

  17. Centre of Biodiversity and Sustainable Land Use, University of Goettingen, Göttingen, Germany

    Holger Kreft

  18. Max Planck Institute for Biogeochemistry, Jena, Germany

    Ernst-Detlef Schulze

  19. Ecological Networks, Technical University of Darmstadt, Darmstadt, Germany

    Nadja Simons

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  1. Lea Heidrich
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Contributions

L.H., J.M., S.B. and S.L. conceived the manuscript. J.M. designed the study. L.H., J.M., P.M., S.B., K.J., M.M.G., M.F., C.B., N.S., S.W., W.W., I.D., M.H., P.K., T.N., A.S. and P.S. acquired and processed the data. L.H., J.M., S.L., P.M., M.M.G., S.S. and W.W. drafted the manuscript. L.H., S.B., S.L., S.S., W.W., P.K., P.M., T.N., P.S., A.S., S.W., C.A., C.B., I.D., M.F., M.M.G., M.H., T.H., K.J., H.K., E.-D.S., N.S., S.T. and J.M. participated in analysing and interpreting the data and contributed critically to the revisions.

Corresponding author

Correspondence to Lea Heidrich.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Locations of the single regions from which data was derived.

The Biodiversity Exploratories project (biodiversity-exploratories.de), comprised three forest areas spanning from south to north: the Biosphere Reserve Schwäbische Alb in the Swabian Jura (ALB, 50 Plots), Hainich National Park and the surrounding area (HAI, 50 plots) and the Schorfheide-Chorin Biosphere Reserve (SCH, 50 plots). This database is supplemented with plots from the Steigerwald project in northern Bavaria (STE, 69 plots) and the BIOKLIM project in the Bavarian Forest National Park (BAY, 278 plots). In all three projects diverse environmental variables and species were monitored in a comparable fashion.

Extended Data Fig. 2 Conceptual considerations of three potential measurements used to describe horizontal heterogeneity of five (A-E) different forest stands.

The number of gaps (lilac line) is a measure which would ignore differences in gap areas (B,C). Gap area (orange line) overestimates heterogeneity when single gaps areas reach thresholds of more than 50% of the plot size (B). Here, the gap becomes the dominant habitat which makes the forest stand actually more homogeneous. Hence, the total gap area per plot would not depict a linear increase in horizontal heterogeneity because both extremes, 100% canopy cover as well as 100% gap area are homogenous in structure. Total gap edge length (red line) steadily increases with horizontal heterogeneity and incorporates both composition and configuration, thereby covering the most important information in one variable.

Extended Data Fig. 3 PCA of structural parameters.

Potential structural parameters, which could have been used to describe either vertical or horizontal heterogeneity, depicted via Principle Component Analysis. Many measures capture not the main axes and represent a mixture of both. The variables which were chosen in our analysis (BE_H_SD and Gap_total_edge length) represent the variation without inferring with each other. Colour-coding referring to the five regions: the Biosphere Reserve Schwäbische Alb (ALB), Hainich National Park and the surrounding area (HAI), the Schorfheide-Chorin Biosphere Reserve (SCH), the Steigerwald project (STE) and the BIOKLIM project in the Bavarian Forest National Park (BAY).

Extended Data Fig. 4 Correlation between selected variables.

Shown are Pearson Correlation Coefficients between the selected variables used in the GAMs, that is taxonomic (DW_TR) and type richness (DW_type) of deadwood, vertical heterogeneity measured as standard deviation of height from vegetation returns (BE_H_SD), vegetation diversity measured as Faith’s PD (in millions of years) of the plant communities (Plant_ObsPD), horizontal heterogeneity measured as the (square-rooted) total gap edge length (sqrtGap), topographic heterogeneity measured as the standard deviation of the slope of the digital terrain model (dtm_slope_sd) as well as the cover of herbs sampled within the plots (HerbCover). All variables have a R of less than 0.6, indicating no problems with multicollinearity. Colour-coding referring to the five regions: the Biosphere Reserve Schwäbische Alb (ALB), Hainich National Park and the surrounding area (HAI), the Schorfheide-Chorin Biosphere Reserve (SCH), the Steigerwald project (STE) and the BIOKLIM project in the Bavarian Forest National Park (BAY).

Extended Data Fig. 5 Correlation between height SD (left) and gap edge length (right) and the proportion of conifers in a forest stand.

Height SD decreased with increasing proportion of coniferous trees but the correlation was relatively weak (F1,495=39.64, t-value = −6.29***, R²=0.07). Horizontal heterogeneity increased with increasing proportion of coniferous trees (F1,495=131.2, t-value = 11.45***, R²=0.21). However, this is likely due to the fact that in the Bavarian Forest, many spruce stands at higher elevations have been infected by bark beetles, which lead to many gaps. Colour-coding referring to the five regions: the Biosphere Reserve Schwäbische Alb (ALB), Hainich National Park and the surrounding area (HAI), the Schorfheide-Chorin Biosphere Reserve (SCH), the Steigerwald project (STE) and the BIOKLIM project in the Bavarian Forest National Park (BAY).

Extended Data Fig. 6 Infliction points under different ranking.

Inflection points, that is, the level of heterogeneity at which species richness was highest, summarized over all six facets of heterogeneity, which were binned from 0 (lowest heterogeneity in all plots) to 1 (highest heterogeneity in all plots), ordered along dispersal ability. In contrast to Fig. 3, we further subdivided into flying and non-flying arthropods and ranked spore-disperses higher than flying vertebrates in terms of dispersal ability. However, both classification and ranking systems did not show any relationship to the infliction point (Asymptotic General Independence Test, alternative “greater”, Z=0.38, p-value=0.35).

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Supplementary notes, methods, Tables 1–3 and Figs. 1–7.

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Heidrich, L., Bae, S., Levick, S. et al. Heterogeneity–diversity relationships differ between and within trophic levels in temperate forests. Nat Ecol Evol 4, 1204–1212 (2020). https://doi.org/10.1038/s41559-020-1245-z

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