Unprecedented species loss in diverse forests indicates the urgent need to test its consequences for ecosystem functioning. However, experimental evaluation based on realistic extinction scenarios is lacking. Using species interaction networks we introduce an approach to separate effects of node loss (reduced species number) from effects of link loss or compensation (reduced or increased interspecific interactions) on ecosystem functioning along directed extinction scenarios. By simulating random and non-random extinction scenarios in an experimental subtropical Chinese forest, we find that species loss is detrimental for stand volume in all scenarios, and that these effects strengthen with age. However, the magnitude of these effects depends on the type of attribute on which the directed species loss is based, with preferential loss of evolutionarily distinct species and those from small families having stronger effects than those that are regionally rare or have high specific leaf area. These impacts were due to both node loss and link loss or compensation. At high species richness (reductions from 16 to 8 species), strong stand-volume reduction only occurred in directed but not random extinction. Our results imply that directed species loss can severely hamper productivity in already diverse young forests.
Subscribe to Journal
Get full journal access for 1 year
only $8.67 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752–1246752 (2014).
Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).
Gibson, L. et al. Near-complete extinction of native small mammal fauna 25 years after forest fragmentation. Science 341, 1508–1510 (2013).
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
McKinney, M. L. Extinction vulnerability and selectivity: combining ecological and paleontological views. Annu. Rev. Ecol. Syst. 28, 495–516 (1997).
Vamosi, J. C. & Wilson, J. R. U. Non-random extinction leads to elevated loss of angiosperm evolutionary history. Ecology Letters 11, 1047–1053 (2008).
Purvis, A., Agapow, P. M., Gittleman, J. L. & Mace, G. M. Non-random extinction and the loss of evolutionary history. Science 288, 328–330 (2000).
Pimm, S. L., Russell, G. J., Gittleman, J. L. & Brooks, T. M. The future of biodiversity. Science 269, 347–350 (1995).
Pan, Y., Birdsey, R. A., Phillips, O. L. & Jackson, R. B. The structure, distribution, and biomass of the world’s forests. Annu. Rev. Ecol. Evol. Syst. 44, 593–622 (2013).
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
García-Valdés, R., Bugmann, H. & Morin, X. Climate change-driven extinctions of tree species affect forest functioning more than random extinctions. Divers. Distrib. 24, 906–918 (2018).
Bunker, D. E. et al. Species loss and aboveground carbon storage in a tropical forest. Science 310, 1029–1031 (2005).
Suding, K. N. et al. Scaling environmental change through the community-level: a trait-based response-and-effect framework for plants. Glob. Change Biol. 14, 1125–1140 (2008).
Larsen, T. H., Williams, N. M. & Kremen, C. Extinction order and altered community structure rapidly disrupt ecosystem functioning. Ecol. Lett. 8, 538–547 (2005).
Greenwood, S. et al. Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area. Ecol. Lett. 20, 539–553 (2017).
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).
Chen, Y. et al. Positive effects of neighborhood complementarity on tree growth in a neotropical forest. Ecology 97, 776–785 (2016).
Laliberte, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).
Solan, M. et al. Extinction and ecosystem function in the marine benthos. Science 306, 1177–1180 (2004).
Bruelheide, H. et al. Designing forest biodiversity experiments: general considerations illustrated by a new large experiment in subtropical China. Methods Ecol. Evol. 5, 74–89 (2014).
Huang, Y. et al. Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science 362, 80–83 (2018).
Muller-Landau, H. C. et al. Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests. Ecol. Lett. 9, 575–588 (2006).
McIntyre, P. B., Jones, L. E., Flecker, A. S. & Vanni, M. J. Fish extinctions alter nutrient recycling in tropical freshwaters. Proc. Natl Acad. Sci. USA 104, 4461–4466 (2007).
Lyons, K. G. & Schwartz, M. W. Rare species loss alters ecosystem function – invasion resistance. Ecol. Lett. 4, 358–365 (2001).
Bracken, M. E. S., Friberg, S. E., Gonzalez-Dorantes, C. A. & Williams, S. L. Functional consequences of realistic biodiversity changes in a marine ecosystem. Proc. Natl Acad. Sci. USA 105, 924–928 (2008).
Smith, M. D. & Knapp, A. K. Dominant species maintain ecosystem function with non-random species loss. Ecol. Lett. 6, 509–517 (2003).
Hubbell, S. P. Neutral theory and the evolution of ecological equivalence. Ecology 87, 1387–1398 (2006).
Uriarte, M. et al. Trait similarity, shared ancestry and the structure of neighbourhood interactions in a subtropical wet forest: implications for community assembly. Ecol. Lett. 13, 1503–1514 (2010).
Cardinale, B. J. et al. The functional role of producer diversity in ecosystems. Am. J. Bot. 98, 572–592 (2011).
Lasky, J. R. et al. The relationship between tree biodiversity and biomass dynamics changes with tropical forest succession. Ecol. Lett. 17, 1158–1167 (2014).
Barrufol, M. et al. Biodiversity promotes tree growth during succession in subtropical forest. PLoS ONE 8, e81246 (2013).
Yang, X. et al. Establishment success in a forest biodiversity and ecosystem functioning experiment in subtropical China (BEF-China). Eur. J. For. Res. 132, 593–606 (2013).
Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156 (2006).
Chen, Y. et al. Data from: directed species loss reduces community productivity in a subtropical forest biodiversity experiment. Figshare digital repository https://doi.org/10.6084/m9.figshare.9192629 (2020).
Huang, Y. et al. Data from: impacts of species richness on productivity in a large-scale subtropical forest experiment. Dryad https://doi.org/10.5061/dryad.t86145r (2018).
Stan Development Team. RStan: the R interface to Stan. R package v.2.14.1 (2019).
Plummer, M. rjags: Bayesian graphical models using MCMC. R package v.4-6 (2016).
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Mouchet, M. A., Villéger, S., Mason, N. W. H. & Mouillot, D. Functional diversity measures: an overview of their redundancy and their ability to discriminate community assembly rules. Funct. Ecol. 24, 867–876 (2010).
Butler, D. asreml: asreml() fits the linear mixed model. R package v.3.0 (2009).
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).
Kirwan, L. et al. Diversity-interaction modeling: estimating contributions of species identities and interactions to ecosystem function. Ecology 90, 2032–2038 (2009).
R Core Team. R: A Language and Environment for Statistical Computing v.3.5.1. (R Foundation for Statistical Computing, Vienna, Austria, 2018).
Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).
Chen, Y. et al. Code from: directed species loss reduces community productivity in a subtropical forest biodiversity experiment. Figshare digital repository https://doi.org/10.6084/m9.figshare.9194555 (2020).
Poorter, H., Niinemets, Ü., Poorter, L., Wright, I. J. & Villar, R. Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis. New Phytol. 182, 565–588 (2009).
Kröber, W., Heklau, H. & Bruelheide, H. Leaf morphology of 40 evergreen and deciduous broadleaved subtropical tree species and relationships to functional ecophysiological traits. Plant Biol. 17, 373–383 (2015).
Losos, J. B. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol. Lett. 11, 995–1003 (2008).
Isaac, N. J. B., Turvey, S. T., Collen, B., Waterman, C. & Baillie, J. E. M. Mammals on the EDGE: conservation priorities based on threat and phylogeny. PLoS ONE 2, e296 (2007).
Cadotte, M. W. et al. Phylogenetic diversity metrics for ecological communities: integrating species richness, abundance and evolutionary history. Ecol. Lett. 13, 96–105 (2010).
China National Specimen Information Infrastructure, Metadata Dataset (NSII, 2017); https://doi.org/10.15468/kmob80/
This study was funded by the EU 7th FP Project IDP-BRIDGES (grant number 608422 to B.S., K.M. and P.A.N.), by the Swiss National Science Foundation (grant number 31003A_166457 to B.S.) and by the BEF-China project, which is supported by the German Science Foundation (grant DFG FOR-891/1-3) and the Institute of Botany of the Chinese Academy of Sciences. B.S. and P.A.N. were additionally funded by the University of Zürich Research Priority Program on Global Change and Biodiversity. We thank C. Lin, Y. Bo and a large number of farmers for help with maintenance of the field experiment. We thank X. Sui for providing the data of species regional rarity.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Panel a shows the overall design. Panel b shows the design of random extinction scenarios. Panel c shows one of the two designed non-random extinction scenarios (rare species going extinct first). The random draws in panel c were done twice. The other designed non-random scenario (species with high SLA going extinct first) is similar. Designed non-random scenarios share the monocultures with the designed random scenarios. 1 mu equals to 0.067 ha.
Extended Data Fig. 2 Difference in community-weighted mean values of specific leaf area (SLA) between pre- and post-extinction communities.
Differences are shown for each extinction step (16→8, 8→4, 4→2 and 2→1 species) from designed random (black circles) and designed non-random, SLA-directed (red circles) extinction scenarios.
Extended Data Fig. 3 Filtering rule to create species richness gradients for effectively non-random extinction scenarios, with regional rarity as an example.
Mixtures at a richness level were selected by matching communities composed by the filtered species pool at the same richness level with the community composition of the 469 plots (plot source). Communities selected from this rule can be both nested and non-nested, and were used in the regression-based analysis of the relationship between species richness and productivity.
Extended Data Fig. 4 Community-weighted means (CWM) and coextinction probabilities for different extinction scenarios.
CWMs; (panels a–d) and coextinction probabilities (panels e–h) are shown across species-richness levels of the plots along five types of extinction scenarios. Black boxes represent the effectively non-random extinction scenarios, while grey boxes represent the designed random extinction scenario. We normalized each trait to have extinction probability spanning from 0.01 to 0.99. Species coextinction probability of each community was calculated as the geometric mean of extinction probability of species present in that community. A small county number indicates a high regional rarity. Horizontal lines at the centres of the boxes and the box edges show the medians and interquartile range (IQR), respectively. Vertical lines extending from the boxes are whiskers, representing values within four times of IQR. Dots are outliers.
Extended Data Fig. 5 Effects of species loss on stand-volume increment across forest ages along five types of extinction scenarios.
Species loss is random (a) or directed by specific leaf area (SLA; b), evolutionary distinctiveness (ED; c), regional rarity (d) or inverse of taxon size (e). Points and vertical lines represent means and two-times standard errors of observed stand-volume increment. Lines are fitted relationships between stand-volume increment (y axis) and species richness in the plot (x axis). Solid lines represent statistically significant declines of stand-volume increment with species loss.
Extended Data Fig. 6 Effects of species loss on stand-volume increment strengthen with forest age along five types of extinction scenarios.
Species loss is random (a) or directed by specific leaf area (SLA; b), evolutionary distinctiveness (ED; c), regional rarity (d) or inverse of taxon size (e). Points and vertical lines represent medians and 95% credible intervals (CI) of estimated net effect of species loss across richness levels. Filled points represent statistically significant effects of species loss on stand-volume increment. Blue lines are the fitted relationships between age and extinction effects. Results are considered as significant if their 95% CI excludes zero.
Extended Data Fig. 7 Relationships between species richness in the plot and average species dissimilarity within communities from the designed random (grey boxes) and effectively non-random (black boxes) extinction scenarios.
Non-random species loss is directed by specific leaf area (SLA; panel a) or evolutionary distinctiveness (ED; panel b). Species dissimilarity was calculated as functional dispersion (FDis) for SLA and mean pairwise phylogenetic distance (MPD) for ED. Horizontal lines at the centres of the boxes and the box edges show the medians and interquartile range (IQR), respectively. Vertical lines extending from the boxes are whiskers, representing values within four times of IQR. Dots are outliers.
Red bars represent the numbers of plots in the regression-based analysis of species loss effects on stand volume. Blue bars represent the numbers of plots of pre-extinction communities from extinction steps used in the analysis of decomposing the net effects of species loss on stand volume. Dashed horizontal lines indicate 10 plots.
Extended Data Fig. 9 Change of species loss effects (net effects and their additive components: node-loss, link-loss and link-compensation effects) on stand volume with forest age for different extinction steps along the designed random (red) and effectively non-random (blue; SLA and ED; abbreviations defined in Fig. 2) extinction scenarios.
Points and vertical lines represent the medians and 95% CI of estimated change of species loss effects through ages. Filled points represent statistically significant changes of species loss effect with forest age. Results are considered significant if their 95% CI excludes zero.
Extended Data Fig. 10 Effects of species loss on stand volume strengthen with forest age along five types of extinction scenarios from the models with positive constraints for the data and parameters associated with stand volume.
Species loss is random (a) or directed by specific leaf area (SLA; b), evolutionary distinctiveness (ED; c), regional rarity (d) or inverse of taxon size (e). Points and vertical lines represent medians and 95% credible intervals (CI) of estimated net effect of species loss across richness levels, respectively. Filled points represent statistically significant effects of species loss on stand volume. Blue lines are the fitted relationships between age and net effect of species loss. Results are considered as significant if their 95% CI excludes zero.
About this article
Cite this article
Chen, Y., Huang, Y., Niklaus, P.A. et al. Directed species loss reduces community productivity in a subtropical forest biodiversity experiment. Nat Ecol Evol 4, 550–559 (2020). https://doi.org/10.1038/s41559-020-1127-4