Species richness is more important for ecosystem functioning than species turnover along an elevational gradient
1.Cardinale, B. J. et al. Effects of biodiversity on the functioning of trophic groups and ecosystems. Nature 443, 989–992 (2006).CAS
PubMed
Article
PubMed Central
Google Scholar
2.Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).CAS
PubMed
Article
PubMed Central
Google Scholar
3.Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 489, 326–326 (2012).CAS
Article
Google Scholar
4.Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).CAS
PubMed
PubMed Central
Article
Google Scholar
5.Mori, A. S., Isbell, F. & Seidl, R. β-Diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564 (2018).PubMed
Article
PubMed Central
Google Scholar
6.Gonzalez, A. et al. Scaling‐up biodiversity–ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).PubMed
PubMed Central
Article
Google Scholar
7.Genung, M. A., Fox, J. & Winfree, R. Species loss drives ecosystem function in experiments, but in nature the importance of species loss depends on dominance. Glob. Ecol. Biogeogr. 29, 1531–1541 (2020).Article
Google Scholar
8.Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).CAS
PubMed
Article
PubMed Central
Google Scholar
9.Wardle, D. A. Do experiments exploring plant diversity–ecosystem functioning relationships inform how biodiversity loss impacts natural ecosystems? J. Veg. Sci. 27, 646–653 (2016).Article
Google Scholar
10.Wardle, D. A., Bardgett, R. D., Callaway, R. M. & Van der Putten, W. H. Terrestrial ecosystem responses to species gains and losses. Science 332, 1273–1277 (2011).CAS
PubMed
Article
PubMed Central
Google Scholar
11.Hillebrand, H. & Matthiessen, B. Biodiversity in a complex world: consolidation and progress in functional biodiversity research. Ecol. Lett. 12, 1405–1419 (2009).PubMed
Article
PubMed Central
Google Scholar
12.Jochum, M. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat. Ecol. Evol. 4, 1485–1494 (2020).PubMed
Article
PubMed Central
Google Scholar
13.van der Plas, F. Biodiversity and ecosystem functioning in naturally assembled communities. Biol. Rev. 94, 1220–1245 (2019).PubMed
PubMed Central
Google Scholar
14.Bannar-Martin, K. H. et al. Integrating community assembly and biodiversity to better understand ecosystem function: the Community Assembly and the Functioning of Ecosystems (CAFE) approach. Ecol. Lett. 21, 167–180 (2018).PubMed
Article
PubMed Central
Google Scholar
15.Leibold, M. A., Chase, J. M. & Ernest, S. K. M. Community assembly and the functioning of ecosystems: how metacommunity processes alter ecosystems attributes. Ecology 98, 909–919 (2017).PubMed
Article
PubMed Central
Google Scholar
16.Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article
Google Scholar
17.Leibold, M. A. et al. The metacommunity concept: a framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).Article
Google Scholar
18.HilleRisLambers, J., Adler, P. B., Harpole, W. S., Levine, J. M. & Mayfield, M. M. Rethinking community assembly through the lens of coexistence theory. Annu. Rev. Ecol. Evol. Syst. 43, 227–248 (2012).Article
Google Scholar
19.Stein, A., Gerstner, K. & Kreft, H. Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecol. Lett. 17, 866–880 (2014).Article
Google Scholar
20.Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).CAS
PubMed
Article
Google Scholar
21.Harpole, W. S. et al. Addition of multiple limiting resources reduces grassland diversity. Nature 537, 93–96 (2016).CAS
PubMed
Article
Google Scholar
22.Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).PubMed
Article
Google Scholar
23.Legendre, P. Interpreting the replacement and richness difference components of beta diversity. Glob. Ecol. Biogeogr. 23, 1324–1334 (2014).Article
Google Scholar
24.Craven, D. et al. A cross‐scale assessment of productivity–diversity relationships. Glob. Ecol. Biogeogr. 29, 1940–1955 (2020).Article
Google Scholar
25.Barry, K. E. et al. A graphical null model for scaling biodiversity–ecosystem functioning relationships. J. Ecol. 109, 1549–1560 (2021).Article
Google Scholar
26.Winfree, R. et al. Species turnover promotes the importance of bee diversity for crop pollination at regional scales. Science 359, 791–793 (2018).CAS
PubMed
PubMed Central
Article
Google Scholar
27.Isbell, F. et al. Quantifying effects of biodiversity on ecosystem functioning across times and places. Ecol. Lett. 21, 763–778 (2018).PubMed
PubMed Central
Article
Google Scholar
28.Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).CAS
PubMed
Article
Google Scholar
29.Bell, T., Newman, J. A., Silverman, B. W., Turner, S. L. & Lilley, A. K. The contribution of species richness and composition to bacterial services. Nature 436, 1157–1160 (2005).CAS
PubMed
Article
PubMed Central
Google Scholar
30.Fox, J. W. & Kerr, B. Analyzing the effects of species gain and loss on ecosystem function using the extended Price equation partition. Oikos 121, 290–298 (2012).Article
Google Scholar
31.Peters, M. K. et al. Predictors of elevational biodiversity gradients change from single taxa to the multi-taxa community level. Nat. Commun. 7, 13736 (2016).CAS
PubMed
PubMed Central
Article
Google Scholar
32.Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).CAS
PubMed
Article
Google Scholar
33.Winfree, R., Fox, J. W., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecol. Lett. 18, 626–635 (2015).PubMed
Article
Google Scholar
34.Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).Article
Google Scholar
35.Stepp, J. R., Castaneda, H. & Cervone, S. Mountains and biocultural diversity. Mt. Res. Dev. 25, 223–227 (2005).Article
Google Scholar
36.Balehegn, M. Unintended consequences: the ecological repercussions of land grabbing in sub-Saharan Africa. Environment 57, 4–21 (2015).
Google Scholar
37.The IPBES Regional Assessment Report on Biodiversity and Ecosystem Services for Africa (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2018); https://doi.org/10.5281/ZENODO.323617838.Maitima, J. et al. The linkages between land use change, land degradation and biodiversity across East Africa. Afr. J. Environ. Sci. Technol. 3, 310–325 (2009).
Google Scholar
39.Clough, Y. et al. Combining high biodiversity with high yields in tropical agroforests. Proc. Natl Acad. Sci. USA 108, 8311–8316 (2011).CAS
PubMed
PubMed Central
Article
Google Scholar
40.Muhumuza, M. & Balkwill, K. Factors affecting the success of conserving biodiversity in national parks: a review of case studies from Africa. Int. J. Biodivers. 2013, 798101 (2013).Article
Google Scholar
41.Mbow, C., van Noordwijk, M., Prabhu, R. & Simons, T. Knowledge gaps and research needs concerning agroforestry’s contribution to Sustainable Development Goals in Africa. Curr. Opin. Environ. Sustain. 6, 162–170 (2014).Article
Google Scholar
42.Kangalawe, R. Y. M., Noe, C., Tungaraza, F. S. K., Naimani, G. & Mlele, M. Understanding of traditional knowledge and indigenous institutions on sustainable land management in Kilimanjaro Region, Tanzania. Open J. Soil Sci. 04, 469–493 (2014).Article
Google Scholar
43.Pretty, J., Toulmin, C. & Williams, S. Sustainable intensification in African agriculture. Int. J. Agric. Sustain. 9, 5–24 (2011).Article
Google Scholar
44.Mbow, C. et al. Agroforestry solutions to address food security and climate change challenges in Africa. Curr. Opin. Environ. Sustain. 6, 61–67 (2014).Article
Google Scholar
45.Ofori, D. A. et al. Developing more productive African agroforestry systems and improving food and nutritional security through tree domestication. Curr. Opin. Environ. Sustain. 6, 123–127 (2014).Article
Google Scholar
46.Munang, R. et al. Ecosystem Based Adaptation (EBA) for Food Security in Africa—Towards a Comprehensive Strategic Framework to Upscale and Out-scale EBA-Driven Agriculture in Africa (United Nations Environment Programme, 2015).47.Albrecht, A. & Kandji, S. T. Carbon sequestration in tropical agroforestry systems. Agric. Ecosyst. Environ. 99, 15–27 (2003).CAS
Article
Google Scholar
48.van der Plas, F. et al. Biotic homogenization can decrease landscape-scale forest multifunctionality. Proc. Natl Acad. Sci. USA 113, 3557–3562 (2016).PubMed
PubMed Central
Article
CAS
Google Scholar
49.Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article
Google Scholar
50.Allen, A. P. Global biodiversity, biochemical kinetics, and the energetic-equivalence rule. Science 297, 1545–1548 (2002).CAS
PubMed
Article
PubMed Central
Google Scholar
51.Nottingham, A. T. et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology 99, 2455–2466 (2018).PubMed
Article
PubMed Central
Google Scholar
52.Hemp, A. Continuum or zonation? Altitudinal gradients in the forest vegetation of Mt. Kilimanjaro. Plant Ecol. 184, 27–42 (2006).Article
Google Scholar
53.Hemp, A. Vegetation of Kilimanjaro: hidden endemics and missing bamboo. Afr. J. Ecol. 44, 305–328 (2006).Article
Google Scholar
54.Appelhans, T. et al. Eco-meteorological characteristics of the southern slopes of Kilimanjaro. Tanzan. Int. J. Climatol. 36, 3245–3258 (2016).Article
Google Scholar
55.van Genuchten, M. T. H. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898 (1980).Article
Google Scholar
56.Gebert, F., Steffan-Dewenter, I., Moretto, P. & Peters, M. K. Climate rather than dung resources predict dung beetle abundance and diversity along elevational and land use gradients on Mt. Kilimanjaro. J. Biogeogr. 47, 371–381 (2019).Article
Google Scholar
57.Dunning, J. B. CRC Handbook of Avian Body Masses (CRC Press, 2008).58.Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article
Google Scholar
59.Kingdon, J. et al. Mammals of Africa (Bloomsbury, 2013).60.Kaspari, M. & Weiser, M. D. The size–grain hypothesis and interspecific scaling in ants. Funct. Ecol. 13, 530–538 (1999).Article
Google Scholar
61.Cane, J. H. Estimation of bee size using intertegular span (Apoidea). J. Kans. Entomol. Soc. 60, 145–147 (1987).
Google Scholar
62.Classen, A., Steffan-Dewenter, I., Kindeketa, W. J. & Peters, M. K. Integrating intraspecific variation in community ecology unifies theories on body size shifts along climatic gradients. Funct. Ecol. 31, 768–777 (2017).Article
Google Scholar
63.Kendall, L. K. et al. Pollinator size and its consequences: robust estimates of body size in pollinating insects. Ecol. Evol. 9, 1702–1714 (2019).PubMed
PubMed Central
Article
Google Scholar
64.Hódar, J. A. The use of regression equations for estimation of arthropod biomass in ecological studies. Acta Oecol. 17, 421–433 (1996).
Google Scholar
65.Ensslin, A. et al. Effects of elevation and land use on the biomass of trees, shrubs and herbs at Mount Kilimanjaro. Ecosphere 6, 45 (2015).Article
Google Scholar
66.Cheng, D.-L. & Niklas, K. J. Above- and below-ground biomass relationships across 1534 forested communities. Ann. Bot. 99, 95–102 (2007).PubMed
Article
Google Scholar
67.Pabst, H., Gerschlauer, F., Kiese, R. & Kuzyakov, Y. Land use and precipitation affect organic and microbial carbon stocks and the specific metabolic quotient in soils of eleven ecosystems of Mt. Kilimanjaro, Tanzania. Land Degrad. Dev. 27, 592–602 (2016).Article
Google Scholar
68.Albrecht, J. et al. Plant and animal functional diversity drive mutualistic network assembly across an elevational gradient. Nat. Commun. 9, 3177 (2018).PubMed
PubMed Central
Article
CAS
Google Scholar
69.Classen, A. et al. Specialization of plant–pollinator interactions increases with temperature at Mt. Kilimanjaro. Ecol. Evol. 10, 2182–2195 (2020).PubMed
PubMed Central
Article
Google Scholar
70.Mayr, A. V. et al. Climate and food resources shape species richness and trophic interactions of cavity-nesting Hymenoptera. J. Biogeogr. 47, 854–865 (2020).Article
Google Scholar
71.Peters, M. K., Mayr, A., Röder, J., Sanders, N. J. & Steffan-Dewenter, I. Variation in nutrient use in ant assemblages along an extensive elevational gradient on Mt Kilimanjaro. J. Biogeogr. 41, 2245–2255 (2014).Article
Google Scholar
72.Genung, M. A. et al. The relative importance of pollinator abundance and species richness for the temporal variance of pollination services. Ecology 98, 1807–1816 (2017).PubMed
PubMed Central
Article
Google Scholar
73.Manly, B. F. J. Randomization, Bootstrap, and Monte Carlo Methods in Biology (Chapman & Hall/CRC, 2007).74.Gelman, A. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1, 515–533 (2006).
Google Scholar
75.Huang, A. & Wand, M. P. Simple marginally noninformative prior distributions for covariance matrices. Bayesian Anal. 8, 439–452 (2013).Article
Google Scholar
76.O’Hara, R. B. & Sillanpää, M. J. A review of Bayesian variable selection methods: what, how and which. Bayesian Anal. 4, 85–117 (2009).
Google Scholar
77.Albrecht, J., Hagge, J., Schabo, D. G., Schaefer, H. M. & Farwig, N. Reward regulation in plant–frugivore networks requires only weak cues. Nat. Commun. 9, 4838 (2018).PubMed
PubMed Central
Article
CAS
Google Scholar
78.Grace, J. B., Johnson, D. J., Lefcheck, J. S. & Byrnes, J. E. K. Quantifying relative importance: computing standardized effects in models with binary outcomes. Ecosphere 9, e02283 (2018).Article
Google Scholar
79.Kass, R. E. & Raftery, A. E. Bayes factors. J. Am. Stat. Assoc. 90, 773–795 (1995).Article
Google Scholar
80.Levy, R. Bayesian data–model fit assessment for structural equation modeling. Struct. Equ. Modeling 18, 663–685 (2011).Article
Google Scholar
81.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article
Google Scholar
82.Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213 (2017).PubMed
PubMed Central
Article
Google Scholar
83.R Development Core Team. R: A Language and Environment for Statistical Computing v. 4.0.3 (R Foundation for Statistical Computing, 2020).84.Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-7 http://cran.r-project.org/package=vegan (2020).85.Plummer, M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling http://mcmc-jags.sourceforge.net (2003)86.Plummer, M. rjags: Bayesian graphical models using MCMC. R package version 4-10 https://CRAN.R-project.org/package=rjags (2016)87.Statisticat, LLC. LaplacesDemon: Complete environment for Bayesian inference. R package version 16.1.4 https://CRAN.R-project.org/package=LaplacesDemon (2021)88.Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R N. 6, 7–11 (2006).
Google Scholar
89.Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D. & Borsboom, D. qgraph: network visualizations of relationships in psychometric data. J. Stat. Softw. 48, 1–18 (2012).Article
Google Scholar
90.Wood, S. N. Generalized Additive Models: An Introduction with R (CRC/Taylor & Francis, 2017).91.Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012).Article
Google Scholar
92.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).Article
Google Scholar
93.Albrecht, J. et al. Data and code from ‘Species richness is more important for ecosystem functioning than species turnover along an elevational gradient’. Figshare https://doi.org/10.6084/m9.figshare.14544207 (2021). More