Agriculture and the exploitation of natural resources have transformed tropical mountain ecosystems across the world, and the consequences of these transformations for biodiversity and ecosystem functioning are largely unknown1,2,3. Conclusions that are derived from studies in non-mountainous areas are not suitable for predicting the effects of land-use changes on tropical mountains because the climatic environment rapidly changes with elevation, which may mitigate or amplify the effects of land use4,5. It is of key importance to understand how the interplay of climate and land use constrains biodiversity and ecosystem functions to determine the consequences of global change for mountain ecosystems. Here we show that the interacting effects of climate and land use reshape elevational trends in biodiversity and ecosystem functions on Africa’s largest mountain, Mount Kilimanjaro (Tanzania). We find that increasing land-use intensity causes larger losses of plant and animal species richness in the arid lowlands than in humid submontane and montane zones. Increases in land-use intensity are associated with significant changes in the composition of plant, animal and microorganism communities; stronger modifications of plant and animal communities occur in arid and humid ecosystems, respectively. Temperature, precipitation and land use jointly modulate soil properties, nutrient turnover, greenhouse gas emissions, plant biomass and productivity, as well as animal interactions. Our data suggest that the response of ecosystem functions to land-use intensity depends strongly on climate; more-severe changes in ecosystem functioning occur in the arid lowlands and the cold montane zone. Interactions between climate and land use explained—on average—54% of the variation in species richness, species composition and ecosystem functions, whereas only 30% of variation was related to single drivers. Our study reveals that climate can modulate the effects of land use on biodiversity and ecosystem functioning, and points to a lowered resistance of ecosystems in climatically challenging environments to ongoing land-use changes in tropical mountainous regions.
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The data that support the findings of this study are documented and archived in the central project database of the DFG-Research Unit FOR1246 (https://www.kilimanjaro.biozentrum.uni-wuerzburg.de), and are available from data owners upon reasonable request. Data will be published in September 2020 via GFBio (https://www.gfbio.org/), following the Rules of Procedure of the German Research Foundation (DFG) and the DFG-Research Unit FOR1246.
Nogués-Bravo, D., Araújo, M. B., Romdal, T. & Rahbek, C. Scale effects and human impact on the elevational species richness gradients. Nature 453, 216–219 (2008).
Körner, C. Mountain biodiversity, its causes and function. Ambio 13, 11–17 (2004).
Payne, D., Spehn, E. M., Snethlage, M. & Fischer, M. Opportunities for research on mountain biodiversity under global change. Curr. Opin. Environ. Sustain. 29, 40–47 (2017).
Sundqvist, M. K., Sanders, N. J. & Wardle, D. A. Community and ecosystem responses to elevational gradients: processes, mechanisms, and insights for global change. Annu. Rev. Ecol. Evol. Syst. 44, 261–280 (2013).
Ferger, S. W. et al. Synergistic effects of climate and land use on avian beta-diversity. Divers. Distrib. 23, 1246–1255 (2017).
Merckx, V. S. F. T. et al. Evolution of endemism on a young tropical mountain. Nature 524, 347–350 (2015).
Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).
Steinbauer, M. J. et al. Accelerated increase in plant species richness on mountain summits is linked to warming. Nature 556, 231–234 (2018).
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
Mittermeier, R. A., Turner, W. R., Larsen, F. W., Brooks, T. M. & Gascon, C. in Biodiversity Hotspots (eds Zachos, F. E. & Habel, J. C.) 3–22 (Springer, Berlin, 2011).
Hemp, A. Climate change-driven forest fires marginalize the impact of ice cap wasting on Kilimanjaro. Glob. Change Biol. 11, 1013–1023 (2005).
Classen, A. et al. Complementary ecosystem services provided by pest predators and pollinators increase quantity and quality of coffee yields. Proc. R. Soc. Lond. B 281, 20133148 (2014).
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
Hof, C., Araújo, M. B., Jetz, W. & Rahbek, C. Additive threats from pathogens, climate and land-use change for global amphibian diversity. Nature 480, 516–519 (2011).
Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23, 453–460 (2008).
Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).
Oliver, T. H. & Morecroft, M. D. Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities: interactions between climate change and land use change. Wiley Interdiscip. Rev. Clim. Change 5, 317–335 (2014).
Mayor, J. R. et al. Elevation alters ecosystem properties across temperate treelines globally. Nature 542, 91–95 (2017).
Hemp, A. Vegetation of Kilimanjaro: hidden endemics and missing bamboo. Afr. J. Ecol. 44, 305–328 (2006).
Karp, D. S. et al. Intensive agriculture erodes β-diversity at large scales. Ecol. Lett. 15, 963–970 (2012).
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).
Barlow, J. et al. Quantifying the biodiversity value of tropical primary, secondary, and plantation forests. Proc. Natl Acad. Sci. USA 104, 18555–18560 (2007).
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).
Meyer, S. T. et al. Biodiversity–multifunctionality relationships depend on identity and number of measured functions. Nat. Ecol. Evol. 2, 44–49 (2018).
Stevens, G. C. The elevational gradient in altitudinal range: an extension of Rapoport’s latitudinal rule to altitude. Am. Nat. 140, 893–911 (1992).
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).
Graham, C. H. et al. The origin and maintenance of montane diversity: integrating evolutionary and ecological processes. Ecography 37, 711–719 (2014).
Blois, J. L., Williams, J. W., Fitzpatrick, M. C., Jackson, S. T. & Ferrier, S. Space can substitute for time in predicting climate-change effects on biodiversity. Proc. Natl Acad. Sci. USA 110, 9374–9379 (2013).
Appelhans, T. et al. Eco-meteorological characteristics of the southern slopes of Kilimanjaro, Tanzania. Int. J. Climatol. 36, 3245–3258 (2016).
Hemp, A. & Hemp, C. Broken bridges: the isolation of Kilimanjaro’s ecosystem. Glob. Change Biol. 24, 3499–3507 (2018).
Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes – eight hypotheses. Biol. Rev. Camb. Philos. Soc. 87, 661–685 (2012).
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).
Junker, R. R. & Keller, A. Microhabitat heterogeneity across leaves and flower organs promotes bacterial diversity. FEMS Microbiol. Ecol. 91, fiv097 (2015).
Högberg, P. Tansley Review No. 95. 15N natural abundance in soil–plant systems. New Phytol. 137, 179–203 (1997).
Robinson, D. δ15N as an integrator of the nitrogen cycle. Trends Ecol. Evol. 16, 153–162 (2001).
Breuer, L., Kiese, R. & Butterbach-Bahl, K. Temperature and moisture effects on nitrification rates in tropical rain-forest soils. Soil Sci. Soc. Am. J. 66, 834 (2002).
Pedersen, A. R., Petersen, S. O. & Schelde, K. A comprehensive approach to soil-atmosphere trace-gas flux estimation with static chambers. Eur. J. Soil Sci. 61, 888–902 (2010).
Gütlein, A., Gerschlauer, F., Kikoti, I. & Kiese, R. Impacts of climate and land use on N2 O and CH4 fluxes from tropical ecosystems in the Mt. Kilimanjaro region, Tanzania. Glob. Change Biol. 24, 1239–1255 (2018).
van Genuchten, M. T. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898 (1980).
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).
Asner, G. P., Scurlock, J. M. O. & Hicke, A. J. Global synthesis of leaf area index observations: implications for ecological and remote sensing studies: global leaf area index. Glob. Ecol. Biogeogr. 12, 191–205 (2003).
Rutten, G., Ensslin, A., Hemp, A. & Fischer, M. Vertical and horizontal vegetation structure across natural and modified habitat types at Mount Kilimanjaro. PLoS ONE 10, e0138822 (2015).
van Praag, H. J., Sougnez-Remy, S., Weissen, F. & Carletti, G. Root turnover in a beech and a spruce stand of the Belgian Ardennes. Plant Soil 105, 87–103 (1988).
Hertel, D. & Leuschner, C. A comparison of four different fine root production estimates with ecosystem carbon balance data in a Fagus–Quercus mixed forest. Plant Soil 239, 237–251 (2002).
Kleyer, M. et al. The LEDA Traitbase: a database of life-history traits of the Northwest European flora. J. Ecol. 96, 1266–1274 (2008).
Schellenberger Costa, D. et al. Community-weighted means and functional dispersion of plant functional traits along environmental gradients on Mount Kilimanjaro. J. Veg. Sci. 28, 684–695 (2017).
Classen, A. et al. Temperature versus resource constraints: which factors determine bee diversity on Mount Kilimanjaro, Tanzania? Glob. Ecol. Biogeogr. 24, 642–652 (2015).
Ferger, S. W., Schleuning, M., Hemp, A., Howell, K. M. & Böhning-Gaese, K. Food resources and vegetation structure mediate climatic effects on species richness of birds: climate and bird species richness. Glob. Ecol. Biogeogr. 23, 541–549 (2014).
Teketay, D. Seedling populations and regeneration of woody species in dry Afromontane forests of Ethiopia. For. Ecol. Manage. 98, 149–165 (1997).
Ky-Dembele, C., Tigabu, M., Bayala, J., Ouédraogo, S. J. & Odén, P. C. The relative importance of different regeneration mechanisms in a selectively cut savanna-woodland in Burkina Faso, West Africa. For. Ecol. Manage. 243, 28–38 (2007).
Westphal, C. et al. Measuring bee diversity in different European habitats and biogeographical regions. Ecol. Monogr. 78, 653–671 (2008).
Estrada-Villegas, S., Meyer, C. F. J. & Kalko, E. K. V. Effects of tropical forest fragmentation on aerial insectivorous bats in a land-bridge island system. Biol. Conserv. 143, 597–608 (2010).
Helbig-Bonitz, M. et al. Bats are not birds – different responses to human land-use on a tropical mountain. Biotropica 47, 497–508 (2015).
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).
Steckel, J. et al. Landscape composition and configuration differently affect trap-nesting bees, wasps and their antagonists. Biol. Conserv. 172, 56–64 (2014).
Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman & Hall and CRC, Boca Raton, 2006).
We thank the Tanzanian Commission for Science and Technology, the Tanzania Wildlife Research Institute and the Mount Kilimanjaro National Park authority for their support, and for granting us access to the Mount Kilimanjaro National Park; all of the companies and private farmers who allowed us to work on their land; and the KiLi field staff for helping to collect data at Mount Kilimanjaro. This study was conducted within the framework of the Research Unit FOR1246 (Kilimanjaro ecosystems under global change: linking biodiversity, biotic interactions and biogeochemical ecosystem processes, https://www.kilimanjaro.biozentrum.uni-wuerzburg.de) funded by the Deutsche Forschungsgemeinschaft (DFG).
Nature thanks Jari Oksanen, Piero Visconti, David Wardle and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, b, Five replicate study sites were selected for each of the six major natural habitat types (circles) and the six major anthropogenic habitat types (square, diamonds and triangles) found on Mount Kilimanjaro. The five study sites of each habitat type were distributed in such a way as to achieve a fine-scale within-habitat elevational gradient. #Study sites, number of study sites per habitat type.
Extended Data Fig. 2 Effects of land use on the composition of plant, animal and microorganism communities.
a–c, The influence of land-use intensity on the overall change in species communities in anthropogenic ecosystems relative to predictions for species communities in natural ecosystems (linear model, for all taxa P < 0.01) is shown. n = 60 study sites for all analyses. d–f, In plants and animals, land-use intensity had stronger effects on the turnover rates in the arid lowlands (ANOVA on residuals of models shown in a–c, P = 0.052) and in higher elevations (ANOVA, P < 0.01), respectively. Box plots show the median (solid line), 25% and 75% quantiles (boxes); whiskers extend to the minimum and maximum within 1.5 times the interquartile range; more-extreme data values are drawn by individual circles. n = 60 study sites for all analyses. g, Calculation of response variables in a–f. Three animals communities that each consist of four species (shown in different colours), which partly overlap, are shown. An increase in MAP is associated with a 25% increase in the dissimilarity (d) of species communities i and ii in natural habitats (di–ii). Community iii is situated in anthropogenic habitats. As communities ii and iii live in the same climate zone, a model that is based only on climate variables would predict the same composition for each species community; however, community iii shows a dissimilarity of 0.5 to community ii (shown in red). In a–c, we analysed the degree to which LUI can explain the difference between these climate-based predictions and the observed composition of species communities (that is, dii–iii as realized in the non-metric multidimensional scaling ordination space).
Extended Data Fig. 3 Effect of climate on ecosystem functions along the natural-habitat elevation gradient.
For soil- and plant-mediated ecosystem functions, the absolute effect strength values are—on average—higher for MAP, whereas animal-mediated ecosystem functions are more strongly influenced by MAT (linear mixed effect model, interaction term (type of ecosystem function × type of climate variable), n = 30 study sites, P < 0.05). The height of the bar shows the mean. Error bars show the standard errors of absolute effect strength values for each type of ecosystem function and climate variable. The bar graphs have been calculated from the data shown in Extended Data Table 3. ESF, ecosystem function.
Extended Data Fig. 4 Analyses of the support for climate and land-use models based on different land-use indices.
For each response variable, 500 different land-use indices were calculated by the random weighting of the four components of the LUI (percentage biomass removal, agricultural inputs, modification of vegetation structure and percentage agriculture in the surrounding landscape) between 0 and 1. For each of the calculated land-use indices, we calculated the support (model weights) for the five major model types (null model, climate model, land-use model, additive climate + land-use model, interactive climate × land-use model), and determined the mean and 90% confidence intervals across the 500 runs. In the majority of runs with differently weighted land-use components, we found similarly high support for the five different model types (as with the original LUI). The climate × land-use interaction model was the single best-supported type of model across response variables and different land-use indicators.
Extended Data Fig. 5 Effects of climate and land use on the multivariate index of multifunctionality.
Dissimilarity in ecosystem multifunctionality across study sites (dots) in natural (red) and anthropogenic (orange) habitats. The position in ordination space illustrates the functional characteristics of sites in relationship to other sites; sites closer to one another have a more-similar ecosystem multifunctionality. Lines in the background show contour lines of elevation.
Extended Data Fig. 6 Average change in ecosystem function with land-use intensity for soil-, plant- and animal-mediated ecosystem functions.
a–c, Average change in ecosystem function (compared to predictions for natural habitats, log-transformed) increased linearly with land-use intensity (linear model, P < 0.01) for soil- (a), plant- (b) and animal-mediated (c) ecosystem functions. n = 50 study sites. d–f, The effect (strength) of land-use intensity on the mean change in ecosystem functioning (grey bars) was, on average, highest for plant-mediated ecosystem functions. The effects of land-use intensity significantly differed among elevation zones in plant- and animal-mediated ecosystem functions (linear model, Pinteraction < 0.05), but did not differ in soil-mediated ecosystem functions. n = 50 study sites.
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