Biodiversity and ecosystem functions depend on environmental conditions and resources rather than the geodiversity of a tropical biodiversity hotspot


  • 1.

    Muellner-Riehl, A. N. et al. Origins of global mountain plant biodiversity: Testing the ‘mountain-geobiodiversity hypothesis’. J. Biogeogr. 46, 2826–2838 (2019).


    Google Scholar
     

  • 2.

    Antonelli, A. et al. Geological and climatic influences on mountain biodiversity. Nat. Geosci. 11, 718–725 (2018).

    ADS 
    CAS 

    Google Scholar
     

  • 3.

    Schrodt, F. et al. Opinion: To advance sustainable stewardship, we must document not only biodiversity but geodiversity. Proc. Natl. Acad. Sci. 116, 16155–16158 (2019).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 4.

    Alahuhta, J. et al. The role of geodiversity in providing ecosystem services at broad scales. Ecol. Indic. 91, 47–56 (2018).


    Google Scholar
     

  • 5.

    Read, Q. D. et al. Beyond counts and averages: Relating geodiversity to dimensions of biodiversity. Glob. Ecol. Biogeogr. 29, 696–710 (2020).


    Google Scholar
     

  • 6.

    Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).

    PubMed 

    Google Scholar
     

  • 7.

    Alahuhta, J., Toivanen, M. & Hjort, J. Geodiversity–biodiversity relationship needs more empirical evidence. Nat. Ecol. Evol. 4, 2–3 (2020).

    PubMed 

    Google Scholar
     

  • 8.

    Boothroyd, A. & McHenry, M. Old processes, new movements: the inclusion of geodiversity in biological and ecological discourse. Diversity 11, 216 (2019).


    Google Scholar
     

  • 9.

    Hunter, M. L., Jacobson, G. L. & Webb, T. Paleoecology and the coarse-filter approach to maintaining biological diversity. Conserv. Biol. 2, 375–385 (1988).


    Google Scholar
     

  • 10.

    Hjort, J. & Luoto, M. Can geodiversity be predicted from space?. Geomorphology 153–154, 74–80 (2012).

    ADS 

    Google Scholar
     

  • 11.

    Benito-Calvo, A., Pérez-González, A., Magri, O. & Meza, P. Assessing regional geodiversity: the Iberian Peninsula. Earth Surf. Process. Landf. 34, 1433–1445 (2009).

    ADS 

    Google Scholar
     

  • 12.

    dos Santos, F. M., de La Corte Bacci, D., Saad, A. R. & da Silva Ferreira, A. T. Geodiversity index weighted by multivariate statistical analysis. Appl. Geomat. 12, 361–370 (2020).


    Google Scholar
     

  • 13.

    Crisp, J. R., Ellison, J. C. & Fischer, A. Current trends and future directions in quantitative geodiversity assessment. Prog. Phys. Geogr. Earth Environ. https://doi.org/10.1177/0309133320967219 (2020).

    Article 

    Google Scholar
     

  • 14.

    Pereira, D. I., Pereira, P., Brilha, J. & Santos, L. Geodiversity assessment of Paraná State (Brazil): An innovative approach. Environ. Manag. 52, 541–552 (2013).

    ADS 

    Google Scholar
     

  • 15.

    Gray, M. Geodiversity and geoconservation: What, why, and how?. George Wright Forum 22, 4–12 (2005).


    Google Scholar
     

  • 16.

    Ruban, D. A. Quantification of geodiversity and its loss. Proc. Geol. Assoc. 121, 326–333 (2010).


    Google Scholar
     

  • 17.

    Hjort, J., Gordon, J. E., Gray, M. & Hunter, M. L. Why geodiversity matters in valuing nature’s stage: Why geodiversity matters. Conserv. Biol. 29, 630–639 (2015).

    PubMed 

    Google Scholar
     

  • 18.

    Beier, P. & Brost, B. Use of land facets to plan for climate change: Conserving the arenas, not the actors. Conserv. Biol. J. Soc. Conserv. Biol. 24, 701–710 (2010).


    Google Scholar
     

  • 19.

    Anderson, M. G. & Ferree, C. E. Conserving the stage: Climate change and the geophysical underpinnings of species diversity. PLoS ONE 5, e11554 (2010).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 20.

    Knudson, C., Kay, K. & Fisher, S. Appraising geodiversity and cultural diversity approaches to building resilience through conservation. Nat. Clim. Change 8, 678–685 (2018).

    ADS 

    Google Scholar
     

  • 21.

    Turner, J. A. Geodiversity: The natural support system of ecosystems. In Landscape Planning with Ecosystem Services: Theories and Methods for Application in Europe 253–265 (eds von Haaren, C. et al.) (Springer, 2019). https://doi.org/10.1007/978-94-024-1681-7_16.

    Chapter 

    Google Scholar
     

  • 22.

    Fox, N., Graham, L. J., Eigenbrod, F., Bullock, J. M. & Parks, K. E. Incorporating geodiversity in ecosystem service decisions. Ecosyst. People 16, 151–159 (2020).


    Google Scholar
     

  • 23.

    Parks, K. E. & Mulligan, M. On the relationship between a resource based measure of geodiversity and broad scale biodiversity patterns. Biodivers. Conserv. 19, 2751–2766 (2010).


    Google Scholar
     

  • 24.

    Comer, P. J. et al. Incorporating geodiversity into conservation decisions: Geodiversity and conservation decisions. Conserv. Biol. 29, 692–701 (2015).

    PubMed 

    Google Scholar
     

  • 25.

    Chakraborty, A. & Gray, M. A call for mainstreaming geodiversity in nature conservation research and praxis. J. Nat. Conserv. 56, 125862 (2020).


    Google Scholar
     

  • 26.

    Lawler, J. et al. The theory behind, and the challenges of, conserving nature’s stage in a time of rapid change. Conserv. Biol. 29, 618–629 (2015).

    PubMed 

    Google Scholar
     

  • 27.

    Beier, P. et al. A review of selection-based tests of abiotic surrogates for species representation. Conserv. Biol. J. Soc. Conserv. Biol. 29, 668–679 (2015).


    Google Scholar
     

  • 28.

    Purvis, A. & Hector, A. Getting the Measure of Biodiversity. Nature 405, 212–219 (2000).

    CAS 
    PubMed 

    Google Scholar
     

  • 29.

    Moreno, C. et al. Measuring biodiversity in the Anthropocene: A simple guide to helpful methods. Biodivers. Conserv. 26, 2993–2998 (2017).


    Google Scholar
     

  • 30.

    Roswell, M., Dushoff, J. & Winfree, R. A conceptual guide to measuring species diversity. Oikos 130, 321–338 (2021).


    Google Scholar
     

  • 31.

    Chiarucci, A., Bacaro, G. & Scheiner, S. M. Old and new challenges in using species diversity for assessing biodiversity. Philos. Trans. R. Soc. B Biol. Sci. 366, 2426–2437 (2011).


    Google Scholar
     

  • 32.

    Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: A consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).


    Google Scholar
     

  • 33.

    Hjort, J., Heikkinen, R. K. & Luoto, M. Inclusion of explicit measures of geodiversity improve biodiversity models in a boreal landscape. Biodivers. Conserv. 21, 3487–3506 (2012).


    Google Scholar
     

  • 34.

    Bailey, J. J., Boyd, D. S., Hjort, J., Lavers, C. P. & Field, R. Modelling native and alien vascular plant species richness: At which scales is geodiversity most relevant?. Glob. Ecol. Biogeogr. 26, 763–776 (2017).


    Google Scholar
     

  • 35.

    Zarnetske, P. L. et al. Towards connecting biodiversity and geodiversity across scales with satellite remote sensing. Glob. Ecol. Biogeogr. 28, 548–556 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 36.

    Bétard, F. Patch-scale relationships between geodiversity and biodiversity in hard rock quarries: Case study from a disused quartzite quarry in NW France. Geoheritage 5, 59–71 (2013).


    Google Scholar
     

  • 37.

    Tukiainen, H. et al. Spatial relationship between biodiversity and geodiversity across a gradient of land-use intensity in high-latitude landscapes. Landsc. Ecol. 32, 1049–1063 (2017).


    Google Scholar
     

  • 38.

    Anderson, M. G. et al. Case studies of conservation plans that incorporate geodiversity. Conserv. Biol. 29, 680–691 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • 39.

    Ren, Y., Lü, Y., Hu, J. & Yin, L. Geodiversity underpins biodiversity but the relations can be complex: Implications from two biodiversity proxies. Glob. Ecol. Conserv. 31, e01830 (2021).


    Google Scholar
     

  • 40.

    Homeier, J., Breckle, S.-W., Günter, S., Rollenbeck, R. T. & Leuschner, C. Tree diversity, forest structure and productivity along altitudinal and topographical gradients in a species-rich Ecuadorian montane rain forest: Ecuadorian Montane forest diversity and structure. Biotropica 42, 140–148 (2010).


    Google Scholar
     

  • 41.

    Krashevska, V., Bonkowski, M., Maraun, M. & Scheu, S. Testate amoebae (protista) of an elevational gradient in the tropical mountain rain forest of Ecuador. Pedobiologia 51, 319–331 (2007).


    Google Scholar
     

  • 42.

    Zhalnina, K. et al. Soil pH determines microbial diversity and composition in the park grass experiment. Microb. Ecol. 69, 395–406 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • 43.

    Fierer, N., Craine, J. M., McLauchlan, K. & Schimel, J. P. Litter quality and the temperature sensiticity of decomposition. Ecology 86, 320–326 (2005).


    Google Scholar
     

  • 44.

    Gibb, H. et al. Climate mediates the effects of disturbance on ant assemblage structure. Proc. R. Soc. B Biol. Sci. 282, 20150418 (2015).


    Google Scholar
     

  • 45.

    Sanders, N. J., Lessard, J.-P., Fitzpatrick, M. C. & Dunn, R. R. Temperature, but not productivity or geometry, predicts elevational diversity gradients in ants across spatial grains. Glob. Ecol. Biogeogr. 16, 640–649 (2007).


    Google Scholar
     

  • 46.

    Paaijmans, K. P. et al. Temperature variation makes ectotherms more sensitive to climate change. Glob. Change Biol. 19, 2373–2380 (2013).

    ADS 

    Google Scholar
     

  • 47.

    McCain, C. M. Global analysis of bird elevational diversity. Glob. Ecol. Biogeogr. 18, 346–360 (2009).


    Google Scholar
     

  • 48.

    Tews, J. et al. Animal species diversity driven by habitat heterogeneity/diversity: The importance of keystone structures: Animal species diversity driven by habitat heterogeneity. J. Biogeogr. 31, 79–92 (2004).


    Google Scholar
     

  • 49.

    Rahbek, C. et al. Humboldt’s enigma: What causes global patterns of mountain biodiversity?. Science 365, 1108–1113 (2019).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • 50.

    Hofhansl, F. et al. Climatic and edaphic controls over tropical forest diversity and vegetation carbon storage. Sci. Rep. 10, 5066 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 51.

    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).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 52.

    Gagic, V. et al. Functional identity and diversity of animals predict ecosystem functioning better than species-based indices. Proc. R. Soc. B Biol. Sci. 282, 20142620 (2015).


    Google Scholar
     

  • 53.

    Kraft, N. J. B., Godoy, O. & Levine, J. M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl. Acad. Sci. 112, 797–802 (2015).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 54.

    Cadotte, M. W. Functional traits explain ecosystem function through opposing mechanisms. Ecol. Lett. 20, 989–996 (2017).

    PubMed 

    Google Scholar
     

  • 55.

    Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: Consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).


    Google Scholar
     

  • 56.

    Whittaker, R. H. Evolution and measurement of species diversity. Taxon 21, 213–251 (1972).


    Google Scholar
     

  • 57.

    Socolar, J. B., Gilroy, J. J., Kunin, W. E. & Edwards, D. P. How should beta-diversity inform biodiversity conservation?. Trends Ecol. Evol. 31, 67–80 (2016).

    PubMed 

    Google Scholar
     

  • 58.

    Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: Dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).

    PubMed 

    Google Scholar
     

  • 59.

    Lichstein, J. W. Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecol. 188, 117–131 (2007).


    Google Scholar
     

  • 60.

    Tuomisto, H. & Ruokolainen, K. Analyzing or explaining beta diversity? Understanding the targets of different methods of analysis. Ecology 87, 2697–2708 (2006).

    PubMed 

    Google Scholar
     

  • 61.

    Peres-Neto, P. R. & Jackson, D. A. How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia 129, 169–178 (2001).

    ADS 
    PubMed 

    Google Scholar
     

  • 62.

    Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: Estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).

    PubMed 

    Google Scholar
     

  • 63.

    Hillebrand, H. & Matthiessen, B. Biodiversity in a complex world: Consolidation and progress in functional biodiversity research: Consolidation and progress in BDEF research. Ecol. Lett. 12, 1405–1419 (2009).

    PubMed 

    Google Scholar
     

  • 64.

    Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • 65.

    Bendix, J. et al. A research framework for projecting ecosystem change in highly diverse tropical mountain ecosystems. Oecologia 195, 589–600 (2021).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 66.

    Beck, E., Bendix, J., Kottke, I., Makeschin, F. & Mosandl, R. Gradients in a Tropical Mountain Ecosystem of Ecuador. ISBN: 978-3-540-73525-0
    (Springer, 2008).


    Google Scholar
     

  • 67.

    Landscape Restoration, Sustainable Use and Cross-Scale Monitoring of Biodiversity and Ecosystem Functions – A Science-directed Approach for South Ecuador (PAK823–825 Platform for Biodiversity and Ecosystem Monitoring and Research in South Ecuador, 2017).

  • 68.

    Beck, E. et al. Ecosystem Services, Biodiversity and Environmental Change in a Tropical Mountain Ecosystem of South Ecuador. ISBN: 978-3-642-38136-2 (Springer, 2013).


    Google Scholar
     

  • 69.

    Homeier, J. & Leuschner, C. Factors controlling the productivity of tropical Andean forests: Climate and soil are more important than tree diversity. Biogeosciences 18, 1525–1541 (2021).

    ADS 
    CAS 

    Google Scholar
     

  • 70.

    Krashevska, V., Sandmann, D., Maraun, M. & Scheu, S. Consequences of exclusion of precipitation on microorganisms and microbial consumers in montane tropical rainforests. Oecologia 170, 1067–1076 (2012).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 71.

    Krashevska, V., Sandmann, D., Maraun, M. & Scheu, S. Moderate changes in nutrient input alter tropical microbial and protist communities and belowground linkages. ISME J. 8, 1126–1134 (2014).

    CAS 
    PubMed 

    Google Scholar
     

  • 72.

    Tiede, Y. et al. Ants as indicators of environmental change and ecosystem processes. Ecol. Indic. 83, 527–537 (2017).


    Google Scholar
     

  • 73.

    Santillán, V. et al. Spatio-temporal variation in bird assemblages is associated with fluctuations in temperature and precipitation along a tropical elevational gradient. PLoS ONE 13, e0196179 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 74.

    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).


    Google Scholar
     

  • 75.

    Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: Interpolation and Extrapolation for Species Diversity. R package version 2.0.20, http://chao.stat.nthu.edu.tw/wordpress/software_download/ (2020).

  • 76.

    Chao, A. et al. Rarefaction and extrapolation with Hill numbers: A framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).


    Google Scholar
     

  • 77.

    Wallis, C. I. B. et al. Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data. Remote Sens. Environ. 225, 77–92 (2019).

    ADS 

    Google Scholar
     

  • 78.

    Keuskamp, J. A., Dingemans, B. J. J., Lehtinen, T., Sarneel, J. M. & Hefting, M. M. Tea Bag Index: A novel approach to collect uniform decomposition data across ecosystems. Methods Ecol. Evol. 4, 1070–1075 (2013).


    Google Scholar
     

  • 79.

    Quitián, M. et al. Elevation-dependent effects of forest fragmentation on plant-bird interaction networks in the tropical Andes. Ecography 41, 1497–1506 (2018).


    Google Scholar
     

  • 80.

    Fries, A. et al. Thermal structure of a megadiverse Andean mountain ecosystem in southern Ecuador and its regionalization. Erdkunde 63, 321–335 (2009).


    Google Scholar
     

  • 81.

    Fries, A., Rollenbeck, R., Nauß, T., Peters, T. & Bendix, J. Near surface air humidity in a megadiverse Andean mountain ecosystem of southern Ecuador and its regionalization. Agric. For. Meteorol. 152, 17–30 (2012).

    ADS 

    Google Scholar
     

  • 82.

    Zvoleff, A. glcm: calculate textures from grey-level co-occurrence matrices (GLCMs). R package version 1.6.1 (2016).

  • 83.

    Wallis, C. I. B. et al. Remote sensing improves prediction of tropical montane species diversity but performance differs among taxa. Ecol. Indic. 83, 538–549 (2017).


    Google Scholar
     

  • 84.

    Wolf, K., Veldkamp, E., Homeier, J. & Martinson, G. O. Nitrogen availability links forest productivity, soil nitrous oxide and nitric oxide fluxes of a tropical montane forest in southern Ecuador: N2 O + NO flux of tropical montane forests. Glob. Biogeochem. Cycles https://doi.org/10.1029/2010GB003876 (2011).

    Article 

    Google Scholar
     

  • 85.

    Fisher, W. D. On Grouping for Maximum Homogeneity. J. Am. Stat. Assoc. 53, 789–798 (1958).

    MathSciNet 
    MATH 

    Google Scholar
     

  • 86.

    Bivand, R. classInt: Choose Univariate Class Intervals (2020).

  • 87.

    Oksanen, J. et al. vegan: Community Ecology Package (2020).

  • 88.

    vegan: Community Ecology Package. https://CRAN.R-project.org/package=vegan.

  • 89.

    Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, 2017).

    MATH 

    Google Scholar
     

  • 90.

    Barbosa, A. M., Real, R., Munoz, A. R. & Brown, J. A. New measures for assessing model equilibrium and prediction mismatch in species distribution models. Divers. Distrib. 19, 1333–1338 (2013).


    Google Scholar
     

  • 91.

    Lotz, T., Nieschulze, J., Bendix, J., Dobbermann, M. & König-Ries, B. Diverse or uniform? Intercomparison of two major German project databases for interdisciplinary collaborative functional biodiversity research. Ecol. Inform. 8, 10–19 (2012).


    Google Scholar
     

  • 92.

    Göttlicher, D. et al. Land-cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling. Int. J. Remote Sens. 30, 1867–1886 (2009).


    Google Scholar
     

  • 93.

    Deng, Y., Wilson, J. P. & DEM Bauer, B. O. resolution dependencies of terrain attributes across a landscape. Int. J. Geogr. Inf. Sci. 21, 187–213 (2007).


    Google Scholar
     

  • 94.

    Weiss, M. & Baret, F. S2ToolBox Level 2 products: LAI, FAPAR, FCOVER Version 1.1. in S2 Toolbox Level 2 Product algorithms v1.1 53.

  • 95.

    Unger, M., Homeier, J. & Leuschner, C. Relationships among leaf area index, below-canopy light availability and tree diversity along a transect from tropical lowland to montane forests in NE Ecuador. Trop. Ecol. 54, 33–45 (2013).


    Google Scholar
     

  • 96.

    Krashevska, V., Maraun, M. & Scheu, S. Micro- and macroscale changes in density and diversity of Testate amoebae of tropical montane rain forests of southern Ecuador. Acta Protozool. 49, 17–28 (2010).


    Google Scholar
     



  • Source link

    Leave a Reply

    Your email address will not be published.