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Intensive farming drives long-term shifts in avian community composition

Nature volume 579, pages 393–396 (2020)Cite this article

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A Publisher Correction to this article was published on 08 May 2020

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Abstract

Agricultural practices constitute both the greatest cause of biodiversity loss and the greatest opportunity for conservation1,2, given the shrinking scope of protected areas in many regions. Recent studies have documented the high levels of biodiversity—across many taxa and biomes—that agricultural landscapes can support over the short term1,3,4. However, little is known about the long-term effects of alternative agricultural practices on ecological communities4,5 Here we document changes in bird communities in intensive-agriculture, diversified-agriculture and natural-forest habitats in 4 regions of Costa Rica over a period of 18 years. Long-term directional shifts in bird communities were evident in intensive- and diversified-agricultural habitats, but were strongest in intensive-agricultural habitats, where the number of endemic and International Union for Conservation of Nature (IUCN) Red List species fell over time. All major guilds, including those involved in pest control, pollination and seed dispersal, were affected. Bird communities in intensive-agricultural habitats proved more susceptible to changes in climate, with hotter and drier periods associated with greater changes in community composition in these settings. These findings demonstrate that diversified agriculture can help to alleviate the long-term loss of biodiversity outside natural protected areas1.

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Fig. 1: Long-term directional shifts in bird community structure in agricultural landscapes contrast with forest communities that are temporally less variable.
Fig. 2: Long-term declines in the number of endemic, range-restricted and IUCN red-list species in intensive-agriculture, but not forest or diversified-agriculture, habitats.
Fig. 3: Annual changes in climate and vegetation drive shifts in bird communities in intensive-agriculture habitats, but not in natural-forest or diversified-agriculture habitats.
Fig. 4: Land use and climate niche determine colonization–extinction dynamics.

Data availability

The bird community data that support the findings of this study have been deposited in Figshare (https://doi.org/10.6084/m9.figshare.11366201).

Change history

References

  1. Kremen, C. & Merenlender, A. M. Landscapes that work for biodiversity and people. Science 362, eaau6020 (2018).

    Article  PubMed  CAS  Google Scholar 

  2. Kremen, C. Reframing the land-sparing/land-sharing debate for biodiversity conservation. Ann. NY Acad. Sci. 1355, 52–76 (2015).

    Article  PubMed  ADS  Google Scholar 

  3. Frishkoff, L. O. et al. Loss of avian phylogenetic diversity in neotropical agricultural systems. Science 345, 1343–1346 (2014).

    Article  CAS  PubMed  ADS  Google Scholar 

  4. Mendenhall, C. D., Shields-Estrada, A., Krishnaswami, A. J. & Daily, G. C. Quantifying and sustaining biodiversity in tropical agricultural landscapes. Proc. Natl Acad. Sci. USA 113, 14544–14551 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kehoe, L. et al. Biodiversity at risk under future cropland expansion and intensification. Nat. Ecol. Evol. 1, 1129–1135 (2017).

    Article  PubMed  Google Scholar 

  6. Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Springmann, M. et al. Options for keeping the food system within environmental limits. Nature 562, 519–525 (2018).

    Article  CAS  PubMed  ADS  Google Scholar 

  8. Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).

    Article  CAS  PubMed  ADS  Google Scholar 

  9. Karp, D. S. et al. Forest bolsters bird abundance, pest control and coffee yield. Ecol. Lett. 16, 1339–1347 (2013).

    Article  PubMed  Google Scholar 

  10. Karp, D. S., Ziv, G., Zook, J., Ehrlich, P. R. & Daily, G. C. Resilience and stability in bird guilds across tropical countryside. Proc. Natl Acad. Sci. USA 108, 21134–21139 (2011).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  11. Ranganathan, J., Daniels, R. J. R., Chandran, M. D. S., Ehrlich, P. R. & Daily, G. C. Sustaining biodiversity in ancient tropical countryside. Proc. Natl Acad. Sci. USA 105, 17852–17854 (2008).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  12. Fukami, T. & Wardle, D. A. Long-term ecological dynamics: reciprocal insights from natural and anthropogenic gradients. Proc. R. Soc. B 272, 2105–2115 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  13. De Palma, A. et al. Chapter four: challenges with inferring how land-use affects terrestrial biodiversity: study design, time, space and synthesis. Adv. Ecol. Res. 58, 163–199 (2018).

    Article  Google Scholar 

  14. Essl, F. et al. Delayed biodiversity change: no time to waste. Trends Ecol. Evol. 30, 375–378 (2015).

    Article  PubMed  Google Scholar 

  15. Hastings, A. et al. Transient phenomena in ecology. Science 361, eaat6412 (2018).

    Article  PubMed  CAS  Google Scholar 

  16. Essl, F. et al. Historical legacies accumulate to shape future biodiversity in an era of rapid global change. Divers. Distrib. 21, 534–547 (2015).

    Article  Google Scholar 

  17. Vellend, M. et al. Extinction debt of forest plants persists for more than a century following habitat fragmentation. Ecology 87, 542–548 (2006).

    Article  PubMed  Google Scholar 

  18. Gibson, L. et al. Near-complete extinction of native small mammal fauna 25 years after forest fragmentation. Science 341, 1508–1510 (2013).

    Article  CAS  PubMed  ADS  Google Scholar 

  19. Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).

    Article  Google Scholar 

  20. Tilman, D., May, R. M., Lehman, C. L. & Nowak, M. A. Habitat destruction and the extinction debt. Nature 371, 65–66 (1994).

    Article  ADS  Google Scholar 

  21. Gilbert, B. & Levine, J. M. Plant invasions and extinction debts. Proc. Natl Acad. Sci. USA 110, 1744–1749 (2013).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  22. Bell, G. & Gonzalez, A. Adaptation and evolutionary rescue in metapopulations experiencing environmental deterioration. Science 332, 1327–1330 (2011).

    Article  CAS  PubMed  ADS  Google Scholar 

  23. Northrup, J. M., Rivers, J. W., Yang, Z. & Betts, M. G. Synergistic effects of climate and land-use change influence broad-scale avian population declines. Glob. Change Biol. 25, 1561–1575 (2019).

    Article  ADS  Google Scholar 

  24. MacLean, S. A., Rios Dominguez, A. F., de Valpine, P. & Beissinger, S. R. A century of climate and land-use change cause species turnover without loss of beta diversity in California’s Central Valley. Glob. Change Biol. 24, 5882–5894 (2018).

    Article  Google Scholar 

  25. Smith, M. D., Knapp, A. K. & Collins, S. L. A framework for assessing ecosystem dynamics in response to chronic resource alterations induced by global change. Ecology 90, 3279–3289 (2009).

    Article  PubMed  Google Scholar 

  26. Pressey, R. L., Cabeza, M., Watts, M. E., Cowling, R. M. & Wilson, K. A. Conservation planning in a changing world. Trends Ecol. Evol. 22, 583–592 (2007).

    Article  PubMed  Google Scholar 

  27. Tulloch, V. J. D. et al. Why do we map threats? Linking threat mapping with actions to make better conservation decisions. Front. Ecol. Environ. 13, 91–99 (2015).

    Article  Google Scholar 

  28. de Camino, R., Segura, O., Guillermo Arias, L. & Perez, I. Costa Rica Forest Strategy and the Evolution of Land Use: Evaluation Country Case Study Series (World Bank, 2000).

  29. Jackson, S. T. & Sax, D. F. Balancing biodiversity in a changing environment: extinction debt, immigration credit and species turnover. Trends Ecol. Evol. 25, 153–160 (2010).

    Article  PubMed  Google Scholar 

  30. Sánchez, J. E., Naoki, K. & Zook, J. New information about Costa Rican birds. Ornitol. Neotrop. 9, 99–102 (1998).

    Google Scholar 

  31. Rosero-Bixby, L., Maldonado-Ulloa, T. & Bonilla-Carrión, R. Bosque y población en la Península de Osa, Costa Rica [Forest and Population in the Osa Peninsula, Costa Rica]. Rev. Biol. Trop. 50, 585–598 (2002).

    PubMed  Google Scholar 

  32. Lobo, J. & Bolaños, F. Historia natural de Golfi to Costa Rica (Instituto Nacional de Biodiversidad, 2005).

  33. Sandoval, L. et al. Recent records of new and rare bird species in Costa Rica. Bull. Br. Ornithol. Club 130, 237–245 (2010).

    Google Scholar 

  34. Sandoval, L. et al. Range expansion and noteworthy records of Costa Rican birds (Aves). Check List 14, 141–151 (2018).

    Article  Google Scholar 

  35. Frishkoff, L. O. et al. Climate change and habitat conversion favour the same species. Ecol. Lett. 19, 1081–1090 (2016).

    Article  PubMed  Google Scholar 

  36. Green, R. E., Cornell, S. J., Scharlemann, J. P. W. & Balmford, A. Farming and the fate of wild nature. Science 307, 550–555 (2005).

    Article  CAS  PubMed  ADS  Google Scholar 

  37. Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71, 319–392 (2009).

    Article  MathSciNet  MATH  Google Scholar 

  38. R Core Team. R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2016).

  39. Simpson, D., Rue, H., Riebler, A., Martins, T. G. & Sørbye, S. H. Penalising model component complexity: a principled, practical approach to constructing priors. Stat. Sci. 32, 1–28 (2017).

    MathSciNet  MATH  Google Scholar 

  40. Zuur, A. F. & Ieno, E. N. A protocol for conducting and presenting results of regression-type analyses. Methods Ecol. Evol. 7, 636–645 (2016).

    Article  Google Scholar 

  41. Oksanen, J. Vegan: ecological diversity. R package version 2.4-4 https://cran.r-project.org/web/packages/vegan/ (2017).

  42. Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).

    Article  Google Scholar 

  43. O’Hara, R. B. & Kotze, D. J. Do not log-transform count data. Methods Ecol. Evol. 1, 118–122 (2010).

    Article  Google Scholar 

  44. IUCN. The IUCN Red List of Threatened Species. version 2019-1 https://www.iucnredlist.org/ (2019).

  45. Stiles, F. G. & Skutch, A. F. A Guide to the Birds of Costa Rica (Cornell Univ. Press, 1989).

  46. Justice, C. O. et al. The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans. Geosci. Remote Sens. 36, 1228–1249 (1998).

    Article  ADS  Google Scholar 

  47. Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol. 14, e1002415 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Anderson, C. B. Biodiversity monitoring, earth observations and the ecology of scale. Ecol. Lett. 21, 1572–1585 (2018).

    Article  PubMed  Google Scholar 

  49. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Article  ADS  Google Scholar 

  50. Wan, Z., Hook, S. & Hulley, G. MOD11A1 v006: MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid https://doi.org/10.5067/MODIS/MOD11A1.006 (NASA, 2015).

  51. Myneni, R. B., Knyazikhin, Y. & Park, T. MCD15A3H v006: MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500m SIN Grid https://doi.org/10.5067/MODIS/MCD15A3H.006 (2015).

  52. Brawn, J. D., Benson, T. J., Stager, M., Sly, N. D. & Tarwater, C. E. Impacts of changing rainfall regime on the demography of tropical birds. Nat. Clim. Change 7, 133–136 (2017).

    Article  ADS  Google Scholar 

  53. Royle, J. A. & Dorazio, R. M. Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities (Academic, 2008).

Download references

Acknowledgements

We thank the landowners, the Organization for Tropical Studies and the Los Cusingos Bird Sanctuary for making the collection of these data possible; P.-J. Ke, E. Mordecai and members of the Fukami and Daily laboratories at Stanford University for feedback; and J. Barlow and T. Tscharntke for comments. Financial support for J.N.H. was provided by the Gerhard Casper and John P. Morgridge Fellowship and the Stanford Graduate Fellowship. J.R.S. was supported by the NSF GRFP DGE-1656518 and the Ward Wilson Woods Jr Environmental Studies Fund. Funding for data collection over all years was through support to G.C.D. from the LuEsther T. Mertz Charitable Trust, the Moore Family Foundation and the Winslow Foundation.

Author information

Authors and Affiliations

  1. Department of Biology, Stanford University, Stanford, CA, USA

    J. Nicholas Hendershot, Jeffrey R. Smith, Christopher B. Anderson, Andrew D. Letten, Tadashi Fukami & Gretchen C. Daily

  2. Center for Conservation Biology, Stanford University, Stanford, CA, USA

    J. Nicholas Hendershot, Jeffrey R. Smith, Christopher B. Anderson & Gretchen C. Daily

  3. Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland

    Andrew D. Letten

  4. The University of Queensland, School of Biological Sciences, Brisbane, Queensland, Australia

    Andrew D. Letten

  5. Department of Biology, University of Texas at Arlington, Arlington, TX, USA

    Luke O. Frishkoff

  6. Unión de Ornitólogos de Costa Rica, Naranjo de Alajuela, Alajuela, Costa Rica

    Jim R. Zook

  7. Woods Institute for the Environment, Stanford University, Stanford, CA, USA

    Gretchen C. Daily

  8. The Natural Capital Project, Stanford University, Stanford, CA, USA

    Gretchen C. Daily

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  1. J. Nicholas Hendershot
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Contributions

G.C.D. and J.R.Z. designed data collection, J.R.Z. collected data, J.N.H. conceived the project idea, J.N.H., J.R.S., C.B.A., A.D.L., L.O.F., T.F. and G.C.D. contributed to analyses and all authors contributed to writing the final manuscript.

Corresponding author

Correspondence to J. Nicholas Hendershot.

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The authors declare no competing interests.

Additional information

Peer review information Nature thanks Jos Barlow, Teja Tscharntke and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Habitat use by IUCN red-list, endemic and range-restricted species across three land-cover types in Costa Rica.

a, Forested habitats contained the greatest number of IUCN red-list, endemic and range-restricted species (LRT, P < 0.001; d.f. = 2, F = 4. 55.090). b, The number of endemic and range-restricted species was lowest in intensive agriculture and highest in forests, although diversified agriculture did not significantly differ from either group (LRT, P < 0.001; d.f. = 2, F = 4.709). c, The number of IUCN red-list species across all habitats. The greatest species richness is found in forest habitats (LRT, P < 0.001; d.f. = 2, F = 90.173). Diversified agricultural and intensive agricultural plots contained 59% and 39% of the endemic and IUCN red-list species found in forested habitats, respectively. Letters denote Tukey post hoc differences between groups in the number of species. Box plots show the median values and the first and third quartiles (25th and 75th percentiles), whiskers extend to 1.5× the interquartile range. Points represent transect-level values. a, nspp. = 62, ntransects = 44, nyears = 18. b, nspp. = 48, ntransects = 44, nyears = 18. c, nspp. = 12, ntransects = 44, nyears = 18. Summary statics for differences between groups in a–c are provided in Supplementary Tables 3–5.

Extended Data Fig. 2 Although all habitats show stable species richness and species abundance across years, interannual community shifts are much more pronounced in intensive agriculture than in natural forests or diversified agriculture.

a, Habitat conversion shifted the rate of community change through time. The most rapid shifts occurred in intensive agriculture sites, and the least change occurred in diversified agriculture. Between years, the average community similarity was 66% in natural forests, 73% in diversified agriculture and 58% in intensive monocultures. b, c, These shifts occur under temporally stable species richness (b) and community abundance (c), highlighting the need to quantify multiple drivers of biodiversity change. Changes in community similarity in a were quantified by comparing communities in each transect to themselves in the previous year using Bray–Curtis similarity. In b, points depict the mean Chao’s estimated species richness. In c, points represent the mean number of counts per transect for each land-cover type. In a–c, error bars depict the standard error of the mean. nspp. = 510, ntransects = 44, nyears = 18.

Extended Data Fig. 3 The total community size, measured as species richness and abundance, was fairly constant in all land-cover classes, with only a small increasing trend in abundance in intensive-agriculture communities.

a–c, Temporal trends in bird species richness (a–c) and annual counts (d–f) in forests (a, d), diversified agriculture (b, e) and intensive agriculture (c, f) across the four regions of Costa Rica. Black lines represent mean trends in species richness (a–c) and annual counts (d–f), modelled as a first-order random walk process for each land-cover type, with shading depicting the 95% Bayesian credible intervals. a–c, The effect of land-cover type on temporal trends in log-transformed species richness was modelled using a Bayesian LMM in INLA. d–f, Annual transect-level abundances (average of wet- and dry-season counts) were modelled using a Bayesian GLMM with a negative binomial distribution in INLA. nspp. = 510, ntransects = 44, nyears = 18.

Extended Data Fig. 4 Declines in IUCN red-list, endemic and range-restricted species in intensive agriculture driven by species loss in the Las Cruces and San Isidro study regions.

a–c, Modelled trends in annual transect richness of endemic, range-restricted and IUCN red-list species in natural forests (a), diversified agriculture (b) and intensive agriculture (c) in the Las Cruces and San Isidro study regions. Trends are standardized and centred around zero. Black lines depict mean trends and the shading represents 95% Bayesian credible intervals from a Bayesian LMM using R-INLA. Overlap of the credible intervals with the zero line indicates that there are no trends in species richness. Analyses were limited to the Las Cruces and San Isidro study regions, as intensive-agriculture communities contained too few IUCN red-list, endemic and range-restricted species to detect a trend. nspp. = 62, ntransects = 22, nyears = 18.

Extended Data Fig. 5 Long-term shifts in community composition in the intensive monoculture habitat are driven by distinct guild-level changes.

a–c, Changes to insectivores in intensive agriculture were a result of turnover in the identity and dominance structure of guild composition and structure. d–i, By contrast, changes in nectarivores (d–f) and granivores (g–i) were primarily driven by declining and increasing relative abundance, respectively (Extended Data Fig. 6). j–l, The high variability in the composition of frugivores without long-term shifts matches the resource tracking of the spatially and temporally irregular availability of fruits in intensive agricultural landscapes. In each plot, black lines depict the mean temporal trend in guild community similarity from 2000 to 2017 in intensive monoculture transects from Bayesian GLMMs. Temporal trends were modelled for each land-cover type as a one-dimensional random walk of Bray–Curtis similarity in each year compared to year 1 (2000) from 2000 to 2017. a, d, g, j, Natural forest. b, e, h, k, Diversified agriculture. c, f, i, l, Intensive agriculture. Mean values are centred around zero and the shading represents 95% Bayesian credible intervals, modelled using INLA. Positive and negative deviation from the zero line indicates the presence of long-term directional trends. nspp. = 510, ntransects = 44, nyears = 18.

Extended Data Fig. 6 Changes in relative abundance differed by land-cover type for the different guilds.

a–l, Changes in the relative abundance differed by land-cover type for insectivores (a–c), nectarivores (d–f), granivores (g–i) and frugivores (j–l). a, d, g, j, Natural forest. b, e, h, k, Diversified agriculture. c, f, i, l, Intensive agriculture. f, i, Significant trends in relative abundance occurred only in intensive monocultures, where a nearly 30% decline in the relative abundance of nectarivores (f) and a 20% increase in relative abundance of granivores (i) was found during the 18-year study. In each plot, black lines depict the mean temporal trend in the relative abundance of each guild from 2000 to 2018 modelled as a first-order random walk process in INLA. Trends are centred around zero, shading represents 95% Bayesian credible intervals. Positive and negative deviation from the zero-line indicates the presence of long-term trends. nspp. = 510, ntransects = 44, nyears = 18.

Extended Data Fig. 7 Crop diversity and agricultural plot size associated with community change.

a, b, Community similarity across 18 years (2000–2017) increases with crop diversity (a) and decreases with plot size (b). Points depict the total change in the community similarity for each agricultural transect (measured as Bray–Curtis Similarity). Higher values denote less change. Blue points are diversified-agricultural communities and yellow points are intensive-agricultural communities. Crop diversity and average plot size were log-transformed to highlight how increases in crop diversity can reduce community change. nspp. = 510, ntransects = 44, nyears = 18.

Extended Data Fig. 8 Long-term trend in all pairwise community combinations shows the same trends as using the first sampling year (2000) as the baseline and when using presence–absence data only.

a–c, Long-term trends in avian community composition in natural forests (a), diversified agriculture (b) and intensive agricultures (c) were quantified using all pairwise temporal Bray–Curtis comparisons within each transect, rather than using the year 2000 as a baseline. This approach was used to validate trends and test for potential bias as a result of using year 2000 as the baseline. In each plot, black lines depict the mean temporal trend in Bray–Curtis community similarity from 2000 to 2018 modelled as a first-order random walk process in INLA. Trends are centred around zero, the shading represents 95% Bayesian credible intervals. Positive and negative deviation from the zero line indicates the presence of long-term trends. Values on the x axis denote the temporal distance, ranging from 1 to 17 years. nspp. = 510, ntransects = 44, nyears = 18. d, Long-term shifts were based on presence–absence data, rather than abundance-weighted data (Fig. 1a); both measures show qualitatively similar results. Community similarity in each year compared to the first year of study (2000) across three land-cover types. Points depict the mean community similarity measured as Bray–Curtis similarity for each transect to itself in the first year of this study; error bars represent the s.e.m. for each land-cover type in each year. nspp. = 510, ntransects = 44, nyears = 18.

Extended Data Fig. 9 Effect of filtering diversified agricultural communities on changes in the temporal composition and species richness.

a, Removing species with low affinity for the diversified agricultural habitat (orange points) and individuals that used elements of the natural landscape (purple points) has little effect on long-term changes in the species composition, though there is some difference in magnitude. b, Removing species with low affinity for the diversified agricultural habitat (orange points) results in moderate reductions in species richness, although there is no effect of removing individuals that used elements of the natural landscape (purple points) on species richness estimates. a, Points depict the mean (±s.e.m.) community similarity measured as Bray–Curtis similarity for each transect to itself in the first year of this study. nspp. = 510, ntransects = 44, nyears = 18. b, Points depict the mean (±s.e.m.) Chao’s estimated species richness. nspp. = 510, ntransects = 44, nyears = 18.

Supplementary information

41586_2020_2090_MOESM1_ESM.pdf

Supplementary Table Supplementary Table 1: Vegetative complexity declines in agricultural landscapes. Vegetative differences between diversified agriculture and intensive agriculture transects used for site classification in 1999. Values show mean values with SD. Ntransects = 44. Table adapted from (10).

Reporting Summary

41586_2020_2090_MOESM3_ESM.pdf

Supplementary Table Supplementary Table 2: Species richness is highest in natural forests and lowest in intensive agriculture. Effect of land conversion on species richness. While the conversion of natural forests and to diversified agriculture does not reduce species richness, intensification results in a dramatic drop in species richness. Species richness for each land cover type estimated using Chao estimated species richness. A likelihood ratio test (Pr(>|z|)) found no significant differences in species richness between forest and diversified agricultural sites. Species richness estimates are presented on the natural scale. Chao estimated species richness was calculated in the package vegan41 in R38. Nspp = 510, Ntransects = 44, Nyears = 18.

41586_2020_2090_MOESM4_ESM.pdf

Supplementary Table Supplementary Table 3: The number of IUCN red-list, endemic and range-restricted species declines in agricultural habitats. Effect of land cover type on habitat use by IUCN red-list and endemic and range-restricted species. ANOVA and Tukey post hoc tests were used to determine the differences in number of species between land cover classes. P-values were adjusted to account for multiple comparisons using the Tukey method. Nspp = 62, Ntransects = 44, Nyears = 18.

41586_2020_2090_MOESM5_ESM.pdf

Supplementary Table Supplementary Table 4: The number of endemic and range-restricted species declines in agricultural habitats. Effect of land cover type on habitat use by endemic and range-restricted species. ANOVA and Tukey post hoc tests were used to determine the differences in number of species between land cover classes. P-values were adjusted to account for multiple comparisons using the Tukey method. See Extended Data Fig. 1B. Nspp = 48, Ntransects = 44, Nyears = 18.

41586_2020_2090_MOESM6_ESM.pdf

Supplementary Table Supplementary Table 5: The number of IUCN red-list species declines in agricultural habitats. Data show the modelled effect of land cover type on habitat use by IUCN red-list species. ANOVA and Tukey post hoc tests were used to determine the differences in number of species between land cover classes. P-values were adjusted to account for multiple comparisons using the Tukey method. See Extended Data Fig. 1C. Nspp =12, Ntransects = 44, Nyears = 18.

41586_2020_2090_MOESM7_ESM.pdf

Supplementary Table Supplementary Table 6: Effect of land cover type on rate of change in community composition between years. Change in community composition was calculated using the Bray-Curtis Similarity index and was modelled under a Beta distribution using Bayesian generalized linear mixed effects model in INLA. Higher values represent less change (more similar communities) between years, with lower values depicting more change (less similar communities) between years. Values and credible intervals were transformed to the natural scale. Nspp = 510, Ntransects = 44, Nyears = 18.

41586_2020_2090_MOESM8_ESM.pdf

Supplementary Table Supplementary Table 7: Effect of land cover type on total change in community composition across 18 yrs. Overall, intensive agriculture communities changed the most, with similar levels of change between natural forest and diversified agricultural communities. Change in community composition was calculated using the Bray-Curtis Similarity index, and communities in each year were compared to themselves in year 1 (2000). Change in composition was modelled under a Beta distribution using Bayesian generalized linear mixed effects model in INLA. The model included individual random walk trends for each land cover type to deal with temporal autocorrelation structure of data. Higher values represent less change (more similar communities) between years, with lower values depicting more change (less similar communities) between years. Values and credible intervals were transformed to the natural scale. Nspp = 510, Ntransects = 44, Nyears = 18.

41586_2020_2090_MOESM9_ESM.pdf

Supplementary Table Supplementary Table 8: Effect of land cover type on long-term trends of all pairwise community combinations, rather than using year 2000 as a baseline. Change in composition was modelled under a Beta distribution using Bayesian generalized linear mixed effects model in INLA. The model included individual random walk trends for each land cover type to deal with temporal autocorrelation structure of data. Higher values represent less change (more similar communities) between years, with lower values depicting more change (less similar communities) between years. Values and credible intervals were transformed to the natural scale using the inverse logit link. Nspp = 510, Ntransects = 44, Nyears = 18.

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Hendershot, J.N., Smith, J.R., Anderson, C.B. et al. Intensive farming drives long-term shifts in avian community composition. Nature 579, 393–396 (2020). https://doi.org/10.1038/s41586-020-2090-6

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