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Agriculture and climate change are reshaping insect biodiversity worldwide | Nature
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- Published: 20 April 2022

# Agriculture and climate change are reshaping insect biodiversity worldwide

### Subjects

## Abstract

Several previous studies have investigated changes in insect biodiversity, with some highlighting declines and others showing turnover in species composition without net declines1, 2, 3, 4, 5. Although research has shown that biodiversity changes are driven primarily by land-use change and increasingly by climate change6, 7, the potential for interaction between these drivers and insect biodiversity on the global scale remains unclear. Here we show that the interaction between indices of historical climate warming and intensive agricultural land use is associated with reductions of almost 50% in the abundance and 27% in the number of species within insect assemblages relative to those in less-disturbed habitats with lower rates of historical climate warming. These patterns are particularly evident in the tropical realm, whereas some positive responses of biodiversity to climate change occur in non-tropical regions in natural habitats. A high availability of nearby natural habitat often mitigates reductions in insect abundance and richness associated with agricultural land use and substantial climate warming but only in low-intensity agricultural systems. In such systems, in which high levels (75% cover) of natural habitat are available, abundance and richness were reduced by 7% and 5%, respectively, compared with reductions of 63% and 61% in places where less natural habitat is present (25% cover). Our results show that insect biodiversity will probably benefit from mitigating climate change, preserving natural habitat within landscapes and reducing the intensity of agriculture.

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Fig. 1: Locations of sites and responses of insect total abundance and species richness to land use and land-use intensity.



Fig. 2: Response of insect total abundance and species richness to interaction between land use and standardized temperature anomaly.



Fig. 3: Response of insect total abundance and species richness to the interaction between land use and standardized temperature anomaly in different realms.



Fig. 4: Responses of insect total abundance and species richness to the interaction between the standardized temperature anomaly, land-use intensity and availability of nearby natural habitat.



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## Data availability

The 2016 release of the PREDICTS database used in this study can be downloaded from the data portal of the Natural History Museum at https://data.nhm.ac.uk/dataset/the-2016-release-of-the-predicts-database. The CRU TS data used for calculating the temperature anomalies can be downloaded from the CRU data website at https://crudata.uea.ac.uk/cru/data/hrg/. The data from ref. 34, including the primary and secondary vegetation layers that were combined into the natural habitat layer, can be downloaded from the Commonwealth Scientific and Industrial Research Organisation data access portal at https://doi.org/10.4225/08/56DCD9249B224.

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## References

1. Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl Acad. Sci. USA 115, E10397–E10406 (2018).

   Article CAS PubMed PubMed Central Google Scholar

2. Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).

   Article CAS PubMed ADS Google Scholar

3. Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384–392 (2020).

   Article PubMed Google Scholar

4. Crossley, M. S. et al. No net insect abundance and diversity declines across US long term ecological research sites. Nat. Ecol. Evol. 4, 1368–1376 (2020).

   Article PubMed Google Scholar

5. Janzen, D. H. & Hallwachs, W. To us insectometers, it is clear that insect decline in our Costa Rican tropics is real, so let’s be kind to the survivors. Proc. Natl Acad. Sci. USA 118, e2002546117 (2021).

   Article CAS PubMed PubMed Central Google Scholar

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

   Article CAS PubMed ADS Google Scholar

7. Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).

   Article ADS Google Scholar

8. Powney, G. D. et al. Widespread losses of pollinating insects in Britain. Nat. Commun. 10, 1018 (2019).

   Article PubMed PubMed Central ADS CAS Google Scholar

9. van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2020).

   Article PubMed ADS CAS Google Scholar

10. Yang, L. H. & Gratton, C. Insects as drivers of ecosystem processes. Curr. Opin. Insect Sci. 2, 26–32 (2014).

    Article PubMed Google Scholar

11. Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).

    Article PubMed PubMed Central ADS Google Scholar

12. Halsch, C. A. et al. Insects and recent climate change. Proc. Natl. Acad. Sci. USA 118, e2002543117 (2021).

    Article CAS PubMed PubMed Central Google Scholar

13. Wagner, D. L., Fox, R., Salcido, D. M. & Dyer, L. A. A window to the world of global insect declines: moth biodiversity trends are complex and heterogeneous. Proc. Natl Acad. Sci. USA 118, e2002549117 (2021).

    Article CAS PubMed PubMed Central Google Scholar

14. Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. Biodiversity: the ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).

    Article CAS PubMed ADS Google Scholar

15. Kerr, J. T. et al. Climate change impacts on bumblebees converge across continents. Science 349, 177–180 (2015).

    Article CAS PubMed ADS Google Scholar

16. Uhler, J. et al. Relationship of insect biomass and richness with land use along a climate gradient. Nat. Commun. 12, 5946 (2021).

    Article CAS PubMed PubMed Central ADS Google Scholar

17. Oliver, T. H. & Morecroft, M. D. Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. Wiley Interdiscip. Rev. Clim. Change 5, 317–335 (2014).

    Article Google Scholar

18. Williams, J. J. & Newbold, T. Local climatic changes affect biodiversity responses to land use: a review. Divers. Distrib. 26, 76–92 (2020).

    Article Google Scholar

19. González del Pliego, P. et al. Thermally buffered microhabitats recovery in tropical secondary forests following land abandonment. Biol. Conserv. 201, 385–395 (2016).

    Article Google Scholar

20. Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evol. 7, 7897–7908 (2017).

    Article PubMed PubMed Central Google Scholar

21. Peters, M. K. et al. Climate–land-use interactions shape tropical mountain biodiversity and ecosystem functions. Nature 568, 88–92 (2019).

    Article CAS PubMed ADS Google Scholar

22. Mantyka-Pringle, C. S., Martin, T. G. & Rhodes, J. R. Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Glob. Change Biol. 18, 1239–1252 (2012).

    Article 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. Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).

    Article CAS PubMed PubMed Central ADS Google Scholar

25. Newbold, T., Oppenheimer, P., Etard, A. & Williams, J. J. Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change. Nat. Ecol. Evol. 4, 1630–1638 (2020).

    Article PubMed Google Scholar

26. Perez, T. M., Stroud, J. T. & Feeley, K. J. Thermal trouble in the tropics. Science 351, 1392–1393 (2016).

    Article CAS PubMed ADS Google Scholar

27. Betts, M. G., Phalan, B., Frey, S. J. K., Rousseau, J. S. & Yang, Z. Old-growth forests buffer climate-sensitive bird populations from warming. Divers. Distrib. 24, 439–447 (2018).

    Article Google Scholar

28. Hendershot, J. N. et al. Intensive farming drives long-term shifts in avian community composition. Nature 579, 393–396 (2020).

    Article CAS PubMed ADS Google Scholar

29. Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes - eight hypotheses. Biol. Rev. 87, 661–685 (2012).

    Article PubMed Google Scholar

30. Suggitt, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).

    Article ADS Google Scholar

31. Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity in Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).

    Article PubMed Google Scholar

32. Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).

    Article PubMed PubMed Central Google Scholar

33. Johansson, F., Orizaola, G. & Nilsson-Örtman, V. Temperate insects with narrow seasonal activity periods can be as vulnerable to climate change as tropical insect species. Sci. Rep. 10, 8822 (2020).

    Article CAS PubMed PubMed Central ADS Google Scholar

34. Hoskins, A. J. et al. Downscaling land-use data to provide global 30′′ estimates of five land-use classes. Ecol. Evol. 6, 3040–3055 (2016).

    Article PubMed PubMed Central Google Scholar

35. Grab, H. et al. Agriculturally dominated landscapes reduce bee phylogenetic diversity and pollination services. Science 363, 282–284 (2019).

    Article CAS PubMed ADS Google Scholar

36. Rusch, A. et al. Agricultural landscape simplification reduces natural pest control: a quantitative synthesis. Agric. Ecosyst. Environ. 221, 198–204 (2016).

    Article Google Scholar

37. Oliver, T. H. et al. Declining resilience of ecosystem functions under biodiversity loss. Nat. Commun. 6, 10122 (2015).

    Article PubMed ADS CAS Google Scholar

38. 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).

    Article CAS PubMed PubMed Central ADS Google Scholar

39. Balmford, A. Extinction filters and current resilience: the significance of past selection pressures for conservation biology. Trends Ecol. Evol. 11, 193–196 (1996).

    Article CAS PubMed Google Scholar

40. Garibaldi, L. A. et al. Stability of pollination services decreases with isolation from natural areas despite honey bee visits. Ecol. Lett. 14, 1062–1072 (2011).

    Article PubMed Google Scholar

41. Carvalheiro, L. G., Seymour, C. L., Veldtman, R. & Nicolson, S. W. Pollination services decline with distance from natural habitat even in biodiversity-rich areas. J. Appl. Ecol. 47, 810–820 (2010).

    Article Google Scholar

42. Dainese, M., Luna, D. I., Sitzia, T. & Marini, L. Testing scale-dependent effects of seminatural habitats on farmland biodiversity. Ecol. Appl. 25, 1681–1690 (2015).

    Article PubMed Google Scholar

43. Fourcade, Y. et al. Habitat amount and distribution modify community dynamics under climate change. Ecol. Lett. 24, 950–957 (2021).

    Article PubMed Google Scholar

44. Alexander, L. V. et al. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res. 111, D05109 (2006).

    ADS Google Scholar

45. Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).

    Article CAS PubMed ADS Google Scholar

46. De Palma, A. et al. Dimensions of biodiversity loss: spatial mismatch in land-use impacts on species, functional and phylogenetic diversity of European bees. Divers. Distrib. 23, 1435–1446 (2017).

    Article PubMed PubMed Central Google Scholar

47. Collen, B., Ram, M., Zamin, T. & McRae, L. The tropical biodiversity data gap: addressing disparity in global monitoring. Trop. Conserv. Sci. 1, 75–88 (2008).

    Article Google Scholar

48. Menéndez, R. How are insects responding to global warming? Tijdschr. Entomol. 150, 355 (2007).

    Google Scholar

49. Bale, J. S. et al. Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8, 1–16 (2002).

    Article ADS Google Scholar

50. Hudson, L. N. et al. The 2016 release of the PREDICTS database. Natural History Museum Data Portal, https://doi.org/10.5519/0066354 (2016).

51. Hudson, L. N. et al. The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts. Ecol. Evol. 4, 4701–4735 (2014).

    Article PubMed PubMed Central Google Scholar

52. De Palma, A. et al. Annual changes in the Biodiversity Intactness Index in tropical and subtropical forest biomes, 2001–2012. Sci. Rep. 11, 20249 (2021).

    Article PubMed PubMed Central CAS Google Scholar

53. Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T.-J. A new statistical approach for assessing similarity of species composition with incidence and abundance data. Ecol. Lett. 8, 148–159 (2005).

    Article Google Scholar

54. Ndakidemi, B., Mtei, K. & Ndakidemi, P. A. Impacts of synthetic and botanical pesticides on beneficial insects. Agric. Sci. 07, 364–372 (2016).

    CAS Google Scholar

55. Wang, X., Hua, F., Wang, L., Wilcove, D. S. & Yu, D. W. The biodiversity benefit of native forests and mixed‐species plantations over monoculture plantations. Divers. Distrib. 25, 1721–1735 (2019).

    Article Google Scholar

56. Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).

    Article ADS Google Scholar

57. Warszawski, L. et al. The inter-sectoral impact model intercomparison project (ISI-MIP): project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014).

    Article CAS PubMed ADS Google Scholar

58. van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).

    Article ADS Google Scholar

59. New, M., Hulme, M. & Jones, P. Representing twentieth-century space–time climate variability. Part I: development of a 1961–90 mean monthly terrestrial climatology. J. Clim. 12, 829–856 (1999).

    Article ADS Google Scholar

60. Hijmans, R. J. Raster: Geographic data analysis and modelling. R package version 2.8-42018, https://CRAN.R-project.org/package=raster (2018).

61. Rigby, R. A., Stasinopoulos, D. M. & Akantziliotou, C. A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution. Comput. Stat. Data Anal. 53, 381–393 (2008).

    Article MathSciNet MATH Google Scholar

62. Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article Google Scholar

## Acknowledgements

This work was supported by UK Natural Environment Research Council grants (NE/R010811/1 and NE/V006533/1) and by a Royal Society University Research Fellowship to T.N.

## Author information

Author notes

1. These authors contributed equally: Charlotte L. Outhwaite, Peter McCann

### Authors and Affiliations

1. Centre for Biodiversity and Environment Research, University College London, London, UK

Charlotte L. Outhwaite, Peter McCann & Tim Newbold

Authors

1. Charlotte L. Outhwaite

2. Peter McCann

3. Tim Newbold

### Contributions

T.N. and P.M. conceived the study. All authors processed and analysed the data and wrote and edited the manuscript.

### Corresponding author

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The authors declare no competing interests.

## Peer review

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## Extended data figures and tables

### Extended Data Fig 1 Global average changes in absolute and standardised temperatures.

a. Absolute change in temperature between the baseline 1901-1930 and 2005 (the median sample year of the insect data). This was calculated as the difference in mean monthly temperatures of insect-active months between the baseline of 1901-1930 and the period 2004-2006 for each 0.5 × 0.5° grid cell of the terrestrial surface, for which estimates were available. b. The standardised temperature anomaly was calculated as the absolute temperate change between the baseline and 2005, divided by the standard deviation of the baseline temperatures across insect-active months (see Methods for more detail). For both absolute and standardised temperature changes, we consider only months in which insects are assumed to be active (i.e., monthly mean temperature ≥ 10 °C). Density plots to the right of each map show the average temperature change at a given latitude. Extreme latitudes and areas of high elevation are blank as they do not have months that meet the assumed temperature threshold for insect activity.

### Extended Data Fig 2 Response of insect total abundance (a) and species richness (b) to the interaction between land use and the maximum temperature anomaly.

(Likelihood-ratio tests: abundance: \({\chi }_{3,11}^{2}=29\), p <0.001; species richness: \({\chi }_{3,11}^{2}=21\), p <0.001). Values represent the percentage difference compared to primary vegetation with no historic climate warming (a maximum temperature anomaly of 0). The maximum temperature anomaly is the difference in the average of the maximum temperatures in the three hottest months each year between the baseline of 1901-1930 and the five years preceding biodiversity sampling, divided by the standard deviation of the baseline monthly maximum temperatures (for the three hottest months per baseline year). Lines correspond to the median predicted value and shaded area represents the 95% confidence interval. Results are plotted for the central 95% of modelled anomaly values for each land use. Number of sites included in the analyses (abundance model, richness model) were: Primary vegetation (Primary) n = 1,458, 1,563; Secondary vegetation (Secondary) n = 1,338, 1,471; Low-intensity agriculture (Agriculture_Low) n = 1,479, 1,499; and High-intensity agriculture (Agriculture_High) n = 1,717, 1,775.

### Extended Data Fig 3 Response of insect total abundance (a) and species richness (b) to the interaction between land use and the standardised temperature anomaly after influential studies were removed from the dataset.

Values represent the percentage difference compared to primary vegetation with no historical climate warming (a standardised temperature anomaly of 0). Details of the calculation of the standardised temperature anomaly are given in the legend of Fig. 2. Lines correspond to the median predicted value and shaded area represents the 95% confidence interval. Results are plotted for the central 95% of modelled anomaly values for each land use. Number of sites removed: abundance, 361 sites; richness, 650 sites.

### Extended Data Fig 4 Response of insect total abundance (a) and species richness (b) to the interaction between land use and the standardised maximum temperature anomaly after influential studies were removed from the dataset.

Values represent the percentage difference compared to primary vegetation with no historical climate warming (a maximum temperature anomaly of 0). Details of the calculation of the maximum temperature anomaly are given in the legend of Extended Data Fig. 2. Lines correspond to the median predicted value and shaded area represents the 95% confidence interval. Results are plotted for the central 95% of modelled anomaly values for each land use. Number of sites removed: abundance, 409 sites; richness, 698 sites.

### Extended Data Fig 5 Response of insect total abundance (a & b) and species richness (c & d) to the interaction between land use and the standardised maximum temperature anomaly in non-tropical (a & c) and tropical (b & d) realms.

(Likelihood-ratio tests: abundance: non-tropical \({\chi }_{3,11}^{2}=8\), p = 0.05, tropical \({\chi }_{3,11}^{2}=24\), p < 0.001; richness : non-tropical \({\chi }_{3,11}^{2}=68\), p < 0.001, tropical \({\chi }_{3,11}^{2}=8.5\), p < 0.05). Values represent the percentage difference compared to primary vegetation with no historical climate warming (a maximum temperature anomaly of 0). Details of the calculation of the maximum temperature anomaly are given in the legend of Extended Data Fig. 2. Lines correspond to the median predicted value and shaded area represents the 95% confidence interval. Results are plotted for the central 95% of modelled anomaly values for each land use.

### Extended Data Fig 6 Response of insect species richness to the interaction between the standardised maximum temperature anomaly, land-use intensity, and the amount of nearby natural habitat.

(Likelihood-ratio test: richness: \({\chi }_{3,13}^{2}=77.3\), p < 0.001). a. Low-intensity agriculture and b. high-intensity agriculture. Lines correspond to the median predicted value and shaded area represents the 95% confidence interval, for sites with differing cover of natural habitat within a 5-km buffer. Values represent the percentage difference compared to primary vegetation with no historical climate warming (a standardised maximum temperature anomaly of 0), and with 100% nearby natural habitat. Details of the calculation of the maximum temperature anomaly are given in the legend of Extended Data Fig. 2. The interaction was non-significant for total abundance ( p > 0.05) and is thus not presented here.

### Extended Data Fig 7 Maps of the standardised temperature anomaly for the years 2018 and 2070 under RCP 8.5.

The standardised temperature anomaly was determined for each 0.5 × 0.5° grid cell of the global terrestrial land area. Based on data on monthly mean temperatures for 2016-2018, and projected temperatures for 2069-2071 under RCP 8.5 as a worst-case scenario should current ambitions to reduce emissions be unsuccessful (see Methods). Negative values indicate a decrease in temperature compared to the historical baseline, while positive values indicate an increase in temperature. A value of 1 indicates warming equivalent to 1 standard deviation of monthly variation during the baseline period 1901-1930 (for insect-active months, i.e., monthly mean temperature ≥ 10 °C).

## Supplementary information

### Supplementary Information

This file contains Supplementary Methods, Figs. 1–31, Tables 1–22 and references. The Supplementary Methods include details on the sensitivity tests outlined in the main paper. Supplementary Tables 1–22 include dataset summaries and model output tables.

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Outhwaite, C.L., McCann, P. & Newbold, T. Agriculture and climate change are reshaping insect biodiversity worldwide.
Nature 605, 97–102 (2022). https://doi.org/10.1038/s41586-022-04644-x

- Received: 19 March 2021

- Accepted: 15 March 2022

- Published: 20 April 2022

- Issue Date: 05 May 2022

- DOI: https://doi.org/10.1038/s41586-022-04644-x

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