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- Published: 20 April 2022
Agriculture and climate change are reshaping insect biodiversity worldwide
- Charlotte L. Outhwaite ORCID: orcid.org/0000-0001-9997-6780 1 na1 ,
- Peter McCann ORCID: orcid.org/0000-0002-1298-6482 1 na1 &
- Tim Newbold 1
Nature volume 605 , pages 97–102 ( 2022 ) Cite this article
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- Biodiversity
- Climate-change ecology
- Macroecology
Several previous studies have investigated changes in insect biodiversity, with some highlighting declines and others showing turnover in species composition without net declines 1 , 2 , 3 , 4 , 5 . Although research has shown that biodiversity changes are driven primarily by land-use change and increasingly by climate change 6 , 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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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 .
Code availability
The code required to run the analyses presented here can be obtained from the GitHub repository at https://github.com/timnewbold/LanduseClimateInsects.
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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
These authors contributed equally: Charlotte L. Outhwaite, Peter McCann
Authors and Affiliations
Centre for Biodiversity and Environment Research, University College London, London, UK
Charlotte L. Outhwaite, Peter McCann & Tim Newbold
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T.N. and P.M. conceived the study. All authors processed and analysed the data and wrote and edited the manuscript.
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Correspondence to Charlotte L. Outhwaite .
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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).
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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
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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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Although research has shown that biodiversity changes are driven primarily by land-use change and increasingly by climate change 6,7, the potential for interaction between these drivers and insect ...