Climate warming promotes pesticide resistance through expanding overwintering range of a global pest

Abstract

Climate change has the potential to change the distribution of pests globally and their resistance to pesticides, thereby threatening global food security in the 21st century. However, predicting where these changes occur and how they will influence current pest control efforts is a challenge. Using experimentally parameterised and field-tested models, we show that climate change over the past 50 years increased the overwintering range of a global agricultural insect pest, the diamondback moth (Plutella xylostella), by ~2.4 million km² worldwide. Our analysis of global data sets revealed that pesticide resistance levels are linked to the species' overwintering range: mean pesticide resistance was 158 times higher in overwintering sites compared to sites with only seasonal occurrence. By facilitating local persistence all year round, climate change can promote and expand pesticide resistance of this destructive species globally. These ecological and evolutionary changes would severely impede effectiveness of current pest control efforts and potentially cause large economic losses.

Fig. 1. Winter survival experiment for the diamondback moth.

a Geographic sites selected for the winter survival experiments under laboratory (different symbols) in 2011 and field conditions (stars) in winter (November–April) of 2008–2013. b Polynomial curves fitted to the daily mean temperatures of the 10 sites during the winter of 1966–2010 for the laboratory experiments. c Simulated winter temperatures representing each of the 10 sites in panel (b). The x-axis represents the number of days since November 1st and the y-axis indicates the mean temperature for a given 10-day interval for panel (b). HRB Harbin, SP Siping, SY Shenyang, DT Datong, BJ Beijing, YC Yinchuan, SJZ Shijiazhuang, TY Taiyuan, HM Huimin, AY Anyang, ZZ Zhengzhou, ZMD Zhumadian, WH Wuhan, CS Changsha, GZ Guangzhou. Source data are provided as a Source data file.

Fig. 2. Optimal winter survival model and field validation.

a Optimal winter survival model of diamondback moth with low-temperature degree-days (LTDD) (n = 220). b Relationship between model predictions vs. field observations of winter survival (n = 556). R² indicates the variability explained by the regression model. Blue and green circles represent the survival rates of diamondback moth in laboratory experiments and field investigations, respectively. The red line represents model predictions and the grey solid line represents the linear regression between LTDD model predicted and observed survival under natural conditions across geographically distinct sites (grey dashed line indicates the 1:1 reference line). Source data are provided as a Source data file.

Fig. 3. Predicted global overwintering survival of the diamondback moth.

a Mean distribution of winter survival in the present 5 years. b Changes in winter survival over the past 50 years. c, d Changes in winter survival for future climate scenarios with a predicted +2 °C or +6 °C increase in mean temperatures. e Predicted global overwintering land area, and f expanded overwintering area at given past and possible future warming scenarios. g Global annual dynamics of low-temperature degree-days (LTDD) in the overwintering marginal belt. In the tendency line, R² indicates the variability explained by the regression line. P value was calculated using two-sided Fisher’s test. All predictions are based on the field validated LTDD-dependent survival model. Source data are provided as a Source data file.

Fig. 4. Pesticide resistance of the diamondback moth.

a Weighted mean resistance (ratio) and relative frequencies of different resistance levels (percentages in the pie charts) in the permanent, marginal and transient sites. b Relationship between the top 15% (0.85 quantile) resistance ratio (log₁₀ transformed) and low-temperature degree-days (LTDD) (n = 1806). The significance of the slope (P value) in the quantile regression was tested by two-sided t-test. c Predicted distribution of the top 15% pesticide resistances in China. d Global distribution of sampling sites for pesticide resistance tests used in meta-analysis and the predicted geographic distribution of the top 15% pesticide resistance. Source data are provided as a Source data file.