Introduction
Approaches used to investigate the effects of climate change on plant pests
Over the past 30 to 40 years, the effects of several factors – increased temperature, CO2, ozone or ultraviolet-B irradiation, and changing water or humidity patterns – on the incidence and severity of plant diseases have been evaluated. Studies have focused on pests affecting field crops such as wheat, barley, rice, soybean and potato (Bregaglio, Donatelli and Confalonieri, 2013; Evans et al., 2008; Launay et al., 2014; Luck et al., 2011; Mikkelsen, Jørgensen and Lyngkjær, 2014), horticultural crops (Gullino et al., 2018; Koo, Hong and Yun, 2016), including tropical and plantation crops (Ghini, Hamada and Bettiol, 2011), and forest trees (Battisti, 2008; Jactel, Koricheva and Castagneyrol, 2019; Sturrock et al., 2011).
A variety of research approaches have been used in such studies, as summarized in Table 1. Some have involved experiments, looking at the effects of changes in one or more weather parameters. Other studies have investigated species along latitudinal or elevational gradients as a proxy for changes in climate over time. In addition to these empirical approaches, “theoretical” approaches have also been adopted, such as the meta-analysis of published results or the analysis of long-term data sets. Finally, some studies have drawn upon expert opinion or have generated simulation models to predict how projected changes in climate or atmospheric composition will alter the distribution, prevalence, severity and management of pests and other organisms.
Table 1. Examples of experimental and theoretical approaches in climate-change biology research
Experimental approaches can yield useful insights into the effects of climate change on plant diseases and pests, but few such studies have realistically mimicked a changing climate (Chakraborty and Newton, 2011; Ingram, Gregory and Izac, 2008; Loustau et al., 2007; Luck et al., 2011; Pautasso et al., 2012). Climate-change studies carried out in free air CO2 enrichment facility (FACE) systems and in open-topped chambers have led to a better understanding of the effects of different parameters on the development of plant diseases in various crops (Eastburn, McElrone and Bilgin, 2011) (Figure 5). Such systems have also been used to investigate weeds (Williams et al., 2007) and insects (Delucia et al., 2012). In general, most of the insect and disease problems studied in FACE systems under elevated CO2 conditions have shown increases, as recently summarized by Ainsworth and Long (2021).
Figure 5A
Figure 5B
Open top chambers for studying the impacts of increased air CO2 concentration in Petrolina, Brazil
Phytotrons – environmental chambers built to test the effect of combinations of environmental parameters (Gullino et al., 2011; Hakata et al., 2017) – enable studies of the effects of short-term increases in CO2 and temperature on host–pathogen relationships (Gullino et al., 2018), to understand how specific diseases may evolve in the future (Figure 6). The results of such studies can be used to develop practical solutions to cope with future scenarios, for instance providing support to the plant-breeding industry. They can also allow investigation into other, more indirect, effects of climate change on plants, such as the effects on mycotoxin production or on disease-management practices (Gilardi et al., 2017; Gullino et al., 2020).
Figure 6
Phytotrons used for vegetative growth under controlled conditions
By simulating multiple environmental factors, phytotrons allow the effects of climate change on plants and their pathogens to be studied.
Field approaches in natural environments include research along an elevation gradient from low- to high-elevation sites (Betz, Srisuka and Puthz, 2020; Garibaldi, Kitzberger and Chaneton, 2011), with associated changes in temperature and air humidity, and research in different habitats along a latitudinal gradient, including, for example, subtropical, temperate and semi-arid climatic conditions (Bairstow et al., 2010; Scalone et al., 2016). The first approach has the advantage of the photoperiod being the same along the elevational gradient. In the second approach, the photoperiod is likely to vary across the latitudinal gradient. In the tropics, for example, days are shorter and nights are longer during summer and the other way round in winter, compared to temperate climatic conditions. These differences in photoperiod need to be considered when interpreting results. Nevertheless, this kind of approach is helpful for identifying broad patterns across wide environmental gradients and a range of climatic regions under real-world conditions, and such studies can help to determine whether a certain species is limited to a specific climate or is widely occurring and may invade locations that are getting warmer (Juroszek and von Tiedemann, 2013a).
Meta-analyses of published data sets have been performed to search for general patterns in the responses of specific pests to differences in climate variables (Koricheva and Larsson, 1998; Massad and Dyer, 2010; Vila et al., 2021). In addition, long-term data sets from field observations have been used to study climate-change effects that are already apparent owing to the warming in recent decades (Altermatt, 2010; Huang and Hao, 2020; Jeger and Pautasso, 2008). Such long-term data sets can serve as a suitable baseline for future studies (Huang and Hao, 2020; Robinet and Roques, 2010) because they can help researchers distinguish impacts due to climate change from those due to other factors (Garrett et al., 2016, 2021). Attempts to improve estimates of climate–warming effects on insects have been made by combining data from long-term data sets, large-scale experiments and computer modelling (Diamond, 2018; Grünig et al., 2020; Lehmann et al., 2020). For example, a meta-analysis of data from laboratory studies concluded that higher trophic levels (e.g. predators) are more susceptible to climate change than lower-order organisms (plants or herbivorous insects) (Fussmann et al., 2014). This is relevant when studying the changing role of natural enemies on insect pest dynamics and biological control under climate change – a subject on which there are very few field data (Thomson, MacFadyen and Hoffman, 2010).
Simulation models can be used to project future climate-change impacts on pests (Sutherst, 1991; Sutherst et al., 2011), and to help determine tactics and strategies to control pests (Ghini, Hamada and Bettiol, 2008; Hill and Thomson, 2015; Salinari et al., 2007; Shaw and Osborne, 2011). One modelling approach, for example, uses “climate matching”, whereby a geographical area that has a present-day climate analogous to the future climate in the area of interest is studied (for pest dynamics in this case), and then the findings extrapolated to a future scenario in the area of interest (Sutherst, Maywald and Russell, 2000). Other modelling approaches may rely on long-term data sets for weather parameters, crop development, and pest distribution and prevalence to develop and validate “pest–crop–climate” models (Angelotti et al., 2017; Madgwick et al., 2011). Other recent examples of modelling studies, listed in Table 2, consider parameters such as the number of generations per year for insect pests, the timing of plant flowering and related disease severity, and the global distribution of weeds.
Table 2. Examples of pest risk simulation studies where pest models were linked to climate-change scenarios
