Modelling the Impact of Climate Change on Sustainable Management of the Codling Moth (Cydia pomonella) as Key Pest in Apple

Transcription

1 Modelling the Impact of Climate Change on Sustainable Management of the Codling Moth (Cydia pomonella) as Key Pest in Apple J. Samietz 1, S. Stoeckli 1, M. Hirschi 2, C. Spirig 2, H. Höhn 1, P. Calanca 3 and M. Rotach 2 1 Agroscope Changins-Wädenswil, Research Station, ACW, Wädenswil, Switzerland 2 Federal Office for Meteorology and Climatology, MeteoSwiss, Zürich, Switzerland 3 Agroscope Reckenholz-Tänikon Research Station, ART, Zürich, Switzerland Keywords: climate change, weather generator, integrated pest management, phenology, model, codling moth Abstract Climate change in temperate regions will lead to higher and more extreme temperature distributions; however, its impact on pests and their control strategies is rarely investigated in detail. One reason is the problem of downscaling climate predictions to the temporal and spatial scale of pest life cycles. In the present study, we have closed that gap by impact modelling on hourly scale and downscaling on spatial scale, by adapting a stochastic weather generator (WG) for pest modelling. Thereby, the codling moth, Cydia pomonella, serves as a relevant model species for a key pest with multiple generations per year that, already under the present climate, requires intensive efforts to control. Stochastic weather generation, followed by a resampling approach, provided hourly synthetic weather data for 10 meteorological sites in Switzerland under future climate conditions ( ). Synthetic weather was analysed by a phenology model implemented in the forecasting system SOPRA. The results showed significant shifts to earlier dates in codling moth phenological events in Swiss apple orchards, increased magnitude of the 2 nd generation, less overlap between stages, and a bigger risk for an additional 3 rd generation. Shifts in phenology and magnitude of later generations require adaptations of plant protection regimes to maintain their sustainability, as we illustrate in the present paper, specifically for the strategies in codling moth control. In general, however, methodologies may easily be adapted in further pest and disease combinations and cropping systems. INTRODUCTION In most parts of temperate Europe, present and projected climate change will lead to higher and more variable temperatures, and less stable precipitation regimes, charging agriculture by increased risks of drought and heat waves or flooding. Beside such direct impacts, agriculture is also largely affected by indirect effects of climate change, such as changes in the distribution and abundance of pests and diseases (e.g., Trnka et al., 2007). Although insects may regulate body temperatures in certain ranges (Samietz et al., 2005), many species especially smaller herbivores are nearly exclusively ectotherms (Willmer et al., 2000) and therefore, development processes, reproduction, and their distribution range are strongly determined by temperature. As a consequence, current climatic projections predict that insect species distribution will shift from lower latitudes polewards and from lower to higher altitudes. Those range shifts in insect distributions have been observed in nature as response of global warming (Walther et al., 2002; Parmesan, 2006). With the projected further temperature increase, agricultural pests and diseases are expected to occur more frequently and possibly extend to previously unaffected regions (Aluja et al., 2011). The earlier onset of the growing season and the longer season also means that already present polyvoltine species may be able to finish more generations per year and consequently, can build up higher population levels towards the end of the season (Stoeckli et al., 2012). Although increased pest outbreaks are judged virtually certain in the IPCC Synthesis Report (IPCC, 2007), their impact on the crops and especially on the crop protection strategies is rarely investigated in detail. One major reason is the problem of Proc. IX th IS on Modelling in Fruit Research and Orchard Management Ed.: G. Bourgeois Acta Hort. 1068, ISHS

2 downscaling climate predictions to the temporal and spatial scale of pest and disease life cycles. Assessing the risk of pest-related damages, as a consequence of climate change, requires information on future weather for the pest relevant habitats and on the appropriate time scale (i.e., hourly temporal resolution). Reliable information on climate change is, however, only available on coarse temporal and spatial resolution from climate models. In the present study, we have approached to close that gap by impact modelling on hourly scale and downscaling on spatial scale (Hirschi et al., 2012). The impact of climate change and variability was focused on apple, one of the most important commercial and rural crops across Europe. Thereby, codling moth, Cydia pomonella (L.), serves as a relevant model species for a pest with multiple generations per year that already, under the present climate, requires intensive efforts to control. MATERIALS AND METHODS Climate Change Scenarios The climate change information, processed in our approach, was taken from the regional climate simulations of the EU-FP6 project ENSEMBLES (Hewitt et al., 2005). This database includes 21 regional climate models (RCMs) driven by 8 global climate models (GCMs), in a transient mode covering at least the time period. Here, the periods vs were considered for the analysis, based on the subset of simulations (14 RCM-GCM chains with six GCMs involved), which ran beyond Using a Bayesian multi-model combination algorithm (Buser et al., 2009), the simulations were processed and aggregated to obtain seasonal probabilistic climate change signals of changes in temperature and precipitation. For details of the approach and scenario combinations, see Fischer et al. (2011) and Hirschi et al. (2012), respectively. Downscaling Procedure To downscale the seasonal climate change information to the time scales relevant for the pest (i.e., hourly weather series at 10 meteorological stations in Switzerland, see Table 1), a stochastic weather generator (WG), combined with a re-sampling approach, was applied (Dubrovský, 2011). The stations were selected based on the availability of long-term (i.e., covering the control period) hourly and daily meteorological observations for the calibration of the downscaling procedure and for in situ pest observations for the validation. First, synthetic daily weather series were produced using the parametric stochastic WG M&Rfi (Dubrovský et al., 2004), a Richardson-type WG (Richardson, 1981), which is based on a Markov chain to model precipitation occurrence, Gamma distribution for the precipitation amount and an autoregressive model for non-precipitation variables (with conditioned statistics of the daily non-precipitation variables on occurrence or nonoccurrence of precipitation). In a second step, a nearest neighbour re-sampling procedure was appended to obtain hourly temporal resolution. For each day of the synthetic weather from the WG, the ten most similar days were selected from the hourly station observations within a ±10-day time window around the corresponding day of year. The similarity was quantified by the Mahalanobis distance, considering daily mean precipitation and temperature, daily temperature range, and solar radiation. From the selected ten daily cycles, one was randomly chosen. In a final step, a fitting was applied to the hourly values of precipitation, solar radiation, and temperature to match the daily values generated by the WG; finally, transitions of temperature at midnight were smoothed (Dubrovský et al., 2011). Modelling Codling Moth Phenology In order to simulate pest phenologies for application of the synthetic weather data, the codling moth model, implemented in the Swiss forecasting system for orchard pests SOPRA, was applied (Samietz et al., 2007). All models of this system are based on time- 36

3 varying distributed delay routines on hourly basis, simulating the proportion of life stages in the population with a temporal resolution of one day in the output. Due to the distributed delay approach, life stages were fully overlapping without limitations to the number of stages. All models included close approximations of the micro-climatic condition, as prerequisite for successful modelling; in case of the codling moth, the simulation of stem temperature over the season. The switch between univoltine and polyvoltine life cycles in the model was determined by a day length signal in nature and accordingly, a latitudinally adapted day of year (DOY) in the model that switches further development to diapause. The codling moth model was adapted to simulate an overwintering generation and three following generations divided into eggs, larvae, pupae and adults. The outputs of the model were used to establish indicative temperature sums for different codling moth life stages. Transferred to dates of occurrence (DOY), these indicative temperature sums permitted to compute many combinations of climate scenarios and therefore, enabled to carry out sensitivity tests exploiting the uncertainty range of the probabilistic climate change signals. For a selected range of climate change signals, the full phenology was simulated with the SOPRA model (cf., Hirschi et al., 2012). RESULTS AND DISCUSSION Validation of Downscaling In a direct validation, climate and weather statistics of the synthetic hourly weather series for present climate conditions were compared to observed statistics to ensure consistency and resulted in nearly perfect matches of observed and simulated daily weather (Hirschi et al., 2012). The downscaling approach was further validated by driving the codling moth model by the synthetic weather series of present climate and the output was then compared with observations in situ. The results show that the median occurrence of the developmental stages does not deviate between those of observed weather series and synthetic weather, based on the current climate (all weather stations: Wilcoxon-Mann- Whitney WMW tests for shift in location, n.s.; Kolmogorov-Smirnov KS tests for the equality probability distributions, n.s.). Results were exemplified for the locations Wädenswil and Magadino (Fig. 1, black dots vs. open black circles). Effect of Climate Change on Codling Moth Considering the median climate change signal and 100 years of synthetic data, the scenarios showed a shift of almost two weeks to an earlier flight start of codling moth, under future climate conditions, for all of the ten stations considered (WMW, KS tests, P<0.05). The life stages following thereafter would be even more advanced with progressing season, reaching about three weeks at larval hatch of the 1 st following generation and up to five weeks at larval hatch of the 2 nd generation in the colder regions of Switzerland. Here, the progressing phenology is exemplified for the median climate change signal at the stations Wädenswil, representing the cooler climate of northern Switzerland, and Magadino, which is located in the warmest regions south of the Alpine divide (Fig. 1, grey dots vs. black dots, WMW, KS tests, P<0.05). The magnitude of the pupae of the 1 st following generation and all later events would significantly increase under changing climate conditions. Additionally, the length of overlap of first and second generation larval emergences would decrease with changing climate. As most important in respect of management, the magnitude of a 2 nd following generation was measured as the probability of a 45% larval emergence for the median signal of the multi-model climate change projections (Table 1). Across all 10 stations considered, the risk of a pronounced 2 nd generation (45% larval emergence) would increase to %, except for the coldest climatic region of St. Gallen (19%, Table 1). In the future, all important apple production areas of Switzerland will face a fully 37

4 manifested 2 nd generation in nearly all years, as it is presently only the case in the warmest regions. With the median scenario applied, even an additional 3 rd generation will be able to develop to the larval stage at the cooler sites in a significant number of years, whereas the chance is marginally under present climate (Fig. 2, Wädenswil). Out of further investigated combinations, with respect to temperature and precipitation changes (cf. Hirschi et al., 2012) here, we only presented data of three scenarios (Table 1). Out of these, even with a scenario of low temperature increase and high increase in precipitation, the risk for an additional 3 rd generation would increase substantially in nearly all Swiss apple growing regions, as for example, from 3% at present to 88% in future at Changins (Table 1). For the median scenario, this risk will be between % in future across Switzerland, except for the coldest region, St. Gallen (10%, Table 1). The high temperature and low precipitation scenario would lead to a risk of 60% for a 3 rd generation, even in the coldest apple production areas (St. Gallen) and a nearly 100% risk for all other regions (Table 1). Consequences for Codling Moth Management Sustainable control of codling moth, under current climatic conditions in Switzerland (Höhn et al., 2010) consisted of three main alternative strategies (Fig. 2, upper graph). The first and most suggested strategy for all commercial orchards was based on (1) pheromone mating disruption using codlemone dispensers, the sexual pheromone of the species. The second main strategy was based on the sole application of (2) codling moth granulosis viruses. It was only suggested for organic farms that were not suitable for pheromone mating disruption due to incompact shape, small size, or lack of isolation. The reason for this restriction was the potential for fast development of resistance against the viruses (Asser-Kaiser et al., 2007). Whenever possible, mating disruption should be applied alone but, under high population pressure, it could be supplemented by granulosis viruses. The third main strategy was suggested for farms of integrated production scheme, that were not suitable for pheromone mating disruption (cf. above) and were based on treatments with (3) insect growth regulators, IGR (Fig. 2, upper graph). Thereby, certified IP farms in Switzerland could only apply two treatments per year and thereby only one treatment of each IGR group per season. The suggested time between the two allowed treatments depended on the development progress, but was usually 4-5 weeks. Usually, either the first treatment was a juvenile hormone mimic targeted against eggs (fenoxycarb), otherwise, a moulting inhibitor (e.g., teflubenzuron) or ecdysone mimic (e.g., tebufenozide), respectively, targeted against larvae. The second treatment was alternated with the IGR group but usually targeted against larvae (Höhn et al., 2010). Under the future climate in Switzerland, codling moth shifts phenology to much earlier dates in consistence with the presently observed trend, showed less overlap between life stages, exhibited much stronger 2 nd following generations and much bigger risk for an additional 3 rd generation. Therefore, it has to be controlled over an at least one month longer season with high infection risk towards harvest (Fig. 2, lower graph). With respect to the first main strategy (1), dispensers were effective for about four months under current climate. With increasing temperature, however, the present amounts of pheromone would most likely not cover the entire season, as emission depended on temperature. Consequently, the dispenser load could have to be increased or new carrier could be developed by the manufacturers. Mating disruption alone could also not be sufficient to control codling moth because, during multiple generations, higher population densities could build up. Therefore, most likely, it had to be combined with other measures like granulosis viruses or, in IP orchards, by IGR later in season or by new products available (Fig. 2). The sole application of granulosis viruses (2) would probably not be suggested anymore in the future due to the increased risk of resistances (Asser- Kaiser et al., 2007). As a consequence, the shapes and sizes of organic apple orchards would have to be adapted in a way that they suffice mating disruption. Alternatively, granulosis viruses would have to be supported by the organic insecticide spinosad. 38

5 The third main strategy would not cover the full season under the climate expected for the time after 2045 (Fig. 2). However, repeated treatments with the same group of active ingredients could provoke the development of resistance to the insecticides. In the past, resistance against IGR had occurred because of too frequent treatments (Höhn et al., 2004; Charmillot et al., 2007) but, it had been overcome with consequent switch to pheromone mating disruption and other anti-resistance measures. Likewise, an IGR strategy will have to be supported by the new products with new and different modes of action, that are already registered or under review in Switzerland. Coragen, for example, could substitute the second IGR treatment in the season since the timing would coincide with the optimum control of other tortricids like summer tortrix (Adoxophyes orana) that could also be controlled by this product. Also, other combinations, including specific agents like emamectin and indoxacarb, were thinkable to substitute IGR strategies. Under very high population pressure threatening harvest, spinosad finally could serve as a product with a short waiting period to be applied late in season. In any case, however, sustainable management of codling moth under future climate would need specific monitoring of phenology, consequent anti-resistance management, right choice and combination of innovative, environmentally friendly products, and precisely timed application, as assured by the Swiss decision support system SOPRA (Samietz et al., 2007). ACKNOWLEDGEMENTS Development of the weather generator was supported by the GAAV Grant Agency (project IAA ). Funding for the downscaling and pest simulation experiments was provided by the Swiss State Secretariat for Education and Research SER (grant number C ) in the framework of COST 734 (CLIVAGRI) and NCCR Climate of the Swiss National Science Foundation. We gratefully acknowledge the use of RCM data sets from the EU-FP6 project ENSEMBLES ( Literature Cited Aluja, M., Guillén, L., Rull, J., Höhn, H., Frey, J., Graf, B. and Samietz, J Is the alpine divide becoming more permeable to biological invasions? - Insights on the invasion and establishment of the Walnut Husk Fly, Rhagoletis completa (Diptera: Tephritidae) in Switzerland. Bull. Entomol. Res. 101: Asser-Kaiser, S., Fritsch, E., Undorf-Spahn, K., Kienzle, J., Eberle, K.E., Gund, N.A., Reineke, A., Zebitz, C.P.W., Heckel, D.G., Huber, J. and Jehle, J.A Rapid emergence of Baculovirus resistance in codling moth due to dominant, sex-linked inheritance. Science 317: Buser, C.M., Künsch, H.R., Lüthi, D., Wild, M. and Schär, C Bayesian multimodel projection of climate: bias assumptions and interannual variability. Clim. Dyn. 33: Charmillot, P.-J., Pasquier, D., Salamin, C., Briand, F., Ter-Hovannesyan, A., Azizian, A., Kutinkova, H., Peeva, P. and Velcheva, N Détection de la résistance du carpocapse Cydia pomonella: Tests d insecticides sur des chenilles diapausantes de Suisse, d Arménie et de Bulgarie. Revue Suisse Vitic. Arboric. Hortic. 39: Dubrovský, M., Buchtele, J. and Žalud, Z High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modelling. Climatic Change 63: Dubrovský, M., Hirschi, M. and Spirig, C HOWGH: an hourly weather generator for pests modelling in present and future climates. Geophys. Rev. Abs. 13. Fischer, A.M., Weigel, A.P., Buser, C.M., Knutti, R., Künsch, H.R., Liniger, M.A., Schärb, C. and Appenzeller, C Climate change projections for Switzerland based on a Bayesian multi-model approach. Int. J. Climatol. 32: Hewitt, C.D The ENSEMBLES project: providing ensemble-based predictions of climate changes and their impacts. EGGS Newsl. 13: Hirschi, M., Stoeckli, S., Dubrovský, M., Spirig, C., Calanca, P., Rotach, M.W., Fisher, 39

6 A.M., Duffy, B. and Samietz, J Downscaling climate change scenarios for apple pest and disease modeling in Switzerland. Earth Syst. Dynam. 3: Höhn, H., Graf, B., Charmillot, P.-J., Pasquier, D. and Grela, C Die Insektizidresistenz beim Apfelwickler breitet sich weiter aus. Schweiz. Z. Obst- Weinbau 9:6-9. Höhn, H., Naef, A., Holliger, E., Widmer, A., Gölles, M., Linder, C., Dubuis, P.-H., Kehrli, P. and Wirth, J Pflanzenschutzempfehlungen für den Erwerbsobstbau 2010/2011. Schweizerische Zeitschrift Für Obst-Und Weinbau 21: IPCC Climate Change 2007: Synthesis Report. IPCC, Geneva, Switzerland. Parmesan, C Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37: Richardson, C.W Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res. 17: Samietz, J., Salser, M.A. and Dingle, H Altitudinal variation in behavioural thermoregulation: local adaptation vs. plasticity in California grasshoppers. J. Evol. Biol. 18: Samietz, J., Graf, B., Höhn, H., Schaub, L. and Höpli, H.U Phenology modelling of major insect pests in fruit orchards from biological basics to decision support: the forecasting tool SOPRA. EPPO Bull. 37: Stoeckli, S., Hirschi, M., Spirig, C., Calanca, P., Rotach, M.W. and Samietz, J Impact of climate change on voltinism and prospective diapause induction of a global pest insect - Cydia pomonella (L.). PLoS ONE 7:1-9. Trnka, M., Mŭska, F., Semerádová, D., Dubrovský, M., Kocmánková, E. and Žalud, Z European corn borer life stage model: regional estimates of pest development and spatial distribution under present and future climate. Ecol. Model. 207: Walther, G.-R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T.J.C., Fromentin, J.-M., Hoegh-Guldberg, O. and Bairlein, F Ecological responses to recent climate change. Nature 416: Willmer, P., Stone, G. and Johnston, I Environmental Physiology of Animals. Blackwell-Science, Oxford. Tables Table 1. Magnitude of the 2 nd codling moth generation, measured as probability of a 45% larval emergence (%), and potential risk for 3 rd generation larvae (%) for present climate and combinations of temperature (T) and precipitation (P) change. Magnitude 2 nd gen. Risk for 3 rd gen. Climate station Med. T Low T Med. T High T Today (med. P) Today (high P) (med. P) (low P) increase increase increase increase Basel (BAS) Bern (BER) Buchs (BUS) Changins (CGI) Chur (CHU) Güttingen (GUT) Magadino (MAG) Sion (SIO) St. Gallen (STG) Wädenswil (WAE)

7 Figures Fig. 1. Mean occurrences of codling moth life phases derived from observed weather (black open circles), synthetic weather for the current climate (ctrl) and for the median scenario for projected climate change after 2045 (scen) at Wädenswil and Magadino. X-axes display the life phases over one year and mean occurrences are only shown if the respective phase was reached in more than 5% of years. 41

8 Principal strategies of Codling moth control (Cydia pomonella) Adults Current Climate Switzerland 2010 Larvae Eggs MD GV IGRs or / plus or or April Mating disruption May June July Aug. Sep. Jh CpGV CpGV CpGV CpGV CpGV MI or Ecd. MI or Ecd. Ecd. or MI CpGV Max. 1 appl. of each group per season (Interval 4-5 weeks) MD: Pheromone Mating Disruption GV: Granulosis Viruses IGRs: Insect Growth Regulators JH = Juvenile hormone Mimics MI = Moulting inhibitors Ecd. = Ecdysone Mimics MD GV IGRs New or / plus or plus April Mating disruption or Jh Supplemented by new products and new combinations MI or Ecd. Ecd. or MI Adults May June July Aug. Sep. CpGV CpGV CpGV CpGV CpGV CpGV MI or Ecd. Coragen e.g., in Exchange of Ecd. or MI Projected Climate Switzerland CpGV Max. 1 appl. of each group per season (Interval 4-5 weeks) Larvae Eggs Indoxacarb, Emamectin, Spinosad Fig. 2. Principal control strategies of codling moth in Switzerland under current conditions (upper graph) and climate projections of the current study simulating hourly weather for scenarios after 2045 (lower graph). Strategies were based on mating disruption with or without support by granulosis viruses, granulosis viruses alone in exceptional cases of small organic farms or insects growth regulators, respectively. 42