Journal of Environmental Science and Management 14(2): 13-20 (December 2011) ISSN 0119-1144
Simulation Modeling of Bunchy Top Epidemics in a Changing Climate Avelino D. Raymundo1 and Ireneo B. Pangga2 ABSTRACT Epidemics of bunchy top, the most destructive viral disease of abaca and banana in the Philippines, were modeled using STELLA version 9.1, a modeling software appropriate for systems analysis of biological populations. A previously developed coupled model of the population dynamics of the insect vector Pentalonia nigronervosa and epidemic progress of the bunchy top disease was improved and modied by incorporating the effects of temperature. Both insect vector and bunchy top epidemic submodels followed the H-L-S-R (healthy (H) - latent (L) - infectious (S) – removed (R)) epidemic modeling approach where the diseased plants or insect population was partitioned into nonoverlapping compartments or states. The modied model satisfactorily simulated P. nigronervosa population dynamics and bunchy top epidemics. The effect of climate change on bunchy top epidemics was simulated through the addition of 1 and 2°C to the average monthly temperature from 1998-2007 in Davao City, Philippines. The increase in monthly average temperatures of 1 and 2°C reduced the simulated bunchy top epidemics as the rates of increase in the number of viruliferous aphids and disease incidence were reduced, and epidemic onset was delayed. Key words: bunchy top disease, abaca, banana, modeling, Pentalonia nigronervosa; simulated climate change
INTRODUCTION Bunchy top disease is the most destructive and widespread viral disease of abaca and banana in the Philippines (Raymundo 2000; Raymundo et al. 2001). Epidemics of this disease have devastated abaca plantations in the Bicol region for more than half a century (Raymundo 2000). A nationwide survey showed that the Bicol and Eastern Visayas regions were “hotspots” or areas with the highest concentration of the bunchy top disease, but the disease was also rampant in the CARAGA (Agusan and Surigao) and Southern Mindanao regions (Raymundo et al. 2001). Estimated ber yield losses in 2002 due to abaca bunchy top and mosaic diseases were valued at PhP 18.3 M in the Bicol region and PhP 8.4 M in the Eastern Visayas region (Raymundo et al. 2002). The epidemiology of the bunchy top disease is inuenced by environmental factors which affect both disease incidence and behavior of its aphid vector, Pentalonia nigronervosa. Epidemics can be triggered by the presence of alternate host plants of the vector such as gabi, cania and caladium. Abaca or banana is a perennial crop that guarantees uninterrupted host availability for the vector and virus. The slow disease expression causes virus detection only after it has already successfully infected another plant (Raymundo 2000; Raymundo and Bajet 2000). Bunchy top can be controlled by eradication of diseased plants but this approach has failed to slow down disease spread due to an inadequate understanding of the epidemiology of the disease and population dynamics of the aphid vector. Epidemics continue to spread at a rapid rate as the virus persists and multiplies rapidly as diseased plants are simply cut down allowing the emergence of infected suckers (De la Cruz and Raymundo, 2007). 1 2
These re-growths, usually not eliminated due to limited resources, serve as ready sources of inoculum. A systems approach, considering all aspects of the host-virus-vector relationship, appears to be needed in bunchy top disease management (Raymundo 2000; Raymundo and Bajet 2000). The complex nature of the host-pathogen relationship of the bunchy top disease justies the need for a holistic approach to manage the disease. Systems modeling allows an integrated view of the whole system behavior and provides a quantitative description and mechanistic understanding of the system (Thornley and Johnson 1990). This study aimed to develop a simulation model linking a bunchy top disease epidemic submodel and a population dynamics submodel of the aphid vector, P. nigronervosa. Outputs of the model can be used in developing integrated and sustainable disease management strategies in response to climate change. MATERIALS AND METHODS Model Development The linked models of bunchy top epidemic and P. nigronervosa population dynamics were developed in STELLA software version 9.1 (ISEE Systems 2008). The original linked models of bunchy top epidemics and P. nigronervosa population dynamics (Raymundo 2001) (Figure 1) were modied by incorporating the effects of temperature using data from the literature. The modied models followed the plant virus - insect vector model of Madden et al. (2000), which was a deterministic model characterizing plant virus disease epidemics in relation to the population dynamics of the insect vectors and four different
Professor, Crop Protection Cluster, College of Agriculture, University of the Philippines Los Baños, E-mail:
[email protected] (corresponding author) Assistant Professor, Crop Protection Cluster, College of Agriculture, UPLB
Simulation Modeling of Bunchy Top Epidemics in a Changing Climate
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Figure 1. Original STELLA models of bunchy top epidemics and Pentalonia nigronervosa population dynamics.
classes of virus transmission mechanisms. The modied models were based on the circulative and persistently transmitted class of the plant virus – insect vector model (Madden et al. 2000) since bunchy top transmission is of the circulative, non-propagative type (CAB International 2007). The modeling approach was based on an H-L-S-R (healthy (H) - latent (L) - infectious (S) – removed (R)) epidemic model where the plant or insect population was partitioned into nonoverlapping compartments or states (Madden et al. 2000). Model assumptions The modied models were based on the following
assumptions. The bunchy top epidemic occurred in an orchard of 900 (30 x 30 grid) healthy plants of uniform growth and nutritional status but susceptible to P. nigronervosa and the bunchy top virus. Each plant was assumed to contain a maximum of 1000 aphids. The bunchy top disease was initiated from one infectious plant by a colony of viruliferous P. nigronervosa aphids containing 500 individuals of the apterae form. No alternate hosts of the virus and vector were considered in the model. Virus acquisition and transmission were assumed to be via adult aphids only. The external meteorological condition considered important in inuencing P. nigronervosa population dynamics and bunchy top epidemics in the
Journal of Environmental Science and Management Vol. 14. No. 2 (December 2011) modeling process was temperature as it has been found to be the key driving variable affecting many insect vector cycles. Model validation The model was validated using two data sets from the literature, a) Umadhay and Raymundo (1999) and b) Opina and Milloren (1996). A paired t-test was used to determine signicant differences between observed and simulated values. Model sensitivity analysis Simulation runs were performed using monthly average temperature data in Davao City, Philippines from 1998-2007. Historical monthly average temperature data for Davao City, Philippines were downloaded from: http://www.tutiempo. net/en/Climate/DAVAO/987540.htm. Planting was set in the beginning of May in each simulated year and harvesting was set after 16 months from planting. Sensitivity analysis was undertaken to determine the effect of climate change on bunchy top epidemics through the addition of 1 and 2°C to the historical Davao City monthly average temperature from 1998-2007. RESULTS AND DISCUSSION The P. nigronervosa population dynamics model (Figure 2) started with the computation of the number of aphids produced (n) using the following equation: dn/dt = r_tempcoef * A * (1-A/K)*aphid_starter where: r _tempcoef = intrinsic rate of increase , A = aphid population, K = carrying capacity, and aphid_starter = variable that starts aphid reproduction. The intrinsic rate of increase (r_tempcoef) computed as ln net reproductive rate (Ro) /generation time (T), was inuenced by temperature (Robson et al. 2007). The carrying capacity (K) was computed as the product of plantmax or maximum number of plants (set to 900 plants) and maxpopn or maximum number of aphids per plant (set to 1000 individuals). Initial aphid population was set to 500 individuals. Aphid starter was based on devtime_tempcoef, which began the reproduction of aphids after the development time or time between birth and production of the rst young of a female of the population as inuenced by temperature (Robson et al. 2007). Nymphal mortality removed dead nymphs from the aphid population via the mortality_tempcoef which was inuenced by temperature (Robson et al. 2007). The aphid population proceeded to the apterous and alate forms through the rates of the number of apterae and alatae produced, respectively. Apterae production (P) was computed as:
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dP/dt = A *apterae_prod_rate * apterae_starter where: A = aphid population, apterae_prod_rate = apterae production rate and apterae_starter = variable that started apterae production. Apterae production rate was set at 98.7% (Rajan 1981) as cited by Robson et al. (2006). The apterae mortality rate was based on longevity_tempcoef that was obtained from apterae longevity data as affected by temperature (Robson et al. 2007). Apterae_starter begins the production of apterae after the development time or time between birth and production of the rst young of a female of the population as inuenced by temperature (Robson et al. 2007). Alate production (L) was computed as: dL/dt = A * alate_prod_rate * alate_starter where: A = aphid population, alate_prod_rate = alate production rate, and alate_starter = variable that started alate reproduction. Alatae are formed after 7 to 10 generations of apterae (Waterhouse and Norris 1987). Alate starter began production of alatae based on the alate generation time set at 7 generations, which was inuenced by temperature (Robson et al. 2007). Alate production rate was set to 1.3 % of the aphid population based on a one year survey according to Rajan (1981) as cited by Robson et al. (2006). The alate mortality rate was based on alate longevity of 19.6 days or 0.65 months (Padmalatha et al. 2002). The number of alatae landed and apterae transferred was obtained via the alate landing rate and apterae transfer rate of 0.4 % of the aphid population based on 0.4 alatae per plant and 4 apterae per plant sampled by Kenyon et al. (1998) and computed as a percentage of the alatae or apterae composition of the maximum number of aphids in the population. The P. nigronervosa population dynamics model was classied into the virus-free, latent, and infective or viruliferous states. The combined number of alate and apterous aphids on the plants acquired the bunchy top virus through the acquisition rate (A), which was computed as: dA/dt = nonvirul_aphids * I * TempCoef1 where: nonvirul_aphids = number of alate and apterae aphids on the plants which are nonviruliferous, I = infectious plants, and TempCoef1 = temperature coefcient. Tempcoef1 was a parameter strictly bound between 0 and 1 indicating the effect of temperature on the virus acquisition rate (Anhalt and Almeida 2008). The alate and apterous aphids passed through the latent viruliferous state at an incubation period of 36 hours or 0.05 months (Anhalt and Almeida 2008), and moved to the viruliferous state (virul_aphids).
Simulation Modeling of Bunchy Top Epidemics in a Changing Climate 16
Figure 2. Modified Pentalonia nigronervosa population dynamics model in STELLA version 9.1.
Figure 3. Modified bunchy top disease model in STELLA version 9.1.
Journal of Environmental Science and Management Vol. 14. No. 2 (December 2011) The bunchy top epidemic model (Figure 3) started with healthy plants becoming latently infected at a transmission rate (T) using the equation: dT/dt = H * Rc * (virul_aphids * TempCoef2)*COFRAGGR where: H = healthy plants, Rc = bunchy top infection rate, virul_aphids = number of viruliferous aphids (alate and apterae combined) on plants, TempCoef2 =temperature coefcient, COFR = correction factor, and AGGR = aggregation factor. The bunchy top infection rate was set as 0.2 infected plants per month (Umadhay and Raymundo 1999). Tempcoef2 was a parameter strictly bound between 0 and 1 indicating the effect of temperature on the virus inoculation rate (Anhalt and Almeida 2008). COFR was a correction factor indicating the proportion of the number of healthy plants to the total number of plants in the plantation area. AGGR is a dimensionless parameter set to 1.6 based on the aggregation of the vector aphid P. nigronervosa in the eld (Young and Wright 2005). The spatial distribution of a banana bunchy top epidemic showed aggregation in a monocropping system (Umadhay and Raymundo 2002). When AGGR=1, the model assumed that the disease was randomly distributed in the population of healthy plants, while AGGR>1 accounted for spatial heterogeneity in the spatial distribution of the disease. The infected plant moved through the latent infectious stage at a transfer rate from 20 to 56 days as inuenced by plant age (Thiribhuvanamala et al. 2001). Infected plants lost infectiousness and proceeded to the removed or postinfectious stage at a removal rate (harvest) of 16 months. In addition, the infected plants can be rogued or not via the rouging starter. If rouging starter=1 then rouging will be implemented at a rouging rate of 0.0164 plants per month (Smith et al. 1998).
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The simulated number of viruliferous aphids per plant is similar to the observed number of aphids sampled per plant in a survey of banana plantations (Young and Wright 2005). The linear progress curve of the number of viruliferous aphids can be caused by the uninterrupted host availability for the vector and virus since abaca or banana is a perennial crop (Raymundo 2000; Raymundo and Bajet 2000). The simulated progress curves of bunchy top incidence using historical monthly average temperature in Davao City, Philippines for all simulated years were sigmoid or S-shaped (Figure 6a). In the early stages of the bunchy top epidemics, the rate of disease increase accelerated up to the inection point or one half (0.5) of the disease incidence where the maximum rate occurs. At later stages of the epidemics, the diminishing number of healthy plants limited the rate of disease increase. The simulated sigmoid progress curves of bunchy top incidence produced by this model were similar to the simulated sigmoid disease progress curves produced by the circulative, persistently transmitted class of the plant virus-insect vector (H-L-S-R) model (Madden et al. 2000) and actual bunchy top disease progress tted with logistic models (Opina and Milloren 1996; Umadhay and Raymundo 1999). The logistic model is appropriate to describe viral diseases where there is plant-to-plant spread (Nutter 1997), which is true for the bunchy top disease.
Model validation Model validation tests using two data sets from the literature, a) Umadhay and Raymundo (1999) and Opina and Milloren (1996), showed proximity of points between observed and predicted values despite the underprediction of disease incidence throughout the observed disease epidemic except for the overprediction of the nal disease incidence (Figure 4). In addition, there were no signicant differences between observed and simulated disease incidences based on paired t-tests for both validation data sets. Model sensitivity analysis The simulated progress of viruliferous aphids per plant throughout the plant growth duration using historical monthly average temperature in Davao City, Philippines for all simulated years followed a linear curve with the onset of increase delayed by ve months after planting (Figure 5a).
Figure 4. Model validation showing simulated and observed bunchy top incidences in two data sets: a) Umadhay and Raymundo (1999) and b) Opina and Milloren (1996).
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Simulation Modeling of Bunchy Top Epidemics in a Changing Climate
The negative effect of climate change via an increase in average monthly temperature of 1 and 2°C on the progress curve of simulated viruliferous aphids per plant is indicated by a delay in onset and reduced rate of increase. An increase of 1°C in the monthly average temperature reduced the rate of increase of viruliferous aphids per plant and delayed the onset of aphid increase by 2 months from planting for all simulated years. An increase of 2°C signicantly reduced the rate of aphid increase in all sites for all simulated years (Figure 5b-c).
The negative effect of climate change via an increase in average monthly temperature of 1 and 2°C on the progress curve of simulated bunchy top incidence is shown by a reduced rate of increase and delay in onset. An increase of 1°C in monthly average temperature reduced the rate of disease increase and delayed the epidemic onset one month later for all simulated years. The 2°C increase signicantly reduced the rate of disease increase for all simulated years. The onset of the epidemics was delayed by 3 months for all simulated years due to a 2°C temperature increase (Figure 6b-c).
Figure 5. Simulated viruliferous aphids per plant using Davao City monthly average temperature from 19982007: actual (a), + 1 °C (b), and + 2 °C (c).
Figure 6. Simulated bunchy top disease incidence using Davao City monthly average temperature from 1998-2007: actual (a), + 1 °C (b), and + 2 °C (c).
Journal of Environmental Science and Management Vol. 14. No. 2 (December 2011) CONCLUSION AND RECOMMENDATION The effects of temperature on the population dynamics of the insect vector P. nigronervosa and on the transmission of the bunchy top virus were incorporated in a previously developed STELLA coupled model of P. nigronervosa population dynamics and bunchy top epidemic. This modied model improved the predictive ability of the previously developed coupled model since the responses of P. nigronervosa and the bunchy top virus to an increase in temperature under future climate change scenarios can now be simulated. It is projected that as future temperature rises due to climate change, bunchy top disease spread will be slower in certain tropical areas as the aphid vector will reproduce less and the bunchy top virus will be transmitted less efciently (Anhalt and Almeida 2008). This modied model tested this hypothesis by simulating bunchy top epidemic progress using monthly average temperature increases of 1 and 2°C in Davao City, Philippines from 1998-2007. Simulation ndings illustrated that an increase in monthly average temperature of 1 and 2°C reduced bunchy top epidemics as the simulated number of viruliferous aphids and disease incidence was reduced, and epidemic onset was delayed. Although the increments of 1 and 20 may not be realistic as wider uctuations of temperature can happen, it is nonetheless a means of sensitivity analysis that can test the effect of microclimatic changes on specic stages of the cycle of the insect vector and of the disease. It must be realized that temperature or any other climatic variable that may play a role inuences these stages which subsequently determines the behavior of the resultant epidemic. Verication of model performance showed a satisfactory simulation of the progress of bunchy top incidence and number of viruliferous aphids. The H-L-S-R epidemic modeling approach shows a realistic representation of the dynamics of plant disease development (Madden et al. 2007), which was also revealed by the performance of this modied bunchy top epidemic model being based on the said approach. However, more model validation eld experiments are needed as the validation using data sets from the literature will yield limited results as these were not designed for model validation. The effect of climate change on host-parasite systems can be positive or negative. It can also be a no-change situation. The reason could be that the relationship, as in the current model, is exceedingly dynamic as it is affected by many outside factors that in themselves are extremely variable. In the Philippines, topography and elevation for instance determine microclimate variability even in closed spaces, such as in the canopy of the young banana plants where P. nigronervosa usually prefers to reside. The microclimate environment therefore can be the deciding factor in the outcome of the relationship of the participants of the system. In this environment,
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there is a concept of compensation usually utilized by the organism whereby it can become very successful even when a part of its cycle is adversely affected as long as the other stages of the cycle can compensate (Rotem 1978). The modied model can be used to explore bunchy top disease management options. Eradication programs by rouging infected plants can be evaluated in this model by comparing the simulated progress curves of bunchy top incidence at different rouging rates. The reduction of the vector population size, such as by insecticide treatment, can be explored in this model. Roguing and reduction in vector population size, as management options, have been explored in an H-L-S-R model (Jeger et al. 1998). This modied model can also be coupled with a geographical information system (GIS) software to map the potential distribution of the bunchy top disease in different geographical locations (Raymundo et al. 2001). Mapping the spread of the bunchy top disease can be explored at different climate change scenarios by increasing the monthly average temperature in simulation runs involving different locations. However, more validation data are needed by comparing simulated and observed bunchy top incidence and P. nigronervosa density in several locations before the model can be used widely in bunchy top disease management. In future simulations, the possible effect of other climatic variables may be explored as these are indeed integral components of the system and of the model. REFERENCES Anhalt M.D. and R.P.P. Almeida. 2008. Effect of temperature, vector life stage and plant access period on transmission of banana bunchy top virus to banana. Phytopathology 98:743748. CAB International. 2007. Crop Protection Compendium. CAB International, Wallington, UK. De la Cruz, C. S. and A. D. Raymundo. 2007. Analysis of spatiotemporal dynamics of abaca mosaic and possible disease risk scenarios in Eastern Visayas. Journal of Tropical Plant Pathology 43: 39-57. ISEE Systems. 2008. Software Reference Guide. STELLA Software Technical Documentation. Wheelock Ofce Park, 31 Old Etna Road, Suite 7N, Lebanon, NH, USA. Jeger M.J., F. Van Den Bosch, L.V. Madden and J. Holt. 1998. A model for analyzing plant-virus transmission characteristics and epidemic development. IMA (Inst. Math Appl.) Journal of Mathematics Applied in Medicine and. Biology 15:1-18. Kenyon L., H. Warburton, T.C.B. Chancellor, J. Holt, M. Smith, M. Brown, R. Thwaites, L.V. Magnaye, L. Herradura, B. Arano, M. Loquias and C. Soguilon. 1998. Identication, vector relationships, epidemiology and control of virus
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