Temperature-dependent phenology of Plutella xylostella (Lepidoptera: Plutellidae): Simulation and visualization of current and future distributions along the Eastern Afromontane

Abstract

There is a scarcity of laboratory and field-based results showing the movement of the diamondback moth (DBM) Plutella xylostella (L.) across a spatial scale. We studied the population growth of the diamondback moth (DBM) Plutella xylostella (L.) under six constant temperatures, to understand and predict population changes along altitudinal gradients and under climate change scenarios. Non-linear functions were fitted to continuously model DBM development, mortality, longevity and oviposition. We compiled the best-fitted functions for each life stage to yield a phenology model, which we stochastically simulated to estimate the life table parameters. Three temperature-dependent indices (establishment, generation and activity) were derived from a logistic population growth model and then coupled to collected current (2013) and downscaled temperature data from AFRICLIM (2055) for geospatial mapping. To measure and predict the impacts of temperature change on the pest's biology, we mapped the indices along the altitudinal gradients of Mt. Kilimanjaro (Tanzania) and Taita Hills (Kenya) and assessed the differences between 2013 and 2055 climate scenarios. The optimal temperatures for development of DBM were 32.5, 33.5 and 33°C for eggs, larvae and pupae, respectively. Mortality rates increased due to extreme temperatures to 53.3, 70.0 and 52.4% for egg, larvae and pupae, respectively. The net reproduction rate reached a peak of 87.4 female offspring/female/generation at 20°C. Spatial simulations indicated that survival and establishment of DBM increased with a decrease in temperature, from low to high altitude. However, we observed a higher number of DBM generations at low altitude. The model predicted DBM population growth reduction in the low and medium altitudes by 2055. At higher altitude, it predicted an increase in the level of suitability for establishment with a decrease in the number of generations per year. If climate change occurs as per the selected scenario, DBM infestation may reduce in the selected region. The study highlights the need to validate these predictions with other interacting factors such as cropping practices, host plants and natural enemies.

Fig 1. Development rates of immature stages of DBM: Egg (a), larva (b) and pupa (c).

Blue dots are the observed means ± SE, the solid red line represents the selected model output, while the dotted blue lines represent the upper and lower 95% confidence limits. Bars represent standard deviations.

Fig 2. Temperature-dependent mortalities of immature life stages of DBM: Egg (a), larva (b) and pupa (c).

Blue dots are the observed means, the solid red line represents the selected model output, while dotted blue lines represent the upper and lower 95% confidence intervals of selected models.

Fig 3. Temperature-dependent total egg production (a) and age-related cumulative proportion of egg production (b).

Age of the females at 50% oviposition is indicated. Dots represent data points. The upper and lower 95% confidence intervals of the model are indicated.

Fig 4. Population growth parameters of DBM estimated over a range of five constant temperatures.

r_m, intrinsic rate of natural increase; Ro, net reproduction rate; GRR, gross reproduction rate; GT, mean generation time; λ, finite rate of increase; and DT, doubling time.

Fig 5. Changes in the establishment, abundance and population growth rates of DBM along altitudinal gradients of Mt. Kilimanjaro and Taita hills.

Establishment risk indices (ERI) of Mt. Kilimanjaro (a) and Taita hills (d); Generation indices (GI) of Mt. Kilimanjaro (b) and Taita hills (e); and Activity indices (AI) of Mt. Kilimanjaro (c) and Taita hills (f) *KisangeB = Kisangesangeni B, KisaMadukani = Kisangesangeni Madukani.

Fig 6. Changed establishment, abundance and population growth rates across climate change scenarios of Mt. Kilimanjaro.

Current 2013 distribution and abundance of DBM: (a) ERI, (b) GI and (c) AI; future 2055 distribution and abundance of DBM: (d) ERI, (e) GI and (f) AI. Absolute change in distribution and abundance between current and future scenarios: (g) ERI, (h) GI and (i) AI. ERI = Establishment Risk Index, GI = Generation Index and AI = Activity Index. *KisangeB = Kisangesangeni B, KisaMadukani = Kisangesangeni Madukani.

Fig 7. Altered establishment, abundance and population growth rates across climate change scenarios of Taita hills.

Current 2013 distribution and abundance of DBM: (a) ERI, (b) GI and (c) AI; future 2055 distribution and abundance of DBM: (d) ERI, (e) GI and (f) AI. Absolute change in distribution and abundance between current and future scenarios: (g) ERI, (h) GI and (i) AI. ERI = Establishment Risk Index, GI = Generation Index and AI = Activity Index.