Potential distribution of Spodoptera frugiperda (J.E. Smith) in China and the major factors influencing distribution

Wang, Rulin; Jiang, Chengyao; Guo, Xiang; Chen, Dongdong; You, Chao; Zhang, Yue; Wang, Mingtian; Li, Qing

doi:10.1016/j.gecco.2019.e00865

Cited by 80 publications

(71 citation statements)

References 25 publications

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“…The accuracy of prediction of FAW MaxEnt model showed high values of AUC con rming a good model performance [33]. Comparing our results with other studies, including Wang et al [56], an excellent AUC was found. For instance, AUC often increases with the size of the study area because it contributes to include background points that have environmental characteristics greatly distant from the species requirement, resulting in arti cial increase of SDM validation [4].…”

Section: Discussionsupporting

confidence: 85%

“…The probability of FAW occurrence is high in environments where mean annual temperature (bio1) is comprised between 19 °C and 23 °C. Du Plessis et al [22] found that the development rate of FAW increased linearly with increasing temperatures between 18 and 30 o C. Additionally, Wang et al [56] found that when the Mean Temperature of the Warmest Quarter varies between 19.15-29.73 °C, the existence probability of the FAW is higher.…”

Section: Discussionmentioning

confidence: 99%

“…It is therefore seen that these four variables also play a major role in the spread of FAW in South Kivu. Wang et al [56] modelled the distribution of FAW through MaxEnt with 19 bioclimatic variables related to temperature and humidity of which 10 in uenced the FAW distribution. However, the FAW distribution may be in uenced by other several non-climatic factors, such as host, natural enemy, management level and human activities [24], soil properties, land cover and agricultural management interventions (such as use of pesticides or fertilizers) [6].…”

Section: Discussionmentioning

confidence: 99%

“…Distribution of FAW has been investigated by Wang et al [56] and Liu et al [32] using SDM MaxEnt (Maximum Entropy). Also, the FAW distribution was modeled on a large scale using CLIMEX software integrating the species model "Wet tropical" [13].…”

Section: Introductionmentioning

confidence: 99%

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Bioclimatic Zonation and Potential Distribution of Spodoptera Frugiperda (Lepidoptera: Noctuidae) in South Kivu Province, DR Congo

Cokola

Mugumaarhahama

Bisimwa

et al. 2020

Preprint

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Abstract Background: the fall Armyworm (FAW) Spodoptera frugiperda (JE Smith), is currently a devastating pest throughout the world due to its dispersal capacity and voracious feeding behaviour on several crops. A MaxEnt species distributions model (SDM) was developed based on collected FAW occurrence and environmental data’s. Bioclimatic zones were identified and the potential distribution of FAW in South Kivu, eastern DR Congo, was predicted.Results: Mean annual temperature (bio1), annual rainfall (bio12), temperature seasonality (bio4) and longest dry season duration (llds) mainly affected the FAW potential distribution. The average area under the curve value of the model was 0.827 demonstrating the model efficient accuracy. According to Jackknife test of variable importance, the annual rainfall was found to correspond to the highest gain when used in isolation. FAWs’ suitable areas where this pest is likely to be present in South Kivu province are divided into two corridors. The Eastern corridor covering the Eastern areas of Kalehe, Kabare, Walungu, Uvira and Fizi territories and the Western corridor covering the Western areas of Kalehe, Kabare, Walungu and Mwenga. Conclusions: This research provides important information on the distribution of FAW and bioclimatic zones in South Kivu. Given the rapid spread of the insect and the climatic variability observed in the region that favor its development and dispersal, it would be planned in the future to develop a monitoring system and effective management strategies to limit it spread and crop damage.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bioclimatic Zonation and Potential Distribution of Spodoptera Frugiperda (Lepidoptera: Noctuidae) in South Kivu Province, DR Congo

Cokola

Mugumaarhahama

Bisimwa

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…It is therefore seen that these four variables also play a major role in the spread of FAW in South Kivu. Wang et al [55] modelled the distribution of FAW through MaxEnt with 19 bioclimatic variables related to temperature and humidity of which 10 in uenced the FAW distribution. However, the FAW distribution may be in uenced by other several non-climatic factors, such as host, natural enemy, management level and human activities [24], soil properties, land cover and agricultural management interventions (such as use of pesticides or fertilizers) [6].…”

Section: Discussionmentioning

confidence: 99%

Bioclimatic zonation and potential distribution of Spodoptera frugiperda (Lepidoptera: Noctuidae) in South Kivu Province, DR Congo

Cokola

Mugumaarhahama

Bisimwa

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: the fall Armyworm (FAW) Spodoptera frugiperda (JE Smith), is currently a devastating pest throughout the world due to its dispersal capacity and voracious feeding behaviour on several crops. A MaxEnt species distributions model (SDM) was developed based on collected FAW occurrence and environmental data’s. Bioclimatic zones were identified and the potential distribution of FAW in South Kivu, eastern DR Congo, was predicted.Results: Mean annual temperature (bio1), annual rainfall (bio12), temperature seasonality (bio4) and longest dry season duration (llds) mainly affected the FAW potential distribution. The average area under the curve value of the model was 0.827 demonstrating the model efficient accuracy. According to Jackknife test of variable importance, the annual rainfall was found to correspond to the highest gain when used in isolation. FAWs’ suitable areas where this pest is likely to be present in South Kivu province are divided into two corridors. The Eastern corridor covering the Eastern areas of Kalehe, Kabare, Walungu, Uvira and Fizi territories and the Western corridor covering the Western areas of Kalehe, Kabare, Walungu and Mwenga.Conclusions: This research provides important information on the distribution of FAW and bioclimatic zones in South Kivu. Given the rapid spread of the insect and the climatic variability observed in the region that favor its development and dispersal, it would be planned in the future to develop a monitoring system and effective management strategies to limit it spread and crop damage.

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Evaluating the predictive performance of presence–absence models: Why can the same model appear excellent or poor?

Abrego,

Ovaskainen

2023

Ecology and Evolution

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When comparing multiple models of species distribution, models yielding higher predictive performance are clearly to be favored. A more difficult question is how to decide whether even the best model is “good enough”. Here, we clarify key choices and metrics related to evaluating the predictive performance of presence–absence models. We use a hierarchical case study to evaluate how four metrics of predictive performance (AUC, Tjur's R2, max‐Kappa, and max‐TSS) relate to each other, the random and fixed effects parts of the model, the spatial scale at which predictive performance is measured, and the cross‐validation strategy chosen. We demonstrate that the very same metric can achieve different values for the very same model, even when similar cross‐validation strategies are followed, depending on the spatial scale at which predictive performance is measured. Among metrics, Tjur's R2 and max‐Kappa generally increase with species' prevalence, whereas AUC and max‐TSS are largely independent of prevalence. Thus, Tjur's R2 and max‐Kappa often reach lower values when measured at the smallest scales considered in the study, while AUC and max‐TSS reaching similar values across the different spatial levels included in the study. However, they provide complementary insights on predictive performance. The very same model may appear excellent or poor not only due to the applied metric, but also how predictive performance is exactly calculated, calling for great caution on the interpretation of predictive performance. The most comprehensive evaluation of predictive performance can be obtained by evaluating predictive performance through the combination of measures providing complementary insights. Instead of following simple rules of thumb or focusing on absolute values, we recommend comparing the achieved predictive performance to the researcher's own a priori expectations on how easy it is to make predictions related to the same question that the model is used for.

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Potential distribution of Spodoptera frugiperda (J.E. Smith) in China and the major factors influencing distribution

Cited by 80 publications

References 25 publications

Bioclimatic Zonation and Potential Distribution of Spodoptera Frugiperda (Lepidoptera: Noctuidae) in South Kivu Province, DR Congo

Bioclimatic Zonation and Potential Distribution of Spodoptera Frugiperda (Lepidoptera: Noctuidae) in South Kivu Province, DR Congo

Bioclimatic zonation and potential distribution of Spodoptera frugiperda (Lepidoptera: Noctuidae) in South Kivu Province, DR Congo

Evaluating the predictive performance of presence–absence models: Why can the same model appear excellent or poor?

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