Phenology Modelling and GIS Applications in Pest Management: A Tool for Studying and Understanding Insect-Pest Dynamics in the Context of Global Climate Change

Fand, Babasaheb B.; Choudhary, Jaipal Singh; Kumar, Mahesh; Bal, Santanu Kumar

doi:10.1007/978-81-322-1620-9_6

Cited by 5 publications

(6 citation statements)

References 61 publications

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“…Climate change is a significant limiting factor affecting the potential global geographical distribution of ICPs, thereby increasing the risk of invasion and expansion of their infestation range [63][64][65]. For example, Spodoptera littoralis, Naupactus leucoloma, and Bemisia tabaci are more widespread due to climate change [66][67][68].…”

Section: Discussionmentioning

confidence: 99%

Estimating the Global Geographical Distribution Patterns of the Invasive Crop Pest Diuraphis noxia Kurdjumov under Current and Future Climatic Scenarios

Jing

Hong-yan

et al. 2023

Insects

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Invasive crop pests (ICPs) are a major cause of crop losses and adversely affect global food security. Diuraphis noxia Kurdjumov is a significant ICP that feeds on the sap of crops, reducing crop yield and quality. Although estimating the geographical distribution patterns of D. noxia under climate change is critical for its management and global food security, such information remains unclear. Based on 533 global occurrence records and 9 bioclimatic variables, an optimized MaxEnt model was used to predict the potential global geographical distribution of D. noxia. The results showed that Bio1, Bio2, Bio7, and Bio12 were significant bioclimatic variables that influenced the potential geographical distribution of D. noxia. Under current climatic conditions, D. noxia was mainly distributed in west-central Asia, most of Europe, central North America, southern South America, southern and northern Africa, and southern Oceania. Under the SSP 1-2.6, SSP 2-4.5, and SSP 5-8.5 scenarios for the 2030s and 2050s, the potential suitable areas increased, and the centroid migrated to higher latitudes. The early warning of D. noxia in northwestern Asia, western Europe, and North America should be attended to further. Our results provide a theoretical basis for early monitoring and warning of D. noxia worldwide.

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Section: Discussionmentioning

confidence: 99%

Estimating the Global Geographical Distribution Patterns of the Invasive Crop Pest Diuraphis noxia Kurdjumov under Current and Future Climatic Scenarios

Jing

Hong-yan

et al. 2023

Insects

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“…For example, Barker et al [46] tested for trends in the date of the first adult emergence of the emerald ash borer for North America and Europe after combining model outputs for each year over a recent 20-year period. Other studies have modeled the phenology of invasive insects under future climate change scenarios to estimate the impacts of global warming and altered precipitation patterns on important phenological events, potential voltinism, and population growth [42,[68][69][70][71][72].…”

Section: Applications Of Phenological Mapsmentioning

confidence: 99%

“…Warming temperatures, an increase in frequency and severity of extreme weather events (e.g., heat waves and droughts), and altered precipitation patterns under global warming are expected to impact pest phenology as well as phenological synchrony between pests and their host plants [23,34,69,72,74,[132][133][134]. Shorter, warmer winters in many regions will likely promote overwintering survival and increase developmental rates of invasive insects, allowing them to emerge earlier and attain higher densities over a longer growing season [42,68,69,71,92,117]. For migratory insects, a trend towards earlier flight activity is expected to change the timing and severity of pest outbreaks [72,135].…”

Section: Recommendations For Future Researchmentioning

confidence: 99%

“…These SYMAP-generated maps depicted the predominant stages of the codling moth (eggs, larvae, pupae, and adults) over two generations for all dates during the growing season. Phenological mapping of IPM pests became more common beginning in the 1990s with further advancements in computers and a growing number of digitized geographic datasets and geographic information systems (GIS) software options [32,[39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

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Phenological Mapping of Invasive Insects: Decision Support for Surveillance and Management

Barker,

Coop

2023

Insects

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Readily accessible and easily understood forecasts of the phenology of invasive insects have the potential to support and improve strategic and tactical decisions for insect surveillance and management. However, most phenological modeling tools developed to date are site-based, meaning that they use data from a weather station to produce forecasts for that single site. Spatial forecasts of phenology, or phenological maps, are more useful for decision-making at area-wide scales, such as counties, states, or entire nations. In this review, we provide a brief history on the development of phenological mapping technologies with a focus on degree-day models and their use as decision support tools for invasive insect species. We compare three different types of phenological maps and provide examples using outputs of web-based platforms that are presently available for real-time mapping of invasive insects for the contiguous United States. Next, we summarize sources of climate data available for real-time mapping, applications of phenological maps, strategies for balancing model complexity and simplicity, data sources and methods for validating spatial phenology models, and potential sources of model error and uncertainty. Lastly, we make suggestions for future research that may improve the quality and utility of phenological maps for invasive insects.

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“…Understanding the phenology of species, the study of periodic events in biological life cycles influenced by seasonal and annual climate fluctuations, is of paramount importance across various domains such as agriculture (e.g., for crop yield forecasts; Fand et al, 2014), nature conservation (e.g., for assessing species responses to global changes; Ettinger, Chamberlain, and Wolkovich, 2022), and addressing public health concerns related to allergens and emerging infectious diseases carried by arthropod vectors (Burkett-Cadena et al, 2011). Ecologists have therefore developed and tested several modelling approaches, i.e.…”

Section: Introductionmentioning

confidence: 99%

Inferring the seasonal dynamics and abundance of an invasive species using a spatio-temporal stacked machine learning model

Da Re,

Marini,

Bonannella

et al. 2023

Preprint

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Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a set of predominantly abiotic covariates. The modelling approach choice, i.e. the algorithm, is a crucial factor for addressing the multiple sources of variability that can lead to disparate outcomes when different models are applied to the same dataset. This inter-model variability has led to the adoption of ensemble modelling techniques, among which stacked generalisation, which has recently demonstrated its capacity to produce robust results. Stacked ensemble modelling incorporates predictions from multiple base learners or models as inputs for a meta-learner. The meta-learner, in turn, assimilates these predictions and generates a final prediction by combining the information from all the base learners. In our study, we utilized a recently published dataset documenting egg abundance observations of Aedes albopictus collected using ovitraps. This dataset spans various locations in southern Europe, covering four countries - Albania, France, Italy, and Switzerland - and encompasses multiple seasons from 2010 to 2022. Utilising these ovitrap observations and a set of environmental predictors, we employed a stacked machine learning model to forecast the weekly average number of mosquito eggs. This approach enabled us to i) unearth the seasonal dynamics of Ae. albopictus for 12 years; ii) generate spatio-temporal explicit forecasts of mosquito egg abundance in regions not covered by conventional monitoring initiatives. Beyond its immediate application for public health management, our work presents a versatile modelling framework adaptable to infer the spatio-temporal abundance of various species, extending its relevance beyond the specific case of Ae. albopictus.

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Phenology Modelling and GIS Applications in Pest Management: A Tool for Studying and Understanding Insect-Pest Dynamics in the Context of Global Climate Change

Cited by 5 publications

References 61 publications

Estimating the Global Geographical Distribution Patterns of the Invasive Crop Pest Diuraphis noxia Kurdjumov under Current and Future Climatic Scenarios

Estimating the Global Geographical Distribution Patterns of the Invasive Crop Pest Diuraphis noxia Kurdjumov under Current and Future Climatic Scenarios

Phenological Mapping of Invasive Insects: Decision Support for Surveillance and Management

Inferring the seasonal dynamics and abundance of an invasive species using a spatio-temporal stacked machine learning model

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