Using district-level occurrences in MaxEnt for predicting the invasion potential of an exotic insect pest in India (original) (raw)
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Mealybug, Phenacoccus solenopsis Tinsley has recently emerged as a serious insect pest of cotton in India. This study demonstrates the use of Maxent algorithm for modeling the potential geographic distribution of P. solenopsis in India with presence-only data. Predictions were made based on the analysis of the relationship between 111 occurrence records for P. solenopsis and the corresponding current and future climate data defined on the study area. The climate data from worldclim database for current and future (SRES A2 emission scenario for 2050) conditions were used. DIVA-GIS, an open source software for conducting spatial analysis was used for mapping the predictions from Maxent. The algorithm provided reasonable estimates of the species range indicating better discrimination of suitable and unsuitable areas for its occurrence in India under both present and future climatic conditions. The fit for the model as measured by AUC was high, with value of 0.930 for the training data and 0.895 for the test data, indicating the high level of discriminatory power for the Maxent. A Jackknife test for variable importance indicated that mean temperature of coldest quarter with highest gain value was the most important environmental variable determining the potential geographic distribution of P. solenopsis. The approaches used for delineating the ecological niche and prediction of potential geographic distribution are described briefly. Possible applications and limitations of the present modeling approach in future research and as a decision making tool in integrated pest management are discussed.
Current Science, 2019
The mango fruit borer, Citripestis eutraphera (Meyrick), originally confined to the Andaman Islands, is a recent invasion in mainland India. With changes in climatic conditions, the pest is likely to spread in other major mango-growing regions of the country and can pose serious threats to mango production. In this backdrop, the present study examines the impact of climate change to develop spatio-temporal distribution of invasive C. eutraphera in India using the maximum entropy (MaxEnt) modelling approach. Integration of point data on current occurrence of pest and corresponding bioclimatic variables in MaxEnt were used to define the potential distribution in India and mapped using spatial analysis tool in ArcGIS. The model framework performed well as indicated by high area under the curve (0.97) value. Jackknife test for estimating predictive power of the variables indicated that 'isothermality' and 'temperature seasonality' significantly affected C. eutraphera distribution. It was found that mango-growing pockets in the southwestern parts of Gujarat, as well as parts of Kerala and Tamil Nadu were moderately to highly suitable for C. eutraphera distribution in 2050 and 2070. The results of this study could be an important guide for selecting monitoring and surveillance sites and designing integrated pest management policies in the context of climate change against this invasive pest of mango.
Current Science
The mango fruit borer, Citripestis eutraphera (Meyrick), originally confined to the Andaman Islands, is a recent invasion in mainland India. With changes in climatic conditions, the pest is likely to spread in other major mango-growing regions of the country and can pose serious threats to mango production. In this backdrop, the present study examines the impact of climate change to develop spatio-temporal distribution of invasive C. eutraphera in India using the maximum entropy (MaxEnt) modelling approach. Integration of point data on current occurrence of pest and corresponding bioclimatic variables in MaxEnt were used to define the potential distribution in India and mapped using spatial analysis tool in ArcGIS. The model framework performed well as indicated by high area under the curve (0.97) value. Jackknife test for estimating predictive power of the variables indicated that 'isothermality' and 'temperature seasonality' significantly affected C. eutraphera distribution. It was found that mango-growing pockets in the southwestern parts of Gujarat, as well as parts of Kerala and Tamil Nadu were moderately to highly suitable for C. eutraphera distribution in 2050 and 2070. The results of this study could be an important guide for selecting monitoring and surveillance sites and designing integrated pest management policies in the context of climate change against this invasive pest of mango.
Ecological Modelling, 2014
The mealybug Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae) is a highly invasive and polyphagous pest of global incidence. The fundamental hypothesis of the present study was that the temperature variations due to global climate change may affect seriously the future distribution and abundance of P. solenopsis, which might further aggravate the crop yield losses. We employed a temperature-based phenology model of P. solenopsis in a geographic information system for mapping population growth potentials of P. solenopsis. The three risk indices viz., establishment risk index, generation index and activity index were computed using interpolated temperature data from worldclim database for current (2000) and future (2050) climatic conditions. The daily minimum and maximum temperature data from four selected weather stations in India were used for analysing within-year variation of pest population. A linear relationship was established between the activity indices and yield losses at various locations reported in literatures for predicting the future trend of yield loss due to climate change. The results revealed that, under current temperature conditions P. solenopsis can complete >4.0 generations per year on ∼80% of the global cotton production areas. Economic losses are likely to occur in areas where at least 8.0 generations can develop in a year; under current climate ∼40% areas fall under this category. The increased geographical suitability at higher latitudes in cotton production areas, additional 2.0 generations per year, and 4.0 fold increase of population abundance of P. solenopsis are expected in tropical and subtropical cotton areas of Brazil, South Africa, Pakistan and India due to predicted climate change. Analysis of within year population increase at various selected locations in India revealed that, P. solenopsis attained maximum potential population increase during the major cotton growing season (May-June to October-November). On the other hand, the innate ability of P. solenopsis population to increase reduced considerably during off season and cooler winter months. The increased pest activity of P. solenopsis due to climate change may intensify the losses in cotton yield, with forecasted losses in India to increase from existing losses of million US$ 1217.10 to future losses of million US$ 1764.85 by the year 2050. Here, we illustrate the possible impact of climate change on future P. solenopsis exacerbation based on temperature-driven population studies, which will help in undertaking agro-ecoregion specific management strategies.
Agriculture, Ecosystems & Environment, 2000
Climatic mapping, which predicts the potential distribution of organisms in new areas and under future climates based on their responses to climate in their home range, has recently been criticised for ignoring dispersal and interactions between species, such as competition, predation and parasitism. In order to determine whether these criticisms are justified, the different procedures employed in climatic mapping were reviewed, with examples taken from studies of the Mediterranean fruit fly (Ceratitis capitata), Karnal bunt of wheat (Tilletia indica) and the Colorado potato beetle (Leptinotarsa decemlineata). All these studies stressed the key role played by non-climatic factors in determining distribution but it was shown that these factors, e.g., the availability of food and synchrony with the host plant, together with the difficulties of downscaling and upscaling data, were different to those highlighted in the criticisms. The extent to which laboratory studies on Drosophila populations, on which the criticisms are based, can be extrapolated to general predictions of species distributions was also explored. The Drosophila experiments were found to illustrate the importance of climate but could not accurately determine potential species distributions because only adult and not breeding population densities were estimated. The experimental design overestimated species interactions and ignored other factors, such as the availability of food. It was concluded that while there are limitations, climatic mapping procedures continue to play a vital role in determining what G.E. Hutchinson defined as the "fundamental niche" in studies of potential distribution. This applies especially for pest species, where natural dispersal is generally less important than transport by man, and species interactions are limited by the impoverished species diversity in agroecosystems. Due to the lack of data, climatic mapping is often the only approach which can be adopted. Nevertheless, to ensure that non-climatic factors are not neglected in such studies, a standard framework should be employed. Such frameworks have already been developed for pest risk analyses and are suitable for general use in studies of potential distribution because, in order to justify the phytosanitary regulation of international trade, they must also consider the potential for pests to invade new areas and the impacts of such invasions.
Mapping risks of pest invasions based on the spatio-temporal distribution of hosts
Management of Biological Invasions
Surveying multiple invasive pest species at the same time can help reduce the cost of detecting new pest invasions. In this paper, we describe a new method for mapping the relative likelihood of pest invasion via plant propagation material in a geographic setting. The method simulates the invasion of a range of pest species, including arrival in an uninvaded area, spread, and survival in a novel landscape, using information on the spatial and temporal distribution of the suitable host crop species and tentative knowledge of the spread and survival capacities of the target pests. The methodology is applied to a gridded map in which each map cell represents a site in a landscape. The method uses stochastic simulations to depict plausible realizations of the invasion outcomes and estimate the distribution of pest invasion likelihood for each cell in the area of concern. The method then prioritizes the cells based on the stochastic invasion outcomes using a pairwise stochastic dominance rule and a hypervolume indicator. We demonstrate the approach by assessing the relative likelihood of pest invasion for strawberry production in Finland. Our method helps to differentiate sites in a landscape using both the estimates of pest invasion risk and their uncertainty. It can be applied to prioritize sites for plant health surveys and allocate survey resources among large geographic regions. The approach is generalizable and can be used in situations where knowledge of the harmful pest species is poor or nonexistent.
Journal of Environmental Biology, 2014
Rhodnius neglectus is frequently found in palm trees and bird nests in sylvatic environments. However, adult specimens infected by Trypanosoma cruzi have been invading houses in central Brazil. Analyzing and predicting the geographical distribution of this species may improve vector surveillance strategies for Chagas disease. Ecological niche modeling using the genetic algorithm for rule-set production (GARP) was applied to predict the geographical distribution of R. neglectus from occurrence records and a set of 23 predictor variables (e.g., temperature, precipitation, altitude, and vegetation). Additionally, the geographical distribution of R. neglectus was compared with the geographical distribution of four species of palm trees and two species of birds from the study region. The models were able to predict, with high probability, the occurrence of R. neglectus as a regular (although nonendemic) species of the Cerrado biome in central Brazil. Caatinga, Amazonian savanna, Pantanal, and the Bolivian Chaco appear as areas with lower probabilities of potential occurrence for the species. A great overlap was observed between the distribution of R. neglectus, palm trees (Acrocomia aculeata and Syagrus oleracea), and birds (Phacellodomus ruber and Pseudoseisura cristata). By including new records for R. neglectus (from both sylvatic and domestic environments), our study showed a distribution increase toward the west and northeast areas of Brazil in the "diagonal of open/dry ecoregions of South America". These results should aid Chagas disease vector surveillance programs, given that household invasion by Rhodnius species maintains the risk of disease transmission and limits control strategies.
Current Science, 2021
The brown planthopper, Nilaparvata lugens (Stål) is the most serious pest of rice across the world. It is also known to transmit stunted viral disease; the insect alone or in combination with a virus causes the breakdown of rice vascular system, leading to economic losses in commercial rice production. Despite its immense economic importance, information on its potential distribution and factors governing the present and future distribution patterns is limited. Thus, in the present study we used maximum entropy modelling with bioclimatic variables to predict the present and future potential distribution of N. lugens in India as an indicator of risk. The predictions were mapped for spatio-temporal variation and area was analysed under suitability ranges. Jackknife analysis indicated that N. lugens geographic distribution was mostly influenced by temperature-based variables that explain up to 68.7% of the distribution, with precipitation factors explaining the rest. Among individual factors, the most important for distribution of N. lugens was annual mean temperature followed by precipitation of coldest quarter and precipitation seasonality. Our results highlight that the highly suitable areas under current climate conditions are 7.3%, whereas all projections show an increase under changing climatic conditions with time up to 2090, and with emission scenarios and a corresponding decrease in low-risk areas. We conclude that climate change increases the risk of N. lugens with increased temperature as it is likely to spread to the previously unsuitable areas in India, demanding adaptation strategies.
Phenological Mapping of Invasive Insects: Decision Support for Surveillance and Management
Insects, 2024
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.