Dataset Information

Predicting the current potential and future world wide distribution of the onion maggot, Delia antiqua using maximum entropy ecological niche modeling.

ABSTRACT: Climate change will markedly impact biology, population ecology, and spatial distribution patterns of insect pests because of the influence of future greenhouse effects on insect development and population dynamics. Onion maggot, Delia antiqua, larvae are subterranean pests with limited mobility, that directly feed on bulbs of Allium sp. and render them completely unmarketable. Modeling the spatial distribution of such a widespread and damaging pest is crucial not only to identify current potentially suitable climactic areas but also to predict where the pest is likely to spread in the future so that appropriate monitoring and management programs can be developed. In this study, Maximum Entropy Niche Modeling was used to estimate the current potential distribution of D. antiqua and to predict the future distribution of this species in 2030, 2050, 2070 and 2080 by using emission scenario (A2) with 7 climate variables. The results of this study show that currently highly suitable habitats for D.antiqua occur throughout most of East Asia, some regions of North America, Western Europe, and Western Asian countries near the Caspian sea and Black Sea. In the future, we predict an even broader distribution of this pest spread more extensively throughout Asia, North America and Europe, particularly in most of European countries, Central regions of United States and much of East Asia. Our present day and future predictions can enhance strategic planning of agricultural organizations by identifying regions that will need to develop Integrated Pest Management programs to manage the onion maggot. The distribution forecasts will also help governments to optimize economic investments in management programs for this pest by identifying regions that are or will become less suitable for current and future infestations.

SUBMITTER: Ning S

PROVIDER: S-EPMC5291381 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Predicting the current potential and future world wide distribution of the onion maggot, Delia antiqua using maximum entropy ecological niche modeling.

Ning Shuoying S Wei Jiufeng J Feng Jinian J

PloS one 20170203 2

Climate change will markedly impact biology, population ecology, and spatial distribution patterns of insect pests because of the influence of future greenhouse effects on insect development and population dynamics. Onion maggot, Delia antiqua, larvae are subterranean pests with limited mobility, that directly feed on bulbs of Allium sp. and render them completely unmarketable. Modeling the spatial distribution of such a widespread and damaging pest is crucial not only to identify current potent ...[more]

PMID: 28158259

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Project description:BackgroundAfrican trypanosomiasis is a tsetse-borne parasitic infection that affects humans, wildlife, and domesticated animals. Tsetse flies are endemic to much of Sub-Saharan Africa and a spatial and temporal understanding of tsetse habitat can aid surveillance and support disease risk management. Problematically, current fine spatial resolution remote sensing data are delivered with a temporal lag and are relatively coarse temporal resolution (e.g., 16 days), which results in disease control models often targeting incorrect places. The goal of this study was to devise a heuristic for identifying tsetse habitat (at a fine spatial resolution) into the future and in the temporal gaps where remote sensing and proximal data fail to supply information.MethodsThis paper introduces a generalizable and scalable open-access version of the tsetse ecological distribution (TED) model used to predict tsetse distributions across space and time, and contributes a geospatial Bayesian Maximum Entropy (BME) prediction model trained by TED output data to forecast where, herein the Morsitans group of tsetse, persist in Kenya, a method that mitigates the temporal lag problem. This model facilitates identification of tsetse habitat and provides critical information to control tsetse, mitigate the impact of trypanosomiasis on vulnerable human and animal populations, and guide disease minimization in places with ephemeral tsetse. Moreover, this BME analysis is one of the first to utilize cluster and parallel computing along with a Monte Carlo analysis to optimize BME computations. This allows for the analysis of an exceptionally large dataset (over 2 billion data points) at a finer resolution and larger spatiotemporal scale than what had previously been possible.ResultsUnder the most conservative assessment for Kenya, the BME kriging analysis showed an overall prediction accuracy of 74.8% (limited to the maximum suitability extent). In predicting tsetse distribution outcomes for the entire country the BME kriging analysis was 97% accurate in its forecasts.ConclusionsThis work offers a solution to the persistent temporal data gap in accurate and spatially precise rainfall predictions and the delayed processing of remotely sensed data collectively in the - 45 days past to + 180 days future temporal window. As is shown here, the BME model is a reliable alternative for forecasting future tsetse distributions to allow preplanning for tsetse control. Furthermore, this model provides guidance on disease control that would otherwise not be available. These 'big data' BME methods are particularly useful for large domain studies. Considering that past BME studies required reduction of the spatiotemporal grid to facilitate analysis. Both the GEE-TED and the BME libraries have been made open source to enable reproducibility and offer continual updates into the future as new remotely sensed data become available.

Project description:Delia antiqua is a major underground agricultural pest widely distributed in Asia, Europe and North America. In this study, we sequenced and annotated the complete mitochondrial genome of this species, which is the first report of complete mitochondrial genome in the family Anthomyiidae. This genome is a double-stranded circular molecule with a length of 16,141 bp and an A+T content of 78.5%. It contains 37 genes (13 protein-coding genes, 22 tRNAs and 2 rRNAs) and a non-coding A+T rich region or control region. The mitochondrial genome of Delia antiqua presents a clear bias in nucleotide composition with a positive AT-skew and a negative GC-skew. All of the 13 protein-coding genes use ATN as an initiation codon except for the COI gene that starts with ATCA. Most protein-coding genes have complete termination codons but COII and ND5 that have the incomplete termination codon T. This bias is reflected in both codon usage and amino acid composition. The protein-coding genes in the D. antiqua mitochondrial genome prefer to use the codon UUA (Leu). All of the tRNAs have the typical clover-leaf structure, except for tRNASer(AGN) that does not contain the dihydrouridine (DHU) arm like in many other insects. There are 7 mismatches with U-U in the tRNAs. The location and structure of the two rRNAs are conservative and stable when compared with other insects. The control region between 12S rRNA and tRNAIle has the highest A+T content of 93.7% in the D. antiqua mitochondrial genome. The control region includes three kinds of special regions, two highly conserved poly-T stretches, a (TA)n stretch and several G(A)nT structures considered important elements related to replication and transcription. The nucleotide sequences of 13 protein-coding genes are used to construct the phylogenetics of 26 representative Dipteran species. Both maximum likelihood and Bayesian inference analyses suggest a closer relationship of D. antiqua in Anthomyiidae with Calliphoridae, Calliphoridae is a paraphyly, and both Oestroidea and Muscoidea are polyphyletic.

Project description:Colonization resistance, i.e., the protective effects of associated microbiota for the animal host against pathogen infection, has been studied widely over the last 100 years. However, few molecules mediating colonization resistance have been identified. In the symbiosis formed by Delia antiqua and its associated microbes, six bacteria protect larvae from infection with the entomopathogen Beauveria bassiana, providing an ideal model to investigate the chemical mechanism for colonization resistance. Subsequently using this symbiotic system, we first compared effects of the six bacterial species, and one control bacterium (Klebsiella oxytoca) that showed no antifungal effects, on B. bassiana and its infection of D. antiqua Second, metabolomic profiles of the six bacteria and K. oxytoca were compared to identify candidate metabolites that may prevent infection. Third, the concentrations of candidate metabolites in situ from axenic and nonaxenic larvae were determined. Finally, effects of artificial metabolite cocktails on B.bassiana and its infection of D. antiqua larvae were determined. Results showed that compared to K. oxytoca, the six bacteria produced a metabolite cocktail showing inhibitory effects on conidial germination, mycelial growth of B.bassiana, and fungal infection. Our work revealed novel molecules that mediate colonization resistance, which could help in developing chemical mechanisms of colonization resistance. Moreover, this work may aid in discovery and expansion of new bioactive antibiotics, promoting development of prophylactic and therapeutic approaches for treating infectious diseases.IMPORTANCE The protection of associated microbiota for their animal hosts against pathogen infection has been studied widely over the last 100 years. However, how those microbes protect the animal host remains unclear. In former studies, body surface microbes of one insect, Delia antiqua, protected the insect larvae from infection with the entomopathogen Beauveria bassiana By comparing the metabolites produced by microbes that protect the insect and microbes that cannot protect the insect, the question of how the microbes protect the insect is answered. It turns out that body surface bacteria produce a metabolite cocktail that inhibits colonization of B.bassiana and consequently protects the insect. This work reveals novel molecules with antifungal activity, which may aid in discovery and expansion of new prophylactic and therapeutic natural chemicals for treating infectious diseases.

Dataset Information

Predicting the current potential and future world wide distribution of the onion maggot, Delia antiqua using maximum entropy ecological niche modeling.

Publications

Predicting the current potential and future world wide distribution of the onion maggot, Delia antiqua using maximum entropy ecological niche modeling.

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