The distribution of <i>Athetis lepigone</i> and prediction of its potential distribution based on <scp>GARP</scp> and MaxEnt

Wang, Y. Q.; F., J.; Li, X. Q.; Wang, Y. F.; Cao, Shuangyin; Xie, Ankun; Ye, S. F.; Dong, Baoqing; Zhao, Wanqing; Qin, Yu; Feng, Xiao-Yong; Zheng, Zhuo; Zhu, Xuemei; Jiang, Junjun; Dong, Zimei

doi:10.1111/jen.12347

Cited by 25 publications

(23 citation statements)

References 25 publications

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“…Athetis lepigone is an important Eurasian and polyphagous agricultural pest with more than 30 species of host plants (e.g., maize, pepper, and wheat; Fu et al, 2014; Wang et al, 2017). The larvae attack bud leaves or drill deep into young prop roots and tender stems in maize, causing severe damage to summer maize in the Northern China Plain (Jiang et al, 2011; Ma et al, 2012; Shi et al, 2011; Wang et al, 2017; Zhang et al, 2011). The adults can undertake seasonal migrations covering long distances (Fu et al, 2014), but their feeding habits remain largely unknown.…”

Section: Introductionmentioning

confidence: 99%

Sensilla of the antenna and proboscis of Athetis lepigone (Möschler) (Lepidoptera: Noctuidae)

Zhang

Wang

et al. 2021

Journal of Morphology

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Sensory structures on the antennae and mouthparts of insects are associated with various activities, such as host location, feeding, attracting a mate, and identifying a suitable oviposition site. Athetis lepigone (Möschler) is an important polyphagous Eurasian pest with more than 30 species of host plants. The larvae target bud leaves, prop roots, and tender stems of many agricultural crops, but the feeding habits of the adults remain poorly known. Aiming to understand the feeding behavior of the species, we investigated the fine morphology of its antennae and proboscis using scanning electron microscopy. The antennae of both sexes are filiform, and bear eight types of sensilla: Böhm's bristles, sensilla squamiformia, trichodea, chaetica, basiconica, coeloconica, styloconica, and auricillica. Sensilla trichodea are the most abundant among these sensillum types. The proboscis consists of two elongated, interlocked maxillary galeae that enclose the food canal by dorsal and ventral legulae. The external galeal surface is covered with numerous triangular microtrichia on Zone 1 and abundant blunt microbumps on Zone 2. The surface of the food canal bears closely connected and smooth semicircular ridges, gradually tapering toward the proboscis tip. Three types of sensilla are noticeable on the proboscis: sensilla trichodea, basiconica, and styloconica. We briefly discuss the putative functional significance of the antennal and proboscis sensilla and, based on the specific structural modifications of the proboscis, predict a flower‐visiting habit for A. lepigone.

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Section: Introductionmentioning

confidence: 99%

Sensilla of the antenna and proboscis of Athetis lepigone (Möschler) (Lepidoptera: Noctuidae)

Zhang

Wang

et al. 2021

Journal of Morphology

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“…Species distribution models (SDMs) have become a prominent tool to predict and evaluate species distributions (Pearson et al, 2007;Elith and Leathwick, 2009;Xu et al, 2012). Different types of SDMs are developed according to the ecological niche, among which maximum entropy (MaxEnt) is widely used (Elith et al, 2006;Wang et al, 2017). MaxEnt acquires the maximum entropy of species distribution and constructs an SDM to predict species' potential distribution based on the species distribution coordinates and environmental variables (Araujo et al, 2005;Phillips et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Habitat Uses and Their Implications for the Conservation of the Chinese Bumblebee Bombus pyrosoma (Hymenoptera: Apidae)

Liu

Ding

et al. 2021

Front. Ecol. Evol.

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Bumblebees are important pollinators for many wild plants and crops. However, the bumblebee populations are seriously declining in many parts of the world. Hence, the bumblebee conservation strategy should be urgently addressed, and the species distribution modeling approach can effectively evaluate the potentially suitable areas for their conservation. Here, one of the most abundant and endemic species of bumblebee in China, Bombus pyrosoma, was selected to assess current and future climates’ influence on its distribution with MaxEnt. Nine high-resolution bioclimatic/environmental variables with high contribution rates and low correlations were used. Four of the nine bioclimatic/environmental variables, min temperature of the coldest month (bio_06), annual mean temperature (bio_01), precipitation of wettest month (bio_13) and radiation of warmest quarter (bio_26), were found to be the most critical factors influencing the distribution of B. pyrosoma. The modeling results showed that the areas with high and moderate suitability for B. pyrosoma covered 141,858 and 186,198 km2 under the current climate conditions. More than 85% of the sampling sites in 2019 were found to be suitable under the current scenario. Under the future A1B and A2 scenarios in 2050 and 2100, the areas with low and moderate suitability for B. pyrosoma increased. However, alarmingly, the high suitability areas decreased under the future A1B and A2 scenarios in 2050 and 2100. Furthermore, regions covering seven provinces of northern China were the most crucial for developing nature reserves for B. pyrosoma, with the following order of suitable areas: Gansu, Shanxi, Ningxia, Qinghai, Shaanxi, Hebei and Beijing. Our study highlights the impact of future climate changes on the distribution of B. pyrosoma, and conservation strategies should mitigate the threats posed by environmental changes, particularly in the current high suitability areas.

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“…Some recent applications include predicting distributions of different species, such as the invasive species (e.g., pignut in India Padalia, Srivastava & Kushwaha, 2014 and creeping oxeye in Central America, Qin et al, 2015), modeling bird abundance patterns (Martínez-Meyer et al, 2013), endangered bird species (Montenegro et al, 2017), and ecological niche of tree species (Prakash Singh et al, 2013), and delineating disease risk areas by estimating the geographical distribution of pathogens (Barro et al, 2016;Chikerema et al, 2017) and vector species (Ramsey et al, 2015;Sloyer et al, 2018;Lippi et al, 2019). Other research compares GARP with some other ENMs (especially MaxEnt) to show how species distributions change using different approaches to provide reliable predictions (Padalia, Srivastava & Kushwaha, 2014;Wang et al, 2017;Ray, Behera & Jacob, 2018), to compare the predictive performance of different methods (Khatchikian et al, 2011;Zhu & Peterson, 2017), or to understand why the differences in the performance exist (Elith & Graham, 2009). Therefore, it is of primary importance to revisit GARP and better understand what biological information can be obtained from rule-set development during the modeling process.…”

Section: Introductionmentioning

confidence: 99%

Exploring environmental coverages of species: a new variable contribution estimation methodology for rulesets from the genetic algorithm for rule-set prediction

Yang

Gómez

Blackburn

2020

PeerJ

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Variable contribution estimation for, and determination of variable importance within, ecological niche models (ENMs) remain an important area of research with continuing challenges. Most ENM algorithms provide normally exhaustive searches through variable space; however, selecting variables to include in models is a first challenge. The estimation of the explanatory power of variables and the selection of the most appropriate variable set within models can be a second challenge. Although some ENMs incorporate the variable selection rubric inside the algorithms, there is no integrated rubric to evaluate the variable importance in the Genetic Algorithm for Ruleset Production (GARP). Here, we designed a novel variable selection methodology based on the rulesets generated from a GARP experiment. The importance of the variables in a GARP experiment can be estimated based on the consideration of the prevalence of each environmental variable in the dominant presence rules of the best subset of models and its coverage. We tested the performance of this variable selection method based on simulated species with both weak and strong responses to simulated environmental covariates. The variable selection method generally performed well during the simulations with over 2/3 of the trials correctly identifying most covariates. We then predict the distribution of Toxostoma rufum (a bird with a cosmopolitan distribution) in the continental United States (US) and apply our variable selection procedure as a real-world example. We found that the distribution of T. rufum could be accurately modeled with 13 or 10 of 21 variables, using an UI cutoff of 0.5 or 0.25, respectively, arriving at parsimonious environmental coverages with good model accuracy. We also provide tools to simulate species distributions for testing ENM approaches using R.

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The distribution of Athetis lepigone and prediction of its potential distribution based on GARP and MaxEnt

Cited by 25 publications

References 25 publications

Sensilla of the antenna and proboscis of Athetis lepigone (Möschler) (Lepidoptera: Noctuidae)

Sensilla of the antenna and proboscis of Athetis lepigone (Möschler) (Lepidoptera: Noctuidae)

An Evaluation of Habitat Uses and Their Implications for the Conservation of the Chinese Bumblebee Bombus pyrosoma (Hymenoptera: Apidae)

Exploring environmental coverages of species: a new variable contribution estimation methodology for rulesets from the genetic algorithm for rule-set prediction

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