Climate Change Increases the Expansion Risk of <i>Helicoverpa zea</i> in China According to Potential Geographical Distribution Estimation

Helicoverpa zea, a well-documented and endemic pest throughout most of the Americas, affecting more than 100 species of host plants. It is a quarantine pest according to the Asia and Pacific Plant Protection Commission (APPPC) and the catalog of quarantine pests for plants imported to the People’s Republic of China. Based on 1781 global distribution records of H. zea and eight bioclimatic variables, the potential geographical distributions (PGDs) of H. zea were predicted by using a calibrated MaxEnt model. The contribution rate of bioclimatic variables and the jackknife method were integrated to assess the significant variables governing the PGDs. The response curves of bioclimatic variables were quantitatively determined to predict the PGDs of H. zea under climate change. The results showed that: (1) four out of the eight variables contributed the most to the model performance, namely, mean diurnal range (bio2), precipitation seasonality (bio15), precipitation of the driest quarter (bio17) and precipitation of the warmest quarter (bio18); (2) PGDs of H. zea under the current climate covered 418.15 × 104 km2, and were large in China; and (3) future climate change will facilitate the expansion of PGDs for H. zea under shared socioeconomic pathways (SSP) 1-2.6, SSP2-4.5, and SSP5-8.5 in both the 2030s and 2050s. The conversion of unsuitable to low suitability habitat and moderately to high suitability habitat increased by 8.43% and 2.35%, respectively. From the present day to the 2030s, under SSP1-2.6, SSP2-4.5 and SSP5-8.5, the centroid of the suitable habitats of H. zea showed a general tendency to move eastward; from 2030s to the 2050s, under SSP1-2.6 and SSP5-8.5, it moved southward, and it moved slightly northward under SSP2-4.5. According to bioclimatic conditions, H. zea has a high capacity for colonization by introduced individuals in China. Customs ports should pay attention to host plants and containers of H. zea and should exchange information to strengthen plant quarantine and pest monitoring, thus enhancing target management

Influence of Crop Residue Management and Soil Tillage Method on Reducing the Carbon Footprint of Winter Wheat Production in the Salt-Affected Arable Land in the North China Plain

Salt-affected arable land is distributed widely in China, especially in the North China Plain. Crop residue management under appropriate tillage is critical to improving salt-affected soil organic carbon and reducing the carbon footprint. This study conducted four-year field experiments including two treatments (residue incorporated into soil with plough tillage (CT+); residue mulching with no-till (NT+)) in two sites (non-saline soil and salt-affected soil); the carbon footprint of wheat production was analyzed by life cycle assessment. The results showed that the carbon footprint of wheat production in the salt-affected soil was significantly larger than that in the non-saline soil, because the salt-affected soil exhibited higher N2O emission than the non-saline soil. CT+ has lower carbon footprint than the NT+, mainly due to the lower N2O emission and higher carbon sequestration in the CT+ compared to NT+. As for the salt-affected soil, the largest contributor of the carbon footprint per unit area was soil N2O emission, with a relative contribution of 40%; the largest contributor of the carbon footprint per unit yield was carbon sequestration, with a relative importance of 47–50%. Our results indicated that wheat production in salt-affected land has a high carbon footprint, while it can be decreased by incorporating crop residue into the soil under the plough tillage

The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing

Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observations used to predict maize yield. It is also unclear how much improvement can be gained using multi-temporal compared to mono-temporal data. We used a systematic scheme to address those gaps employing UAV multispectral observations at nine development stages of maize (from second-leaf to maturity). Next, the spectral and texture indices calculated from the mono-temporal and multi-temporal UAV images were fed into the Random Forest model for yield prediction. Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (R2 increased by 8.1% and RMSE decreased by 27.4%). For single temporal UAV observation, the fourteenth-leaf stage was the earliest suitable time and the milking stage was the optimal observing time to estimate grain yield. For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (R2 = 0.93, RMSE = 0.77 t·ha−1). Furthermore, we found that the Normalized Difference Red Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), and dissimilarity of the near-infrared image at milking stage were the most promising feature variables for maize yield prediction

Predicting Climate Change Effects on the Potential Distribution of Two Invasive Cryptic Species of the Bemisia tabaci Species Complex in China

Middle East-Asia Minor 1 (MEAM1) and Mediterranean (MED) are two invasive cryptic species of the Bemisia tabaci species complex (Hemiptera: Aleyrodidae) that cause serious damage to agricultural and horticultural crops worldwide. To explore the possible impact of climate change on their distribution, the maximum entropy (MaxEnt) model was used to predict the potential distribution ranges of MEAM1 and MED in China under current and four future climate scenarios, using shared socioeconomic pathways (SSPs), namely SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, over four time periods (2021&ndash;2040, 2041&ndash;2060, 2061&ndash;2080, and 2081&ndash;2100). The distribution ranges of MEAM1 and MED were extensive and similar in China under current climatic conditions, while their moderately and highly suitable habitat ranges differed. Under future climate scenarios, the areas of suitable habitat of different levels for MEAM1 and MED were predicted to increase to different degrees. However, the predicted expansion of suitable habitats varied between them, suggesting that these invasive cryptic species respond differently to climate change. Our results illustrate the difference in the effects of climate change on the geographical distribution of different cryptic species of B. tabaci and provide insightful information for further forecasting and managing the two invasive cryptic species in China

Differential Influences of Wind-Blown Sand Burial on Bacterial and Fungal Communities Inhabiting Biological Soil Crusts in a Temperate Desert, China

Biological soil crusts (BSCs) are an integration of external photoautotrophs and internal heterotrophic communities. Sand burial is a ubiquitous disturbance that affects the biodiversity and ecological function of BSCs, but little is known about the influence of sand burial on microbial communities in arid sandy deserts. Here, based on a long-term field experiment and utilizing high-throughput sequencing, we assessed the influence of sand burial on bacterial and fungal communities inhabiting two typical successional stages of BSCs (cyanobacterial crusts for early successional stage and mixed crusts for late successional stage) at the three-sand buried depth (0, 0.5, and 10 mm) in the Tengger Desert, Northern China. We found that the diversity, abundance, and composition of the bacterial and fungal communities were all altered by the sand burial treatment. Different indicator taxa were identified in unburied and buried (shallow and deep) BSCs. Changes in soil properties caused by sand burial have been suggested as a possible cause of changes in the bacterial and fungal community composition in BSCs

The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing

Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observations used to predict maize yield. It is also unclear how much improvement can be gained using multi-temporal compared to mono-temporal data. We used a systematic scheme to address those gaps employing UAV multispectral observations at nine development stages of maize (from second-leaf to maturity). Next, the spectral and texture indices calculated from the mono-temporal and multi-temporal UAV images were fed into the Random Forest model for yield prediction. Our results indicated that multi-temporal UAV data could remarkably enhance the yield prediction accuracy compared with mono-temporal UAV data (R2 increased by 8.1% and RMSE decreased by 27.4%). For single temporal UAV observation, the fourteenth-leaf stage was the earliest suitable time and the milking stage was the optimal observing time to estimate grain yield. For multi-temporal UAV data, the combination of tasseling, silking, milking, and dough stages exhibited the highest yield prediction accuracy (R2 = 0.93, RMSE = 0.77 t&middot;ha&minus;1). Furthermore, we found that the Normalized Difference Red Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), and dissimilarity of the near-infrared image at milking stage were the most promising feature variables for maize yield prediction

Increased Invasion Risk of <i>Tagetes minuta</i> L. in China under Climate Change: A Study of the Potential Geographical Distributions

Tagetes minuta L., a member of the Tageftes genus belonging to the Asteraceae family, is a well-documented exotic plant native to South America that has become established in China. In this study, 784 occurrence records and 12 environmental variables were used to predict the potential geographical distributions (PGDs) of T. minuta under current and future climatic changes using an optimized MaxEnt model. The results showed that (1) three out of the twelve variables contributed the most to the model performance: isothermality (bio3), precipitation in the driest quarter (bio17), and precipitation in the warmest quarter (bio18); (2) the PGDs of T. minuta under the current climate covered 62.06 × 104 km2, mainly in North, South, and Southwest China; and (3) climate changes will facilitate the expansion of the PGDs of T. minuta under three shared socioeconomic pathways (SSP 1-2.6, SSP2-4.5, and SSP5-8.5) in both the 2030s and 2050s. The centroid of suitable habitats under SSP2-4.5 moved the longest distance. T. minuta has the capacity to expand in China, especially in Yunnan, where there exist no occurrence records. Customs, ports, and adjacent regions should strengthen the quarantine of imported goods and mobile personnel for T. minuta, and introduced seedlings should be isolated to minimize their introduction risk

Similar Bacterial Communities among Different Populations of a Newly Emerging Invasive Species, Tuta absoluta (Meyrick)

Microorganisms in the guts of insects enhance the adaptability of their hosts with different lifestyles, or those that live in different habitats. Tuta absoluta is an invasive pest that is a serious threat to tomato production in China. It has quickly spread and colonized Xinjiang, Yunnan and other provinces and regions. We used Illumina HiSeq next generation sequencing of the 16S rRNA gene to study and analyze the composition and diversity of the gut microbiota of three geographical populations of T. absoluta. At the phylum level, the most common bacteria in T. absoluta across all three geographical populations were Proteobacteria and Firmicutes. An uncultured bacterium in the Enterobacteriaceae was the dominant bacterial genus in the T. absoluta gut microbiotas. There were no significant differences in alpha diversity metrics among the Spanish, Yunnan and Xinjiang populations. The structures of the gut microbiota of the three populations were similar based on PCoA and NMDS results. The results confirmed that the microbial structures of T. absoluta from different regions were similar.info:eu-repo/semantics/publishedVersio

Krüppel-homologue 1 regulates the development of Tuta absoluta and its cascade regulation pattern in the juvenile hormone signalling pathway

Tomato leaf miner, Tuta absoluta (Meyrick), is one of the most destructive quarantine pests globally. It has been confirmed that Krüppel-homologue 1 (kr-h1) plays a key role in the regulation of juvenile hormone (JH). However, it is unclear how kr-h1 regulates the synthesis of JH and its cascade regulation pattern in tomato leaf miner. Here, we obtained the six JH signalling genes (kr-h1, Methoprene-tolerant, Forkhead box O, Juvenile acid methyltransferase, Juvenile hormone esterase and Fatty acid synthase 2), and applied RNA interference to explore the role of kr-h1 and the seven genes (plus Vitellogenin) regulation relationship in T. absoluta. Bioinformatics analysis revealed the structural characteristics of kr-h1 protein and JH receptor Met, which contained eight C2H2 zinc finger structures and three typical domains of the bHLH-PAS family, respectively. The expression levels of Met and Vg were upregulated after RNAi of kr-h1 gene, while the gene levels of JHAMT and FAS2 were downregulated. Furthermore, topical application of JH analogue to second instar larvae could induce the expression of kr-h1 and inhibit the expression of Met. Our study reveals the mechanism by which kr-h1 regulates JH pathway genes, which could be applied to control the growth of tomato leaf miners