Genetic basis and origin of resistance to acetolactate synthase inhibitors in Amaranthus palmeri from Spain and Italy

BACKGROUND

Amaranthus palmeri is an aggressive annual weed native to the United States, which has become invasive in some European countries. Populations resistant to acetolactate synthase (ALS) inhibitors have been recorded in Spain and Italy, but the evolutionary origin of the resistance traits remains unknown. Bioassays were conducted to identify cross-resistance to ALS inhibitors and a haplotype-based genetic approach was used to elucidate the origin and distribution of resistance in both countries.

RESULTS

Amaranthus palmeri populations were resistant to thifensulfuron-methyl and imazamox, and the 574-Leu mutant ALS allele was found to be the main cause of resistance among them. In two Spanish populations, 376-Glu and 197-Thr mutant ALS alleles were also found. The haplotype analyses revealed the presence of two and four distinct 574-Leu mutant haplotypes in the Italian and Spanish populations, respectively. None was common to both countries, but some mutant haplotypes were shared between geographically close populations or between populations more than 100 km apart. Wide genetic diversity was found in two very close Spanish populations.

CONCLUSION

ALS-resistant A. palmeri populations were introduced to Italy and Spain from outside Europe. Populations from both countries have different evolutionary histories and originate from independent introduction events. ALS resistance then spread over short and long distances by seed dispersal. The higher number and genetic diversity among mutant haplotypes from the Spanish populations indicated recurrent invasions. The implementation of control tactics to limit seed dispersal and the establishment of A. palmeri is recommended in both countries. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

Tick-borne encephalitis epidemic in Hungary 1951–2021: The story and lessons learned

The authors analysed epidemiological data of the Hungarian tick-borne encephalitis epidemic from the past seven decades. A total of 911 meningitis serosa cases were described from 1930-1950 s by local hospital physicians, indicating that the virus had been present in the country decades before its official identification in 1952. The virus spread freely in the 1950s–1960s, occupying almost all habitats where ticks occurred in large numbers. The increasing number of cases drove authorities to classify this illness as a notifiable disease in 1977 and to organize the first measures to stop the epidemic. Statistical analysis revealed that the large-scale vaccination launched from the 1990s was responsible for the sharp decrease in the number of human cases from 1997. A significant negative correlation was found between the number of vaccine doses sold and human cases 6 years later. The TBEV endemic area covers 16.57% of the territory and 16.65% of the population of the country. In the last 10 years, 186,000 vaccine doses/year in average were enough to keep the incidence of human TBEV infections between 0.45 and 0.06/100,000 persons. A 20-year-long study found evidence for easing clinical signs in TBEV-infected hospitalized patients. Statistics found a sharp decrease in the number of samples sent for TBEV diagnosis after 1989. Male dominance of patients was characteristic of the epidemics since the 1940s, but now analysis of detailed data from the 1981–2021 period (60.5%–87.5%) proved the statistical significance of this dominance. Obviously, the voluntary vaccination programme was the tool which broke the spread of the epidemic. Widespread public awareness of the disease and the tick vector, probable evolutionary spread of less pathogenic virus strains supplemented with the vaccination campaign led to a negligible level of human TBE cases in Hungary in the last years.     

Fertility of Holstein cows and heifers submitted to timed artificial insemination and receiving one or two doses (12 h apart) of semen

The objective of this retrospective study was to assess the effect of receiving a single (n = 50,285) or double (n = 4392) artificial insemination (AI), 12 h apart, within a timed artificial insemination protocol on pregnancy per AI (P/AI) in nulliparous heifers (inseminated with either sex-sorted or conventional semen) and pluriparous Holstein cows in a commercial dairy herd. Also, this study aimed to investigate the relationship between temperature-humidity index (THI) and time of the first AI and fertility. Fertility of cows receiving two AI with normothermia (THI <68) was higher (p < .05) than cows receiving a single AI (42.9% vs. 36.4%). P/AI of cows receiving two AI with severe heat stress (THI >85) was higher (p < .05) than cows receiving a single AI (21.0% vs. 12.6%). Regardless of heat stress conditions, applying the first AI in the morning increased (p < .05) P/AI in cows with double AI than in cows whose first AI occurred in the afternoon (38.4 vs. 33.3%). With moderate heat stress, and sexed-sorted semen, P/AI to timed AI was higher (65.0 vs. 51.9%; p < .05) in heifers receiving double AI than those serviced once. It was concluded that double AI, 12 h apart, enhanced fertility at timed AI than herd mates with a single AI, particularly with heat stress at breeding.     

Histomorphological changes between short and long days in ovary guanacos (Lama guanicoe)

This is the first morpho-histological comparison of guanaco ovaries between reproductive (long-days) and non-reproductive (short-days) seasons, and oestrogen receptor-alpha (ERα) and beta (ERβ) detection. Different stages of follicle development were found in the cortical area, but no corpus luteum was detected. The size and frequency of antral follicles and large atretic follicles were higher in long-day ovaries than short-days, consistent with ovarian activity in this season. Differential expression of ERα and ERβ was observed in follicles at different stages of development between short and long days. These data reveal histological and molecular differences between reproductive and non-reproductive seasons of guanaco ovaries.     

The effect of salinity on corn yield using the CERES-maize model

In most cases, when calculating soil water availability, only thewater content is considered. The effect of salinity on the wiltingpoint is neglected. The objective of this work is to use asimulation model (CERES-maize) in order to predict cornyields as a function of water salinity under severalenvironmental, agrotechnical, and plant characteristics. A modelis presented in which the wilting point is a function of the soilsalt content. At high salinity, the water content at wilting pointis higher than at low salinity, resulting in an insufficient amountof available water and, therefore, a reduced yield. The modelwas used to simulate several theoretical and experimentalsituations for forage corn and grain corn. Simulation resultsshowed that nitrogen fertilization increases the salinity thresholdvalue and the yield sensitivity (rate of yield reduction per unitof salinity). The also showed that forage corn is more sensitiveto salinity than grain corn. If the soil is not leached, a heaviersoil texture has a higher salinity threshold value. On the otherhand, if the soil is leached, the soil texture has no influence onthe salinity threshold value and the yield is less sensitive tosalinity in sandy soils. The determination coefficient (r²= 0.75) indicated that the results of the simulations were in goodagreement with the field data.     

Habitat quality,not habitat amount,drives mammalian habitat use in the Brazilian Pantanal

Landscape Ecology - An understanding of species-habitat relationships is required to assess the impacts of habitat fragmentation and degradation. To date, habitat modeling in fragmented landscapes...     

Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil

Portable X-ray fluorescence (pXRF) spectrometry and magnetic susceptibility (MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest (RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models (DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO₂ contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.