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The emergence of antibiotic-resistant bacteria in coastal waters is a global health problem posing potential risks to the health of humans who depend on coastal resources. Monitoring and increased efforts are needed to maintain the health of marine ecosystems. The sea surface microlayer (SML) is poorly studied for antibiotic resistance of the inhabiting bacteria. Therefore, we examined the antibiotic resistance patterns of 41 bacterial strains isolated from the SML in a harbor in the southern North Sea. The strains are affiliated with 17 genera typically found in the marine environment. Using the disc diffusion assay, we found extensive resistance, particularly to gentamycin, kanamycin, nalidixic acid, penicillin, sulfadimidine, and streptomycin. A broth microdilution assay showed high minimum inhibitory concentrations (MICs) for most isolates for amikacin, aztreonam, ceftazidime, cefepime, minocycline, and tobramycin. Genome analysis of three strains affiliated with the genera Pseudoseohaeicola, Nereida, and Vibrio, all showing a highly resistant phenotype, revealed the presence of 57, 42, and 90 genes, respectively, associated with antibiotic resistance. Over 50 % of these genes are multidrug efflux pumps. Our study shows that the SML in anthropogenic-influenced coastal regions harbors a wide diversity of antibiotic-resistant bacteria equipped with a broad range of multidrug efflux pumps.

Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an ’18-tree’ stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards.

Abstract Background The RCA (Roseobacter clade affiliated) cluster belongs to the family Roseobacteracea and represents a major Roseobacter lineage in temperate to polar oceans. Despite its prevalence and abundance, only a few genomes and one described species, Planktomarina temperata, exist. To gain more insights into our limited understanding of this cluster and its taxonomic and functional diversity and biogeography, we screened metagenomic datasets from the global oceans and reconstructed metagenome-assembled genomes (MAG) affiliated to this cluster. Results The total of 82 MAGs, plus five genomes of isolates, reveal an unexpected diversity and novel insights into the genomic features, the functional diversity, and greatly refined biogeographic patterns of the RCA cluster. This cluster is subdivided into three genera: Planktomarina, Pseudoplanktomarina, and the most deeply branching Candidatus Paraplanktomarina. Six of the eight Planktomarina species have larger genome sizes (2.44–3.12 Mbp) and higher G + C contents (46.36–53.70%) than the four Pseudoplanktomarina species (2.26–2.72 Mbp, 42.22–43.72 G + C%). Cand. Paraplanktomarina is represented only by one species with a genome size of 2.40 Mbp and a G + C content of 45.85%. Three novel species of the genera Planktomarina and Pseudoplanktomarina are validly described according to the SeqCode nomenclature for prokaryotic genomes. Aerobic anoxygenic photosynthesis (AAP) is encoded in three Planktomarina species. Unexpectedly, proteorhodopsin (PR) is encoded in the other Planktomarina and all Pseudoplanktomarina species, suggesting that this light-driven proton pump is the most important mode of acquiring complementary energy of the RCA cluster. The Pseudoplanktomarina species exhibit differences in functional traits compared to Planktomarina species and adaptations to more resource-limited conditions. An assessment of the global biogeography of the different species greatly expands the range of occurrence and shows that the different species exhibit distinct biogeographic patterns. They partially reflect the genomic features of the species. Conclusions Our detailed MAG-based analyses shed new light on the diversification, environmental adaptation, and global biogeography of a major lineage of pelagic bacteria. The taxonomic delineation and validation by the SeqCode nomenclature of prominent genera and species of the RCA cluster may be a promising way for a refined taxonomic identification of major prokaryotic lineages and sublineages in marine and other prokaryotic communities assessed by metagenomics approaches. Video Abstract

Abstract Background Heterotopic ossifications (HO) are a common complication after total hip arthroplasty (THA). Nonsteroidal anti-inflammatory drugs have proven to reduce the occurrence of HO. It is still unclear when the formation of HO is finished. Aim of our study was to answer this question. Methods In a prospective study, the occurrence of periarticular HO was checked during the follow-up (FU) examinations. In total, 75 consecutive patients who underwent THA were included. To ensure a high follow-up rate, only patients with a life expectancy of at least 10 years were included. A medical ossification prophylaxis with mostly etoricoxib (90 mg once daily) was administered. Follow-up examinations were performed at 3 months, 1 year, 3, 5, and 10 years postoperatively. Each time, a clinical and radiological examination was carried out. The HO was graded according to Brooker’s method. Results Low-grade HO classified by Brooker grade I and II occurred significantly more frequent than HO grade III. In patients with present HO, a possible increase in Brooker stage could further be observed within 3 years postoperatively. After 3 years, the formation of HO was completed in all patients. Conclusion Three years after THA, the formation of HO is complete. After more than 3 years postoperatively, if HO occurs or increases, other triggering causes such as new trauma, periarticular infection, or implant loosening should be considered.

ABSTRACT Cancer pagurus is highly susceptible to shell disease syndrome. However, little is known about concomitant changes in the epibacterial community. We compared the bacterial communities of black spot affected and nonaffected areas of the carapace by amplicon sequencing of 16S rRNA genes and 16S rRNA. Within each spot, bacterial communities of affected areas were less diverse compared to communities from nonaffected areas. Communities of different affected spots were, however, more divergent from each other, compared to those of different nonaffected areas. This indicates a reduced and shifted microbial community composition caused by the black spot disease. Different communities found in black spots likely indicate different stages of the disease. In affected areas, Flavobacteriaceae rose to one of the most abundant and active families due to the increase of Aquimarina spp., suggesting a significant role in shell disease syndrome. We isolated 75 bacterial strains from diseased and healthy areas, which are primarily affiliated with Proteobacteria and Bacteroidetes, reflecting the dominant phyla detected by amplicon sequencing. The ability to degrade chitin was mainly found for Gammaproteobacteria and Aquimarina spp. within the Flavobacteriia, while the ability to use N-acetylglucosamine, the monomer of the polysaccharide chitin, was observed for most isolates, including many Alphaproteobacteria. One-third of the isolates, including most Aquimarina spp., showed antagonistic properties, indicating a high potential for interactions between the bacterial populations. The combination of bacterial community analysis and the physiological properties of the isolates provided insights into a functional complex epibacterial community on the carapace of C. pagurus. IMPORTANCE In recent years, shell disease syndrome has been detected for several ecologically and economically important crustacean species. Large proportions of populations are affected, e.g., >60% of the widely distributed species Cancer pagurus in different North Sea areas. Bacteria play a significant role in the development of different forms of shell disease, all characterized by microbial chitinolytic degradation of the outer shell. By comparing the bacterial communities of healthy and diseased areas of the shell of C. pagurus, we demonstrated that the disease causes a reduced bacterial diversity within affected areas, a phenomenon co-occurring also with many other diseases. Furthermore, the community composition dramatically changed with some taxa rising to high relative abundances and showing increased activity, indicating strong participation in shell disease. Characterization of bacterial isolates obtained from affected and nonaffected spots provided deeper insights into their physiological properties and thus the possible role within the microbiome.

In the original publication [...]

Tank bromeliads are unique canopy microhabitats that offer freshwater and organic nutrient-rich substrates in the Neotropics. In them it is possible to thoroughly characterize environmental factors and species composition of terrestrial and aquatic biota. Therefore, these plants have been used as natural models to study how communities are distributed and assembled. Here we used amplicon sequencing of the 16S rRNA gene and their functional annotations to study the diversity and metabolic potential of prokaryotic communities in tank bromeliads in five different forests along an elevation gradient in tropical Mexico. Furthermore, we analyzed the effects of vegetation type and environmental factors inside the tanks on prokaryotic composition. We found a high prokaryotic diversity in tank bromeliads along the elevation gradient. Prokaryotes commonly observed in acidic environments rich in organic carbon, and the potential pathogen Pasteurella multocida, were present in all samples, but few amplicon sequence variants were shared between forests. The prokaryotic composition was affected by forest type, and comparisons against null models suggest that it was shaped by non-neutral processes. Furthermore, prokaryotic community changes significantly covaried with tank water temperature, pH, and inorganic carbon. We found a high diversity of putative metabolic groups dominated by chemoheterotrophs and fermenters, but taxonomic groups involved in nitrogen and sulfur cycling were also present in all samples. These results suggest that tank bromeliads promote taxonomic and metabolic diversity of the prokaryotic community at a local and regional scale and play an important role in the biogeochemistry of forest canopies in the Neotropics.

Abstract Trait variation among heterospecific and conspecific organisms may substantially affect community and food web dynamics. While the relevance of competition and feeding traits have been widely studied for different consumer species, studies on intraspecific differences are more scarce, partly owing to difficulties in distinguishing different clones of the same species. Here, we investigate how intraspecific trait variation affects the competition between the freshwater ciliates Euplotes octocarinatus and Coleps hirtus in a nitrogen‐limited chemostat system. The ciliates competed for the microalgae Cryptomonas sp. (Cry) and Navicula pelliculosa (Nav), and the bacteria present in the cultures over a period of 33 days. We used monoclonal Euplotes and three different Coleps clones (Col 1, Col 2, and Col 3) in the experiment that could be distinguished by a newly developed rDNA‐based molecular assay based on the internal transcribed spacer (ITS) regions. While Euplotes feeds on Cry and on bacteria, the Coleps clones cannot survive on bacteria alone but feed on both Cry and Nav with clone‐specific rates. Experimental treatments comprised two‐species mixtures of Euplotes and one or all of the three different Coleps clones, respectively. We found intraspecific variation in the traits “selectivity” and “maximum ingestion rate” for the different algae to significantly affect the competitive outcome between the two ciliate species. As Nav quickly escaped top‐down control and likely reached a state of low food quality, ciliate competition was strongly determined by the preference of different Coleps clones for Cry as opposed to feeding on Nav. In addition, the ability of Euplotes to use bacteria as an alternative food source strengthened its persistence once Cry was depleted. Hence, trait variation at both trophic levels codetermined the population dynamics and the outcome of species competition.

Accurate pre-harvest yield forecasting of mango is essential to the industry as it supports better decision making around harvesting logistics and forward selling, thus optimizing productivity and reducing food waste. Current methods for yield forecasting such as manually counting 2–3% of the orchard can be accurate but are very time inefficient and labour intensive. More recent evaluations of technological solutions such as remote (satellite) and proximal (on ground) sensing have provided very encouraging results, but they still require infield in-season sampling for calibration, the technology comes at a significant cost, and commercial availability is limited, especially for vehicle-mounted sensors. This study presents the first evaluation of a ”time series”—based remote sensing method for yield forecasting of mango, a method that does not require infield fruit counts and utilizes freely available satellite imagery. Historic yield data from 2015 to 2022 were sourced from 51 individual orchard blocks from two farms (AH and MK) in the Northern Territory of Australia. Time series measures of the canopy reflectance properties of the blocks were obtained from Landsat 7 and 8 satellite data for the 2015–2022 growing seasons. From the imagery, the following vegetation indices (VIs) were derived: EVI, GNDVI, NDVI, and LSWI, whilst corresponding weather variables (rainfall (Prec), temperature (Tmin/Tmax), evapotranspiration (ETo), solar radiation (Rad), and vapor pressure deficit (vpd)) were also sourced from SILO data. To determine the relationships among weather and remotely sensed measures of canopy throughout the growing season and the yield achieved (at the block level and the farm level), six machine learning (ML) algorithms, namely random forest (RF), support vector regression (SVR), eXtreme gradient boosting (XGBOOST), RIDGE, LASSO and partial least square regression (PLSR), were trialed. The EVI/GNDVI and Prec/Tmin were found to be the best RS and weather predictors, respectively. The block-level combined RS/weather-based RF model for 2021 produced the best result (MAE = 2.9 t/ha), marginally better than the RS only RF model (MAE = 3.4 t/ha). The farm-level model error (FLEM) was generally lower than the block-level model error, for both the combined RS/weather-based RF model (farm = 3.7%, block (NMAE) = 33.6% for 2021) and the RS-based model (farm = 4.3%, block = 38.4% for 2021). Further testing of the RS/weather-based RF models over six additional orchards (other than AH and MK) produced errors ranging between 24% and 39% from 2016 to 2020. Although accuracies of prediction did vary at both the block level and the farm level, this preliminary study demonstrates the potential of a ”time series” RS method for predicting mango yields. The benefits to the mango industry are that it utilizes freely available imagery, requires no infield calibration, and provides predictions several months before the commercial harvest. Therefore, this outcome not only presents a more adoptable option for the industry, but also better supports automation and scalability in terms of block-, farm-, regional, and national level forecasting.

Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an '18-tree' stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards. [ABSTRACT FROM AUTHOR]