The study used an exploratory, descriptive and contextual qualitative design at a single public, multi-campus nursing education institution in South Africa. Its interviews were originally conducted as part of a broader, multi-phased research project on Ubuntu-informed health sciences education; professionalism was not the focus of the original interview guide. Nevertheless, comments concerning professional conduct, ethical responsibility, role modelling, educator–student relationships and assessment appeared across all five focus groups, providing a coherent body of material for a focused analysis. This embedded design offers important context: the reported results concern a specific analytical strand within a wider project rather than an independently designed trial of an educational intervention.

Researchers analyzed the interview material using reflexive thematic analysis, combining primarily data-led coding with theoretically informed development of themes. Ubuntu provided the main relational lens for interpretation, while decoloniality served as a critical orientation. The analysis produced one overarching theme—relational professionalism as ethical formation and collective responsibility—and four interconnected subthemes: relational ethical formation, relational accountability, embodied pedagogical praxis, and communal responsibility shaped by institutional, curricular and structural conditions. Together, these themes suggest that professionalism involves both how educators understand their obligations and the practical and organizational conditions that enable or constrain those obligations.

Relational ethical formation, as described in the study, places relationships and ethical development at the center of professional identity. Relational accountability extends this idea by presenting responsibility as something established through interaction rather than detached from people and circumstances. Embodied pedagogical praxis indicates that professionalism is also performed in everyday educational practice, while communal responsibility highlights how institutional, curricular and structural conditions influence whether professional norms can be enacted. The participants’ accounts therefore challenge an exclusively compliance-oriented account centered on behavioral competencies, ethical conduct and regulatory accountability. That model may illuminate what educators do, but the study indicates that it can understate the interpersonal and contextual processes through which professional identity becomes possible.

The findings are best understood as contextually grounded accounts from one institutional setting, not as a universal measurement of nursing professionalism or proof that a particular teaching model causes better ethical outcomes. The sample was limited to 44 nurse educators within one South African public nursing education institution, and the themes were developed from material gathered for a broader project. Although the data were collected across five focus groups and the researchers used both inductive coding and an explicit interpretive framework, the study did not test changes in student behavior, clinical competence or professional compliance. It was also not a clinical trial, and no clinical outcomes were evaluated.

Even with those boundaries, the research shifts attention toward a practical question that compliance frameworks alone may not answer: how does a professional identity form, and who or what shares responsibility for sustaining it? For nursing educators, the answer emerging from the interviews depends on relationships, pedagogical practice and the environments in which teaching and assessment occur. The study consequently frames ethical formation as a collective endeavor embedded in institutions and curricula, while also documenting the limits of treating professionalism solely as a personal attribute. Its contribution is an Ubuntu-informed interpretation from a particular public education setting that may inform further research on how nursing programs balance regulatory accountability with relational and culturally responsive professional formation.

Subject of Research: How South African nurse educators describe relational professionalism, ethical formation and collective responsibility in nursing education

Article Title: Relational professionalism in nursing education: a qualitative study of nurse educators’ narratives of ethical formation and collective responsibility

Article References: Matahela, V. E., & van Rensburg, G. H. (2026). Relational professionalism in nursing education: a qualitative study of nurse educators’ narratives of ethical formation and collective responsibility. BMC Nursing. https://doi.org/10.1186/s12912-026-05543-6

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05543-6

Keywords: nursing education, professional identity, ethical formation, collective responsibility, nurse educators, Ubuntu, focus groups, qualitative research, South Africa, decolonial pedagogy, Relational, professionalism

The observational study treated 20 source-specific pools as its independent analytical units. Human material included 12 clinical isolates associated with bloodstream infections or urinary tract infections, divided into two pools. Animal sources contributed 328 pig-associated and 16 poultry-associated isolates, organized into pools representing retail meat, litter and longitudinal pig-fecal collections. The environmental collection comprised 37 soil and litter-fertilizer samples and 146 wastewater and surface-water samples, including influent, effluent, upstream and downstream river water, pig wastewater and chicken wastewater. Pool composition was unbalanced, ranging from three to 46 isolates, and the original farm-to-fork design made pig-associated groups disproportionately numerous. Only two pools represented humans, limiting replication and the strength of comparisons among sectors.

Each isolate was cultured before DNA from colonies within a predefined source group was combined, and the preparations were sequenced as paired-end libraries on an Illumina NextSeq 500 platform. The libraries produced more than 23 gigabytes of uncompressed data. Reads were quality-filtered, assembled into scaffolds and screened for genes predicted to encode resistance. The Comprehensive Antibiotic Resistance Database, ACLAME and MetaStorm database were used to annotate ARGs, plasmid-associated sequences and MGE-associated sequences, respectively. Because the sequencing input came from pooled E. coli cultures rather than untargeted environmental samples, the results describe culture-derived genomic profiles. They do not provide unbiased estimates of microbial-community abundance, isolate-specific carriage, strain prevalence or the distribution of resistance determinants among individual genomes.

Chromosomal determinants accounted for most of the classified resistance signal in every sector: 79.67% in animal pools, 81.07% in environmental pools and 85.82% in human pools. Intrinsic determinants contributed another 3.52%, 3.89% and 3.15%, respectively. The acquired fraction was smaller, representing 16.82% of animal profiles, 15.04% of environmental profiles and 11.03% of human profiles. Antibiotic inactivation and target protection were the largest contributors to the reported mechanism profiles across sectors, while target replacement accounted for 14.15% in animals, 14.18% in the environment and 17.13% in humans. Prominent acquired determinants included vgaC, msrB, tet(D), tet(34), qacH and several aminoglycoside-modifying, dihydrofolate reductase and sulfonamide-resistance genes. Detection indicates resistance potential, but sequence annotation alone cannot establish gene expression, phenotypic resistance or transferability.

Neither clustering nor ordination cleanly separated the sectors. Hierarchical analyses placed human, animal and environmental pools on overlapping branches, while pig-fecal time points, retail meat, litter, wastewater and soil samples were distributed across multiple subclusters. Principal coordinate analysis and nonmetric multidimensional scaling likewise showed extensive overlap. The two clinical pools were close to one another and to several environmental pools, and environmental groups largely occupied the same region as animal groups. A permutational multivariate analysis of variance detected a statistically significant but modest association between source sector and resistome composition, with pseudo-F = 1.585, R² = 0.157 and p = 0.022. Sector therefore accounted for 15.7% of compositional variation, leaving 84.3% unexplained by that classification.

Multivariate dispersion did not differ significantly among sectors in a PERMDISP analysis (F_2,17 = 2.498, p = 0.086), although the limited and uneven sample design limits the strength of that conclusion. Alpha diversity did not differ significantly among human, animal and environmental groups. Tests of ARG richness gave p = 0.940, Shannon diversity p = 0.820 and Simpson diversity p = 0.490, with all adjusted pairwise comparisons remaining nonsignificant. The shared signatures could reflect related bacterial lineages, common selective pressures, repeated independent acquisition from upstream sources, environmental persistence or genetic exchange. The pooled data cannot distinguish among those mechanisms or determine the direction of any movement between sectors. Proximity between pools in the analyses also does not establish epidemiological connectivity.

Plasmid- and MGE-associated matches were widespread across the sampled groups. Human-only detection was uncommon: one plasmid-associated feature was found exclusively in human pools, along with 14 MGE-associated features. In contrast, 285 plasmid-associated features occurred in animal and environmental pools but not in human pools, and 267 MGE-associated features had the same animal-environment distribution. The animal and environmental sectors also shared 59 ARGs exclusively between them, the largest pairwise-only ARG intersection. These intersections are consistent with overlapping material and resistance features among livestock, manure, litter, soil and wastewater systems, but database matches do not demonstrate that the sequences belonged to complete plasmids, transposons or integrons. The analysis did not establish whether an ARG and an MGE were located on the same contig, plasmid, chromosome or bacterial strain, nor did it show that a mobile element was active or capable of transfer.

An exploratory random forest model provided weak evidence that source sector could be predicted from resistance profiles. Its best repeated five-fold cross-validated accuracy was 63.1%, while a tree-matched observed accuracy of 60.5% was compared with a mean permuted-label accuracy of 47.3%. The resulting permutation p value of 0.053 did not meet the study’s threshold for statistical significance. Mean testing accuracy across repeated partitions was 56.2%, with a standard deviation of 15.7 percentage points, and mean out-of-bag error was 47.5%. Although 102 variable ARG predictors were assessed for only 20 pools, and correlated resistance features complicate interpretation, recurrent rankings identified candidates for future investigation. dfrA12 appeared among the top 35 predictors in all 100 runs; Ecol_emrE, mdtN and tet(D) each appeared in 99%, followed by pmrF in 98%. The rankings are internal to the fitted models and do not demonstrate epidemiological relevance or causal source attribution.

MetaCompare produced comparative resistome-risk scores from co-occurrence patterns among ARGs, MGEs and sequences associated with human pathogens. The unweighted mean was 23.26% for animal pools, 19.13% for environmental pools and 19.04% for human pools. Individual pools varied more widely: the January pig-fecal group reached 37%, while chicken wastewater reached 29.16%, compared with 22% for the urinary-tract-infection pool and 16% for the bloodstream-infection pool. Because MetaCompare was designed for assembled metagenomic datasets and used relatively permissive sequence thresholds in this analysis, the scores were treated as within-dataset prioritization measures. They are not probabilities of clinical harm, evidence of active gene mobility or confirmation that any pool was a transmission hub. Variation in sequencing depth, assembly completeness, the number and composition of contributing isolates, and partial sequence recovery can all affect such estimates.

Overall, the results show a broadly shared but heterogeneous E. coli resistome across the sampled human, animal and environmental interfaces, superimposed on modest sector-associated differences. The predominance of chromosomal and intrinsic determinants indicates that the genetic background of E. coli strongly shaped the profiles, while the shared acquired ARGs and mobile-associated features identify targets for integrated surveillance. Resolving whether the similarities arose from clonal dissemination, horizontal gene transfer, common sources or similar local pressures will require balanced longitudinal sampling, isolate-resolved whole-genome sequencing, phenotypic susceptibility testing, phylogenomics, contig-level ARG–MGE analysis and long-read reconstruction of plasmids. Until those steps are completed, the study’s strongest conclusion is shared resistance potential across One Health sectors rather than confirmed transmission.

Subject of Research: One Health metagenomic comparison of resistance and mobile-element signatures in multidrug-resistant Escherichia coli

Article Title: Shared resistome and mobilome signatures across One Health Escherichia coli sources

Article References: Nnah, E. P., Ismail, A., Abia, A. L. K., Essack, S., & Amoako, D. G. (2026). Shared resistome and mobilome signatures across One Health Escherichia coli sources. Frontiers in Bioinformatics, 6, Article 1930054. https://doi.org/10.3389/fbinf.2026.1930054

Image Credits: AI Generated

DOI: 10.3389/fbinf.2026.1930054

Keywords: Escherichia coli, antimicrobial resistance, One Health, resistome, mobile genetic elements, shotgun metagenomics, South Africa, plasmid signatures, environmental surveillance, food animals, Shared, mobilome

The analysis was conducted under the Global Conservation Consortia framework by the GCCErica working group, which drew on taxonomic records, occurrence data, protected-area coverage, conservation assessments and PlantSearch records. Researchers consolidated names through World Flora Online, filtered Global Biodiversity Information Facility occurrences to produce three increasingly precise datasets, compared South African distributions with protected areas, and assessed representation in seed banks and botanic gardens. Working groups also discussed obstacles, required actions and measures of success in March 2025.

World Flora Online documented 830 accepted non-hybrid species in June 2025, alongside 20 accepted non-horticultural hybrid names. The genus contains 1,214 taxa when species, subspecies and varieties represented in the identification aid are counted. All accepted subspecific taxa are included in the Erica Identification Aid, but 220, all from South Africa, are not character coded.

Of 572,990 occurrence records retained initially, the most precise filtering stage retained 388,411 records representing 782 species and 1,026 taxa. This represented reductions of 32% in records, 7% in species and 13% in taxa compared with the first filter. Among non-hybrid taxa represented in GBIF, seven species, comprising 61 taxa, had no records; 656 species had been recorded within the previous 10 years.

Threat coverage was broad but uneven. About 90% of species had been assessed, but 53 species were Data Deficient and 518 had not been assessed within the preceding 10 years. The source combined South African Red List and IUCN assessments, noting that many South African assessments relied on the 2009 assessment and later updates, while most species outside South Africa, including nearly all Malagasy species, lacked assessments.

Ex situ representation was concentrated among common European and northern species. Of 830 species, 209 were held by the Millennium Seed Bank, 230 occurred in two or more collections, 187 were represented in one collection and 413 were not known to be held in any collection. Among threatened species, 32 of 48 Endangered and 44 of 95 Vulnerable species were absent from ex situ collections. Critically Endangered and Endangered accessions were concentrated mainly in SANBI gardens and the Millennium Seed Bank, which together held 88 of 109 such accessions.

The analysis also identified major ecological and research gaps. Pollinators had been identified for only 1.8% of Erica species, and seed longevity, dispersal and germination requirements remained poorly studied. Around one-third of species lacked DNA sequence data, limiting reliable inclusion in phylogenetic analyses, while undescribed diversity and unclear species or subspecies boundaries persisted.

The authors concluded that threatened-species coverage remains below the Convention on Biological Diversity target of 75%, although the available collection data provide a minimum estimate rather than a complete accession-level assessment. The Erica Identification Aid can support prioritisation, but further collection-level data, updated threat assessments, targeted fieldwork and improved ecological knowledge are required to guide in situ protection, cultivation and the development of distributed metacollections.

Subject of Research: Conservation status and collection gaps for the flowering plant genus Erica

Article Title: Conservation gap analysis for Erica ( Ericaceae )

Article References: Pirie, M. D., Bester, S. P., Blackhall-Miles, R., Coetzee, A., Crowley, D., Ebrahim, I., Elliott, A. C., Fagúndez, J., Forest, F., Forshaw, N., Hoekstra, R. D., Kandziora, M., Klopper, R. R., Koopman, R., Musker, S. D., Nürk, N. M., Osborne, J., Pasta, S., Pipins, S., … Wilman, V. (2026). Conservation gap analysis for Erica (Ericaceae). Nature Conservation, 63, 199-236. https://doi.org/10.3897/natureconservation.63.175466

Image Credits: AI Generated

DOI: 10.3897/natureconservation.63.175466

Keywords: Erica, Ericaceae, conservation gap analysis, ex situ conservation, botanic gardens, seed banks, threat assessment, biodiversity data, plant taxonomy, South Africa, Conservation, analysis

The collection, launched in 2021 by Vegetation Classification and Survey and the African Section of the International Association for Vegetation Science, contains studies from Morocco, Benin, South Africa and Namibia. It therefore covers three of Africa’s major regions rather than the entire continent.

At the continental scale, available classifications are broad physiognomic systems. White’s 1983 classification recognized 16 natural vegetation types, while a remote-sensing-based system by Mayaux and colleagues in 2003 grouped vegetation into five main types. The review reports that comprehensive fine-scale overviews exist for relatively few countries or territories, including Morocco and South Africa, Lesotho and Eswatini, while many other studies address local areas or selected vegetation types.

African flora and vegetation data are less available and accessible than in Europe and the Americas, according to the review. It identifies data availability, language barriers, limited research funding and political instability as obstacles. African Union member states committed in 2006 to spend 1% of gross domestic product on research and development, but the continent’s funding reached 0.42% in 2019, compared with a global average of 1.7%.

Vegetation-plot records are particularly sparse in international databases. The review cites the working version of sPlot 4.0, which contains 45,202 plots from African countries out of 2,531,784 worldwide, or 1.8%, although Africa covers 22.5% of Earth’s ice-free surface. Thirty regional databases contributed data from 38 African countries, but some countries had no plots in sPlot. Namibia and Morocco had relatively good coverage, while South Africa’s National Vegetation Database contained more than 46,000 plot-based records but had not joined sPlot.

Two studies examined environmental gradients. In Namibia, Naftal and colleagues classified vegetation from 1,986 relevés into 12 units and used a Random Forest model to project distributions under 2080 climatic conditions. The model achieved 82% prediction accuracy and projected greater dominance of broad-leaved and degraded thornbush savannas, while mountain, dwarf-shrub and dry thornbush savanna units would decrease in area or disappear. These are model projections rather than observed changes.

In South Africa’s Tankwa Karoo National Park, Samuels and colleagues compared three vegetation types across elevations from 338 to 1,147 metres above sea level. Species composition and environmental variables distinguished the habitats, while slope and rockiness were associated with variation within vegetation types. In Benin’s Pendjari Biosphere Reserve, Assèdé and colleagues analysed 202 Braun-Blanquet relevés, identifying 249 species in 11 associations associated with a dryland-to-wetland moisture gradient.

The other studies examined traditional agroecosystems in Morocco, land-cover change on Beninese inselbergs and Karstveld vegetation in Namibia. Chakkour and colleagues recorded 209 species in 94 relevés across six Moroccan study areas. Ayeko and colleagues used Sentinel-2 and Spot-5 imagery at 10-metre resolution to assess land cover from 2003 to 2018, finding expansion of savanna, grassland, fields, fallow land and plantations alongside conversion of natural vegetation. Strohbach and Strohbach classified Namibian Karstveld from 889 relevés into four major types and formally described 17 new associations.

The authors recommend stronger cross-country collaboration, expanded plot sampling, improved databases, training and development of an African Vegetation Archive. They cite Europe’s long-term Vegetation Survey as an example of cooperation, while emphasizing that Africa’s scale, resource constraints and existing regional initiatives require a tailored approach. The review itself is explicitly incomplete and regionally biased, so the collection does not constitute a comprehensive assessment of African vegetation.

Subject of Research: Continental gaps and regional advances in African vegetation classification

Article Title: African vegetation studies: introduction to a Special Collection

Article References: Guuroh, R. T., Brown, L. R., Alvarez, M., Finckh, M., Schmiedel, U., Tsheboeng, G., & Dengler, J. (2024). African vegetation studies: introduction to a Special Collection. Vegetation Classification and Survey, 5, 279-288. https://doi.org/10.3897/vcs.143360

Image Credits: AI Generated

DOI: 10.3897/VCS.143360

Keywords: African vegetation, vegetation classification, vegetation plots, sPlot database, remote sensing, phytosociology, Namibia, South Africa, Benin, Morocco, African, vegetation

The Kilpdrift Complex lies within the coastal cliffs of the De Hoop Nature Reserve and is a Middle and Later Stone Age archaeological site. Investigators analysed archived sediment samples taken from the ochre-rich surface in an experimental palaeoproteomic study. Because the recovered proteins were not readily visible, the researchers used molecular analysis to identify their animal source. The analysis found type I collagen assigned to the common eland, Taurotragus oryx, providing evidence that eland material had been associated with the archaeological deposit.

Proteins were extracted from the sediment and separated and analysed using liquid chromatography-tandem mass spectrometry, commonly abbreviated LC-MS/MS. This method breaks extracted proteins into measurable peptide fragments. The resulting fragments were compared with large protein databases and a custom collagen reference database to determine the species of origin. The eland identification was based on this biomolecular comparison, allowing researchers to detect animal material that could not be characterized by visual examination of the sediment alone.

The successful recovery of identifiable collagen from archaeological sediment demonstrates a potential role for palaeoproteomics at sites where ancient DNA rarely survives. Ancient proteins can therefore supplement other forms of archaeological analysis by revealing biological traces associated with otherwise inconspicuous deposits. In this case, the method recovered eland collagen from a challenging African environment and documented an association between animal material and ochre use. The study does not establish the precise origin of the collagen, whether it was intentionally added to the ochre, or whether the deposit reflects a specific technical or symbolic activity.

Evidence of organic components mixed with ochre has been extremely rare in Africa, according to the research report, making the eland collagen an unusual addition to the record of Middle Stone Age behaviour. The authors interpret the association as evidence of the complex technical and possibly symbolic behaviours of early Homo sapiens, but they acknowledge that the protein evidence alone cannot distinguish between deliberate mixing and the presence of ochre-treated hides. Further archaeological context would be needed to determine the sequence of events that created the deposit and the function of the ochre and animal material. The principal demonstrated result is therefore the identification of common eland collagen in approximately 60,000-year-old ochre-rich sediment through palaeoproteomic analysis.

Subject of Research: Palaeoproteomic identification of eland collagen associated with Middle Stone Age ochre use in South Africa

Article Title: Invisible eland identified from 60,000-year-old ochre-rich sediment

Article References: Invisible eland identified from 60,000-year-old ochre-rich sediment. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: palaeoproteomics, ancient collagen, common eland, ochre, Middle Stone Age, archaeology, mass spectrometry, South Africa, Kilpdrift Complex, Invisible, eland, identified

The research team conducted floristic surveys on 20 quartz islands between February and March 2024, using systematic walk-through observations to ensure comprehensive species detection. The surveys yielded a complete species list per island and recorded 220 vascular plant species across the archipelago. Roughly one third of the species belonged to just three families: Asteraceae, Aizoaceae, and Poaceae.

Species richness varied dramatically among islands, ranging from 23 to 105 species, a difference of 82 species between the poorest and richest islands. Beyond counting species, the researchers measured functional traits for 134 perennial species, representing more than 80 percent of all perennial species recorded across the islands. Nine traits were quantified following standardized protocols: plant height, specific leaf area, leaf water content, relative leaf phosphorus content, stem succulence, and the presence or absence of epidermal trichomes, prickles, wax coating, and glands.

Each trait captures a different ecological strategy, from drought tolerance to defense against herbivory. From these measurements the team calculated functional dispersion, defined as the mean distance of individual species to the centroid of the community in trait space. This metric is considered robust to outliers and largely independent of species richness, and the authors confirmed that their estimates showed no significant correlation with species counts.

To capture the evolutionary dimension of diversity, the team reconstructed a phylogeny for all recorded species using a megatree backbone and calculated the mean pairwise distance, which measures the average branch distance between all species pairs in a community. This metric is conceptually similar to functional dispersion but reflects deeper lineage history rather than trait values. Island geography was quantified through remote sensing.

The researchers mapped quartz islands by supervised classification of Sentinel-2 multispectral imagery at 10-meter resolution, using a Maximum Entropy Presence Prediction tool and, at two sites, a one-class Support Vector Machine. Model accuracy was evaluated against withheld ground-truth points. From the resulting island polygons and a digital elevation model, the team derived eleven biogeographic parameters grouped into three categories: island shape (area, perimeter, shape index), isolation (nearest neighbour distance, neighbour index, proximity index, and area-based isolation metrics), and habitat diversity (habitat Shannon diversity, habitat richness, and related indices based on slope, aspect, convexity, and surface texture).

Statistical analysis revealed strikingly divergent responses across the three diversity facets. For species richness, the best-supported model contained four parameters and reduced deviance by about 80 percent relative to the null model. Island area emerged as the strongest predictor, exerting a highly significant positive effect consistent with classical island biogeographic theory.

Larger islands offer more resources and space, reduce the risk of population extinction, and enhance the chance of colonization by erratic dispersal. The shape index showed a significant negative effect, indicating that more irregular island outlines flatten the species-area relationship. Partitioning of explained deviance showed that island shape accounted for more than 80 percent of the variation in species richness, while habitat diversity and isolation jointly contributed roughly 15 percent.

Functional dispersion told a different story. The best model explained 70 percent of the variation, but island connectivity, quantified by the neighbour index, was the strongest positive predictor, while nearest neighbour distance and habitat Shannon diversity were significantly negatively related. Variance partitioning revealed that isolation and connectivity metrics accounted for roughly 60 percent of the explained variation, with habitat diversity contributing the remainder.

Notably, island area showed no detectable effect on functional dispersion. The authors attribute this to trait redundancy in species-rich communities: additional species on larger islands likely occupy similar trait combinations rather than expanding the occupied trait space, so functional diversity becomes decoupled from species richness. The narrow range of functional dispersion values across islands, from 0.1187 to 0.1564, further points to strong environmental filtering and broadly similar functional composition among communities.

Phylogenetic diversity responded to yet another parameter. After excluding one highly influential observation identified through diagnostic checks, the best-supported model retained only the area of the nearest neighbour island as a significant positive predictor, explaining about one third of the variance in mean pairwise distance. This suggests that phylogenetic diversity is primarily shaped by the immediate size of the closest potential source area rather than by broader network connectivity.

The absence of stronger isolation signals aligns with studies on other habitat island systems showing weak or random relationships between phylogenetic diversity and insularity. The divergent responses across taxonomic, functional, and phylogenetic facets carry important implications for conservation. Quartz islands occur largely on privately owned land, where threats such as poaching of rare and endemic dwarf succulents persist.

The findings suggest that conservation should safeguard both large islands, given the strong area effect on species richness, and well-connected networks of intact islands, which appear crucial for maintaining functional diversity. Maintaining connectivity is also likely to support dispersal and gene flow among islands, enhancing the adaptive potential of quartz island floras under ongoing global change. More broadly, the study underscores the need for multidimensional biodiversity assessments in habitat islands.

Focusing solely on species richness would have missed the connectivity effects on functional diversity and the area-based isolation effects on phylogenetic diversity. Because quartz islands occur across multiple archipelagos along a wide bioclimatic gradient in South Africa and southern Namibia, they offer an exceptional natural system for comparative habitat island biogeography. Future research extending taxonomic, functional, and phylogenetic analyses across regions at larger scales could identify the main drivers of diversity patterns and determine how climate shapes functional trait space and island-biogeographic relationships in otherwise comparable edaphic environments.

Subject of Research: Biogeographic drivers of taxonomic, functional, and phylogenetic plant diversity on quartz habitat islands in southern Africa

Article Title: Diverging responses of taxonomic, functional, and phylogenetic plant diversity to biogeographic parameters in arid habitat islands of southern Africa

Article References: Bürger, A. M., Meyer, K., Eibes, P. M., Lauel, C., Oldeland, J., Schmiedel, U., & Irl, S. D. H. (2026). Diverging responses of taxonomic, functional, and phylogenetic plant diversity to biogeographic parameters in arid habitat islands of southern Africa. Frontiers of Biogeography, 19, Article e167957. https://doi.org/10.21425/fob.19.167957

Image Credits: AI Generated

DOI: 10.21425/fob.19.167957

Keywords: quartz islands, habitat islands, island biogeography, plant diversity, functional diversity, phylogenetic diversity, species richness, South Africa, Nama-Karoo, environmental filtering, conservation, remote sensing

The researchers used data collected from 2010 to 2016 through the North-West Child-Health-Integrated with Learning and Development longitudinal study. The same children were followed at three ages: Grade 1, when participants were 6–7 years old; Grade 4, when they were 9–10; and Grade 7, when they were 12–13. The cohort included 186 children classified as low SES and 147 as high SES, with boys and girls analyzed in each group. Undernutrition risk was assessed using height-for-age, weight-for-age, and body mass index-for-age, while overnutrition was assessed using body mass index-for-age, waist-to-height ratio, and body fat percentage.

Across Grades 1 to 7, low-SES children had a significantly higher risk of being at risk of stunting and underweight, with all reported p-values at or below 0.001. Their relative risk of classification as at risk of stunting was at least 178%, and the relative risk of being underweight was at least 259%. Conversely, low-SES children had lower measured prevalence of overweight or obesity by body mass index-for-age, waist-to-height ratio, and body fat percentage. The reported reductions in relative risk were at least 68% for waist-to-height ratio, at least 68% for body fat percentage, and at least 71% for body mass index-for-age, with all p-values at or below 0.003.

Some patterns changed during puberty. Stunting-risk prevalence increased by 24.1% among pubertal boys. Among pubertal girls, the higher risk of classification as at risk of stunting and the lower risk of overweight or obesity by body mass index-for-age each lost statistical significance over time. The abstract describes these results as indicating a population-level, but not individual-level, double burden of malnutrition, characterized by persistent undernutrition risk among low-SES children and greater overnutrition among high-SES children. Because the available evidence is the study abstract, it does not provide the full methods, detailed statistical estimates, or results needed to assess individual-level classification across all measures or the study’s wider limitations.

Subject of Research: Longitudinal differences in malnutrition measures by socioeconomic status among South African children aged 6 to 13

Article Title: Trajectories of the double burden of malnutrition and social inequality in 6–12-year-old South African children: The NW-child study

Article References: van Niekerk, E., Pienaar, A. E., & Monyeki, M. A. (2026). Trajectories of the double burden of malnutrition and social inequality in 6–12-year-old South African children: The NW-child study. BMC Pediatrics. https://doi.org/10.1186/s12887-026-07548-1

Image Credits: AI Generated

DOI: 10.1186/s12887-026-07548-1

Keywords: child malnutrition, South Africa, longitudinal study, socioeconomic status, stunting, underweight, overweight, obesity, body composition, puberty, Trajectories, double

Researchers at a public university in South Africa interviewed 10 Bachelor of Education students who had recently completed Life Sciences teaching practicums in public secondary schools. Six had taught in low-quintile schools, classified as Quintiles 1–3, and four in high-quintile schools, classified as Quintiles 4–5. The study was interpretive and qualitative, so it was intended to provide contextual insight rather than statistically generalizable estimates about teachers nationally.

South Africa’s school-quintile system classifies public schools according to the socioeconomic circumstances of the communities they serve and is used in resource allocation. Quintile 1–3 schools generally serve poorer communities and may have limited infrastructure, learning materials and access to electricity and digital technologies. Quintile 4–5 schools are generally located in more affluent communities and tend to have better access to laboratories, digital resources and teaching materials, according to the study’s description of the system.

Each participant took part in an individual online interview lasting approximately 45 to 60 minutes. The researchers used an open-ended interview schedule, piloted it with three pre-service teachers and reviewed it with a three-person panel of experts in science education and educational technology. Interviews were recorded with consent, transcribed verbatim and analysed with Braun and Clarke’s reflexive thematic approach. The researchers coded the data manually, reviewed the developing themes collaboratively and attended to cases that differed from the dominant patterns, but they did not conduct member checking.

The analysis identified three connected themes concerning technology and infrastructure, perceived usefulness of AI, and pedagogical value. Participants in higher-quintile schools described environments with robotics, smart classrooms and supportive leadership, and generally saw AI as useful for engagement, interactivity and visualizing complex scientific content. Participants in lower-quintile schools emphasized problems including load-shedding, limited internet access and shortages of basic equipment such as printers and projectors. Their reservations were often described as concerns about whether AI could be used at all, rather than as opposition to its potential in principle.

School resources did not fully determine participants’ views. Neereshnie, who had practised in a Quintile 4 school, said, “I’m not particularly enthusiastic about AI. It sounds good in theory, but I’m not sure it suits our learners’ needs.” Nomathemba also described herself as more traditional and said AI did not feel like a tool she would be comfortable using. Conversely, Luxolo, whose practicum was in a Quintile 1 school, recognized that automation might save time on marking despite expressing low confidence. These accounts indicated that personal teaching preferences, digital literacy and prior exposure also shaped perceptions.

Many participants linked their confidence directly to what they had encountered during practicum. Those with little or no exposure to AI reported uncertainty, while those who had used even basic digital tools were more oriented toward its possible classroom applications. The researchers also identified a pattern they termed conditional epistemic self-efficacy: participants did not describe themselves simply as ready or unready, but said adoption depended on conditions such as demonstrable learner benefit, reliable infrastructure, appropriate training and assurance that AI would not widen existing learning gaps.

Several participants described ways they had built competence without formal AI instruction. Karabo, placed in a Quintile 2 school, said she had learned through “bits and pieces I’ve picked up from webinars and online courses,” while Ololade, in a Quintile 3 placement, described learning through informal networks of technology-proficient peers. Participants also proposed sustained training, mentorship, pilot phases, communities of practice, reliable internet and dependable electricity, supported by school governing bodies and education departments. These were participants’ recommendations, not tested interventions or measured outcomes.

The authors interpret the findings as evidence that a uniform rollout could distribute benefits unevenly because resource access, practicum exposure and institutional support differed across school settings. They also argue that these differences can compound: limited infrastructure may be accompanied by fewer opportunities to encounter AI during training, which may in turn reduce teachers’ confidence. Based on these qualitative accounts, the study calls for justice-oriented teacher preparation that combines context-responsive AI training with attention to infrastructure and learner access. The authors connect this approach to the United Nations Sustainable Development Goals on inclusive and equitable education and on innovation and resilient infrastructure.

The study has important limitations. Its sample was small and limited to pre-service Life Sciences teachers at one South African public university, and the researchers described differences between quintile groups as illustrative rather than definitive. The data were self-reported, interview questions sometimes directly referenced resource inequalities, and some wording may have implied that AI adoption was desirable. Race, home language, ability, prior AI experience and other individual background characteristics were not systematically collected. The authors therefore caution that the findings offer contextual insight rather than population-level proof, and they recommend mixed-method and longitudinal research to examine how AI-related confidence changes once teachers enter service.

Subject of Research: How school socioeconomic context and practicum experiences shape South African pre-service Life Sciences teachers’ perceived readiness to integrate artificial intelligence into teaching.

Article Title: Preservice life sciences teachers’ readiness for AI integration in teaching across varying socioeconomic contexts

Article References: Mnguni, L., Nuangchalerm, P., Sari, I. J., Mokhele, M., Camara, J. S., Van Bien, N., & Islami, R. A. Z. E. (2026). Preservice life sciences teachers’ readiness for AI integration in teaching across varying socioeconomic contexts. Discover Education, 5(1), Article 889. https://doi.org/10.1007/s44217-026-02140-2

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02140-2

Keywords: artificial intelligence in education, pre-service teachers, life sciences education, South Africa, school quintiles, digital inequality, teacher readiness, epistemic self-efficacy, teacher education, practicum experience, Preservice, life

The review was conducted in three major stages and eight steps using thematic synthesis. The researchers searched relevant Health and Medicine databases for primary studies published in any language with an English abstract and followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines. Studies focusing on nurse educator challenges and nursing student attrition were critically appraised with the Mixed Methods Appraisal Tool. Thematic content analysis combined deductive and inductive reasoning; where included studies presented both nurse educators and students, the analysis used only data from educators. Of 1,330 initially identified studies, 37 met the inclusion criteria.

The abstract identifies persistent challenges in nursing education environments and highlights the importance of effective clinical and educational learning environments. Proposed responses include enhancing the nursing profession’s image, revising curricula and implementing student-focused teaching methods. It also calls for support for students during clinical placements, learning environments with adequate infrastructure, fair workload distribution and competitive remuneration to support educator motivation and retention. The authors additionally suggest that revised policies could improve continuous professional development and mentorship for new nurse educators. These are presented in the abstract as possible solutions rather than outcomes evaluated by the review.

South Africa provided the motivating context for the research. The abstract reports that problems in nursing training have contributed to a shortage of newly qualified nurses, while nursing education institutions accredited by the South African Nursing Council can register only limited numbers of students. Despite assistance from nurse educators, some students still fail to complete their training, and the authors suggest that educators may face challenges in addressing factors related to attrition. The findings are described as globally relevant, but the available evidence is limited to the abstract. It does not provide the geographic distribution, detailed characteristics or study-level quality findings for the 37 included studies, so the scope and limitations of the underlying evidence cannot be assessed further from the supplied material. The article is an early-access version in BMC Nursing and remains subject to further editorial changes.

Subject of Research: Nurse educator challenges associated with nursing student attrition in South African and global nursing education

Article Title: Challenges experienced by nurse educators to minimise the attrition of nursing students: a systematic review

Article References: Kent, A., Scholtz, S., Lubbe, W., & Rabie, T. (2026). Challenges experienced by nurse educators to minimise the attrition of nursing students: a systematic review. BMC Nursing. https://doi.org/10.1186/s12912-026-05455-5

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05455-5

Keywords: nursing student attrition, nurse educators, systematic review, mixed-studies review, thematic synthesis, nursing education, clinical placements, South Africa, Challenges, experienced, nurse, educators

WhSMV is a vector-borne viral disease of wheat that has been reported in southern Brazil and South Africa. Traditional recognition is based on field inspection by trained specialists, which can be labor-intensive, subjective and difficult to scale. The research addressed two practical constraints: early disease signs may be difficult to distinguish from healthy plants using ordinary visible-light images, and high-performing deep-learning models can require substantial computing resources. The investigators tested whether spectral-filter images captured with an affordable camera could support machine-learning classification under natural field conditions, and whether knowledge distillation could transfer that performance to a much smaller model.

The dataset contained 3,840 images distributed across six filter configurations: 1,002 images labeled healthy, 1,338 labeled early-stage diseased and 1,500 labeled diseased. Images were collected from 22 to 25 August and 2–6 October 2023 at farms in KwaZulu-Natal, Mpumalanga and North West provinces. A Canon EOS 800D equipped with the six filter configurations was used to capture the data under sunlight, with camera angle, distance and zoom kept consistent during collection. Soil moisture and pH readings were also recorded, with the dataset maintained within a pH range of 6.5 to 7.0. Plant health labels were assigned through visual assessment by a specialist WhSMV panel from the Forestry and Agricultural Biotechnology Institute at the University of Pretoria. The panel was led by senior pathologists involved in molecular isolation and the first published report of WhSMV in South Africa, but the individual image labels in this dataset were not supported by molecular confirmation for every sample.

The raw Canon files were processed in RawTherapee and converted from CR2 to JPG, reducing file size and making the images easier to handle in the model-training workflow. The Retinex algorithm was applied to retain detail under uneven lighting, with manually adjusted settings that included a scale and slope of 3, a color-restoration factor of 1.3, and highlight and shadow tonal widths of 80. Training data received geometric augmentation consisting of rotation, shear, zoom and horizontal and vertical flipping. Cropping and positional shifts were deliberately excluded so that the location of leaves within the images remained representative. Each model also used 50% dropout, and early stopping with a patience of 15 epochs was used during training. Bayesian optimization selected the optimizer and learning rate from Adam, AdamW and stochastic gradient descent, with learning rates searched between 1 × 10−8 and 1 × 10−3.

Twelve transfer-learning architectures were evaluated: DenseNet121, Xception, InceptionV3 and ResNet50V2 convolutional neural networks; ViT-S16, ViT-B16 and ViT-L16 Vision Transformers; two hybrid ViT models; and three Swin Transformers. DenseNet121 gave the strongest overall performance, reaching 91.25 ± 0.93% accuracy on K590 and 90.21 ± 0.57% on BlueIR. ViT-B16 was the strongest transformer, with 89.17 ± 0.93% accuracy on K590. Performance was lower for some models and filters: across the 12 architectures, HotMirror accuracy ranged from 60.21% to 84.79%, while K850 ranged from 61.46% to 82.08%. Standard deviations among the five fold-trained models ranged from approximately 0.47 to 1.65 percentage points, indicating that the reported results were relatively stable across the training-validation partitions.

The researchers then used response-based knowledge distillation to transfer predictions from the four strongest teacher architectures to MobileNetV2. In this method, the teacher’s class probabilities were softened using a temperature parameter, while alpha controlled the balance between the distillation loss and the student’s categorical cross-entropy loss. Bayesian optimization searched temperature values from 1 to 10 and alpha values from 0.1 to 0.9. DenseNet121, with approximately 8 million parameters, produced the strongest students. ViT-B16, with approximately 87 million parameters, was the second-most effective teacher. The larger ViT_R50_L32, at approximately 329 million parameters, and Swin_large, at approximately 197 million, did not transfer as consistently.

Relative to standalone MobileNetV2, the student distilled from DenseNet121 improved mean accuracy on every filter by 7.29 to 12.50 percentage points. It matched its teacher at 91.25% on K590 and averaged 87.88% across all six filters, compared with 77.92% for the standalone student. The DenseNet121-distilled student also improved the standalone student’s accuracy on every filter for DenseNet121, ViT-B16 and Swin_large teachers, and on five of six filters for ViT_R50_L32, with no change on HotMirror. This difference between the largest teacher and the more effective teachers is consistent with the authors’ conclusion that teacher–student capacity matching can influence how much knowledge is successfully transferred; teacher size alone did not determine student performance.

Statistical analysis found evidence that the model configuration affected accuracy. A Shapiro–Wilk test did not reject normality of the ANOVA residuals, and Levene’s tests found no significant evidence against equal variances across teachers or filters. ANOVA gave an F-statistic of 7.34 and a p-value of 0.00047. In the pooled Tukey HSD comparison, students distilled from DenseNet121 and ViT-B16 significantly outperformed standalone MobileNetV2, while the gains from Swin_large and ViT_R50_L32 were not significant. These tests concerned the pooled model comparisons; the researchers did not report separate per-filter significance tests for every teacher–student pair. The results therefore support the overall conclusion that DenseNet121 and ViT-B16 were the most effective teachers in this experiment, without establishing that every individual filter-level improvement was statistically significant.

For the DenseNet121-distilled MobileNetV2, K590 produced the strongest class-level results: 93.42% F1 for diseased plants, 90.00% for early-stage disease and 90.34% for healthy plants. The early-stage category was more difficult across the filters, with minimum values of 83.65% precision, 83.12% recall and 83.39% F1. Grad-CAM visualizations for K590, K850 and HotMirror showed a concentrated activation region for K590 and broader or more diffuse regions for the other two filters. These qualitative maps indicate where the model focused, but they do not by themselves establish which spectral or physiological features caused the performance difference. The study also did not directly compare the learned features with vegetation indices such as NDVI or NDRE.

Although K590 covered 590–1,000 nanometers, including the red-edge and near-infrared regions, the experiment did not isolate spectral information outside the visible range as the cause of improved classification. The study’s direct comparison was between filter configurations and model performance, not a controlled analysis of individual spectral mechanisms. The authors discuss relationships between plant stress, pigment changes, leaf structure and water content, while noting that quantitative spectral analysis and physiological measurements would be needed to establish those explanations. MobileNetV2 also had the lowest estimated computational cost among the tested models, at approximately 0.3 GFLOPs, and an inference time below 1 millisecond for 224 × 224-pixel inputs on the experimental hardware. The model was not tested on actual smartphones, drones or embedded processors, so its field speed, memory use, power consumption and accuracy on representative edge devices remain unverified.

Several limitations affect how the results can be interpreted and generalized. The dataset was small, with 640 images per filter, and was drawn from private farms in South Africa using the same wheat cultivar. Cloud cover and shadowing varied during acquisition, but their independent effect was not quantified. The independent test set was balanced at 32 images per disease stage per filter and was separated from training and validation data by plot, reducing leakage, but the small number of test plots still limits statistical power. The early-stage labels depended on expert visual assessment rather than a quantitative disease-severity index or molecular confirmation for every image. Subtle early symptoms may overlap with nutrient deficiencies, drought stress or other abiotic stresses, making the ground truth less certain for the most difficult class. Larger and more geographically diverse datasets, repeated imaging of individual plants, laboratory-supported labels and tests on representative edge hardware would be needed before the approach could be considered ready for operational farm use.

Subject of Research: Three-stage wheat stripe mosaic virus classification using multispectral field images and a knowledge-distilled MobileNetV2 model.

Article Title: Early detection of wheat stripe mosaic virus using economical multispectral imaging and model compression

Article References: De Silva, M., & Brown, D. (2026). Early detection of wheat stripe mosaic virus using economical multispectral imaging and model compression. Smart Agricultural Technology, 15, Article 102637. https://doi.org/10.1016/j.atech.2026.102637

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102637

Keywords: wheat stripe mosaic virus, multispectral imaging, knowledge distillation, MobileNetV2, DenseNet121, plant disease classification, near-infrared imaging, computer vision, South Africa, edge AI, Early, detection