The research team, led by Sambiani D. Y. Tindjiete and Terence Epule Epule of the Institut de Recherche en Agriculture et Agroalimentaire at the Université du Québec en Abitibi-Témiscamingue, with colleagues from the University of Seychelles, the Université du Québec à Montréal, and the Institut National de la Recherche Scientifique, applied a systematic tracking approach to map where digital technologies intersect with Quebec agriculture. The team screened and analyzed a total of 59 peer-reviewed publications and grey literature reports, a corpus spanning everything from government agency assessments to industry reports. Rather than testing a single tool in a single field, the review took an inventory: which technologies exist in the province, where the scientific attention is concentrated, and which combinations of tools could, in principle, support the province’s sustainable development objectives.

The stocktake documented 14 categories of digital technologies operating within Quebec’s agricultural sector. But the scientific literature is far from evenly distributed across them. Three dominant categories absorbed most of the research attention: web and mobile applications and software accounted for 31 percent of the documented representation, machine learning and sensors for 14 percent, and satellite technologies for 13 percent. That concentration matters, because it signals where innovation energy and funding have flowed, and, by extension, which corners of the digital agriculture landscape remain underexplored. Technologies that could be transformative but attract little research attention risk remaining stuck in pilot-project purgatory, never reaching the farm gate at scale.

What can these technologies actually do for sustainability? The review identified significant potential across five fronts. Digital tools can improve pesticide monitoring, allowing farmers and regulators to track and reduce chemical inputs with greater precision. They can support soil health assessment and conservation, using sensor networks and remote sensing to detect degradation before it becomes irreversible. They can optimize fertilizer management, matching nutrient application to crop need rather than blanket schedules that waste money and leak nitrogen and phosphorus into waterways. They can improve water-use efficiency, an increasingly urgent capability as climate change reshapes precipitation patterns across the province. And they can contribute to biodiversity conservation, for example by mapping habitats and monitoring the ecological footprint of farming practices.

The 65 distinct combinations figure is the study’s most striking quantitative result. The researchers systematically assessed how the 14 technology categories could be paired and grouped to support sustainable development objectives, arriving at 65 possible configurations. If only 54 percent of that potential is currently being exploited, nearly half of the plausible ways digital agriculture could serve sustainability in Quebec remain unrealized. Some of those unexploited combinations likely involve integrating mature tools, such as coupling satellite imagery with machine learning models to predict pest outbreaks, or linking soil sensors with mobile decision-support applications that translate raw data into actionable recommendations. Others may involve technologies that are documented but rarely deployed in combination with anything else, isolated by cost, complexity, or lack of institutional support.

The study, funded through Quebec’s Fonds de recherche du Québec—Société et culture under the Pan-Québec Economic Impacts of Climate Change on Agriculture and Adaptation project, arrives at a moment when the province’s agricultural sector faces intensifying pressure. Climate change is altering growing seasons, pest dynamics, and water availability, while Quebec has made sustainable development a stated priority for its agricultural sector. Digital agriculture, the authors argue, is an important component of sustainable agricultural systems and a promising pathway toward achieving sustainable development goals. But promise and practice are separated by a chasm of practical barriers, and the review’s most important contribution may be its diagnosis of where those barriers lie.

That diagnosis points squarely at the social dimensions of technology adoption. The review reveals important knowledge gaps, particularly regarding the social factors influencing the diffusion and adoption of digital technologies in agriculture. A limited understanding of the factors affecting Quebec farmers’ adoption decisions remains, in the authors’ assessment, a major barrier to maximizing the benefits of digital agriculture. This is a deceptively simple point with profound implications. A sensor that measures soil moisture with laboratory-grade accuracy is worthless if the farmer who owns it does not trust the readings, cannot integrate them into existing workflows, or finds the subscription cost unjustified at current commodity prices. Adoption research has long shown that technology uptake in agriculture depends on cost, perceived usefulness, technical literacy, age, farm size, land tenure, and trust in the institutions promoting the tools, yet the review found these behavioral dimensions underexplored in the Quebec context.

The gap is especially consequential because Quebec agriculture is structurally distinct. The province’s farm sector includes large grain operations in the St. Lawrence valley, dairy farms, and a maple syrup industry of global significance, alongside smaller and diversified operations. The review’s partners, which included La Financière Agricole du Québec, the Producteurs et Productrices de Grains du Québec, Quebec’s maple syrup producers, and the provincial agriculture ministry MAPAQ, reflect that institutional landscape. Technologies that work for a thousand-hectare grain operation may not translate to a mid-sized dairy farm or a sugarbush, and the review suggests that the social and behavioral research needed to understand those differences has not kept pace with the technical research documenting what the tools can do.

The authors argue that addressing these social and behavioral dimensions will be essential for accelerating the transition toward more sustainable, digitally enabled agricultural systems in Quebec. In practical terms, that means research programs that treat farmer decision-making as seriously as they treat sensor calibration, extension services that build trust and technical capacity, and policy frameworks that lower the financial and cognitive barriers to adoption. It also means studying the full diffusion pathway, from initial awareness through trial, adoption, and sustained use, rather than celebrating pilot projects that never scale. The review’s systematic approach, tracking both the technologies and the literature that describes them, provides a baseline against which future progress can be measured.

The broader lesson extends well beyond Quebec. Jurisdictions worldwide are investing heavily in precision agriculture, smart farming, and agricultural artificial intelligence, often with the implicit assumption that technology deployment automatically produces sustainability outcomes. This stocktake suggests otherwise: the binding constraint is frequently not the technology itself but the human systems surrounding it. With 14 technology categories documented, 65 potential sustainability combinations identified, and only 54 percent of that potential exploited, Quebec offers a quantified case study of the gap between what digital agriculture could achieve and what it currently does. Closing that gap, the review makes clear, will require as much investment in understanding farmers as in building tools for them.

Subject of Research: A stocktake of opportunities and knowledge gaps in advancing digital agriculture in Quebec

Article Title: A stocktake of opportunities and knowledge gaps in advancing digital agriculture in Quebec

Article References: Tindjiete, S. D. Y., Epule, T. E., Etongo, D., Peng, C., & Célicourt, P. (2026). A stocktake of opportunities and knowledge gaps in advancing digital agriculture in Quebec. Discover Sustainability. https://doi.org/10.1007/s43621-026-04697-2

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04697-2

Keywords: digital agriculture, precision agriculture, smart farming, Quebec, sustainable development, machine learning, satellite technologies, technology adoption, soil health, agricultural sustainability, stocktake, opportunities

Researchers from Polytechnique Montreal, led by Virginie Francoeur with Joliann Morissette and Catherine Beaudry, spent nine months in 2022 embedded with Culture Saguenay-Lac-Saint-Jean, a non-profit organization in Quebec that supports workers in the arts and cultural sectors. The organization estimated it was roughly ten years behind on its technological transition, and in 2022 it launched a digital platform designed to centralize the region’s cultural offerings and let artists, cultural workers and institutional representatives create profiles showcasing their expertise and services. For a region that had never developed a tool capable of consolidating its cultural forces, the researchers describe the platform as a radical change, and radical change is precisely where most transformation projects fail.

The stakes are high because change management research has long estimated that between 20 and 80 percent of change projects fail, and one of the main reasons is the neglect of the human factor. To understand what drives or blocks adoption in a creative environment, the team turned to a framework from the 1940s: psychologist Kurt Lewin’s three-step model of change. Lewin argued that change requires creating a state of imbalance between the current and desired situation, and his model unfolds through unfreezing, in which established behaviors and habits are questioned; changing, in which new behaviors and structures are implemented; and refreezing, in which new practices are stabilized and integrated into daily operations. Although the model has been criticized for its simplicity, the authors argue it remains empirically grounded and flexible enough to analyze change in contexts where it has never been tested, including museums, theaters, publishing houses and artist studios.

The study tested a moderated mediation model linking individual dispositions such as skills and beliefs, organizational dispositions such as support, and technology readiness, operationalized through optimism, to the intention to use the new platform. Beliefs were split into two dimensions: appropriateness, the conviction that the proposed solution fits the challenges of one’s environment, and valence, the expectation that one will personally benefit from the change. Affective commitment, defined as the emotional attachment a person feels toward the change process, served as the mediating variable. The research question was direct: to what extent do individual, organizational and technology-readiness dispositions influence the intention to use a digital platform among artists and cultural workers?

Because the arts and cultural sector has been little studied in change management research, which traditionally focuses on white- and blue-collar workplaces, the team adopted an action research approach that combined knowledge production with deliberate support for the organization’s transformation. Data collection ran from January to October 2022 and was unusually rich for a case study. The researchers conducted one 75-minute group interview with three Culture SLSJ employees, six 60-minute individual semi-structured interviews with potential users, and 18 informal discussions during 25 hours of in situ observation across eight cultural sites in four cities, including Chicoutimi, Alma, La Baie and Val-Jalbert, some 470 kilometers from Montreal. They then distributed a 29-question questionnaire on the LimeSurvey platform, drawing 70 respondents, a 30 percent response rate of the target population, with artists and self-employed workers making up just over half of the sample.

The quantitative analysis used Hayes’s MACRO process for Model 7, a bootstrap procedure with 5,000 resamples, to test the moderated mediation. The results confirmed that all four individual and organizational dispositions significantly predicted intention to use the platform. Ranked by explanatory power, appropriateness led with a t-value of 5.75, followed closely by support at 5.70, then valence at 4.99, and finally skills at 2.22. In other words, believing the platform is well suited to the sector’s needs, perceiving visible support from leaders, and expecting personal benefit predicted adoption far more strongly than users’ self-assessed digital competence. Support alone explained roughly 32 percent of the variance in intention to use, while skills accounted for less than 7 percent.

Affective commitment emerged as a powerful mediator, and in one case a complete one. For skills, the direct path to intention to use was statistically non-significant once affective commitment entered the model, meaning that perceived competence influences adoption only through emotional attachment to the change. For support, valence and appropriateness, the mediation was partial, but it consistently improved the model’s overall explanatory power. The qualitative data helped explain why: artists and cultural workers described their relationship to their work in terms of attachment, belonging, creation, mutual aid and passion, vocabulary that maps directly onto the affective dimension of commitment. One cinema director captured the mechanism plainly, saying that people have to be engaged in the process from the start because it is then easier to get buy-in.

Perhaps the most counterintuitive finding concerned optimism. The researchers expected technology readiness to strengthen the path from positive beliefs to commitment, but the moderating effect of optimism on the relationship between valence and affective commitment was negative. When optimism was low, the effect of valence on commitment was 0.3122; when optimism was high, it dropped to 0.1524, with a significant difference between the slopes. Drawing on conservation of resources theory, the authors suggest that optimism and valence function as interchangeable psychological resources: highly optimistic individuals, who already view technology as beneficial, depend less on positive beliefs about the specific platform to sustain their commitment. Alternatively, high optimism may inflate expectations about what the platform will deliver, and unmet expectations produce disillusionment that erodes the value of the change.

The study also documented resistance in real time and showed how it can be converted into momentum. Early interviews recorded reactions ranging from an artist’s exasperated declaration of not wanting yet another platform to a literary director’s doubts about whether Culture SLSJ had the staffing capacity to carry such a large project. Acting on the researchers’ recommendations, the organization strengthened the legitimacy of the project leader, communicated the platform’s benefits through newsletters and social networks, and held four discussion-day sessions where potential users could understand the reasons for the change. Data security concerns raised during field visits prompted ABCD Data training and educational sheets in spring and summer 2022. By the time the questionnaire was distributed, attitudes had measurably shifted, with one artist noting that the project was supported by serious people with expertise and legitimacy. Notably, every artist interviewed ultimately enrolled on the platform.

The authors distill their findings into a practical guide mapped onto Lewin’s three steps. Unfreezing requires challenging traditional ways of working and showing that digital transformation increases artists’ visibility and the accessibility of their work, while offering visible support from cultural leaders who act as ambassadors and providing training personalized to specific needs. The change stage calls for demonstration sessions and built-in appropriation time so users can interact directly with project leaders. Refreezing demands consolidating acquired knowledge through cultural days dedicated to technology, assessing user satisfaction and impact, and mobilizing the sector continuously through creative formats, from comic strips about the technological transition to poetry readings on the benefits of digital tools. The researchers acknowledge limitations, including a modest sample size that prevented distinguishing between worker categories and a single-region case that limits generalizability, but they argue the framework offers cultural organizations everywhere a roadmap for digital change built not on software but on support, belief and emotional investment.

Subject of Research: A mixed-methods action research study applying Lewin's change management model to digital transformation in the arts and cultural sector

Article Title: Understanding digital transformation through the lens of change management: a case study in the art and cultural sector

Article References: Francoeur, V., Morissette, J., & Beaudry, C. (2026). Understanding digital transformation through the lens of change management: a case study in the art and cultural sector. European Journal of Cultural Management and Policy, 15, Article 15467. https://doi.org/10.3389/ejcmp.2025.15467

Image Credits: AI Generated

DOI: 10.3389/ejcmp.2025.15467

Keywords: digital transformation, change management, cultural sector, arts, Lewin's change model, affective commitment, technology readiness, action research, Quebec, COVID-19, Understanding, digital

The study, published in the journal Environmental Monitoring and Assessment, was conducted by Samuel de la Sablonnière, Philippe Vigneault and Etienne Lord of Agriculture and Agri-Food Canada, together with Samuel Foucher and Yacine Bouroubi of the University of Sherbrooke. Their target was the L’Acadie River watershed in southern Quebec, a 562.5 square kilometer basin where agriculture covers 67 percent of the land and rotations of maize and soybean dominate the fields. The region is a classic example of the intensively farmed lowlands where nutrient runoff threatens aquatic ecosystems. In the nearby Rivière aux Brochets watershed, for instance, annual crops contributed 69 percent of the phosphorus load in 2003 while occupying only 22 percent of the territory, a stark illustration of why the last line of defense along the water’s edge matters so much.

To measure buffer quality, the researchers relied on Quebec’s Riparian Strip Quality Index, or RSQI, a metric used for decades by provincial watershed agencies. The index is a weighted sum of the land covers found within a 10 meter wide strip along the bank: forest earns the highest weight of 10.0, shrubs 8.7, herbaceous vegetation 5.8, while crops, infrastructure and bare soil receive weights as low as 1.9 and 1.7. Values range from 17 to 100, with classes running from very poor to excellent. Traditionally, evaluators walk the streams and estimate the proportions of each cover type by hand, section by section. The COVABAR watershed organization provided exactly such a dataset for the L’Acadie River, covering 697 kilometers of watercourses surveyed between 2012 and 2015, a treasure trove of expert ground truth that most machine learning studies can only dream of.

The satellite side of the equation came from the Pléiades constellation, two optical Earth observation satellites operated by CNES and Airbus Defence and Space. Three images acquired in July 2013 and July 2015 were sharpened to a resolution of 0.5 meters using the panchromatic band, orthorectified with a LiDAR derived elevation model, and mosaicked into a single high resolution portrait of the watershed. From this mosaic, the team automatically extracted more than 46,000 small image patches, each 23 meters on a side and centered 10 meters from the nearest stream, and labeled each patch with the field measured RSQI value at that location. The patches were grouped into 3,489 riparian strips, each strip carrying a single index score that summarized conditions along its length.

That grouping posed a genuine technical puzzle. A convolutional neural network normally learns from one image paired with one label, but a riparian strip can stretch for hundreds of meters and cannot be captured in a single 46 by 46 pixel patch. The researchers’ solution was to borrow an architecture from an unexpected corner of computer vision: a Multi-View Convolutional Neural Network, or MVDCNN, originally designed to recognize three dimensional objects by looking at many snapshots of the same object from different angles. In the original design, embeddings from multiple views pass through a shared encoder and are combined in a view pooling layer. The team swapped the original VGG-M backbone for the leaner and more efficient RESNET-18, and made one ecologically motivated change: because the RSQI is itself a weighted average, they replaced the max pooling operator with an average, so the network synthesizes local assessments along the strip into a global quality estimate, mirroring how the index is actually calculated.

The team systematically tested how many views the network should see at once, trying 1, 2, 4, 8, 12 and 16 simultaneous patches per strip. Four views emerged as the sweet spot, delivering an RMSE of 7.35 and a regression slope of 0.970, closest to the ideal 1:1 line. A single view already performed surprisingly well, which the authors attribute to the relative homogeneity of agricultural riparian strips, but it systematically overestimated the worst buffers and underestimated the best ones. Beyond four views, accuracy actually declined and results became less stable, suggesting that additional patches mostly introduced redundant information. The flexibility of the multi-view design, however, means the network can accept any number of views at inference time, so the length of a river poses no practical constraint.

Perhaps the most counterintuitive finding concerned spectral bands. The researchers trained the network with seven different band combinations, from full RGB and RGB plus near infrared down to single bands repeated across all three input channels. When training from scratch, a lone green band actually beat the four band combination, and overall performance varied little across configurations. The explanation lies in texture and shape rather than color: a degraded buffer planted with crops and a pristine buffer shaded by forest can both look lush and green in a satellite image, so what distinguishes them is the pattern, texture and geometry of the vegetation, not its spectral signature. This is good news for budget conscious monitoring programs, because plain RGB imagery, the cheapest and most widely available option, works nearly as well as anything else. Fine tuning a network pre-trained on ImageNet also outperformed training from scratch by roughly 10 to 17 percent, confirming that low level features like edge and texture detection learned from everyday photographs transfer remarkably well to satellite scenes.

How does this stack up against the conventional approach? The team also computed RSQI the traditional way, using object based classification of the same imagery: segmenting the landscape into 186,936 objects, classifying them into land cover classes with a support vector machine, and then calculating the index from the classified map. That pipeline achieved an R squared of 0.87 and an RMSE of 11.25 on the 75 strip test set, respectable but clearly behind the neural network’s R squared of 0.93 and RMSE of 7.35 with RGB bands, an error reduction of about 28 percent. The conventional workflow also demands expert photo interpretation and laborious parameter tuning, whereas the trained network requires minimal preprocessing and no segmentation step at all. Simulated tests reinforced the model’s credibility: when the network was fed mixed views from two different strips, its predictions tracked the averaged index closely, and when views were synthesized along a forest to crop gradient, predicted values followed the calculated index with errors comparable to the real test set.

The authors are candid about the limitations. The model was trained on a single watershed, a single season, and a single image resolution, so applying it elsewhere would require retraining with local field data. Mid range RSQI values between 30 and 80 proved hardest to predict, likely because such strips are both rare in the dataset and genuinely heterogeneous. Dense tree canopies can hide what lies beneath them from a top down camera, a problem the authors suggest could be eased by incorporating LiDAR or radar data. And because the training labels themselves came from human evaluators working over three years with multiple teams, some subjectivity is baked into the ground truth, particularly when distinguishing natural grassland from pasture. Temporal mismatches of a year or two between field campaigns and image acquisition add further noise.

Even so, the implications are striking. This is, to the authors’ knowledge, the first direct application of deep convolutional neural networks to riparian buffer assessment, and it outperforms earlier remote sensing efforts by a wide margin, with a normalized RMSE of 0.089 compared to 0.34 in a recent European study using Sentinel 2 data. Because the method piggybacks on field campaigns that many agencies have already completed, it could enable annual, watershed wide monitoring at a fraction of the cost of repeated surveys, flagging degraded reaches for intervention and identifying regions where field campaigns are still needed. The researchers also point to a broader opportunity: if agencies make their characterization datasets publicly available, teams worldwide could build models that are more versatile and resilient, paving the way for large scale, highly recurrent monitoring of the vegetated ribbons on which so much of freshwater health depends.

Subject of Research: Deep learning-based monitoring of riparian buffer strip quality in agricultural watersheds using very high resolution satellite imagery

Article Title: A CNN-based approach to riparian buffer strip quality monitoring in agricultural areas using satellite imagery

Article References: de la Sablonnière, S., Foucher, S., Bouroubi, Y., Vigneault, P., & Lord, E. (2026). A CNN-based approach to riparian buffer strip quality monitoring in agricultural areas using satellite imagery. Environmental Monitoring and Assessment, 198(10), Article 1042. https://doi.org/10.1007/s10661-026-15760-w

Image Credits: AI Generated

DOI: 10.1007/s10661-026-15760-w

Keywords: riparian buffers, convolutional neural networks, remote sensing, satellite imagery, water quality, RSQI, agricultural watersheds, deep learning, environmental monitoring, Pléiades, Quebec, land cover classification

The research team, led by Audrey Nantel and Véronique Gingras of the Department of Nutrition at Université de Montréal and the Research Center of the Centre Hospitalier Universitaire Sainte-Justine, set out to answer a deceptively simple question: what determines how parents introduce their babies to solid food? To do so, they recruited twenty-four mother and father dyads in Quebec, deliberately splitting the sample between first-time parents and parents who had already raised a child through this stage. Each parent, forty-eight individuals in total, completed a phone questionnaire assessing breastfeeding and complementary feeding practices, and then sat for a semi-structured qualitative interview when their child reached nine months of age. The interviews were structured around the Socio-Ecological Model, a framework that organizes influences on behavior into individual, interpersonal and environmental levels, and the team used a directed content analysis to extract the salient determinants of feeding behavior from the transcripts.

The technical details of the sample matter for interpreting the findings. Most participants came from households with favorable socio-economic status, holding university degrees and reporting high incomes, and forty-two percent identified as first- or second-generation immigrants. The first complementary foods in these families were introduced at a median age of 5.5 months, with an interquartile range of just one month, a figure that sits close to international recommendations but reveals considerable variation in practice. The World Health Organization and national bodies recommend introducing solids around six months while continuing breastfeeding, yet adherence to such guidelines remains variable worldwide, which is precisely why the researchers argue that understanding the lived experience behind the numbers is essential for designing effective interventions.

One of the most striking findings concerns the emotional texture of the feeding experience. Parents across the sample described looking forward to creating pleasurable first experiences with food and were strongly motivated by the perceived health and developmental benefits for their children. But beneath this positive framing, the researchers detected meaningful differences between groups. First-time mothers, in particular, showed a strong adherence to official guidelines, and some expressed feelings of anxiety about getting every detail right. This anxiety-driven compliance represents a double-edged phenomenon: it may drive closer adherence to recommended practices, but it also signals a psychological burden that current support systems may be failing to relieve. The study suggests that the very precision of feeding guidelines, intended to help parents, can become a source of stress when parents feel solely responsible for executing them perfectly.

That burden has a name in the study’s findings: the mental workload. Many mothers reported feeling weighed down by the cognitive and organizational labor that complementary feeding demands, from planning meals and tracking allergen introductions to anticipating textures, timing and safety. This invisible labor, largely shouldered by mothers even in households where fathers were engaged, adds a dimension that quantitative studies of feeding practices rarely capture. The researchers note that experienced parents took a markedly different approach. Non-first-time parents tended to seek simplicity and practicality, streamlining the process based on what had worked before, while first-time parents leaned heavily on external guidance. The contrast suggests that confidence, accumulated through experience, changes not only what parents do but how they feel while doing it.

Fathers emerged as a distinct and underexamined group within the data. Across the interviews, fathers typically adopted a supportive role in the feeding process rather than a leading one, and they reported lower levels of both confidence and knowledge regarding the introduction of complementary foods. This asymmetry has practical implications. If fathers are less informed and less confident, they are less likely to make independent feeding decisions, reinforcing a division of labor that concentrates both expertise and stress in one parent. Interventions that treat the parental dyad, rather than the mother alone, as the unit of support could redistribute this workload and potentially improve consistency in what and how babies are fed across caregivers.

The interpersonal level of the Socio-Ecological Model revealed where parents actually turn for help. Mothers in the study tended to seek information and support from close friends and sisters, from online support groups and from parent-baby activities such as community classes and meetups. These informal networks functioned as parallel knowledge systems, often more accessible and more trusted than official channels. Meanwhile, although governmental resources were generally appreciated by participants, guidance from in-person healthcare providers was frequently perceived as insufficient. Many parents reported filling the gap by consulting social media and other non-governmental sources, a pattern that carries obvious risks in an information environment where evidence-based advice competes with commercial marketing and unqualified influencers.

The environmental findings carry particular weight for public health policy. The gap between what parents want from healthcare systems and what those systems currently deliver represents a clear intervention target. If clinical encounters during the complementary feeding period provided more structured, more detailed and more accessible guidance, parents might rely less on social media feeds of variable quality. The researchers also point to the diversity of the sample, with its substantial proportion of immigrant-background families, as evidence that support strategies must account for cultural food practices, family structures and varying levels of familiarity with Canadian feeding guidelines. A one-size-fits-all campaign, the findings imply, will miss the specific anxieties of first-time mothers, the knowledge gaps of fathers and the practical shortcuts of experienced parents simultaneously.

Methodologically, the study distinguishes itself as the first to identify individual, interpersonal and environmental determinants of complementary feeding practices specifically in Quebec. The use of dyads, interviewing both members of each parental couple separately, guards against the common bias of relying solely on maternal reports, and the nine-month timing of the interviews placed parents close enough to the introduction period for their recollections to be fresh. The team acknowledges the limitations inherent in qualitative work with a relatively affluent sample, and the findings may not generalize to families facing food insecurity or other structural constraints. Still, the directed content analysis anchored in the Socio-Ecological Model provides a systematic map of where leverage points lie.

The ultimate significance of the research lies in its implications for intervention design. By documenting the anxiety of first-time mothers, the mental load carried by mothers broadly, the supportive but underconfident role of fathers and the insufficiency of professional guidance relative to informal and online sources, the study provides an array of concrete opportunities for evidence-based programs. Support that builds paternal confidence, distributes the mental workload, reassures guideline-adherent parents and strengthens the clinical voice in a crowded information landscape could reshape the complementary feeding period for thousands of families. As the authors note, this window of opportunity for healthy child development is too important to leave to social media algorithms and parental guesswork.

Subject of Research: Determinants of complementary feeding practices among mothers and fathers in Quebec, Canada

Article Title: Determinants of complementary feeding practices – a qualitative study exploring mothers and fathers’ experiences in Canada

Article References: Nantel, A., Bégin, S., Van Hulst, A., Desroches, S., Pelaez, S., & Gingras, V. (2026). Determinants of complementary feeding practices – a qualitative study exploring mothers and fathers’ experiences in Canada. BMC Pediatrics. https://doi.org/10.1186/s12887-026-07689-3

Image Credits: AI Generated

DOI: 10.1186/s12887-026-07689-3

Keywords: complementary feeding, infant nutrition, qualitative research, parenting, Socio-Ecological Model, breastfeeding, baby-led weaning, mental workload, fathers, public health, Quebec, child development

The motivation comes from a well-known weakness of classical interpolation. Techniques such as inverse distance weighting, ordinary kriging, and kriging with external drift estimate values at unmeasured points by weighting observations from surrounding stations. These methods work reasonably well when gauges are dense, but their accuracy collapses as networks thin out, because the statistical relationships they exploit are inferred from the stations themselves. In sparsely instrumented regions, the reconstructed fields often lose realistic spatial variability, and those errors cascade directly into hydrological models used for flood forecasting, reservoir management, and climate impact assessment. Previous studies have shown that during extreme rainfall events, forecast quality depends strongly on how many gauges are available to capture the storm’s structure.

SPR takes a fundamentally different route. The key insight is that regional climate model simulations, even when biased in absolute terms, encode physically consistent patterns of how meteorological variables vary across space. Rather than using these simulations only as a mean climatology, as some existing methods do, SPR extracts their dominant modes of spatial variability through principal component analysis, computed via singular value decomposition. Each mode, known as a spatial pattern or empirical orthogonal function, is an eigenvector of the spatial covariance structure and captures a coherent way in which values across a region tend to rise and fall together, independently of their magnitude on any given day.

Once these patterns are extracted, the method works in two steps. First, during an auxiliary period, the full archive of daily model fields is decomposed into a fixed basis of spatial patterns ordered by the variance they explain. Second, for each day of the interpolation period, the observed values at the available stations are regressed onto the patterns restricted to the station locations. Ordinary least squares estimates a set of coefficients that describe how strongly each spatial pattern contributes on that particular day. The full gridded field is then rebuilt as a linear combination of the patterns, scaled by those daily coefficients and anchored to the model-derived mean field. In effect, the model supplies the shape of the weather, while the sparse observations set its amplitude.

This design has several practical advantages. The regression is performed independently for each day, so the method does not require a fixed station network over time; the pattern matrix simply adapts to however many stations report on a given day. Nor do the observations need to be temporally aligned with the period used to extract the patterns, which gives considerable flexibility when working with archives of different lengths. Because the patterns come from model simulations that can span both historical and future climates, SPR can, in principle, adapt to changing spatial structures in a warming world, something earlier reduced-space reconstruction methods built on observational archives cannot easily do.

To test the method rigorously, the researchers designed a battery of controlled synthetic experiments. They used high-resolution simulations from the ClimEx project, produced with the Canadian Regional Climate Model over a 280 by 280 cell grid at roughly 11 kilometer resolution covering North America. Virtual stations were created by sampling grid cells at densities ranging from 90 percent down to 10 percent, across nested regions of large, medium, and small extent in both a southern and a northern study area, yielding 30 distinct experiments per variable for daily precipitation, minimum temperature, and maximum temperature. Because the full model field serves as ground truth by design, every method could be evaluated with perfect knowledge of what it was trying to reconstruct.

The results were striking. Across the vast majority of the 90 synthetic experiments, SPR delivered the best combination of low root mean squared error and high structural similarity, the latter measured by the Structural Similarity Index, a metric borrowed from image quality assessment that captures how well spatial texture and contrast are preserved. At moderate to high station densities, SPR dominated regardless of region size or location. Kriging with external drift, the strongest baseline, edged out SPR in only six of the 90 experiments, mostly for precipitation in small regions, and even then SPR typically reproduced the spatial patterns better. Ordinary kriging and inverse distance weighting consistently trailed both.

The most dramatic test came from a stress experiment mimicking the harshest conditions of remote monitoring: a large northern Quebec region of 2,970 grid cells with only three virtual stations, roughly 0.1 percent density. Here SPR again produced lower average and median errors than kriging with external drift for all three variables, and higher structural similarity for both temperature variables. A revealing diagnostic showed that the kriging fields were almost perfectly correlated with the climatological background, a Spearman correlation of 0.9997, meaning the method essentially reproduced its auxiliary information. SPR’s fields were less tied to the background, yet far more faithful to the true spatial organization, with an average structural similarity of 0.4151 against 0.2504 for kriging.

Crucially, the method also held up against real-world data. Using quality-controlled observations from Environment and Climate Change Canada stations in southern Quebec, with 15 complete precipitation records and 44 temperature records over two-year windows, the researchers ran 100 independent random subsamplings at 10 and 30 percent station density, corresponding to as few as two to four training stations across a 70,000 square kilometer region. At the sparser level, SPR systematically achieved lower median errors and higher structural similarity than kriging with external drift, and its performance varied less from one station configuration to another, indicating genuine robustness rather than luck. At 30 percent density the advantage narrowed, confirming that the method’s main value lies in data-scarce settings.

The authors are candid about limitations. SPR inherits some of the smoothness of its 11 kilometer source model, and any systematic spatial bias in the simulation can propagate into the reconstructed fields, so pairing the method with convection-permitting models running at one to four kilometer resolution is a promising next step. The current implementation also ignores temporal dependence between days and uses a fixed number of patterns, both natural targets for future refinement. Still, the conceptual shift is significant: rather than treating auxiliary gridded data as a drift term that constrains absolute values, SPR uses it to define spatial organization itself. For hydrologists calibrating models against historical observations while projecting future change with climate simulations, that consistency between past and future spatial structures could prove quietly transformative, turning sparse gauges and model archives into weather maps that are finally good enough to trust.

Subject of Research: A new interpolation method combining climate model spatial patterns with sparse station observations to reconstruct daily meteorological fields

Article Title: Spatial pattern regression for meteorological fields interpolation

Article References: Houssou, V., & Carreau, J. (2026). Spatial pattern regression for meteorological fields interpolation. Hydrology and Earth System Sciences, 30(18), 5791-5807. https://doi.org/10.5194/hess-30-5791-2026

Image Credits: AI Generated

DOI: 10.5194/hess-30-5791-2026

Keywords: spatial interpolation, meteorological fields, regional climate model, principal component analysis, hydrology, kriging, precipitation, temperature, station networks, empirical orthogonal functions, climate data, Quebec

The research, led by Yan Lévesque of the Institut National de La Recherche Scientifique and Université du Québec à Chicoutimi, together with Romain Chesnaux, Julien Walter and Lamine Boumaiza, tackles one of the most persistent problems in hydrogeology: calibrating a numerical model when direct measurements of groundwater levels are sparse. Groundwater models are the workhorses of water resource management, used to predict how aquifers will respond to pumping, drought and climate change. Yet a model is only as good as the data used to constrain it, and in many regions of the world, observation wells are few and far between.

To build their model, the team assembled an unusually rich geological picture of the moraine system. They drew on 94 boreholes drilled through the glacial deposits, five stratigraphic cross-sections that traced the layering of sediments across the landscape, and three complementary geophysical techniques: 20 transient electromagnetic surveys, 6 electrical resistivity tomography surveys, and 6 ground-penetrating radar surveys. Each method probes the subsurface in a different way. Transient electromagnetic surveys induce currents in the ground and measure how they decay, revealing contrasts in electrical conductivity that distinguish saturated from unsaturated materials. Electrical resistivity tomography injects current through electrodes and maps resistivity variations in two-dimensional slices, while ground-penetrating radar uses high-frequency radio waves to image shallow stratigraphy with centimeter-scale resolution.

From this combined dataset, the researchers constructed an integrated three-dimensional geological framework capturing the complex, heterogeneous and anisotropic character of the aquifer system. Glacial deposits such as moraines are notoriously difficult to model because they were deposited by ice in chaotic, laterally discontinuous layers. A single borehole may reveal coarse sand at one location while a neighboring hole, only a few hundred meters away, encounters dense till. Geophysics helps bridge those gaps by interpolating between the hard constraints of the boreholes with continuous geophysical profiles.

With the geological architecture in place, the team turned to numerical simulation. They used FEFLOW, a widely applied finite element software package for modeling groundwater flow, mass and heat transport in porous media, to run flow simulations and define initial estimates of hydraulic parameters such as hydraulic conductivity, which describes how easily water moves through the subsurface. These initial values were then refined through calibration, the process of adjusting model parameters until simulated groundwater levels match observed ones as closely as possible.

The crucial innovation lay in what the researchers used as calibration targets. Instead of relying solely on water levels measured in wells, they employed groundwater levels derived from the geophysical data themselves. Because the electrical resistivity of sediments changes sharply at the water table, where dry unsaturated material gives way to saturated material, geophysical surveys can be inverted to estimate the depth of the saturated zone. The team used the FePEST module, an interface between FEFLOW and the PEST parameter estimation software, to automatically adjust hydraulic parameters until the model reproduced these geophysics-derived water levels.

The real test came afterward. The researchers evaluated the calibrated model against an entirely independent dataset: piezometric measurements from 26 observation wells distributed across the study area. These wells had not been used in the calibration, so they provided an unbiased measure of the model’s predictive skill. The comparison between calibrated and observed groundwater levels yielded a root mean square error of 3.69 meters, a relatively low value for a regional-scale model of heterogeneous glacial terrain, indicating good agreement with field observations.

That result carries weight beyond one moraine in Quebec. In many parts of the world, including remote northern communities, agricultural regions with sparse monitoring networks, and developing countries investing in groundwater resources for the first time, the cost of drilling observation wells limits how well aquifers can be understood. Geophysical surveys are faster and often cheaper per unit of spatial coverage than drilling, and this study demonstrates that their output can serve not merely as a qualitative check on a model but as quantitative calibration data that actively refines hydraulic parameters.

The approach also reframes the traditional division of labor between geophysics and hydrogeology. Conventionally, geophysical data have been used to build geological models, delineating aquifer boundaries and layer thicknesses, while calibration has depended on hydraulic measurements. The new work shows that geophysically derived groundwater levels can play both roles, supporting the geological framework and simultaneously constraining the flow model. This dual use of a single dataset maximizes the value of every field campaign and reduces the uncertainty that accumulates when models are calibrated against too few points.

The findings arrive at a moment when pressure on groundwater is intensifying worldwide. Aquifers feed rivers, sustain ecosystems and supply roughly half of the world’s drinking water, yet many are being depleted faster than they recharge. Reliable regional models are essential for managing this invisible resource, and the Quebec study offers a practical template: combine boreholes, multiple geophysical methods and automated calibration to extract the maximum information from limited field data. As the authors note, integrating geophysical datasets with conventional hydrogeological observations improves parameter estimation and enhances the reliability of regional groundwater flow models, a step toward water management decisions grounded in a clearer picture of what lies beneath.

Subject of Research: Calibration of a 3D regional groundwater flow model using groundwater levels derived from geophysical surveys in Quebec

Article Title: Calibration of a 3D regional groundwater model using geophysics-derived groundwater levels

Article References: Lévesque, Y., Chesnaux, R., Walter, J., & Boumaiza, L. (2026). Calibration of a 3D regional groundwater model using geophysics-derived groundwater levels. Hydrogeology Journal. https://doi.org/10.1007/s10040-026-03144-w

Image Credits: AI Generated

DOI: 10.1007/s10040-026-03144-w

Keywords: groundwater modeling, geophysics, hydrogeology, aquifer calibration, transient electromagnetics, electrical resistivity tomography, ground-penetrating radar, FEFLOW, Saint-Narcisse Moraine, Quebec, hydraulic conductivity, water table

The study, published in the Journal of Population Research by Georges Alain Tchango Ngalé and Solène Lardoux of the Université de Montréal and Vissého Adjiwanou of the Université du Québec à Montréal, draws on retrospective data from the research project Trajectoires individuelles et dynamiques de participation des femmes et des hommes à la société québécoise, known as TrajIPaQ. That project reconstructed the detailed job and residential histories of 1,392 Quebec residents across the fifteen-year window from 2005 to 2020. By stitching together year-by-year records of employment entries, exits and address changes, the team could ask a question that most surveys cannot answer: not simply whether jobs and moves are related, but exactly when the housing response occurs relative to the employment event.

The methodological core of the paper is a family of survival models known as Prentice-Williams-Peterson, or PWP, models, which are designed for analyzing recurrent events such as repeated residential moves. Rather than treating each person as a single data point, these models treat each successive move as a distinct event in an ordered sequence, allowing the researchers to estimate how the risk of moving shifts in the same year as a job transition, in the years before it, and in the years after it. The team complemented this approach with fixed-effects logistic models, a robustness check that controls for stable individual characteristics, ensuring the results were not simply an artifact of some people being more mobile in general than others.

This temporal decomposition matters because life course theory suggests that major events do not influence behavior only at the moment they occur. People may anticipate a transition, for example by scouting neighborhoods before signing an employment contract, or they may lag behind it, postponing a move until savings accumulate or a lease expires. The researchers therefore modeled three distinct effects: synchronous effects, which capture movement in the same year as the job transition; anticipatory effects, which capture movement in the year before; and lagged effects, which capture movement in the one or two years following. The resulting picture is one of the job-housing relationship as a temporally extended process rather than a single synchronized decision.

Among the general population of Quebec residents, job entries emerged as the strongest and most consistent predictor of residential mobility. The synchronous effect was robust, but so were the lagged effects, meaning that people who started a new job remained at elevated risk of moving for up to two years afterward. This persistence fits the classical interpretation from urban economics and sociology: a new job changes the calculus of commuting, income and housing needs, and households adjust their dwelling when circumstances allow. Job exits also predicted mobility, though with a weaker and less extended temporal signature, consistent with the idea that leaving a job reduces income and often delays rather than accelerates relocation, at least until financial pressures or unemployment duration force a housing change.

The sharpest finding, however, concerns immigrants. For this group, the extended temporal structure largely disappeared. Only the synchronous effect of job entry remained statistically visible, and neither anticipatory nor lagged effects could be detected. In other words, immigrants who moved in connection with a new job did so in the same year, and the cascade of delayed adjustments seen among the broader population was absent. The authors interpret this not as evidence that immigrants are less responsive to employment change, but as evidence that their capacity to fine-tune housing over time is constrained. Rental market pressures in metropolitan Montreal, tenure asymmetries between renters and owners, and the difficulties of navigating an unfamiliar housing system all plausibly compress the window in which immigrants can act on changed employment circumstances.

Within the immigrant population itself, further heterogeneity emerged when the researchers disaggregated by age at arrival and length of residence. Immigrants who arrived before the age of 25, a threshold chosen because it broadly marks the end of emerging adulthood and the point at which residential independence and labor market entry typically consolidate, displayed anticipatory effects, moving in the year before a job transition. This suggests that those socialized largely within Quebec, who completed their education and entered adulthood there, behave more like the native-born population, preparing their housing in advance of employment changes. By contrast, recent immigrants displayed a reversed temporality: for them, residential moves tended to precede job exits rather than follow job entries. The authors suggest this pattern may reflect the well-documented secondary migration of newcomers during their first years in Canada, when initial settlement locations are often abandoned in favor of areas with better employment prospects or established community networks.

The Quebec context sharpens the significance of these findings. The province has experienced sustained rental market tension, particularly in the Montreal metropolitan area, where vacancy rates have fallen and rents have risen sharply, and advocacy organizations have documented the growing difficulty that tenants, and especially immigrant tenants, face in finding affordable and adequate housing. Homeownership, which stabilizes housing and anchors households in place, is also unevenly distributed by immigration status, and tenure transitions themselves were tracked in the study as part of the residential histories. Against this backdrop, the compression of the job-housing temporal chain among immigrants looks less like a cultural preference and more like an institutional outcome, produced by housing scarcity, discrimination risks in the rental market and the precarious position many immigrants occupy in the labor market during their early years in the province.

The policy implications that the authors draw are correspondingly specific. If job entries mark a critical period during which immigrants would benefit from relocating, but structural barriers prevent them from doing so except in an immediate and often reactive way, then housing support targeted around the period of labor market entry becomes a rational intervention. The authors point to the importance of targeted housing assistance for immigrants as they transition into employment, alongside broader policy attention to the structural barriers, rental market pressures and tenure asymmetries that delay residential adjustment across the population as a whole. More generally, the study adds to a growing body of life course research insisting that the timing of linked events, not merely their co-occurrence, is central to understanding how people navigate jobs, homes and family lives.

What makes the study methodologically notable is its demonstration that a familiar statistical relationship can conceal fundamentally different processes underneath. A simple cross-tabulation of job changes and moves would show an association for both immigrants and non-immigrants, but the survival analysis reveals that the underlying temporal machinery differs: an extended, phased adjustment for most residents, and a compressed, synchronous response for immigrants, with anticipatory behavior among those who arrived young and a reversed sequence among newcomers. The job-housing relationship, the authors conclude, is not universal. It is conditioned by migration profile and by the institutional features of the receiving context, and understanding it requires data and models capable of distinguishing when things happen, not just whether they happen together. For a province negotiating rapid demographic change alongside a strained housing market, that distinction may prove consequential for both research and policy in the years ahead.

Subject of Research: The relationship between job transitions and residential mobility in Quebec and how its timing differs by immigration status

Article Title: Job transitions and residential mobility among Quebec residents: synchronous, lagged, and anticipatory effects by immigration status

Article References: Tchango Ngalé, G. A., Lardoux, S., & Adjiwanou, V. (2026). Job transitions and residential mobility among Quebec residents: synchronous, lagged, and anticipatory effects by immigration status. Journal of Population Research, 43(4), Article 29. https://doi.org/10.1007/s12546-026-09435-z

Image Credits: AI Generated

DOI: 10.1007/s12546-026-09435-z

Keywords: Quebec, residential mobility, job transitions, immigration status, survival analysis, life course, housing market, rental market, age at arrival, length of residence, labour market entry, Journal of Population Research

The scale of the analysis is notable. The cohort consisted of patients recruited from addiction treatment centers across Quebec in 2012 and 2013 who received a diagnosis of gambling disorder at any point between 2009 and 2022. The investigators linked these clinical records with provincial health administrative databases, allowing them to track diagnoses, service use, and hospitalizations over more than a decade. Using the discharge date from each patient’s last hospitalization carrying a gambling disorder diagnosis as the index date, they identified 470 hospitalized patients and matched them by age and sex to 1,606 patients with gambling disorder who had never been hospitalized. Controls qualified only if they had received their gambling disorder diagnosis within the three years preceding the index date, a constraint designed to ensure that the two groups were genuinely comparable in the recency of their diagnosis.

Methodologically, the study relied on conditional logistic regression stratified by age and sex, a technique appropriate for matched case-control designs because it compares each case with its matched controls while automatically accounting for the matching variables. The models produced adjusted odds ratios, a statistical measure expressing how much each factor changes the odds of hospitalization after controlling for everything else in the model. This approach matters because hospitalization in psychiatric populations is rarely the product of a single cause; it emerges from an interplay of illness severity, treatment history, social circumstances, and the way health systems respond to crises. By separating these strands statistically, the researchers could ask which factors carry independent weight.

The answer, in descending order of magnitude, was striking. Patients with comorbid mental disorders in the three years before the index date had nearly ten times the odds of hospitalization compared with those without such comorbidity, an adjusted odds ratio of 9.81. Suicidal behaviors more than doubled the odds, at 2.88, and comorbid substance-related disorders raised them by more than half, at 1.58. These figures align with a growing international literature. A Finnish nationwide register study published in Addiction in 2024 documented elevated somatic and psychiatric comorbidity among people with diagnosed gambling disorder, and a systematic review and meta-analysis in the Australian and New Zealand Journal of Psychiatry found psychiatric comorbidity to be the rule rather than the exception among treatment-seeking problem gamblers. Research from Denmark has similarly linked gambling disorder treatment with heightened comorbidity, criminality, and healthcare costs.

The suicide signal deserves particular emphasis. Gambling disorder carries one of the highest suicide rates of any addiction, and studies of treatment samples in Austria and national inpatient data in the United States have repeatedly documented elevated rates of suicidal ideation and attempts among people with gambling problems, especially when depression is present. The Quebec findings quantify this danger in service terms: a history of suicidal behavior in the preceding three years nearly triples the odds that a patient with gambling disorder will require hospitalization. For clinicians, this suggests that suicide risk assessment should be a routine, systematic component of every encounter with a patient receiving treatment for gambling disorder, not an occasional add-on reserved for obvious crises.

Service use patterns in the year before the index date also proved predictive. High emergency department use carried an adjusted odds ratio of 2.45, and any hospitalization in the prior year raised the odds by 79 percent. These findings echo a broader pattern documented in patients with substance-related disorders, where frequent emergency department visits and prior hospitalizations reliably flag patients at risk of subsequent acute care. Emergency departments, in this sense, function as early warning systems, but also as symptoms of a system under strain. Studies of frequent emergency department visitors have shown that these patients often report unmet primary care needs, suggesting that heavy emergency use reflects gaps in community-based care rather than simply greater illness. The Quebec data now extend that insight to the gambling disorder population.

Perhaps the most actionable findings concern what protects patients. High-intensity outpatient care in the year before the index date halved the odds of hospitalization, with an adjusted odds ratio of 0.50, and high continuity of care reduced the odds by 26 percent, at 0.74. Continuity of care, a concept with a long history in health services research dating back to work on primary care in the 1970s, refers to the extent to which a patient sees the same providers or a coherent team over time rather than a rotating cast of strangers. Research on patients with serious mental illness has shown that family practice continuity reduces unplanned hospital use, and studies of general practitioner regularity have linked it to lower rates of high-use hospitalization. The Quebec study demonstrates that the same principle applies to behavioral addiction: patients whose outpatient care is intensive and consistent are markedly less likely to crash into the acute care system.

Sociodemographic factors added their own texture. Unemployment raised the odds of hospitalization by 45 percent, and living in semi-urban or rural areas increased them by 51 percent, a pattern that likely reflects both the socioeconomic stress associated with joblessness and the thinner supply of specialized addiction services outside major urban centers. Intriguingly, living alone was associated with lower odds of hospitalization, at 0.61, a counterintuitive result the authors note as significant. One possible interpretation is that patients living alone who reach treatment may differ systematically from those with co-residing partners or family, or that cohabiting patients are more often pushed into care by distressed relatives at moments of crisis that otherwise might have escalated to hospitalization. The authors frame the overall picture through the lens of vulnerability: unemployment, rural residence, and social isolation mark patients whose clinical needs intersect with limited community support.

The study’s framework draws on Andersen’s behavioral model of health services use, a foundational framework in health services research that organizes predictors into predisposing, enabling, and need characteristics. This theoretical scaffolding helps explain why both clinical severity and service system factors appear side by side in the results. Gambling disorder, classified as a behavioral addiction in both the DSM-5 and the ICD-11, remains frequently underdiagnosed and undertreated, partly because of stigma. Research on perceived social stigmatization of gambling disorders shows that stigma deters help-seeking and shapes how patients and professionals view treatment, while systematic reviews of general practitioners’ management of gambling disorder reveal persistent gaps in knowledge and screening. Patients may therefore cycle through primary care and emergency departments with depression, anxiety, or substance problems while the underlying gambling disorder goes unrecognized.

The Lancet Public Health Commission on gambling, published in 2024, framed gambling as a significant and growing public health concern, and the Quebec study adds a concrete service-delivery dimension to that call. The authors’ conclusion is direct: the key clinical needs and vulnerabilities among patients with gambling disorder should be addressed through outpatient interventions that prioritize high continuity and intensity of care. In practical terms, that means building treatment pathways in which a patient with gambling disorder sees a stable, coordinated team that can manage comorbid depression, screen for suicide risk, treat concurrent substance use, and maintain contact over time, rather than relying on episodic crisis-driven care. The chronic disease management model long advocated for addiction, which treats addiction as a relapsing condition requiring sustained monitoring rather than a discrete episode to be cured, fits these findings closely. As jurisdictions worldwide expand legalized gambling and online platforms multiply access, the population of diagnosed patients will likely grow, and the Quebec data offer a template for identifying, early and precisely, those most at risk of hospitalization, and for designing the outpatient systems that could keep them out of hospital beds altogether.

Subject of Research: Clinical, service use, and sociodemographic predictors of hospitalization among patients diagnosed with gambling disorder in Quebec, Canada.

Article Title: Predictors of Hospitalization Among Patients with Gambling Disorder: a Retrospective Matched Case–Control Study

Article References: Tatar, O., Sharker, S., Huỳnh, C., & Fleury, M.-J. (2026). Predictors of Hospitalization Among Patients with Gambling Disorder: a Retrospective Matched Case–Control Study. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-026-01729-w

Image Credits: AI Generated

DOI: 10.1007/s11469-026-01729-w

Keywords: gambling disorder, hospitalization, comorbid mental disorders, suicidal behavior, substance-related disorders, emergency department use, continuity of care, outpatient treatment, case-control study, Quebec, behavioral addiction, health services research

Global electricity demand is growing at roughly twice the rate of overall energy demand, and the expansion of transmission networks shows no sign of slowing. While linear infrastructure is often associated with habitat loss and fragmentation, ecologists have increasingly recognized that power line corridors can act as novel habitats, supporting diverse plant communities and providing forage for pollinators such as bees and butterflies. What has been missing, the authors argue, is a mechanistic understanding of how these corridors shape vegetation—not simply how many species they contain, but what ecological strategies those species represent.

To address this gap, a research team led by Samuel Roy Proulx of Université du Québec à Chicoutimi, working with colleagues including Évelyne Thiffault, David Paré, Jean-François Boucher, Yann Chavaillaz, and Maude Larochelle, surveyed 18 power line rights-of-way across an enormous territory spanning roughly 205,000 square kilometers of Québec. Their sites ranged from the northern mixed forest biome in the south to the edge of the continuous boreal forest in the north, crossing four distinct bioclimatic domains and a mean annual temperature gradient from 4.6 °C down to −1.6 °C. Corridor widths ranged from 30 to 160 meters, and the lines had been installed at various points between 1927 and 2021.

At each site, the team laid out a transect of seven circular sampling plots running perpendicular to the corridor: three within the cleared right-of-way itself, two in the forest edge approximately 20 meters from the corridor margin, and two in control forest interiors about 60 meters away. Vegetation was surveyed in three strata—trees, shrubs, and forbs—using nested plots of different sizes, from 400-square-meter circles for mature trees down to one-square-meter quadrats for low-growing herbs. In total, the surveys recorded 139 plant species across the study area.

Rather than relying solely on species counts, the researchers assembled functional traits for each species, drawing on the TOP and TRY plant trait databases. The traits included specific leaf area, plant height, foliage persistence, flower color, inflorescence type, and flowering period—characteristics that together capture how plants respond to disturbance and how they might support pollinators. From these traits, the team calculated four complementary functional diversity indices: functional richness, which measures the volume of trait space occupied; functional evenness, which describes how evenly abundances are distributed within that space; functional dispersion, the mean trait distance of species from the community centroid; and Rao’s quadratic entropy, which combines abundance and functional differences. A Shannon diversity index provided a taxonomic benchmark for comparison.

The statistical analysis used linear mixed models with bioclimatic domain and site identity as random effects, and candidate predictors including corridor position, right-of-way width, installation year, topographic wetness, growing degree-days above 5 °C, seasonal precipitation, and soil clay and silt content. Model selection via the corrected Akaike Information Criterion produced a striking result: the position relative to the right-of-way, and its interaction with climatic variables, dominated the patterns—while the corridor’s own attributes, such as width and age, mattered far less than the researchers had hypothesized.

For the forb layer, forest edges emerged as functional hotspots. Every functional index except evenness peaked at the edge, followed by control forests, with corridors scoring lowest. Functional richness of forbs was strongly and positively related to growing degree-days interacting with position, with edge and forest predictions substantially exceeding corridor values as thermal sums increased. Functional evenness told a subtler story: within corridors, evenness remained stable as seasonal precipitation rose from 430 to 640 millimeters, whereas it declined sharply in edge and control forests over the same gradient. Rao’s quadratic entropy was roughly 63 percent higher at forest edges and 50 percent higher in control forests than in corridors.

The shrub stratum showed an even clearer positional signature. Functional evenness in corridors averaged 0.31 compared with 0.74 in control forests, and functional dispersion and Rao’s entropy were dramatically lower inside corridors—expected consequences of mechanical clearing that removes woody vegetation on a four-to-ten-year cycle to prevent electrical arcing and fire. Notably, corridor characteristics such as installation year and width had little explanatory power for shrub diversity, suggesting that the recurring management regime, rather than corridor history, is the dominant filter. For trees, which were entirely absent from corridors, the only significant effects were higher functional richness at edges with increasing degree-days and a decline in functional dispersion as corridor width increased from 30 to 91 meters—a hint that very wide clearings homogenize the surrounding forest’s tree communities.

Why did corridors, with their open, sun-drenched conditions, fail to show the highest functional diversity? The authors point to several mechanisms. Repeated cutting leaves behind accumulated trunks, branches, and twigs that can create suboptimal seedbeds for forbs, while decomposing woody debris and canopy openings elevate soil inorganic nitrogen, favoring a narrow set of nitrophilous species such as raspberry and strawberry. Recurrent disturbance may also act as an environmental filter, culling species whose traits do not tolerate regular removal and thereby compressing the community’s functional trait space even when species richness persists. Notably, the study found little evidence that corridors promote invasives: of the 139 species recorded, only six were exotic or invasive, and their mean cover across plots was a mere 0.52 percent.

The broader significance lies in what functional diversity implies for ecosystem services. Because floral traits such as flower color, inflorescence type, and flowering period are known predictors of pollinator communities, the high functional diversity of forbs and shrubs at forest edges suggests these transitional zones may offer particularly suitable pollinator habitat—consistent with European studies linking corridor vegetation traits to bee and butterfly diversity. The authors are careful to note that pollinators were not directly surveyed, so their results indicate habitat potential rather than demonstrated biodiversity support. Still, the message for land managers is clear: the forest edge, not the corridor interior, is where the richest functional vegetation persists, and management strategies that maintain diverse, low-growing communities while removing cut biomass could enhance the ecological value of these ubiquitous linear infrastructures across boreal and temperate forests.

Subject of Research: Vegetation functional diversity across power line rights-of-way, forest edges, and control forests in eastern Canadian boreal and temperate forests.

Article Title: Vegetation functional diversity along power line rights-of-way in eastern Canadian forests

Article References: Proulx, S. R., Thiffault, É., Paré, D., Boucher, J.-F., Chavaillaz, Y., & Larochelle, M. (2026). Vegetation functional diversity along power line rights-of-way in eastern Canadian forests. Environmental and Sustainability Indicators, 32, Article 101507. https://doi.org/10.1016/j.indic.2026.101507

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101507

Keywords: functional diversity, power line rights-of-way, plant functional traits, boreal forest, forest edges, Québec, pollinator habitat, vegetation management, forbs, shrubs, boreal forest ecology, linear infrastructure