The study, published in Frontiers in Environmental Science, began with a 30-meter-resolution ASTER digital elevation model and built every thematic layer from scratch within ArcGIS Pro. The researchers derived slope and landform maps from the elevation data, produced a drainage density map by modeling flow direction and drainage networks, and estimated groundwater depth by overlaying soil, precipitation, and drainage intensity data. Because high water tables increase the fluidity of loose alluvial sediments, groundwater depth was included as a proxy for liquefaction potential, the phenomenon in which saturated soil temporarily loses its strength and behaves like a liquid during strong shaking. Active fault maps and a catalog of historical earthquakes with magnitudes greater than 3.0, retrieved from Turkey’s disaster management agency AFAD for the period 2000 to 2025, completed the dataset.

At the heart of the analysis lies the Analytic Hierarchy Process, a multi-criteria decision-making framework developed by Thomas Saaty that breaks complex problems into pairwise comparisons. Experts in seismotectonics, geomorphology, GIS, and disaster management judged the relative importance of each risk factor on Saaty’s nine-point scale, and the judgments were aggregated using the geometric mean method. The resulting weights were then applied to the spatial layers through weighted overlay analysis to produce the final earthquake risk map. A consistency ratio of 0.0324 confirmed that the expert judgments were internally coherent, well below the 0.10 threshold that AHP methodology requires for scientific validity.

The two dominant factors shaping earthquake risk in Çemişgezek turned out to be distance from fault lines, which accounted for 39.53 percent of the total risk weighting, and lithology, the rock and soil type underlying the surface, which contributed 30.41 percent. Together these two criteria explain nearly 70 percent of the overall seismic threat. Groundwater potential carried a weight of 17.98 percent, while slope contributed 12.08 percent. The findings underscore a fundamental principle of seismic hazard assessment: where a structure sits relative to an active fault, and what kind of ground it stands on, matter far more than the steepness of the terrain or the amount of rainfall the area receives.

When the weighted overlay was complete, the risk map revealed a district divided into five classes. Very low-risk terrain covers 17.87 percent of the district’s 821 square kilometers, while low-risk areas encompass 43.92 percent. Medium-risk zones account for 36.74 percent, high-risk areas for 1.34 percent, and very high-risk zones for a mere 0.07 percent, equivalent to just one square kilometer. That single square kilometer of extreme danger clusters around the villages of Tekeli and Cihangir, where the active fault line runs directly beneath the settlements and where the highest-magnitude earthquakes in the regional record have been recorded. The alignment between the model’s predicted hot spots and the historical earthquake catalog provides strong validation of the approach.

By contrast, 61.81 percent of the district falls into the low or very low risk categories, largely because it lies far from the active fault segments and rests on geologically older, more consolidated formations. The town center of Çemişgezek itself sits within the low and medium risk classes. This spatial concentration of hazard presents both a challenge and an opportunity for planners: rather than distributing limited resources uniformly across the entire district, disaster management equipment, structural reinforcement programs, and emergency preparedness initiatives can be targeted precisely at the small but critical corridor where the fault line passes.

Yet the researchers emphasize that physical hazard is only part of the story. Çemişgezek’s population has declined steadily over recent decades, with rural villages emptying faster than the district center, leaving behind an aging population with reduced physical mobility and diminished capacity for self-organized emergency response. Many buildings in the district are old, poorly maintained, and constructed without modern seismic engineering standards. The ferry line crossing the Keban Dam Lake, which serves as the region’s primary connection to the outside world, passes through high-risk geological units, meaning that a significant earthquake could sever the district’s only transportation link and leave residents isolated from external aid.

The study frames earthquake risk not as a purely tectonic phenomenon but as a socio-spatial vulnerability shaped by the interaction of natural conditions and demographic change. Continuous emigration of younger residents has eroded local economic investment and institutional capacity, while the remaining elderly population is less able to evacuate or rebuild after a disaster. The researchers argue that effective risk reduction requires a combination of measures: relocating new settlements to safer ground, reinforcing existing structures in the high-risk corridor, raising public awareness of earthquake hazards, and developing alternative evacuation routes, including helicopter landing areas and water-based transport options, to compensate for the district’s dependence on a single vulnerable crossing.

The methodology has clear limitations. The model relies on open-access data with a spatial resolution that limits micro-level accuracy, and it does not incorporate building-level structural parameters such as construction age, number of floors, or load-bearing materials. Seismic engineering parameters like peak ground acceleration and Vs30 shear-wave velocity measurements were also absent. The researchers acknowledge that the resulting map is better described as a physical susceptibility assessment than a comprehensive risk model, and they recommend that future work integrate dynamic datasets, building inventories, and machine learning approaches to refine predictions further.

Nevertheless, the Çemişgezek study offers a replicable template for other small, isolated districts across Turkey and beyond. By demonstrating that earthquake risk can be spatially quantified with freely available satellite data and a transparent weighting procedure, the research provides local governments with a practical decision-support tool aligned with the United Nations Sustainable Development Goal 11 on sustainable cities and communities. The message is clear: even in places far from major population centers, the ground beneath one’s feet can be mapped, measured, and planned for, long before the next earthquake arrives.

Subject of Research: GIS-based multi-criteria earthquake risk assessment in Çemişgezek District, Tunceli, Türkiye

Article Title: Multi-criteria GIS analysis of earthquake risk and spatial resilience in Eastern Anatolia

Article References: Engin, F., Giyik, C., Şengün, M. T., & Kaya, A. Y. (2026). Multi-criteria GIS analysis of earthquake risk and spatial resilience in Eastern Anatolia. Frontiers in Environmental Science, 14, Article 1800445. https://doi.org/10.3389/fenvs.2026.1800445

Image Credits: AI Generated

DOI: 10.3389/fenvs.2026.1800445

Keywords: earthquake risk, GIS, AHP, Çemişgezek, Tunceli, fault proximity, lithology, liquefaction, disaster planning, spatial analysis, Eastern Anatolia, risk mapping

The research team, led by Sudeep Jogi Kanwar and Milan Dhakal of the Agriculture and Forestry University in Nepal together with Ashok Parajuli of the Division Forest Office, focused on Rasuwa because it embodies the mounting dangers facing mountain ecosystems worldwide. The district spans roughly 154,400 hectares, with elevations ranging from 614 to 7,227 meters, and about 68 percent of its territory lies above 3,000 meters. Long-term records from the Dhunche meteorological station show maximum temperatures rising by about 0.05 degrees Celsius per year between 1989 and 2018, while winter precipitation has declined. This elevation-dependent warming is eroding the natural flammability barriers that once kept fire out of the high mountains, and the region has already witnessed catastrophic blazes, including a 2012 wildfire that burned unchecked for months near the Gatlang forest.

To build the model, the researchers assembled eleven predictive layers spanning four categories of fire drivers. Biophysical factors included the 2022 National Land Cover Map of Nepal and the Normalized Difference Vegetation Index, or NDVI, derived from Landsat imagery, which captures vegetation density and moisture status. Topographic variables—elevation, slope, aspect, and the topographic wetness index—were calculated from the Shuttle Radar Topography Mission digital elevation model. Anthropogenic pressure was represented by Euclidean distance layers computed from settlement and road datasets, while climatic conditions came from WorldClim precipitation data, MODIS land surface temperature composites spanning 2000 to 2024, and long-term average wind speeds from the Global Wind Atlas. All layers were resampled to a common 30-meter resolution to reduce uncertainty arising from mismatched spatial scales.

The fire record itself came from the Visible Infrared Imaging Radiometer Suite aboard the Suomi National Polar-orbiting Partnership satellite, which detects active fires at 375-meter resolution—finer than the 1-kilometer MODIS product and therefore better suited to catching small, scattered mountain ignitions. The team extracted incidents between 2012 and 2024, retained only detections with confidence levels above 30 percent, and applied a spatial thinning procedure requiring a minimum 100-meter separation between points. That filtering left 757 well-distributed fire locations for model training, a critical step because clustered records can bias machine learning classifiers toward overrepresented landscapes.

Before training, the researchers screened their predictors for multicollinearity using variance inflation factors, a standard diagnostic that flags variables whose information overlaps so heavily that the model cannot attribute influence cleanly. Precipitation failed the test spectacularly, with a VIF of 20.14 against a threshold of 10, and was excluded. The authors attribute this to the orographic precipitation mechanism of the Hindu Kush Himalaya, in which monsoon moisture interception is controlled almost entirely by topographic position—meaning elevation already encodes much of the rainfall signal. The remaining ten variables passed, and the team proceeded to train a Random Forest classifier, an ensemble method introduced by Leo Breiman that grows hundreds of decision trees on randomly resampled subsets of the data and averages their votes.

Model tuning was systematic rather than ad hoc. Using five-fold cross-validation on 80 percent of the data, the team searched a grid of hyperparameters covering the number of trees, the minimum size of terminal nodes, and the number of variables considered at each split, selecting the combination that maximized the area under the receiver operating characteristic curve. The final configuration achieved an AUC of 0.90 on the held-out 20 percent test set, with a standard deviation of just 0.018 across folds. The True Skill Statistic, a metric favored in ecological modeling because it remains reliable under class imbalance, reached 0.67, and a Brier score of 0.12 confirmed that the model’s predicted probabilities were well calibrated rather than merely well ranked.

When the researchers interrogated which variables drove the predictions, elevation emerged as the single most important factor, followed by land surface temperature, NDVI, and proximity to settlements. Partial dependence plots revealed the shape of these relationships in striking detail. Fire probability peaked at lower elevations between roughly 1,000 and 2,500 meters, then plateaued with notable fluctuations between 2,500 and 4,000 meters before stabilizing near 4,200 meters—a pattern suggesting that subalpine forests and shrublands, long assumed to be too cold and wet to burn, now carry genuine risk. NDVI values between 0.4 and 0.6, corresponding to open needle-leaved forests rich in resinous, low-moisture conifer fuels, proved most dangerous. Fire likelihood also climbed steadily with temperature above 15 degrees Celsius and was highest on slopes of 30 to 60 degrees and on sun-exposed south-facing aspects, where fuel dries fastest in the Northern Hemisphere.

The resulting risk map, classified into five zones using the Natural Breaks method, proved operationally meaningful rather than statistically arbitrary: 87.47 percent of all recorded fire incidents fell within the very high risk class, while fewer than 1 percent landed in the low and very low categories combined. In total, 19,523 hectares of forest face high or very high risk. The geography of danger is uneven. Kalika Rural Municipality has the largest proportion of high-risk forest at 53.7 percent, while Gosaikunda has the least at 19.5 percent, though Gosaikunda holds the greatest absolute area of very high risk at 6,235 hectares. The western flank of Langtang National Park stands out as a hotspot, combining low elevations, high land surface temperatures, and rugged terrain that hampers suppression once ignition occurs. Temporally, the picture is equally concentrated: 91.86 percent of incidents occurred between January and April, April alone recorded 283 fires, and the jump from 24 incidents in 2022 to 223 in 2023 signals an accelerating trend. Notably, about 30 percent of fires occurred above 3,000 meters, with detections as high as 4,525 meters.

The implications reach beyond fire management alone. The authors argue that wildfire, landslide, and flood hazards in mountain catchments are interconnected—burned soils shed water faster, lowering the rainfall threshold for slope failure—so a validated fire risk map doubles as an input to multi-hazard planning. The study is candid about its limits: Random Forest remains a black box without regression coefficients or confidence intervals, the static NDVI and land cover layers cannot capture landscape change across the 13-year fire record, and coarse-resolution inputs plus the absence of ground-verified incident data may blur spatial precision. Future work, the team suggests, should apply explainable AI techniques such as SHAP values, incorporate ensemble methods and higher-resolution data, and validate predictions through field surveys and community-based participatory mapping. For now, the map gives district and municipal authorities in Rasuwa something they have never had before: a locally calibrated, statistically validated picture of where the next fire is most likely to start, and where early warning systems and fuel reduction work should begin first.

Subject of Research: Machine learning-based forest fire risk mapping using earth observation data in the high-mountain district of Rasuwa, Nepal

Article Title: Integrating earth observation and machine learning for forest fire risk assessment in Rasuwa district, Nepal

Article References: Kanwar, S. J., Dhakal, M., & Parajuli, A. (2026). Integrating earth observation and machine learning for forest fire risk assessment in Rasuwa district, Nepal. Discover Forests, 2(1), Article 78. https://doi.org/10.1007/s44415-026-00138-0

Image Credits: AI Generated

DOI: 10.1007/s44415-026-00138-0

Keywords: forest fire risk, Random Forest, machine learning, remote sensing, Nepal, Himalaya, GIS, VIIRS, NDVI, Langtang National Park, climate change, risk mapping

The research team drew on a decade of surveillance data, spanning 2014 to 2023, obtained from the China Information System for Disease Control and Prevention, the national backbone for notifiable infectious disease reporting in the country. These anonymized case records, collected through routine public health surveillance and therefore exempt from individual ethics review, provided the occurrence records needed to anchor a spatial model. To explain and predict where cases occur, the investigators assembled a multisource set of environmental variables covering three broad domains: meteorological factors such as temperature and precipitation, socioeconomic indicators including population density, and land cover characteristics captured through satellite-derived indices such as the Normalized Difference Vegetation Index, a standard remote-sensing measure of green vegetation vigor.

The analytical engine of the study was the Maximum Entropy model, universally abbreviated as MaxEnt, which has become one of the most widely used tools in ecological niche modeling. MaxEnt estimates the probability distribution of a species, or in this case a disease, by finding the distribution that is most spread out, or maximally entropic, while still remaining consistent with the known occurrence points and the environmental conditions measured at those locations. Its advantage for disease mapping lies in its ability to work with presence-only data, to handle complex non-linear relationships between predictors and risk, and to quantify the relative contribution of each environmental variable. In essence, the model asks: given the places where hemorrhagic fever with renal syndrome has been reported, what combination of climatic, ecological, and human conditions best characterizes those places, and where else in the landscape do those same conditions prevail?

Model performance was evaluated with the Area Under the Curve of the Receiver Operating Characteristic, a standard metric in which a value of 0.5 indicates predictions no better than random chance, while values approaching 1.0 indicate near-perfect discrimination between suitable and unsuitable areas. The MaxEnt model achieved a mean AUC of 0.902, a figure that places it firmly in the range conventionally regarded as excellent predictive accuracy. This robust performance means the resulting risk maps can be interpreted with reasonable confidence as genuine reflections of the disease’s ecological niche in the region, rather than statistical artifacts of sparse or noisy surveillance data.

The variable contribution analysis delivered the study’s most consequential result. Population density alone accounted for 73.5 percent of the model’s explanatory contribution, dwarfing every environmental factor. The next most important predictor was the Normalized Difference Vegetation Index measured in May, contributing 9.2 percent, followed by annual mean temperature at 5.8 percent. The prominence of May vegetation is biologically plausible: spring green-up in the mountainous southwest influences rodent food availability and population dynamics, which in turn shape the abundance of infected reservoir hosts such as Apodemus and Rattus species that shed hantavirus in their excreta. Yet the overwhelming dominance of a single anthropogenic variable marks a departure from many earlier hantavirus studies, in which climatic oscillations and land cover change typically claimed the largest shares of explained variation.

The spatial pattern that emerged from the model was described by the authors as a core aggregation with sporadic dispersion. High-risk zones concentrated overwhelmingly in the Chengdu-Chongqing urban agglomeration, one of the largest and fastest-growing megaregions in western China, with additional localized clusters scattered across Yunnan Province. This geography tells a coherent story. The Chengdu Plain and the Sichuan Basin combine fertile agricultural land, dense human settlement, and fragmented natural habitat at the interface where people, crops, storage facilities, and rodents meet. Yunnan’s clusters, by contrast, likely reflect the province’s extraordinary ecological diversity and its mosaic of forest, farmland, and rural settlements that sustain persistent enzootic viral cycles spilling over into human populations.

Response curves, which plot modeled suitability against each variable while holding others constant, revealed the functional shapes underlying these relationships. Population density showed a sigmoidal positive correlation with disease risk: risk rises slowly at low densities, then climbs steeply as human numbers increase, before potentially plateauing at very high densities. Temperature and precipitation behaved differently, exhibiting non-linear inverted U-shaped and U-shaped relationships respectively. Such hump-shaped curves are a hallmark of ecological tolerance limits: transmission is favored within an intermediate climatic window but suppressed at extremes, where conditions become either too cold and dry or too hot and wet for the virus, its rodent reservoirs, or the environmental persistence of infectious excreta. In this way, meteorological factors act less as primary engines of transmission and more as boundary-setters, constraining the spatial limits within which the human-driven amplification can unfold.

The authors interpret the dominance of population density through the lens of what they call the human behavior amplification effect in natural focal diseases. Dense human populations do not simply encounter more rodents; they generate the very conditions that favor contact. Urban fringes undergoing rapid construction disturb rodent habitat and drive displacement into human settlements. Agricultural intensification, food storage, waste streams, and informal housing provide abundant rodent resources. Occupational and recreational activities in peri-urban green spaces increase exposure to aerosolized virus from contaminated soil and dust. In effect, the anthropogenic signal in the model captures a web of behavioral and land-use pathways that traditional climate-only models have tended to miss, and it explains why incidence and endemic ranges are expanding even as the region’s fundamental climate remains comparatively stable.

The practical implications of the study point toward a strategic reorientation of prevention. Rather than applying blanket interventions across entire provinces, the authors advocate a shift to precision control: prioritizing active surveillance, rodent monitoring, and public health preparedness in densely populated urban fringes and in areas undergoing infrastructure development, where the model indicates risk is rising fastest. Targeted measures might include pre-construction rodent surveys, environmental management around new housing and industrial sites, and risk communication aimed at workers and residents in the highest-suitability zones. Because the model is built on a decade of data and open environmental layers, its framework can in principle be updated as urbanization proceeds, offering health authorities a dynamic rather than static picture of where the next cases are most likely to appear.

Caveats remain, as they do in all niche-modeling exercises. Surveillance-based occurrence records reflect where cases are detected, which is itself influenced by healthcare access and diagnostic capacity, factors that may correlate with population density and could inflate its apparent contribution. The study region’s complex topography also means that fine-scale microclimates and local rodent ecology may not be fully resolved by the environmental layers used. Nevertheless, the study’s core message stands on solid quantitative ground: in Southwest China, the future geography of hemorrhagic fever with renal syndrome is being written less by the weather than by the growth and movement of people themselves. For a disease that has historically been framed as a rural, environmentally determined zoonosis, that reframing carries real weight for how China, and other rapidly urbanizing regions with hantavirus circulation, plan the next decade of control.

Subject of Research: Ecological niche modeling of hemorrhagic fever with renal syndrome distribution in Southwest China

Article Title: Predicting the potential distribution of hemorrhagic fever with renal syndrome in Southwest China using the ecological niche modeling

Article References: Zhang, D., Zhu, Z., Wang, Y., Zhu, L., Zheng, Z., Qu, R., Shao, F., Chen, Q., Xu, Y., & Zhang, W. (2026). Predicting the potential distribution of hemorrhagic fever with renal syndrome in Southwest China using the ecological niche modeling. Parasites & Vectors. https://doi.org/10.1186/s13071-026-07631-7

Image Credits: AI Generated

DOI: 10.1186/s13071-026-07631-7

Keywords: hemorrhagic fever with renal syndrome, hantavirus, ecological niche modeling, MaxEnt, Southwest China, population density, rodent-borne disease, risk mapping, spatial epidemiology, NDVI, urbanization, public health surveillance

The study, published in the journal Parasites & Vectors, exploits a regulatory quirk that most surveillance systems can only dream of. Unlike passive reporting schemes, which depend on farmers choosing to notify disease, the Imizol notification requirement captures treatment events systematically, because compliance is mandated whenever the drug is used. Between 2016 and 2024, these notifications provided a proxy dataset for clinically recognised bovine babesiosis at a resolution fine enough to map disease presence onto a grid of two-kilometre squares covering Great Britain. The disease itself is caused by protozoan parasites of the genus Babesia, transmitted by ticks, with the sheep tick Ixodes ricinus serving as the principal vector in the British Isles. Infected cattle can develop fever, anaemia, red urine and, in severe cases, death, making early detection and treatment critical for herd health and farm economics.

To transform the notification records into a predictive map, the team turned to machine learning, specifically boosted regression trees, a method that excels at capturing complex, nonlinear relationships between environmental variables and disease occurrence. The predictor variables fell into four broad categories. Seasonal climate metrics characterised temperature and moisture conditions across the year. Vegetation greenness, derived from satellite observations, served as a proxy for habitat quality and tick-friendly landscapes. Livestock densities captured the availability of hosts on which ticks could feed. Finally, the researchers included predicted habitat suitability for three species of deer, red, roe and fallow, which act as key reproductive hosts for adult Ixodes ricinus ticks and are known to shape tick population dynamics across British landscapes.

The modelling strategy was deliberately rigorous. Models were trained on notification data from southwest England, a recognised hotspot for the disease, using records from 2016 to 2022. They were then temporally validated against data from the same region for 2023 and 2024, testing whether the models could anticipate disease patterns in years they had never seen. Crucially, the models were also evaluated externally against independent notification data from outside the training region, a demanding test of whether the relationships learned in one part of Britain could transfer to environments elsewhere on the island.

The results were striking. In internal cross-validation, the models achieved a mean area under the receiver operating characteristic curve, or AUC, of 0.81, with a mean correlation of 0.55, indicating strong discrimination between areas with and without notified cases. Temporal validation yielded a continuous Boyce Index of 0.81, confirming that the models reliably ranked habitat suitability in unseen years. External validation outside southwest England produced a Boyce Index of 0.58, suggesting moderate transferability, a respectable figure for a disease model extrapolated beyond its training domain. For a condition that has historically lacked any systematic national mapping, these performance metrics represent a substantial advance in the epidemiology of bovine babesiosis.

Perhaps the most revealing findings concerned which variables actually drove the predicted patterns. Climatic predictors accounted for the greatest relative influence by a wide margin, with autumn cooling alone contributing a mean relative influence of 37.3 percent and atmospheric moisture adding a further 8.5 percent. This dominance of autumnal temperature decline aligns closely with the ecology of Ixodes ricinus, whose activity depends on specific thermal and humidity windows. Ticks in Britain typically show peaks of questing activity in spring and autumn, and the timing and intensity of autumn cooling appears to constrain how long ticks remain active late in the season, shaping the window during which cattle can be exposed to infected bites.

Among the non-climatic predictors, habitat suitability for roe deer and sheep density emerged as the most influential factors, each contributing moderate explanatory power, while vegetation greenness exerted a smaller effect. The prominence of roe deer is ecologically coherent: deer of this species are widespread across woodland and fragmented rural landscapes in Britain and serve as efficient hosts for adult ticks, amplifying tick populations in areas where they thrive. Sheep density, meanwhile, may reflect both the presence of additional hosts and the movement of livestock between farms, which can transport ticks and, in some contexts, the parasites themselves. Notably, the proportional land-cover variables that are staples of many landscape epidemiology studies showed consistently low individual influence, suggesting that the composition of habitat types matters less for this disease than the climatic and host-related conditions that govern tick survival and abundance.

Beyond its ecological insights, the study carries immediate practical value for British agriculture. The risk maps generated by the models can help anticipate areas of elevated babesiosis risk and guide disease surveillance at regional scales. Of particular interest are the locations where environmental conditions appear suitable for disease occurrence but treated cases have not yet been reported. Such mismatches between predicted suitability and observed notifications may indicate genuine gaps in surveillance, under-recognition of disease by veterinarians, or areas where the disease is emerging as conditions change. Identifying these frontier zones allows veterinary authorities and farmers to target awareness campaigns, diagnostic efforts and tick control measures before outbreaks become established.

The research also demonstrates a methodological lesson with implications far beyond a single disease. Mandatory veterinary medicines notification data, the authors show, can serve as a credible proxy for disease occurrence at national scale, opening a surveillance channel that exists independently of voluntary reporting. Many countries maintain compulsory reporting requirements for the use of specific veterinary drugs, and the British experience suggests these records can be mined for spatial epidemiology at little additional cost. As climate change alters the seasonal rhythms of temperature and moisture across Europe, the activity patterns of Ixodes ricinus and the diseases it carries are expected to shift, making robust, repeatable mapping systems increasingly valuable.

For British cattle farmers, the message is that the geography of babesiosis risk is now legible in unprecedented detail, and that the levers shaping that geography are largely environmental: the coolness of autumn, the dampness of the air, the abundance of roe deer in the surrounding woods and the density of sheep grazing nearby fields. The work, funded through a University of Liverpool PhD studentship in partnership with the Animal and Plant Health Agency and supported by the BBSRC-DEFRA OPTICK project, exemplifies how collaboration between academic modellers and government surveillance units can convert routine regulatory data into actionable ecological intelligence. As warming winters and shifting rainfall patterns continue to redraw the map of tick-borne disease across the British Isles, studies of this kind provide the baseline against which future change will be measured, and the tools with which farmers and veterinarians can stay one step ahead of a parasite that thrives in the margins of the season.

Subject of Research: Spatial modelling of bovine babesiosis risk in Great Britain using mandatory imidocarb treatment notifications and machine learning

Article Title: Climate, host and landscape determinants of bovine babesiosis in Great Britain: analysis of Imidocarb treatment notifications, 2016–2024

Article References: Shanks, S., Duncan, J., Johnson, N., Millins, C., Hassall, R., & Purse, B. V. (2026). Climate, host and landscape determinants of bovine babesiosis in Great Britain: analysis of Imidocarb treatment notifications, 2016–2024. Parasites & Vectors. https://doi.org/10.1186/s13071-026-07665-x

Image Credits: AI Generated

DOI: 10.1186/s13071-026-07665-x

Keywords: bovine babesiosis, Ixodes ricinus, tick-borne disease, imidocarb dipropionate, machine learning, boosted regression trees, climate determinants, roe deer, sheep density, Great Britain, veterinary surveillance, risk mapping

The review focuses on three interlocking technologies. Geographic Information Systems, or GIS, are computerized platforms for capturing, storing, integrating, analyzing, and visualizing spatially referenced data, allowing researchers to overlay disease reports with environmental variables and livestock densities. The Global Positioning System and its international counterparts under the Global Navigation Satellite Systems umbrella supply precise coordinates for tracking herds, geotagging outbreak cases, and monitoring pastoral movements. Remote sensing completes the triad, drawing on satellite and aerial platforms to measure the ecological drivers of disease—vegetation cover, rainfall, temperature, humidity, and landscape structure—that shape where vectors and pathogens thrive.

The evidence base assembled by the authors is striking. GIS-based mapping of Rift Valley fever outbreaks in East Africa has enabled rapid identification of epidemiological hotspots and guided vaccination campaigns to the areas of greatest risk. In southern Africa, remotely sensed vegetation indices have successfully predicted tick distribution patterns, sharpening targeted acaricide interventions and reducing livestock morbidity. In Ethiopia itself, GIS has been used to map the distribution of Peste des Petits Ruminants along pastoral mobility corridors, letting veterinary authorities pinpoint high-risk zones and prioritize vaccination. Risk mapping of lumpy skin disease virus in the Oromia region revealed strong correlations between outbreak intensity and humid lowland agro-ecologies, underscoring how environmental drivers shape disease distribution.

What makes these tools particularly powerful is their synergy with molecular diagnostics. While genomic assays identify pathogens with precision, GIS and remote sensing contextualize those findings—mapping pathogen distribution, tracing transmission pathways, and linking them to ecological drivers. The review cites the pairing of genomic confirmation of PPR outbreaks in Ethiopia with GIS-based mapping of pastoral mobility routes, a combination that produces both a precise diagnosis and a spatially informed intervention strategy. This fusion of laboratory science and landscape analysis represents a comprehensive framework for modern veterinary epidemiology.

The analytical machinery behind these maps has grown considerably more sophisticated. Spatial autocorrelation statistics such as Moran’s I and Geary’s C test whether disease cases cluster, disperse, or scatter randomly. Scan statistics identify statistically significant outbreak clusters, supporting early detection of epidemic hotspots in diseases like foot-and-mouth and avian influenza. Kernel density estimation and Getis-Ord Gi hotspot analysis visualize areas of unusually high or low risk, and have been applied to rabies and tick-borne disease foci. Geographically weighted regression captures risk-factor relationships that vary across space, while Bayesian frameworks such as BYM2 provide robust disease mapping for endemic infections like bovine tuberculosis, accounting for uncertainty and spatial dependence even in sparse datasets. Livestock movement network analysis identifies high-risk corridors and nodes for disease transmission, critical for controlling transboundary diseases.

Beyond livestock, the technologies are reshaping wildlife and zoonotic disease monitoring—a domain of growing urgency given that zoonotic diseases represent nearly half of all known human pathogens. GPS collars on wildlife have revealed movement patterns that overlap with livestock grazing areas, raising risks of tick-borne diseases such as East Coast fever and Lyme disease and helping anticipate spillover at the wildlife-livestock-human interface. Unmanned aerial vehicles have been deployed to estimate free-roaming dog populations for rabies surveillance and to map ecological risk zones for fascioliasis. These applications align with international frameworks including the World Organisation for Animal Health standards, the FAO’s EMPRES-i system, and the WHO-FAO-WOAH Global Early Warning System, all of which emphasize geospatial integration within the One Health approach.

Food safety and international trade represent another frontier. GIS has been used to trace contamination sources in meat and dairy supply chains, while GPS tracking of transport routes improves compliance with international standards for animal products—capabilities particularly relevant for trade-sensitive diseases such as bovine tuberculosis and brucellosis. Geospatially referenced datasets like the FAO’s Gridded Livestock of the World strengthen population monitoring, and GIS-based mapping of livestock transport routes and abattoir locations in Ethiopia has been used to trace potential contamination pathways and support export certification requirements, operationalizing Codex Alimentarius traceability principles in practice.

Yet the review is candid about the obstacles. In Ethiopia, recent surveys indicate that fewer than 20 percent of veterinary institutions actively use GIS platforms for routine surveillance, and remote sensing remains limited to pilot projects within national veterinary services. Technical and infrastructure barriers loom large: grassroots organizations and veterinary services often lack the time, skills, and financial resources to deploy GIS effectively, and persistent cloud cover over highland and humid regions degrades satellite imagery, demanding multi-sensor integration and radar-based alternatives. Data quality problems—incomplete datasets, coarse spatial resolution, limited nighttime data, and the unstructured nature of big data—continue to undermine reliability. Adoption, the authors stress, is not merely a technical exercise but a long-term institutional commitment requiring skilled personnel, supportive frameworks, and sustained investment.

The statistical pitfalls are equally consequential. Ecological fallacy can lead researchers to wrongly infer that associations observed at the district level apply to individual herds. The modifiable areal unit problem means analytical results can shift depending on the spatial boundaries chosen. Ignoring spatial autocorrelation inflates statistical significance, while uneven surveillance coverage and under-reporting in resource-limited settings distort risk maps, making well-monitored areas appear artificially high-risk. Temporal mismatches between satellite-derived environmental data and disease occurrence records further weaken causal inference. Ground-truthing and careful validation against local ecological conditions remain essential, since remotely sensed variables are not always reliable proxies for pathogen ecology—satellite temperature or vegetation indices may miss the microclimatic conditions critical for vector survival.

Ethical and policy questions add another layer of complexity. Publishing high-resolution disease maps can inadvertently identify individual farms or owners, exposing them to economic losses, trade restrictions, or social stigma, while disclosing wildlife locations may increase poaching risks. Veterinary geospatial datasets typically involve multiple stakeholders—farmers, veterinary services, research institutions, and governments—making clear policies on data ownership, access, and confidentiality essential. The authors argue for responsible data governance in which outputs are anonymized, aggregated, and contextualized, balancing the benefits of open data for rapid outbreak response against the risks of exposing sensitive information.

The path forward, the review suggests, runs through open-source platforms and international collaboration. Tools such as QGIS and Google Earth Engine offer cost-effective entry points—QGIS has been applied to mapping African swine fever risk zones and rabies surveillance in resource-constrained settings, while Google Earth Engine has enabled large-scale integration of Sentinel-1 and Sentinel-2 imagery for pasture productivity monitoring and disease risk prediction. Training initiatives like the FAO’s ISAVET program have already taught veterinarians GIS-based surveillance across sub-Saharan Africa, and the WOAH Twinning Programmes connect institutions in Africa and Asia with advanced reference centers. Lessons from COVID-19 dashboards demonstrate how real-time GIS can be adapted to veterinary threats including foot-and-mouth disease, PPR, and avian influenza.

Looking ahead, the authors see artificial intelligence and machine learning transforming the field: AI-driven outbreak forecasting, convolutional neural networks that classify livestock behaviors, satellite-image classification, and vector-habitat modeling all promise to anticipate epidemics before they escalate. Internet-of-Things-enabled animal tracking and mobile GIS will push data collection directly into pastoral and rural areas, while drones extend surveillance into inaccessible terrain. But technology alone will not suffice. The review’s recommendations—national geospatial data repositories with clear policy backing, mapping of pastoral livestock mobility, integration of drought monitoring into veterinary early warning systems, and geotagging of clinics and laboratories to identify underserved areas—point to a future where geospatial thinking is embedded in the institutional fabric of veterinary medicine. Closing the gap between what satellites can see and what veterinary services can act upon, the authors conclude, will be essential for disease control, public health, and the resilience of the livestock systems that feed a growing world.

Subject of Research: Applications and challenges of geospatial technologies in veterinary disease surveillance

Article Title: Applications and challenges of geospatial technologies in veterinary medicine

Article References: Fentie, A. W., & Asefa, D. H. (2026). Applications and challenges of geospatial technologies in veterinary medicine. Discover Animals, 3(1), Article 95. https://doi.org/10.1007/s44338-026-00259-y

Image Credits: AI Generated

DOI: 10.1007/s44338-026-00259-y

Keywords: geospatial technologies, GIS, remote sensing, GPS, veterinary epidemiology, disease surveillance, risk mapping, One Health, zoonotic diseases, livestock, Ethiopia, machine learning

The problem with traditional risk assessment, the authors argue, is fragmentation. Environmental agencies typically rely on a menu of chemical indices, each capturing one slice of the hazard: the Pollution Load Index tracks overall enrichment, the Nemerow Index highlights worst-case exceedances, the Potential Ecological Risk Index weighs toxicity, and the Geo-accumulation Index compares concentrations against background levels. Each index is defensible on its own, but they often disagree, and none of them directly measures the thing that matters most ecologically: whether organisms in the soil are being harmed. Chemical measurements describe exposure, not effect, and the gap between the two is where risk management decisions frequently go wrong.

That is where biomarkers come in. Biological markers, such as the activity of antioxidant enzymes or the production of metal-binding proteins in earthworms, respond to contaminant stress in ways that chemical assays cannot capture. They register early biological effects before populations collapse, and they integrate the effects of multiple metals and their interactions in a way no single chemical metric can. Yet biomarkers bring their own complications. Their responses are often non-monotonic, meaning that stress indicators can rise, fall, and rise again as exposure duration and dose change. A biomarker measured at one moment in time may tell a very different story than the same biomarker measured a week later, which has made it difficult to fold biomarker data into standard risk scoring.

The study’s first major innovation addresses exactly this problem. The researchers built what they call the CRITIC-Fuzzy Biomarker Response Index, or CFBRI, an upgraded version of the existing Biomarker Response Index. The conventional BRI compresses multiple biomarker readings into a single score, but the team found it explained only about 35 percent of the variance in toxicity outcomes across different time points, a goodness of fit of R² = 0.35. By incorporating the CRITIC weighting method, which weighs each biomarker according to how much information it carries and how strongly it correlates with other indicators, together with fuzzy comprehensive evaluation, a mathematical framework for handling ambiguous categories, the new index dynamically balances biomarker data across multiple time points. The payoff was substantial: the fit improved to R² = 0.62, a meaningful gain in a field where noisy biological data routinely defeats simple scoring schemes.

With a robust biological index in hand, the team turned to the second half of the problem: merging biology with chemistry. Using Principal Component Analysis, they fused the CFBRI with the four classical abiotic indices into a single comprehensive index, or CI. Crucially, they built separate versions for different land uses, reasoning that an agricultural field and a construction site face different exposure pathways and different protection goals. The resulting Agricultural Land CI achieved a fit of R² = 0.9388 and the Construction Land CI reached R² = 0.9438, both dramatically outperforming any individual index. In statistical terms, these composite scores capture more than 93 percent of the variation in overall ecological risk, which makes them reliable enough to serve as ground-truth labels for the next stage of the work.

Those labels then became the teaching material for machine learning. The researchers trained multi-class classification models using five different algorithms, tasking each with predicting the comprehensive risk category of a soil sample from its underlying measurements. Random Forest, an ensemble method that aggregates the votes of hundreds of decision trees each trained on random subsets of the data, emerged as the clear winner. The agricultural land model achieved a cross-validation accuracy of 0.888 and the construction land model reached 0.905, meaning the algorithms correctly assigned risk categories roughly nine times out of ten when tested on data they had never seen during training.

The real test came in a regional case study, where the Random Forest models were asked to draw risk zones across an actual landscape. The resulting maps showed zoning patterns highly consistent with both land use functions and the underlying pollution logic, identifying high-risk areas where contamination sources and vulnerable land uses coincide. This is precisely what ecological risk zoning is supposed to do: not merely flag hotspots of high metal concentrations, but delineate areas where the combination of contamination and land function creates genuine ecological danger. The agreement between the model output and the on-the-ground reality of pollution sources suggests the framework captures causal structure rather than statistical coincidence.

The broader significance of the work lies in what it connects. Biomarker studies have traditionally lived in the laboratory world of ecotoxicology, producing elegant dose-response curves that rarely influence a zoning decision at a planning office. Regional risk mapping, meanwhile, has lived in the world of geographic information systems and chemical surveys, producing colorful maps with no biological grounding. By converting biomarker responses into a weighted, time-aware index, then embedding that index in a composite score, and finally using the composite score as a supervised learning target, the researchers built a pipeline that runs unbroken from the enzyme activity in an earthworm’s tissue to a color-coded risk zone on a regional map. The authors describe it as a transferable decision-support tool, meaning the same architecture could be retrained with local biomarker and chemical data in other regions and for other contaminant mixtures.

The study, published as volume 48, article 589 of the journal and led from Central South University’s Institute of Environmental Science and Engineering with collaborators at the Chinese National Engineering Research Center for Control and Treatment of Heavy Metal Pollution, was funded by China’s National Key Research and Development Program and Power Construction Corporation of China. Its timing reflects a wider shift in environmental science, as machine learning methods move from novelty to necessity in handling the multi-dimensional data that modern risk assessment demands. As soils around the world continue to accumulate cadmium, lead, arsenic, and other legacy contaminants from mining, smelting, and industrial activity, the ability to translate molecular stress signals into landscape-scale zoning decisions may prove one of the more consequential bridges ecotoxicology has built in years. For land managers, the message is blunt: the worms knew about the problem first, and now the algorithms can listen to them at regional scale.

Subject of Research: Ecological risk zoning of soil heavy metal pollution using biomarker-enhanced indexing and machine learning

Article Title: Ecological risk zoning of soil heavy metal pollution using biomarker-enhanced indexing and machine learning

Article References: Li, C., Li, K., Yu, L., Xia, H., Si, M., Yang, W., Li, Q., Liao, Q., & Yang, Z. (2026). Ecological risk zoning of soil heavy metal pollution using biomarker-enhanced indexing and machine learning. Environmental Geochemistry and Health, 48(15), Article 589. https://doi.org/10.1007/s10653-026-03476-2

Image Credits: AI Generated

DOI: 10.1007/s10653-026-03476-2

Keywords: soil heavy metal pollution, ecological risk assessment, biomarker response index, machine learning, random forest, comprehensive risk index, land use zoning, Principal Component Analysis, earthworm biomarkers, environmental geochemistry, CRITIC weighting, risk mapping