The scale of the field effort was considerable. Between August 2018 and September 2019, the research team deployed three complementary trap types—Biogents Sentinel traps, CDC Gravid traps, and CDC Light Traps—at 19 study locations across the Ouest Department. Over the thirteen-month sampling window, they captured a total of 22,504 mosquitoes. Two species dominated the catch: Culex quinquefasciatus, which accounted for 53.4 percent of all specimens, and Aedes aegypti, which made up 16.9 percent. These are not incidental species. Cx. quinquefasciatus is the principal vector of lymphatic filariasis in Haiti, while Ae. aegypti transmits the arboviruses responsible for dengue, Zika, chikungunya, and yellow fever. Their numerical dominance underscores how heavily the region’s disease burden is concentrated in a small number of highly adapted urban vectors.

Alongside the two dominant species, the collections included Anopheles albimanus, the country’s key malaria vector, and a broader cast of secondary species, including Psorophora columbiae. All mosquitoes were identified morphologically, and the team then quantified biodiversity using a standard ecological toolkit: species richness measures of alpha diversity, Shannon’s index, and Simpson’s index. Each of these metrics captures a slightly different aspect of community structure—raw species counts, the balance between richness and evenness, and the probability that two randomly drawn individuals belong to the same species, respectively. Applying all three allowed the researchers to distinguish sites that merely hosted many species from those that supported genuinely balanced, multi-species communities.

A crucial methodological finding emerged from this diversity assessment: the metrics varied significantly both by trap type and by month. No single trapping method painted a complete picture of the mosquito community. Biogents Sentinel traps, which target host-seeking container-breeding Aedes, CDC Gravid traps, which attract egg-laying females enriched for Culex, and CDC Light Traps, which sample nocturnal flying insects broadly, each bias collections toward particular behaviors and species. The implication for surveillance programs is direct—reliance on a single trap design risks systematically undercounting the very species that matter most for a given disease system, and the authors emphasize the need for diversified trapping methods in future monitoring efforts.

To move beyond description and identify the environmental forces structuring these communities, the team turned to boosted regression trees, a machine-learning technique increasingly favored in disease ecology for its ability to model nonlinear relationships and interactions among many predictors simultaneously. The models incorporated both static and temporally varying covariates, including temperature, precipitation, vegetation indices measured through the normalized difference vegetation index (NDVI), and human population density. By relating the diversity indices computed for each collection event to these environmental layers, the BRTs could rank the relative influence of each factor and reveal the shapes of their effects—something classical linear models often obscure.

The results were striking in their consistency. Human population density, temperature, and vegetation indices emerged as the key predictors of mosquito diversity across the study area. This triad tells an ecologically coherent story. Dense human settlement creates the artificial containers, storm drains, and water-holding debris that container-breeding Aedes require, while also supplying the blood meals that sustain high vector populations. Temperature governs mosquito development rates, viral replication within vectors, and seasonal activity windows. Vegetation, as indexed by NDVI, shapes the availability of shaded resting sites, organic-rich larval habitats, and the microclimates that favor some species over others. In a highly urbanized tropical setting, these three forces interact to determine which mosquito species coexist at any given site and in any given month.

Perhaps the most novel component of the study was its use of species co-occurrence networks to describe the relationships among mosquito species. The researchers constructed two types of networks—one based on a cross-product approach and one on a probabilistic framework—to test whether pairs of species occurred together more or less often than expected by chance. The networks revealed significant positive associations between Aedes species and Cx. quinquefasciatus, suggesting that the vectors of arboviruses and lymphatic filariasis frequently share the same habitats and may respond similarly to urban environmental conditions. For control programs, this is a double-edged finding: interventions targeting shared breeding habitats could suppress multiple disease vectors simultaneously, but conditions that favor one dominant species may also be quietly building populations of the others.

Not all relationships in the networks were positive. Psorophora columbiae exhibited negative associations with multiple other species, standing out as the community’s apparent contrarian. Negative co-occurrence patterns can arise from several mechanisms, including competition for larval resources, differing habitat preferences, or predation, although the study’s observational design means the underlying cause cannot be pinned down with certainty. What the pattern does establish is that inter-species dynamics are a real and measurable feature of Haiti’s mosquito communities, not a statistical artifact, and that single-species control strategies may produce ecological ripple effects that are currently poorly understood.

The public health stakes of this work are substantial. Haiti continues to confront endemic malaria transmitted by An. albimanus, recurring dengue outbreaks driven by Ae. aegypti, and lymphatic filariasis sustained by Cx. quinquefasciatus. Ae. albopictus, the Asian tiger mosquito, is also present as an arbovirus vector. By identifying the environmental conditions under which these vectors co-occur, the study provides a foundation for multi-species control measures—interventions designed not against one target insect but against the ecological communities that collectively transmit the country’s major vector-borne diseases. The finding that human population density is a central driver also highlights how urbanization itself functions as a disease ecology variable, concentrating vectors and humans in the same environmental envelope.

The authors are careful to frame their conclusions as a beginning rather than an endpoint. They call for further research on inter-species dynamics to build a more unified understanding of vector ecology in Haiti, and their study design—combining year-round field surveillance, multiple trap types, rigorous biodiversity metrics, machine-learning environmental modeling, and network analysis—offers a template that other resource-limited settings could adapt. The work was funded by the Armed Forces Health Surveillance Branch’s Global Emerging Infections Surveillance section and through a Battelle Memorial Institute contract supporting Navy force health protection efforts, reflecting the strategic importance placed on vector surveillance in the Caribbean region. As climate change alters temperature regimes and urbanization accelerates across the tropics, the environmental levers identified in this study—population density, heat, and vegetation—will only grow in importance for predicting where mosquito biodiversity, and the diseases it carries, will flourish next.

Subject of Research: Environmental and demographic drivers of mosquito biodiversity and species co-occurrence in urban Haiti

Article Title: Vegetation, temperature, and human population density influence mosquito biodiversity in Haiti

Article References: Pshea-Smith, I. A., Okech, B. A., Boncy, J., Existe, A., So, J., Hamilton, T., Blanton, J., Matulis, G. A., Metrailer, M. C., Cleary, N. G., Dunford, J. C., Koehler, J. W., Blackburn, J. K., & von Fricken, M. E. (2026). Vegetation, temperature, and human population density influence mosquito biodiversity in Haiti. Parasites & Vectors. https://doi.org/10.1186/s13071-026-07724-3

Image Credits: AI Generated

DOI: 10.1186/s13071-026-07724-3

Keywords: mosquito biodiversity, Haiti, Aedes aegypti, Culex quinquefasciatus, boosted regression trees, species co-occurrence networks, vector ecology, NDVI, urban ecology, dengue, malaria, lymphatic filariasis

The study, published in the journal Ecology and Evolution, focused on a roughly 30 square kilometre area on the western flank of the Nimba range, home to two chimpanzee communities totalling around 89 individuals. Although local customs and national law strictly prohibit chimpanzee hunting — a taboo documented in the region since the 1970s — hunters from outside the area do not necessarily share those prohibitions. Even when chimpanzees are not the target, poaching for smaller mammals generates by-catch, injuries and pervasive human disturbance that can reshape how these intelligent apes use their forest home. Previous research has shown that where hunting occurs, chimpanzees tend to spatially avoid human presence, making fine-scale disturbance a genuine conservation concern even without direct killing.

Between August 2022 and August 2023, teams of Guinean researchers combed the mountainous terrain twenty days per month, logging 901 GPS occurrence points: 633 records of chimpanzee feeding and travelling, 185 nesting records, and 83 points of hunting evidence ranging from empty bullet casings and snare traps to hunting camps and campfires. The researchers deliberately separated nesting from feeding and travelling, reasoning that the two activities — one vulnerable and stationary, the other mobile — might respond differently to human pressure. After thinning the data to one record per 30-metre pixel, they generated pseudo-absence points only along surveyed tracks, a careful design choice that prevented the models from mistaking unsurveyed areas for genuinely unused habitat.

The methodological heart of the study lies in its multi-scale approach. Recognising that species respond to their environment at different spatial grains, the team transformed sixteen environmental variables — including elevation, slope, topographic position, vegetation indices, wetness and distances to villages, roads, rivers and research camps — into seven spatial scales ranging from 30 metres to 2 kilometres using Gaussian kernel smoothing in Google Earth Engine. For each activity type, univariate models identified the optimal scale for every predictor, a step that fewer than 5 percent of habitat modelling studies typically perform. The team then compared two complementary modelling frameworks: generalised linear models, prized for their statistical interpretability, and boosted regression trees, a machine learning method capable of capturing non-linear relationships and threshold effects that simpler models miss.

The results paint a nuanced picture of two species — human and chimpanzee — navigating the same landscape with partly overlapping, partly divergent logic. For hunters, distance to research camps emerged as the single most consistent predictor, with hunting probability rising sharply away from the camps. In the boosted regression tree models, tree cover was the most influential variable at the optimised 500-metre scale, with hunting activity dropping markedly where canopy cover exceeded 90 percent — dense forest apparently presenting obstacles and limited visibility that deter hunters pursuing smaller prey. Wetness at 1,500 metres and proximity to rivers also shaped hunting patterns, suggesting that difficult, waterlogged terrain offers chimpanzees a measure of natural protection.

Chimpanzees, for their part, revealed their own sophisticated habitat calculus. Feeding and travelling activity peaked above 750 metres in elevation, with the animals favouring steeper slopes, warmer south-facing aspects and areas with high values of the Enhanced Vegetation Index — a satellite-derived signal of dense, healthy primary forest. Intriguingly, the multi-scale models showed that chimpanzees responded to warm slopes at a broad 2-kilometre scale while selecting steeper terrain at a medium 250-metre scale, evidence that these apes integrate landscape features across nested spatial hierarchies. Nesting behaviour followed a subtly different recipe: slope was key, with a pronounced threshold at 15 degrees above which nesting suitability climbed, and landscape-scale topographic position and wetness at 2,000 metres featured prominently, indicating that elevated ground relative to surroundings provides the security these apes seek during their most vulnerable resting hours.

When the researchers intersected the predicted surfaces, the maps exposed a striking geography of risk. The western lowlands of the study area showed high hunting probability but low chimpanzee suitability, while the highest-elevation zones — precisely the steep, remote areas chimpanzees favour for nesting — emerged as the zones of greatest potential overlap between apes and hunters. Correlation analyses confirmed strong spatial associations between the predictive surfaces, meaning chimpanzees and hunters largely select and avoid similar terrain. The authors suggest this overlap may indicate that chimpanzees either tolerate human presence or employ temporal displacement, avoiding areas only when hunting activity is underway. Over the long term, however, sustained human activity in these shared zones could gradually suppress habitat use and force chimpanzees into increasingly marginal environments.

The most unexpected finding concerns the research camps themselves. Both chimpanzees and hunters avoided areas near the camps, but the hunters’ avoidance was markedly stronger. Because the reduction in predicted hunting activity exceeded the reduction in chimpanzee suitability, the camps effectively cast a protective buffer across the surrounding forest — a case of conservation science inadvertently shielding the very animals it studies. The authors caution, however, that this benefit comes with a caveat: because chimpanzees generally avoid humans, siting new camps in the heart of prime chimpanzee habitat could paradoxically drive the apes away from resources they need, so strategic placement must balance deterrence of hunters against disturbance of the studied communities.

Methodologically, the study offers lessons that extend well beyond Nimba. Scale-optimised boosted regression tree models consistently outperformed their single-scale counterparts, and the multi-scale approach improved the hunting models across both frameworks, confirming that habitat features invisible to single-scale analyses can prove critical for conservation planning. The two modelling approaches also diverged instructively: the linear models delivered clearer inference through model-averaged coefficients, while the machine learning models captured thresholds and extrapolated more reliably beyond surveyed ground. The authors are candid about limitations — survey effort targeted chimpanzee-frequented areas, potentially biasing the pseudo-absence sample, and variables such as fruit availability could not be included — and they call for extended surveys, ground-truthing and additional data on disturbances like slash-and-burn agriculture.

For the Mount Nimba Strict Nature Reserve, a UNESCO World Heritage Site listed as In Danger, the practical implications are immediate. High-elevation zones should be prioritised for intensive monitoring and patrolling, dense forests deserve protection as natural refuges, and the strategic positioning of research and monitoring infrastructure could be harnessed deliberately as a conservation tool. More broadly, the study demonstrates how pairing fine-scale remote sensing with multi-scale ecological niche modelling can turn scattered field observations into actionable maps of human-wildlife conflict — a template that, if replicated across other montane refuges, could help secure a future for the western chimpanzee in the fragmented forests of West Africa.

Subject of Research: The impact of illegal hunting on the spatial distribution of western chimpanzees in the Mount Nimba Strict Nature Reserve, Guinea

Article Title: Illegal Hunting and Chimpanzee Distribution in the Seringbara Region of the Mount Nimba Strict Nature Reserve: A Multi‐Scale Modelling Approach

Article References: Degrève, L., Erazo, D., Fitzgerald, M., Camara, H. D., Mamy, G., Dellicour, S., Kaszta, Ż., & Koops, K. (2026). Illegal Hunting and Chimpanzee Distribution in the Seringbara Region of the Mount Nimba Strict Nature Reserve: A Multi‐Scale Modelling Approach. Ecology and Evolution, 16(10), Article e74384. https://doi.org/10.1002/ece3.74384

Image Credits: AI Generated

DOI: 10.1002/ece3.74384

Keywords: western chimpanzee, Mount Nimba, illegal hunting, poaching, ecological niche modelling, boosted regression trees, habitat suitability, conservation, Guinea, UNESCO World Heritage, spatial overlap, multi-scale modelling

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