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Divisek, J.

Publications and source records attributed to Divisek, J..

2 recordsLinked to original sources

Predictive Approach to Understanding Angiostrongylus cantonensis Distribution in the Canary Islands

Angiostrongylus cantonensis is an invasive parasitic nematode and zoonotic pathogen responsible for eosinophilic meningitis. Originally native to Southeast Asia, it is now globally distributed across tropical and subtropical regions and is approaching Europe, with Tenerife as a key hotspot. This study investigates the distribution and prevalence of A. cantonensis in Tenerife across three host groups (rats, gastropods, lizards). Based on prevalence data, we modelled its potential distribution using species distribution models (SDMs) and compared climatic conditions with Hawaii, a region with frequent human cases. Field surveys confirmed A. cantonensis in endemic and introduced gastropods (25.6%; 179/698), rats (21.5%; 14/79), and lizards (24.0%; 31/129), with local prevalence ranging from 2.4% to 41.6%. MaxEnt and Boosted Regression Tree models identified precipitation seasonality as the main driver of distribution, while prevalence was influenced primarily by tree cover density and climatic variability. Northeastern Tenerife, La Gomera, La Palma, and El Hierro showed the highest habitat suitability. However, overlap with densely populated areas was limited, possibly explaining the absence of reported human cases. The MESS analysis, based on climatic data from Hawaii, indicated moderate to high environmental similarity across most of the Canary Islands, except in northeastern Tenerife, where conditions were outside the range observed in Hawaii. A. cantonensis is firmly established in Tenerife, but human cases remain absent, likely due to limited human exposure, cultural practices, and geographic separation of parasite hotspots from urban zones. Our findings highlight the importance of integrating ecological and epidemiological data in zoonotic risk assessments. Author SummaryThe rat lungworm, Angiostrongylus cantonensis, is a parasitic nematode that can infect the human brain and cause a serious disease known as eosinophilic meningitis. Although it was once limited to Southeast Asia, it has now spread across many tropical regions, and its arrival in the Canary Islands places it close to mainland Europe. In this study, we explored how widespread the parasite is on Tenerife and what environmental conditions allow it to thrive. We examined rats, snails, and lizards from different parts of the island and found that the parasite is well established in all three groups. By combining these field data with environmental information, we built models to predict where the parasite is most likely to occur and compared Tenerifes climate to Hawaii, where human infections are common. Our results show that suitable conditions exist across much of the Canary Islands, but areas where people live densely overlap only slightly with the parasites hotspots. This may explain why no human cases have been recorded so far, even though the parasite is abundant in wildlife.

ecology↗

The relationship between spectral and plant diversity: disentangling the influence of metrics and habitat types

Biodiversity monitoring is crucial for ecosystem conservation, yet field data collection is limited by costs, time, and extent. Remote sensing represents a convenient approach providing frequent, near-real-time information over wide areas. According to the Spectral Variation Hypothesis (SVH), spectral diversity (SD) is an effective proxy of environmental heterogeneity, which ultimately relates to plant diversity. So far, studies testing the relationship between SD and biodiversity have reported contradictory findings, calling for a thorough investigation of the key factors (e.g., metrics applied, ecosystem type) and the conditions under which such a relationship holds true. This study investigates the applicability of the SVH for plant diversity monitoring at the landscape scale by comparing the performance of three different types of SD metrics. Species richness and functional diversity were calculated for more than 2000 cells forming a grid covering the Czech Republic. Within each cell, we quantified SD using a Landsat-8 "greenest pixel" composite by applying: i) the standard deviation of NDVI, ii) Raos Q entropy index, and iii) richness of "spectral communities". Habitat type (i.e., land cover) was included in the models describing the relationship between SD and ground biodiversity. Both species richness and functional diversity show positive and significant relationships with each SD metric tested. However, SD alone accounts for a small fraction of the deviance explained by the models. Furthermore, the strength of the relationship depends significantly on habitat type and is highest in natural transitional areas. Our results underline that, despite the stability in the significance of the link between SD and plant diversity at this scale, the applicability of SD for biodiversity monitoring is context-dependent and the factors mediating such a relationship must be carefully considered to avoid drawing misleading conclusions. HighlightsO_LIPlant species richness and functional diversity show significant and positive relationships with spectral diversity C_LIO_LISpectral diversity alone explains a small fraction of the total variability in ground biodiversity C_LIO_LISlight differences among the performances of the spectral diversity metrics tested C_LIO_LIThe relationship between spectral and plant diversity is context-dependent C_LI

ecology↗