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Background: More than 170 species of tabanids are known in Europe, with many occurring only in limited areas or having become very rare in the last decades. They continue to spread various diseases in animals and are responsible for livestock losses in developing countries. The current monitoring and recording of horseflies is mainly conducted throughout central Europe, with varying degrees of frequency depending on the country. To the detriment of tabanid research, little cooperation exists between western European and Eurasian countries.
Methods: For these reasons, we have compiled available sources in order to generate as complete a dataset as possible of six horsefly species common in Europe. We chose Haematopota pluvialis, Chrysops relictus, C. caecutiens, Tabanus bromius, T. bovinus and T. sudeticus as ubiquitous and abundant species within Europe. The aim of this study is to estimate the distribution, land cover usage and niches of these species. We used a surface-range envelope (SRE) model in accordance with our hypothesis of an underestimated distribution based on Eurocentric monitoring regimes.
Results: Our results show that all six species have a wide range in Eurasia, have a broad climatic niche and can therefore be considered as widespread generalists. Areas with modelled habitat suitability cover the observed distribution and go far beyond these. This supports our assumption that the current state of tabanid monitoring and the recorded distribution significantly underestimates the actual distribution. Our results show that the species can withstand extreme weather and climatic conditions and can be found in areas with only a few frost-free months per year. Additionally, our results reveal that species prefer certain land-cover environments and avoid other land-cover types.
Conclusions: The SRE model is an effective tool to calculate the distribution of species that are well monitored in some areas but poorly in others. Our results support the hypothesis that the available distribution data underestimate the actual distribution of the surveyed species.
The Culex pipiens complex encompasses five species and subspecies of the genus Culex. Over time, a multitude of morphologically indistinguishable species has been assigned to this complex with several species being classified as important vectors for different diseases. Some species of this complex hibernate in subterranean habitats, and it has been proven that viruses can survive this phase of hibernation. However, studies focusing on the environmental requirements, ecology and spatial and temporal distribution patterns of mosquitos in underground habitats are sparse. Here, we investigate the main environmental factors and dependencies of Culex, considering the number of individuals and survival probabilities in underground habitats during the winter months. Methods. Since the State of Hesse, Germany harbors about 3500 to 4000 subterranean shelters ample availability of subterranean habitats there provides a good opportunity to conduct detailed investigations of the Culex pipiens complex. In this study, we identified a sample of 727 specimens of overwintering females within the Culex pipiens complex from 52 different underground sites collected over a period of 23 years using qPCR. A complete data set of samplings of hibernating mosquitos from 698 subterranean habitats in Central Germany over the same period was available to study the spatial and temporal patterns and the effect of temperature and precipitation conditions on these hibernating populations using a generalized linear model (GLM). Results. Our qPCR-results show, similar to aboveground studies of mosquitos, that Culex pipiens pipiens and Culex torrentium occur sympatrically. On the other hand, Culex pipiens molestus occurred very rarely. The GLM revealed no shifts in species composition over time, but different preferences for subterranean hibernacula, chemical effects on overwintering populations as well as effects of annual and seasonal mean temperature and precipitation during the active phase from March to November. Cx. p. pipiens and Cx. torrentium are the most common species within Hessian caves and other underground habitats during winter. They co-occur with different frequency without any patterns in species composition. Weather conditions influence the number of overwintering mosquitos during the activity phase. Depending on cave parameters, the number of mosquitos decreases during the winter months.
Background: Zika is of great medical relevance due to its rapid geographical spread in 2015 and 2016 in South America and its serious implications, for example, certain birth defects. Recent epidemics urgently require a better understanding of geographic patterns of the Zika virus transmission risk. This study aims to map the Zika virus transmission risk in South and Central America. We applied the maximum entropy approach, which is common for species distribution modelling, but is now also widely in use for estimating the geographical distribution of infectious diseases.
Methods: As predictor variables we used a set of variables considered to be potential drivers of both direct and indirect effects on the emergence of Zika. Specifically, we considered (a) the modelled habitat suitability for the two main vector species Aedes aegypti and Ae. albopictus as a proxy of vector species distributions; (b) temperature, as it has a great influence on virus transmission; (c) commonly called evidence consensus maps (ECM) of human Zika virus infections on a regional scale as a proxy for virus distribution; (d) ECM of human dengue virus infections and, (e) as possibly relevant socio-economic factors, population density and the gross domestic product.
Results: The highest values for the Zika transmission risk were modelled for the eastern coast of Brazil as well as in Central America, moderate values for the Amazon basin and low values for southern parts of South America. The following countries were modelled to be particularly affected: Brazil, Colombia, Cuba, Dominican Republic, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Puerto Rico and Venezuela. While modelled vector habitat suitability as predictor variable showed the highest contribution to the transmission risk model, temperature of the warmest quarter contributed only comparatively little. Areas with optimal temperature conditions for virus transmission overlapped only little with areas of suitable habitat conditions for the two main vector species. Instead, areas with the highest transmission risk were characterised as areas with temperatures below the optimum of the virus, but high habitat suitability modelled for the two main vector species.
Conclusion: Modelling approaches can help estimating the spatial and temporal dynamics of a disease. We focused on the key drivers relevant in the Zika transmission cycle (vector, pathogen, and hosts) and integrated each single component into the model. Despite the uncertainties generally associated with modelling, the approach applied in this study can be used as a tool and assist decision making and managing the spread of Zika.
Environmental niche modelling is an acclaimed method for estimating species’ present or future distributions. However, in marine environments the assembly of representative data from reliable and unbiased occurrences is challenging. Here, we aimed to model the environmental niche and distribution of marine, parasitic nematodes from the Pseudoterranova decipiens complex using the software Maxent. The distribution of these potentially zoonotic species is of interest, because they infect the muscle tissue of host species targeted by fisheries. To achieve the best possible model, we used two different approaches. The land distance (LD) model was based on abiotic data, whereas the definitive host distance (DHD) model included species-specific biotic data. To assess whether DHD is a suitable descriptor for Pseudoterranova spp., the niches of the parasites and their respective definitive hosts were analysed using ecospat. The performance of LD and DHD was compared based on the variables’ contribution to the model. The DHD-model clearly outperformed the LD-model. While the LD-model gave an estimate of the parasites’ niches, it only showed the potential distribution. The DHD-model produced an estimate of the species’ realised distribution and indicated that biotic variables can help to improve the modelling of data-poor, marine species.