@article{KuhlsMoskalenkoSukiasyanetal.2021, author = {Kuhls, Katrin and Moskalenko, Olga and Sukiasyan, Anna and Manukyan, Dezdemonia and Melik-Andreasyan, Gayane and Atshemyan, Liana and Apresyan, Hripsime and Strelkova, Margarita V. and Jaeschke, Anja and Wieland, Ralf and Frohme, Marcus and Cortes, Sofia and Keshishyan, Ara}, title = {Microsatellite based molecular epidemiology of Leishmania infantum from re-emerging foci of visceral leishmaniasis in Armenia and pilot risk assessment by ecological niche modeling}, series = {PLoS Neglected Tropical Diseases}, volume = {15}, journal = {PLoS Neglected Tropical Diseases}, number = {4}, publisher = {Public Library of Science (PLoS)}, issn = {1935-2735}, doi = {10.1371/journal.pntd.0009288}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-14004}, pages = {e0009288}, year = {2021}, abstract = {Leishmaniasis is a vector-borne disease caused by protozoan parasites of the genus Leishmania. In Armenia visceral leishmaniasis (VL) is re-emerging since 1999 after a long break of 30 years, with 167 cases recorded until 2019. Molecular diagnosis of VL was implemented only in 2016, and the causative agent was identified as L. infantum. In the present study we expanded the investigation of the causative agent to a characterization at strain level and the identification of its phylogenetic position among the L. infantum genotypes circulating worldwide. This is the first study addressing genetic diversity and population structure of L. infantum in Armenia and in Transcaucasia. Armenia is an extremely interesting region due to its bio-geographic specificities e.g. the high number of different climates in this small mountainous country and the observed high diversity of sand fly species, part of which occurring in very high altitudes. Ecological niche modeling based on registered VL cases and sand fly vectors collected in active VL foci revealed that the risk of further spread of VL is very high due to climate change. Studies of this region should be expanded to enable targeted control measures.}, language = {en} } @article{KoenigKiffnerKuhlsetal.2023, author = {K{\"o}nig, Hannes J. and Kiffner, Christian and Kuhls, Katrin and Uthes, Sandra and Harms, Verena and Wieland, Ralf}, title = {Planning for wolf-livestock coexistence: landscape context predicts livestock depredation risk in agricultural landscapes}, series = {animal}, volume = {17}, journal = {animal}, number = {3}, publisher = {Elsevier}, issn = {1751-7311}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17032}, year = {2023}, abstract = {Extensive pastoral livestock systems in Central Europe provide multiple ecosystem services and support biodiversity in agricultural landscapes but their viability is challenged by livestock depredation (LD) associated with the recovery of wolf populations. Variation in the spatial distribution of LD depends on a suite of factors, most of which are unavailable at the appropriate scales. To assess if LD patterns can be predicted sufficiently with land use data alone at the scale of one federal state in Germany, we employed a machine-learning-supported resource selection approach. The model used LD monitoring data, and publicly available land use data to describe the landscape configuration at LD and control sites (resolution 4 km * 4 km). We used SHapley Additive exPlanations to assess the importance and effects of landscape configuration and cross-validation to evaluate the model performance. Our model predicted the spatial distribution of LD events with a mean accuracy of 74\%. The most influential land use features included grassland, farmland and forest. The risk of livestock depredation was high if these three landscape features co-occurred with a specific proportion. A high share of grassland, combined with a moderate proportion of forest and farmland, increased LD risk. We then used the model to predict the LD risk in five regions; the resulting risk maps showed high congruence with observed LD events. While of correlative nature and lacking specific information on wolf and livestock distribution and husbandry practices, our pragmatic modelling approach can guide spatial prioritisation of damage prevention or mitigation practices to improve livestock-wolf coexistence in agricultural landscapes.}, language = {en} } @article{WielandKuhlsLentzetal.2021, author = {Wieland, Ralf and Kuhls, Katrin and Lentz, Hartmut H.K. and Conraths, Franz and Kampen, Helge and Werner, Doreen}, title = {Combined climate and regional mosquito habitat model based on machine learning}, series = {Ecological Modelling}, volume = {452}, journal = {Ecological Modelling}, address = {Elsevier}, issn = {0304-3800}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-15649}, year = {2021}, abstract = {Besides invasive mosquito species also several native species are proven or suspected vectors of arboviruses as West Nile or Usutu virus in Western Europe. Habitat models of these native vectors can be a helpful tool for assessing the risk of autochthonous occurrence, outbreaks and spread of diseases caused by such arboviruses. Modelling native mosquitoes is complicated because of the perfect adaptation to the climatic and landscape conditions and their high abundance in contrast to invasive species. Here we present a new approach for such a habitat model for native mosquito species in Germany, which are considered as vectors of West Nile virus (WNV). Epizootic emergence of WNV was registered in Germany since 2018. The models are based on surveillance data of mosquitoes from the German citizen science project "M{\"u}ckenatlas" complemented by data from systematic trap monitoring in Germany, and on data freely available from the Deutscher Wetterdienst (DWD) and OpenStreetMap (OSM). While climatic factors still play an important role, we could show that habitat suitability is predictable only by the combination of the climate model with a regional model. Both models were based on a machine-learning approach using XGBoost. Evaluation of the accuracy of the models was done by statistical analysis, determining among others feature importances using the SHAP-Library. Final output of the combined climatic and regional models are maps showing the superposed habitat suitability which are generated through a number of steps described in detail. These maps also include the registered cases of WNV infections in the selected region of Germany.}, language = {en} }