Can data from native mosquitoes support determining invasive species habitats? Modelling the climatic niche of Aedes japonicus japonicus (Diptera, Culicidae) in Germany.

Dapatkah data dari nyamuk asli mendukung penentuan habitat spesies invasif? Pemodelan relung iklim *Aedes japonicus japonicus* (Diptera, Culicidae) di Jerman.

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ID: 67849
2019
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Abstract
Invasive mosquito species and the pathogens they transmit represent a serious health risk to both humans and animals. Thus, predictions on their potential geographic distribution are urgently needed. In the case of a recently invaded region, only a small number of occurrence data is typically available for analysis, and absence data are not reliable. To overcome this problem, we have tested whether it is possible to determine the climatic ecological niche of an invasive mosquito species by using both the occurrence data of other, native species and machine learning. The approach is based on a support vector machine and in this scenario applied to the Asian bush mosquito (Aedes japonicus japonicus) in Germany. Presence data for this species (recorded in the Germany since 2008) as well as for three native mosquito species were used to model the potential distribution of the invasive species. We trained the model with data collected from 2011 to 2014 and compared our predicted occurrence probabilities for 2015 with observations found in the field throughout 2015 to evaluate our approach. The prediction map showed a high degree of concordance with the field data. We applied the model to medium climate conditions at an early stage of the invasion (2011-2015), and developed an explanation for declining population densities in an area in northern Germany. In addition to the already known distribution areas, our model also indicates a possible spread to Saarland, southwestern Rhineland-Palatinate and in 2015 to southern Bavaria, where the species is now being increasingly detected. However, there is also evidence that the possible distribution area under the mean climate conditions was underestimated.
Spesies nyamuk invasif dan patogen yang ditularkannya menimbulkan risiko kesehatan yang serius bagi manusia maupun hewan. Oleh karena itu, prediksi mengenai potensi distribusi geografisnya sangat dibutuhkan. Pada kasus wilayah yang baru saja terinvasi, biasanya hanya sedikit data keberadaan yang tersedia untuk dianalisis, dan data ketiadaan tidak dapat diandalkan. Untuk mengatasi permasalahan ini, kami menguji apakah penentuan relung ekologi iklim suatu spesies nyamuk invasif dapat dilakukan dengan memanfaatkan data keberadaan spesies asli lainnya dan pembelajaran mesin. Pendekatan ini didasarkan pada *support vector machine* dan, dalam skenario ini, diterapkan pada nyamuk semak asia (*Aedes japonicus japonicus*) di Jerman. Data keberadaan spesies ini (yang tercatat di Jerman sejak 2008) serta data dari tiga spesies nyamuk asli digunakan untuk memodelkan potensi distribusi spesies invasif tersebut. Kami melatih model dengan data yang dikumpulkan dari tahun 2011 hingga 2014 dan membandingkan probabilitas keberadaan yang diprediksi untuk tahun 2015 dengan hasil observasi lapangan sepanjang tahun 2015 guna mengevaluasi pendekatan kami. Peta prediksi menunjukkan tingkat kesesuaian yang tinggi dengan data lapangan. Kami menerapkan model tersebut pada kondisi iklim sedang pada tahap awal invasi (2011–2015), serta mengembangkan penjelasan mengenai penurunan kepadatan populasi di suatu wilayah di Jerman utara. Selain wilayah persebaran yang telah diketahui sebelumnya, model kami juga mengindikasikan kemungkinan penyebaran ke Saarland, bagian barat daya Rhineland-Palatinate, dan pada tahun 2015 ke Bavaria selatan, tempat spesies tersebut kini semakin banyak terdeteksi. Namun demikian, terdapat pula bukti bahwa potensi wilayah distribusi pada kondisi iklim rata-rata telah diperkirakan terlalu rendah (*underestimated*).
Reference Key
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Authors Kerkow, Antje;Wieland, Ralf;Früh, Linus;Hölker, Franz;Jeschke, Jonathan M;Werner, Doreen;Kampen, Helge;
Journal parasitology research
Year 2019
DOI
10.1007/s00436-019-06513-5
URL
Keywords
Machine learning support vector machine citizen science invasive species distribution models occurrence probability

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