Predicting the potential distribution of the invasive Common Waxbill Estrilda astrild (Passeriformes: Estrildidae)

Abstract

Human transport and commerce have led to an increased spread of non-indigenous species. Alien invasive species can have major impacts on many aspects of ecological systems. Therefore, the ability to predict regions potentially suitable for alien species, which are hence at high risk, has become a core task for successful management. The Common Waxbill Estrilda astrild is a widespread African species, which has been successfully introduced to many parts of the world. Herein, we used MAXENT software, a machine-learning algorithm, to assess its current potential distribution based on species records compiled from various sources. Models were trained separately with records from the species' native range and from both invaded and native ranges. Subsequently, the models were projected onto different future climate change scenarios. They successfully identified the species known range as well as some regions that seem climatically well suited, where the Common Waxbill is not yet recorded. Assuming future conditions, the models suggest poleward range shifts. However, its potential distribution pattern within its tropical native and invasive ranges appears to be more complex. Although the results of both separate analyses showed general similarities, many differences have become obvious. Niche overlap analysis shows that the invasive range includes only a small fraction of the ecological space that can be found in the native range. Thus, we tentatively prefer the model based on native locations only, but in particular, we highlight the importance of the selection process of species records for modelling invasive species.Zusammenfassung Weltweiter Handel und Mobilität haben zu einer zunehmenden Ausbreitung nicht-heimischer Arten geführt. Invasive Arten können großen Einfluss auf zahlreiche Aspekte ökosystemarer Zusammenhänge haben. Deshalb ist die Fähigkeit, Regionen vorherzusagen, die für solche Arten potentiell geeignet und daher möglicherweise bedroht sind, eine Kernaufgabe erfolgreichen Managements. Der Wellenastrild Estrilda astrild ist eine weit verbreitete afrikanische Art, die erfolgreich in viele Gebiete der Welt eingeführt wurde. Mit Hilfe der Software MAXENT, einem Algorithmus, der auf maschinellem Lernen basiert, haben wir seine gegenwärtige, potentielle Verbreitung basierend auf Fundpunkten aus verschiedenen Quellen modelliert. Die Modelle wurden sowohl mit Nachweisen aus dem heimischen als auch dem invasiven und heimischen Verbreitungsgebiet gemeinsam trainiert. Nachfolgend wurden beide auf unterschiedliche zukünftige Klimawandelszenarien projiziert. Die Modelle identifizierten erfolgreich sowohl das bekannte Verbreitungsgebiet der Art, als auch Gebiete, die klimatisch gut geeignet erscheinen, in denen der Wellenastrild aber noch nicht Communicated by T. Friedl. Electronic supplementary materialThe online version of this article (

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