Combining genetic and distributional approaches to sourcing introduced species: a case study on the Nile monitor (Varanus niloticus) in Florida

Abstract

Three separate breeding populations of the Nile monitor (Varanus niloticus) have been identified in Florida, USA, located in Cape Coral, West Palm Beach and Homestead Air Reserve Base. This large, predatory lizard could have negative effects on Florida's native wildlife. Here, we infer the source of the introduced populations using genetic and statistical approaches, as well as estimate the potential non-native distribution of V. niloticus in North America. We collected genetic data from 25 Florida individuals as well as utilized genetic datasets from reference individuals spanning the full native distribution throughout sub-Saharan Africa. Using occurrence data from the inferred source population and the full species range, we built ecological niche models (ENMs) and projected them onto North America to determine regions with suitable climate. Our results indicated that the introduced populations resulted from three separate introduction events, and all originated from the southern coastal region of West Africa. The ENM built from the West African source population predicted only the southernmost portions of North America to be suitable. Conversely, the model derived from the full species’ range predicted suitable climates across a large portion of the United States. This information can be used to focus management and eradication efforts.

From pet to pest? Differences in ensemble SDM predictions for an exotic reptile using both native and nonnative presence data

“…2). Comparing with other SDM efforts (Dowell et al 2016), we note that the most predictive model here does not project spread northward in continental North America. High areas of suitability in South America include (but are not limited to) coastal Table 3).…”

“…These projections included both native and nonnative presences in our analyses, highlighting the differences that can result in model predictions based on different initial data with different geographic scales. Our ensemble SDMs based on native and native + nonnative presence data showed suitable Nile monitor habitat across many tropical, subtropical, and warm temperate regions, with less potential spread into cooler regions, contrary to that in Dowell et al (2016). This distribution is consistent with our a priori understanding of the monitor's native range and habitat preferences in Sub-Saharan Africa (de Buffrenil and Francillon-Viellot 2001, Bayless 2002, Berny et al 2006, Ciliberti et al 2011, encompassing many regions that are evolutionarily naïve to any of the known 53 Varanus species, let alone the Nile monitor (Campbell 2003).…”

“…To date, only Dowell et al (2016) have attempted to predict suitable habitat for Nile monitors in North America, but did so using only 71 native presences, one algorithm (i.e., Maxent), and only climate and elevation predictor variables. These predictions showed highly suitable habitat in places as disparate as the Yucatan and Olympic peninsulas, Florida and western Montana, and southern California and southern Maine.…”

In situ adaptation and ecological release facilitate the occupied niche expansion of a non‐native Madagascan day gecko in Florida

“…Correlative "climate-matching" SDMs use the bioclimatic characteristics of a taxon's observed range to identify regions of potential bioclimatic suitability outside of its known range (Engeman et al, 2011;Hattab et al, 2017;Uden et al, 2015) and are considered a useful tool for the management of non-native herpetofauna (reptiles and amphibians) (Bomford et al, 2009;Fujisaki et al, 2009;van Wilgen et al, 2009). Maxent (Phillips et al, 2006) is one of the most popular methods for modeling species distributions (Merow et al, 2013) and is widely used in the study of non-native reptiles (Angetter et al, 2011;Buckland et al, 2014;Cohen, 2017;Dowell et al, 2016;Falcón et al, 2012;Jarnevich et al, 2018;Mothes et al, 2019;Mutascio et al, 2018;Nania et al, 2020;Pyron et al, 2008;Rödder et al, 2008;Weterings & Vetter, 2018). Maxent has been shown to generally outperform equivalent methods (Elith et al, 2006;Gogol-Prokurat, 2011), returning highly accurate predictions even with small sets of presence-only data (Gogol-Prokurat, 2011;Merow et al, 2013;Pearson et al, 2007).…”

“…Maxent (Phillips et al., 2006) is one of the most popular methods for modeling species distributions (Merow et al., 2013) and is widely used in the study of non‐native reptiles (Angetter et al., 2011; Buckland et al., 2014; Cohen, 2017; Dowell et al., 2016; Falcón et al., 2012; Jarnevich et al., 2018; Mothes et al., 2019; Mutascio et al., 2018; Nania et al., 2020; Pyron et al., 2008; Rödder et al., 2008; Weterings & Vetter, 2018). Maxent has been shown to generally outperform equivalent methods (Elith et al., 2006; Gogol‐Prokurat, 2011), returning highly accurate predictions even with small sets of presence‐only data (Gogol‐Prokurat, 2011; Merow et al., 2013; Pearson et al., 2007).…”