Using survival regression to study patterns of expansion of invasive species: will the common waxbill expand with global warming?

Abstract

Different approaches can be used to model the spread of invasive species. Here we demonstrate the use of survival regression, an approach that can be used to study a variety of events, not just death, to model the time to colonization. The advantage of survival regression to study colonisation of new areas is that information on those areas that have not been invaded by the end of a study can be included in the analysis, thus potentially increasing the accuracy of parameter estimation. We use proportional hazards regression (PHR; a type of survival regression) to model the spread of the common waxbill Estrilda astrild in Portugal. The species invaded Portugal in two peaks of invasion between 1964 and 1999. We built a PHR model with the information available up to the first invasion peak, then used this model to predict the pattern of invasion in the second peak. PHR had useful forecasting capabilities: areas that were actually colonised by 1999 had significantly higher hazards of colonization based on information from the first wave of invasion than areas that were not colonised. We then built a final model of expansion of the common waxbill that combined all available data up to 1999. Among climate variables, the most important predictor of colonization was temperature, followed by relative humidity. We used this model to estimate the invasion potential of the species under climate change scenarios, observing that an increase of 18C in mean annual temperature increased the risk of a new invasion by 47%. Our analyses suggest that survival regression may be a useful tool for studying the geographical spread of invasive species. However, PHR was conceived as a descriptive technique rather than as a predictive tool, and thus further research is needed to empirically test the predictive capabilities of PHR.

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

“…During the last decades, the species has increased its invaded range at least within the Iberian Peninsula (Silva et al 2002). Based on data presented by Reino et al (2009), the species might further expand its invaded range in south-western Europe making future predictions on a global scale especially appealing.…”

Avian SDMs: current state, challenges, and opportunities

“…General application Examples Specific issues Examples Conservation Identification of priority areas for bird conservation Guisan et al 2013, Frick et al 2014 Seabirds and marine environments Lavers et al 2014 Identifying protected areas to meet specific targets Naoe et al 2015 Identifying no-go areas to reduce human-wildlife conflicts in wind power planning Reid et al 2015 Identifying specific habitats for certain species needs Brambilla and Saporetti 2014 Validating umbrella species to match conservation goals Fourcade et al 2017 Evaluating or forecasting the effect of environmental changes Green et al 2008 Future effectiveness of protected areas over different spatial scales Coetzee et al 2009, Hole et al 2009, Veloz et al 2013, Virkkala et al 2013, Brambilla et al 2015 Kissling 2013, Tracewski et al 2016 Including changes in demography Haché et al 2016 Including nest predation and food limitation Harris et al 2012 Including wind farm construction Bastos et al 2016 Invasive birds Predictions of invasion risk Muñoz and Real 2006, Nyári et al 2006, Real et al 2008, Strubbe and Matthysen 2009, Herrando et al 2010, Di Febbraro and Mori 2015, Fraser et al 2015 Range dynamics under climate change Huntley et al 2007, Reino et al 2009, Graham et al 2011 (Continued) …”

Combining multiple models to predict the geographical distribution of the Baru tree (Dipteryx alata Vogel) in the Brazilian Cerrado

“…These processes relate the known occurrences of a species and relevant environmental data to produce predicted geographic occurrences (Guisan and Zimmermann, 2000). There are several techniques that can be employed for niche modelling, such as Generalised Linear Models (McCullagh and Nelder, 1989), Generalised Additive Models (Hastie and Tibshirani, 1990), Principal Component Analysis (Robertson et al, 2003), Artificial Neural Networks (Mastrorillo et al, 1997), Maximum Entropy and others (see Elith et al, 2006;Reino et al, 2009). Many recent papers discuss these statistical techniques, how to evaluate the models they generate, and how to detect which model is more robust or accurate (Segurado and Araújo, 2004;Elith et al, 2006).…”

“…Geographical distribution modelling has become an important tool in filling this gap and in furthering ecology, conservation and evolution studies. Specific applications include the study of the expansion of invasive species (Carroll et al, 2001;Peterson et al, 2007;Zhu et al, 2007;Reino et al, 2009), the effects of climatic changes on biodiversity (Heikkinen et al, 2006) and the establishment of conservation plans for threatened species (Solano and Feria, 2007;Marage et al, 2008).…”