ARPHA Preprints, doi: 10.3897/arphapreprints.e108136
Predicting the expansion of invasive species: how much data do we need?
expand article infoJoana Santana, Neftalí Sillero§, Joana Ribeiro|#, Cesar Capinha¤«, Ricardo Lopes#», Luís Reino|#
‡ BIOPOLIS-CIBIO/InBIO, Porto, Portugal§ CICGE-Centro de Investigação em Ciências Geo-Espaciais, Spatial Biology Lab, Faculdade de Ciências da Universidade do Porto, 4430-146 Vila Nova de Gaia, Portugal, Porto, Portugal| CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017 Lisboa, Portugal, Lisboa, Portugal¶ CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, 4485-661 Vairão, Portugal, Vairão, Portugal# BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, 4485-661 Vairão, Portugal, Vairão, Portugal¤ Centro de Estudos Geográficos, Instituto de Geografia e Ordenamento do Território, Universidade de Lisboa, Rua Branca Edmée Marques, Lisboa, Portugal, Lisboa, Portugal« Laboratório Associado Terra, Lisboa, Portugal, Lisboa, Portugal» MHNC-UP, Natural History and Science Museum of the University of Porto, 4099-002 Porto, Portugal, Porto, Portugal
Open Access
Abstract
Ecological niche models (ENMs) are a powerful tool to predict the spread of invasive alien species (IAS) and support the implementation of actions aiming to reduce the impact of biological invasions. While calibrating ENMs with distribution data from species' native ranges can underestimate the invasion potential due to possible niche shifts, using distribution data combining species’ native and invasive ranges may overestimate the invasion potential due to a reduced fitness and environmental tolerance of species in invaded ranges. An alternative may be using the increasingly available distribution data of IAS as they spread their invaded ranges, to iteratively forecast invasions as they unfold. However, while this approach accounts for possible niche shifts, it may also underestimate the species’ potential range, particularly at the early stages of the invasion when the most suitable conditions may not yet be represented in the distribution range data set. Here, we evaluate the capacity of ENMs to forecast the distribution of IAS based on distribution data on invaded ranges as these data become available. We further use dispersion models to assess the expansion process using the predicted potential distributions. Specifically, we used the common waxbill (Estrilda astrild) in the Iberia Peninsula as a model system, building ENMs with distribution records for each decade from 1960 to 2019 and yearly bioclimatic variables, to forecast the species potential range in the coming decades. Then, we analysed the performance of the models for each decade in forecasting the species observed range expansion in the following decades and evaluated how the number of distribution records determined the quality of the forecasts. Finally, we performed dispersal estimates (based on species traits, topography, climate and land cover) to analyse the prediction capacity of models as their uncertainty may be reduced when projecting them to the next decades. Our results show that invasion-only ENMs successfully forecasted the species’ range expansion over three decades after invasion, while dispersion models were not important in forecasting common waxbill expansion. Our study highlights the importance of constantly monitoring alien species, suggesting that iterative updating of ENMs with observed distribution data may accurately forecast the range expansion of alien species.
Keywords
alien species, common waxbill, dispersal analyses, ecological niche models, Estrilda astrild, forecasts