Incorporating functional connectivity into species distribution models improves the prediction of invasiveness of an exotic species not at niche equilibrium - Pereira, Pedro; Francesco, Valerio; Lourenço, Rui (Springer, 2023)
Biological invasion assessments have often used species distribution models (SDMs) assuming species equilibrium with the environment. However, the identification of invaded areas seems more accurate when incorporating movement constraints as landscape connectivity (e.g., circuit theory models). We studied an introduced population of an Asian bird species (Leiothrix lutea) in Portugal during its spreading stage between 2014 and 2019 to: (1) compare accuracy in inferring year-based invasion stages between resistance surfaces models (from SDMs) and circuit theory models; (2) quantify the consistency of niche conservatism; and (3) map a long-term connectivity pattern (2014–2050) to predict future invaded areas. We considered three environmental variables: two static (distance to rivers and altitude) and a dynamic (normalized difference vegetation index: NDVI). SDMs were projected during the species dispersal period to infer range expansion, and then converted into resistance surfaces. We compared SDM performances with those of circuit theory models, built with resistance surfaces plus reachable habitat patches as nodes. Overall, our results showed the superiority of circuit theory models over SDMs in inferring invasion. Along the years, SDMs showed that the relative importance of river proximity decreased while NDVI increased, with landscape metrics suggesting an increasing niche generalism. We examined niche conservatism across years by comparing continuous distribution to binary maps (habitat patches) through landscape metrics. We found no evidence for niche conservatism after accounting for landscape variation. Our findings highlight the importance of following each invasion stage of an established exotic species, as well as incorporating niche breadth and dispersal constraints into frameworks to enhance population monitoring and control strategies.