Model limitations, and methodological problems
Recently, numerous SDM have been based on presence-only data and employ
so-called background points (pseudo-absences). Nonetheless, data not
only on species presence but also their true (i.e., confirmed) absence
are considered more relevant for modelling (Brotons et al., 2004;
Barbet-Massin et al., 2012; Elith et al., 2020). Unfortunately,
confirmed absence data are problematic because they need a high sampling
effort (Barbet-Massin et al., 2012; MacKenzie & Royle, 2005) to be
realistic. Our results show that in spite of the high-quality data
employed here, exclusion of squares with a richness of neophytes
(considered here as target species group) improves the model’s
performance. This suggests an issue of sampling bias, which can be
mediated by appropriate procedures. Our approach seems to be promising,
but it needs further study in order to better understand its operation.
The typical assumption, such as higher sampling effort in densely
populated areas and near roads, is not adequate for invasive species
because they typically occur in urban areas and along communication
routes (Niinemets & Peñuelas 2008; Szymura et al., 2016).
Another problem consists of
causality in our model: the approach applied represents a correlative
type of model that is unable to directly capture the underlying
processes driving the observed patterns of distribution. Contrary to
this, the mechanistic (or process-based) models, which are built using
explicit descriptions of biological mechanisms, are free from this
disadvantage (Yates et al., 2018). However, they need appropriate
formulation including detailed data on species response to environment,
preferably coming from experiments, which are typically unavailable. In
practice, the models rely to a considerable degree on parametrization
based on observational data, and as a result, the difference between
correlative and mechanistic models is often fuzzy (Yates et al., 2018).
To conclude, regarding the recent state of knowledge regarding processes
driving Solidago invasion, the mechanistic models do not have a
lot of advantages compared with correlative models, especially given the
lack of data for parametrization. Such data will come from experiments,
not from observational study.