Data from: Powerful yet challenging: Mechanistic Niche Models for predicting invasive species potential distribution under climate change

Risk assessments of invasive species present one of the most challenging
applications of species distribution models (SDMs) due to the fundamental
issues of distributional disequilibrium, niche changes, and truncation.
Invasive species often occupy only a fraction of their potential
environmental and geographic ranges, as their spatiotemporal dynamics are
shaped by intraspecific variability, human-mediated introductions, novel
biotic interactions, climate change, rapid selection, and ecological niche
shifts. Traditional correlative SDMs struggle to capture these processes
because they implicitly assume distributions are at equilibrium and rely
on observed occurrences that seldom represent the full environmental niche
of invasive species. Predicting future potential distributions therefore
requires moving beyond simple climate-matching approaches to models that
explicitly capture the mechanisms underlying species responses to their
environment. Mechanistic Niche Models (MNMs) are process-explicit models
that address these limitations by capturing species' performance
across environmental gradients. By incorporating physiological constraints
and vital rates, MNMs offer a mechanistic understanding of
species-environment relationships and enable more robust predictions onto
novel environments. However, a unified MNM framework remains elusive. In
this review, we delve into the theoretical foundations of MNMs,
emphasizing their advantages over correlative approaches, focusing on
invasive species. We provide insights into diverse modelling techniques
across taxa and examine the benefits and limitations of MNMs for
predicting species distributions under novel conditions. Our systematic
review reveals that MNMs have been applied sparingly to invasive species,
focusing primarily on insects and plants, likely due to high data
requirements. MNMs constitute the most suitable approach for defining
species distribution limits under novel conditions, but their success
depends on the relevance of input data and effective parameterisation,
including genotype selection, model type, experimental conditions, and
physiological curve-fitting techniques. MNMs offer significant potential
for advancing ecological research and providing robust tools for
evidence-based management decisions for populations in disequilibrium
under changing environmental conditions.