biophysical models; marine larval dispersal; demographic process; sea
Larval dispersal is arguably the most important but least understood demographic process in the sea.
The likelihood of a larva dispersing from its birthplace to successfully recruit in another location
is the culmination of many intrinsic and extrinsic factors that operate in early life. Empirically
estimating the resulting population connectivity has been immensely difficult because of the
challenges of studying and quantifying dispersal in the sea. Consequently, most estimates are based
on predictions from biophysical models. Although there is a long history of dispersal modelling,
there has been no comprehensive review of this literature. We conducted a systematic quantitative
review to address the following questions: (1) Is there any bias in the distribution of research effort
based on geographical or taxonomic coverage? (2) Are hydrodynamic models resolving ocean
circulation at spatial scales (resolution and extent) relevant to the dispersal process under study?
(3) Where, when and how many particles are being tracked, and is this effort sufficient to capture
the spatiotemporal variability in dispersal? (4) How is biological and/or behavioural complexity
incorporated into Lagrangian particle tracking models. (i.e. are key attributes of the dispersal
process well captured.)? Our review confirms strong taxonomic and geographic biases in published
work to date. We found that computational ‘effort’ (i.e. model resolution and particle number) has
not kept pace with dramatic increases in computer processor speed. We also identified a number of
shortcomings in the incorporation of biology, and behaviour specifically into models. Collectively,
these findings highlight some important gaps and key areas for improvement of biophysical models
that aspire to inform larval dispersal processes. In particular, we suggest the need for greater
emphasis on validation of model assumptions, as well as testing of dispersal predictions with
empirically derived data.