10.5061/DRYAD.69SQ3
Sullivan, Lauren L.
University of Minnesota
Li, Bingtuan
University of Louisville
Miller, Tom E. X.
Rice University
Neubert, Michael G.
Woods Hole Oceanographic Institution
Shaw, Allison K.
University of Minnesota
Data from: Density dependence in demography and dispersal generates
fluctuating invasion speeds
Dryad
dataset
2018
Allee effects
integrodifference equations
National Science Foundation
https://ror.org/021nxhr62
DMS-1515875, DEB-1501814, DEB-1257545, DEB-1145017
2018-04-19T00:00:00Z
2018-04-19T00:00:00Z
en
https://doi.org/10.1073/pnas.1618744114
84747984 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Density dependence plays an important role in population regulation and is
known to generate temporal fluctuations in population density. However,
the ways in which density dependence affects spatial population processes,
such as species invasions, are less understood. Although classical
ecological theory suggests that invasions should advance at a constant
speed, empirical work is illuminating the highly variable nature of
biological invasions, which often exhibit nonconstant spreading speeds,
even in simple, controlled settings. Here, we explore endogenous density
dependence as a mechanism for inducing variability in biological invasions
with a set of population models that incorporate density dependence in
demographic and dispersal parameters. We show that density dependence in
demography at low population densities—i.e., an Allee effect—combined with
spatiotemporal variability in population density behind the invasion front
can produce fluctuations in spreading speed. The density fluctuations
behind the front can arise from either overcompensatory population growth
or density-dependent dispersal, both of which are common in nature. Our
results show that simple rules can generate complex spread dynamics and
highlight a source of variability in biological invasions that may aid in
ecological forecasting.
Figure 1Code to run models and plot Figure 1Fig1.zipFigure 2Code to run
the models and plot Figure 2Fig2.zipFigure 3Code to run model and plot
Figure 3Fig3.zip