10.5061/DRYAD.J6Q573NFN
DeLong, John
0000-0003-0558-8213
University of Nebraska - Lincoln
Coblentz, Kyle
University of Nebraska - Lincoln
Prey diversity constrains the adaptive potential of predator foraging traits
Dryad
dataset
2021
eco-evolutionary dynamics
food web stability
GEM
keystone predator
James S. McDonnell Foundation
https://ror.org/03dy4aq19
2021-11-08T00:00:00Z
2021-11-08T00:00:00Z
en
https://doi.org/10.5281/zenodo.5585382
22405863 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Predators are generally under selective pressure to get better at
foraging, leading to steeper functional responses and stronger
predator-prey interactions. Yet strong interactions can de-stabilize food
webs, and most interactions across ecological communities are thought to
be weak. This conflict between evolutionary and community expectations for
the strength of predator-prey interactions represents a fundamental gap in
our understanding of how the evolution of foraging plays out in food webs.
Here we help to resolve the conflict by showing analytically that the
expectation for the evolution of steeper functional responses is relaxed
in communities with diverse prey types. We simulate communities with
varying prey richness and show that increasing prey richness can indeed
constrain the adaptive potential of predator foraging traits, but that at
low prey richness predators can evolve to have a stronger interaction with
prey that have high net energy yields. Our results also indicate that
handling time plays a role in determining whether predators may evolve to
have a stronger interaction with abundant prey, suggesting that the
evolution of keystone predator modules in food webs is most likely when
handling times are negligible. Our results also provide a new mechanism
predicting more diffuse interactions in diverse tropical communities
relative to more species-poor communities at higher latitudes.
The data uploaded here are abundances and traits through time generated in
simulations. Each file is a replicate given a level of prey species
richness (1, 2, 4, 8) and interspecific competition (0.5 or 1, with 1
reflecting neutrality). Each file is named according to the run, e.g.
"GEM_output_alpha=0.5_2_1_prey_high_h" is the output for
competition of 0.5 (complementary prey species) with 2 prey types and
replicate 1. In this case, we are also specifying a high handling time.
These data can be imported and analyzed however you want, but the code we
used is provided in the file called
"Plot_GEMv5_multi_prey_multi_run4.m". This is a Matlab file. It
will import each of the simulation files and reproduce the figures in the
manuscript. It requires the function "jbfill.m". The remaining
files are code to run the simulations. See "Usage notes" for
explanation.
This set of Matlab scripts and functions will initiate and perform the GEM
simulation. Start with "Call_multi_prey_GEM.m", which is where
you will specify prey levels to use, type of competition, and number of
replicates. This file then calls the primary GEM function in a loop. The
primary GEM function saves the simulations each in turn as the script runs
through the levels and replicates. The primary GEM function is
"GEMv5_multi_prey.m". This is a function and needs to be called.
It is set up to run in parallel, so careful attention must be paid to
changes in the script structure. This function depends on other
subfunctions: "V4_pick_individuals.m",
"V5_medians_and_cis.m", "V4_initiate_populations.m",
and "create_multi_prey_model.m" that handle the repeated tasks.