TY - JOUR
T1 - Combining the strengths of agent-based modelling and network statistics to understand animal movement and interactions with resources
T2 - example from within-patch foraging decisions of bumblebees
AU - Chudzinska, Magda
AU - Dupont, Yoko L.
AU - Nabe-Nielsen, Jacob
AU - Maia, Kate P.
AU - Henriksen, Marie V.
AU - Rasmussen, Claus
AU - Kissling, W. Daniel
AU - Hagen, Melanie
AU - Trøjelsgaard, Kristian
N1 - During the writing of this paper, YLD was supported by the Danish Centre for Environment and Energy (DCE).
PY - 2020/8/15
Y1 - 2020/8/15
N2 - Understanding interactions between
individual animals and their resources is fundamental to ecology.
Agent-Based Models (ABMs) offer an opportunity to study how individuals
move given the spatial distribution and characteristics of their
resources. When contrasted with empirical individual-resource network
data, ABMs can be a powerful method to detect the processes behind
observed movement patterns, as they allow for a complete and
quantitative analysis of the agent-to-environment relationships. Here we
use the small-scale, within-patch movement of bumblebees (Bombus pascuorum)
as a case study to demonstrate how ABMs can be combined with network
statistics to provide a deeper understanding of the mechanisms behind
the interactions between individuals and their resources.We
build an ABM that explicitly simulates the influence of distance to the
nearest flowering plant (allowing minimal energy expenditure and
maximum time spent foraging), plant height and number of flower heads
(as a proxy of food availability) on local foraging decisions of
bumblebees. The relative importance of these three elements is
determined using pattern-oriented modelling (POM), where we confront the
network statistics (number of visited plants, number of interactions,
nestedness and modularity) of a real B. pascuorum individual-resource network with the emergent patterns of our ABM. We also explore the model results using spatial analysis.The
model is able to reproduce the observed network statistics. Despite the
complex behaviour of bumblebees, our results show a surprisingly
precise match between the structure of the simulated and empirical
networks after adjusting a single model parameter controlling the
importance of distance to the next plant visited.Our
study illustrates the potential of combining field data, ABMs and
individual-resource networks for evaluating small-scale, within-patch
movement decisions to better understand animal movements in natural
habitats. We discuss the benefits of our approach when compared to more
classical statistical methods, and its ability to test various scenarios
in a new or altered environment.
AB - Understanding interactions between
individual animals and their resources is fundamental to ecology.
Agent-Based Models (ABMs) offer an opportunity to study how individuals
move given the spatial distribution and characteristics of their
resources. When contrasted with empirical individual-resource network
data, ABMs can be a powerful method to detect the processes behind
observed movement patterns, as they allow for a complete and
quantitative analysis of the agent-to-environment relationships. Here we
use the small-scale, within-patch movement of bumblebees (Bombus pascuorum)
as a case study to demonstrate how ABMs can be combined with network
statistics to provide a deeper understanding of the mechanisms behind
the interactions between individuals and their resources.We
build an ABM that explicitly simulates the influence of distance to the
nearest flowering plant (allowing minimal energy expenditure and
maximum time spent foraging), plant height and number of flower heads
(as a proxy of food availability) on local foraging decisions of
bumblebees. The relative importance of these three elements is
determined using pattern-oriented modelling (POM), where we confront the
network statistics (number of visited plants, number of interactions,
nestedness and modularity) of a real B. pascuorum individual-resource network with the emergent patterns of our ABM. We also explore the model results using spatial analysis.The
model is able to reproduce the observed network statistics. Despite the
complex behaviour of bumblebees, our results show a surprisingly
precise match between the structure of the simulated and empirical
networks after adjusting a single model parameter controlling the
importance of distance to the next plant visited.Our
study illustrates the potential of combining field data, ABMs and
individual-resource networks for evaluating small-scale, within-patch
movement decisions to better understand animal movements in natural
habitats. We discuss the benefits of our approach when compared to more
classical statistical methods, and its ability to test various scenarios
in a new or altered environment.
KW - Individual-resource network
KW - Bumblebee foraging patterns
KW - Pattern-oriented modelling
KW - Small-scale foraging
U2 - 10.1016/j.ecolmodel.2020.109119
DO - 10.1016/j.ecolmodel.2020.109119
M3 - Article
SN - 0304-3800
VL - 430
JO - Ecological Modelling
JF - Ecological Modelling
M1 - 109119
ER -