TY - JOUR
T1 - Integrated Population Models: Achieving their Potential
AU - Frost, Fay
AU - McCrea, Rachel
AU - King, Ruth
AU - Gimenez, Olivier
AU - Zipkin, Elise
N1 - Funding Information:
FF and RM were supported by EPSRC grant EP/S020470/1. RK was supported by the Leverhulme research fellowship RF-2019-299. EFZ was supported by the US National Science Foundation grant DBI-1954406. OG was supported by the French National Research Agency (grant ANR-16-CE02-0007). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/11/15
Y1 - 2022/11/15
N2 - Precise and accurate estimates of
abundance and demographic rates are primary quantities of interest within wildlife
conservation and management. Such quantities provide insight into population
trends over time and the associated underlying ecological drivers of the
systems. This information is fundamental in managing ecosystems, assessing
species conservation status and developing and implementing effective conservation
policy. Observational monitoring data are typically collected on wildlife populations
using an array of different survey protocols, dependent on the primary
questions of interest. For each of these survey designs, a range of advanced
statistical techniques have been developed which are typically well understood.
However, often multiple types of data may exist for the same population under
study. Analysing each data set separately
implicitly discards the common information contained
in the other data sets. An alternative approach that aims to optimise the shared
information contained within
multiple data sets is to use a “model-based data integration” approach, or more commonly referred to as an “integrated model”. This integrated modeling approach simultaneously analyses
all the available data within a single, and robust, statistical
framework. This paper provides a statistical overview
of ecological integrated models, with a focus on
integrated population models (IPMs) which include abundance and demographic rates as quantities of interest.
Four
main challenges within
this area are discussed, namely
model specification,
computational aspects, model assessment and forecasting. This should encourage researchers to explore further and develop new practical
tools to ensure
that full utility
can be made of IPMs for future
studies.
AB - Precise and accurate estimates of
abundance and demographic rates are primary quantities of interest within wildlife
conservation and management. Such quantities provide insight into population
trends over time and the associated underlying ecological drivers of the
systems. This information is fundamental in managing ecosystems, assessing
species conservation status and developing and implementing effective conservation
policy. Observational monitoring data are typically collected on wildlife populations
using an array of different survey protocols, dependent on the primary
questions of interest. For each of these survey designs, a range of advanced
statistical techniques have been developed which are typically well understood.
However, often multiple types of data may exist for the same population under
study. Analysing each data set separately
implicitly discards the common information contained
in the other data sets. An alternative approach that aims to optimise the shared
information contained within
multiple data sets is to use a “model-based data integration” approach, or more commonly referred to as an “integrated model”. This integrated modeling approach simultaneously analyses
all the available data within a single, and robust, statistical
framework. This paper provides a statistical overview
of ecological integrated models, with a focus on
integrated population models (IPMs) which include abundance and demographic rates as quantities of interest.
Four
main challenges within
this area are discussed, namely
model specification,
computational aspects, model assessment and forecasting. This should encourage researchers to explore further and develop new practical
tools to ensure
that full utility
can be made of IPMs for future
studies.
U2 - 10.1007/s42519-022-00302-7
DO - 10.1007/s42519-022-00302-7
M3 - Article
SN - 1559-8608
VL - 17
JO - Journal of Statistical Theory and Practice
JF - Journal of Statistical Theory and Practice
M1 - 6
ER -