Integrated population models: powerful methods to embed individual processes in population dynamics models

Abstract

Population dynamics models have long assumed that populations are composed of a restricted number of groups, where individuals in each group have identical demographic rates and where all groups are similarly affected by density‐dependent and ‐independent effects. However, individuals usually vary tremendously in performance and in their sensitivity to environmental conditions or resource limitation, such that individual contributions to population growth will be highly variable. Recent efforts to integrate individual processes in population models open up new opportunities for the study of eco‐evolutionary processes, such as the density‐dependent influence of environmental conditions on the evolution of morphological, behavioral, and life‐history traits. We review recent advances that demonstrate how including individual mechanisms in models of population dynamics contributes to a better understanding of the drivers of population dynamics within the framework of integrated population models (IPMs). IPMs allow for the integration in a single inferential framework of different data types as well as variable population structure including sex, social group, or territory, all of which can be formulated to include individual‐level processes. Through a series of examples, we first show how IPMs can be beneficial for getting more accurate estimates of demographic traits than classic matrix population models by including basic population structure and their influence on population dynamics. Second, the integration of individual‐ and population‐level data allows estimating density‐dependent effects along with their inherent uncertainty by directly using the population structure and size to feedback on demography. Third, we show how IPMs can be used to study the influence of the dynamics of continuous individual traits and individual quality on population dynamics. We conclude by discussing the benefits and limitations of IPMs for integrating data at different spatial, temporal, and organismal levels to build more mechanistic models of population dynamics.

Innovations in data integration for modeling populations

“…Plard et al. () discuss how individual‐level processes can be incorporated into IPMs to examine the effects of density‐dependence on population dynamics while also incorporating continuous trait data, unifying IPMs with integral projection models (the other IPM, integrating demographic functions over continuous individual traits).…”

“…While IPMs are typically used because of deficiencies in various data sets, Saunders et al (2019) highlight how IPMs can also be used to resolve discrepancies in inferences from individual analysis of independent data sets. Plard et al (2019) discuss how individual-level processes can be incorporated into IPMs to examine the effects of density-dependence on population dynamics while also incorporating continuous trait data, unifying IPMs with integral projection models (the other IPM, integrating demographic functions over continuous individual traits). Fletcher et al (2019) and Pacifici et al (2019) discuss opportunities and challenges for integrating multiple types of data (typically count, presence-absence, and/or presence-only) to estimate species distributions.…”

Size‐ and stage‐dependence in cause‐specific mortality of migratory brown trout

“…In our case, not just one but two distinct data sources provide information on growth: length measurements from trout captured in the fish ladder (markrecapture data) and lengths back-calculated from scale year rings of a subset of marked individuals. This provides a unique opportunity for integrated analysis of multiple data sets which is likely to result in more precise estimates of vital rates, more comprehensive understanding of variation therein and insights into potential discrepancies among different types of data (Plard, Fay, Kéry, Cohas, & Schaub, 2019;Saunders et al, 2019). The framework of integrated population models (Plard, Fay, et al, 2019) in general, and recent extensions for populations structured by continuous traits in particular (Plard, Turek, Grüebler, & Schaub, 2019), lend themselves well to the study of these questions for our system and will follow naturally from the integration of growth and survival estimation.…”

“…This provides a unique opportunity for integrated analysis of multiple data sets which is likely to result in more precise estimates of vital rates, more comprehensive understanding of variation therein and insights into potential discrepancies among different types of data (Plard, Fay, Kéry, Cohas, & Schaub, 2019;Saunders et al, 2019). The framework of integrated population models (Plard, Fay, et al, 2019) in general, and recent extensions for populations structured by continuous traits in particular (Plard, Turek, Grüebler, & Schaub, 2019), lend themselves well to the study of these questions for our system and will follow naturally from the integration of growth and survival estimation. Fully integrated, size-structured population models will further provide new opportunities to study the joint impacts of harvesting, stocking, habitat alteration, climate change and disease dynamics (Plard, Fay, et al, 2019) and are thus highly relevant for future studies aiming to improve understanding and inform management of the trout in lake Mjøsa and of animal population inhabiting ecosystems heavily impacted by human activity in general.…”

Integrating social and ecological data to model metapopulation dynamics in coupled human and natural systems

“…Government institutions impose policies that incentivize or discourage different decisions of resource users (e.g., landowners) under their jurisdiction. Land-use change decisions of resource users are then modeled as functions of these policies, interactions with neighbors, and their own heterogeneous characteristics, as well as those of their patches (equivalent to individual variation; Plard et al 2019). For example, in this article we use regression on social survey data to estimate the probability of landowners responding to policies in different ways (a heuristic rule-based decision-making model), but there are many other types of land-use change submodels (see Parker et al 2003 for reviews).…”