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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.

Persecution and overexploitation by humans are major causes of species extinctions. Rare species, often confined to small geographic ranges, are usually at highest risk, whereas extinctions of superabundant species with very large ranges are rare. The Yellow‐breasted Bunting (Emberiza aureola) used to be one of the most abundant songbirds of the Palearctic, with a very large breeding range stretching from Scandinavia to the Russian Far East. Anecdotal information about rapid population declines across the range caused concern about unsustainable trapping along the species’ migration routes. We conducted a literature review and used long‐term monitoring data from across the species’ range to model population trend and geographical patterns of extinction. The population declined by 84.3–94.7% between 1980 and 2013, and the species’ range contracted by 5000 km. Quantitative evidence from police raids suggested rampant illegal trapping of the species along its East Asian flyway in China. A population model simulating an initial harvest level of 2% of the population, and an annual increase of 0.2% during the monitoring period produced a population trajectory that matched the observed decline. We suggest that trapping strongly contributed to the decline because the consumption of Yellow‐breasted Bunting and other songbirds has increased as a result of economic growth and prosperity in East Asia. The magnitude and speed of the decline is unprecedented among birds with a comparable range size, with the exception of the Passenger Pigeon (Ectopistes migratorius), which went extinct in 1914 due to industrial‐scale hunting. Our results demonstrate the urgent need for an improved monitoring of common and widespread species’ populations, and consumption levels throughout East Asia.

Many animal populations are subject to hunting or fishing in the wild. Detailed knowledge of demographic parameters (e.g. survival, reproduction) and temporal dynamics of such populations is crucial for sustainable management. Despite their relevance for management decisions, structure and size of exploited populations are often not known, and data limited. Recently, joint analysis of different types of demographic data, such as population counts, reproductive data and capture-mark-recapture data, within integrated population models (IPMs) has gained much popularity as it may allow estimating population size and structure, as well as key demographic rates, while fully accounting for uncertainty. IPMs built so far for exploited populations have typically been built as age-structured population models. However, the age of harvested individuals is usually difficult and/or costly to assess and therefore often not available. Here, we introduce an IPM structured by body size classes, which allows making efficient use of data commonly available in exploited populations for which accurate information on age is often missing. The model jointly analyzes size-at-harvest data, capture-mark-recapture-recovery data and reproduction data from necropsies, and we illustrate its applicability in a case study involving heavily hunted wild boar. This species has increased in abundance over the last decades despite intense harvest, and the IPM analysis provides insights into the roles of natural mortality, body growth, maturation schedules and reproductive output in compensating for the loss of individuals to hunting. Early maturation and high reproductive output contributed to wild boar population persistence despite a strong hunting pressure. We thus demonstrate the potential of size-class-structured IPMs as tools to investigate the dynamics of exploited populations with limited information on age, and highlight both the applicability of this framework to other species and its potential for follow-up analyses highly relevant to management.

Assessing the effects of climate and interspecific relationships on communities is challenging because of the complex interplay between species population dynamics, their interactions, and the need to integrate information across several biological levels (individuals – populations – communities). Usually used to quantify single‐species demography, integrated population models (IPMs) have recently been extended to communities. These models allow fitting multispecies matrix models to data from multiple sources while simultaneously accounting for uncertainty in each data source. We used multispecies IPMs accommodating climatic variables to quantify the relative contribution of climate vs. interspecific interactions on demographic parameters, such as survival and breeding success, in the dynamics of a predator–prey system. We considered a stage‐structured predator–prey system combining 22 years of capture–recapture data and population counts of two seabirds, the Brown Skua (Catharacta lönnbergi) and its main prey the Blue Petrel (Halobaena caerulea), both breeding on the Kerguelen Islands in the Southern Ocean. Our results showed that climate and predator–prey interactions drive the demography of skuas and petrels in different ways. The breeding success of skuas appeared to be largely driven by the number of petrels and to a lesser extent by intraspecific density‐dependence. In contrast, there was no evidence of predation effects on the demographic parameters of petrels, which were affected by oceanographic factors. We conclude that bottom‐up mechanisms are the main drivers of this skua–petrel system.

The objective of this research is to address saiga conservation challenges amid significant anthropogenic pressures through a population model-based approach. The study involved an analysis of herd composition in terms of sex, age, and size over the years 2019-2022, with a focus on lamb weight measurements in 2022. Notably, 2021 witnessed a substantial decline in offspring numbers, with lambs being three times scarcer compared to 2020 and 5.2 times fewer than in 2022 (p ≤ 0.05). Male saigas exhibited a 3.1-fold reduction in birth rates in 2021 relative to 2020 and a 5.6-fold decrease compared to 2022 (p < 0.05). The fluctuations in the number of females and males across the three years were found to be statistically comparable (p ≥ 0.05). Since 2022, there has been a notable increase in the size of saiga herds, with counts reaching 450-500 animals compared to 2019-2020 (p ≤ 0.05). The observation identified a total of 93 saiga herds, with six of them consisting of more than 1,000 antelopes. The augmentation of male presence within herds is anticipated to contribute to sustaining saiga population growth. Achieving this outcome may necessitate the implementation of captive breeding initiatives or an integrated approach.

Many historical processes exhibit recurrent patterns of change. Century-long periods of population expansion come before long periods of stagnation and decline; the dynamics of prices mirror population oscillations; and states go through strong expansionist phases followed by periods of state failure, endemic sociopolitical instability, and territorial loss. Peter Turchin and Sergey Nefedov explore the dynamics and causal connections between such demographic, economic, and political variables in agrarian societies and offer detailed explanations for these long-term oscillations--what the authors call secular cycles.

A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long-term data sets (1963–2015) within an IPM to estimate continental-scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock’s geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.

Individual-based models are an exciting and widely used new tool for ecology. These computational models allow scientists to explore the mechanisms through which population and ecosystem ecology arises from how individuals interact with each other and their environment. This book provides the first in-depth treatment of individual-based modeling and its use to develop theoretical understanding of how ecological systems work, an approach the authors call \"individual-based ecology.\" Grimm and Railsback start with a general primer on modeling: how to design models that are as simple as possible while still allowing specific problems to be solved, and how to move efficiently through a cycle of pattern-oriented model design, implementation, and analysis. Next, they address the problems of theory and conceptual framework for individual-based ecology: What is \"theory\"? That is, how do we develop reusable models of how system dynamics arise from characteristics of individuals? What conceptual framework do we use when the classical differential equation framework no longer applies? An extensive review illustrates the ecological problems that have been addressed with individual-based models. The authors then identify how the mechanics of building and using individual-based models differ from those of traditional science, and provide guidance on formulating, programming, and analyzing models. This book will be helpful to ecologists interested in modeling, and to other scientists interested in agent-based modeling.

Ecologists widely acknowledge that a complex interplay of endogenous (density‐dependent) and exogenous (density‐independent) factors impact demographic processes. Individuals respond differently to those forces, ultimately shaping the dynamics of wild populations. Most comprehensive studies disentangling simultaneously the effects of density dependence, climate, and prey abundance while taking into account age structure were conducted in terrestrial ecosystems. However, studies on marine populations are lacking. Here we provide insight into the mechanisms affecting four vital rates of an apex Antarctic marine predator population, the South Polar Skua Catharacta maccormicki, by combining a nearly half‐century longitudinal time series of individual life histories and abundance data, with climatic and prey abundance covariates. Using multistate capture–mark–recapture models, we estimated age classes effects on survival, breeding, successful breeding with one or two chicks and successful breeding with two chicks probabilities, and assessed the different effects of population size, climate, and prey abundance on each age‐specific demographic parameter. We found evidence for strong age effects in the four vital rates studied. Vital rates at younger ages were lower than those of older age classes for all parameters. Results clearly evidenced direct and indirect influences of local climate (summer sea ice concentration), of available prey resources (penguins), and of intrinsic factors (size of the breeding population). More covariate effects were found on reproductive rates than on survival, and younger age classes were more sensitive than the older ones. Results from a deterministic age‐structured density‐dependent matrix population model indicated greater effects of prey abundance and sea ice concentration on the total population size than on the breeding population size. Both total population size and the number of breeders were strongly affected by low values of sea ice concentration. Overall, our results highlight the greater sensitivity of reproductive traits and of younger age classes to prey abundance, climate variability, and density dependence in a marine apex predator, with important consequences on the total population size but with limited effects on the breeding population size. We discuss the mechanisms by which climate variability, prey abundance, and population size may affect differentially age‐specific vital rates, and the potential population consequences of future environmental changes.