Mechanistic description of population dynamics using dynamic energy budget theory incorporated into integral projection models

Are some species more sensitive to environmental change than others? It may all depend on the context

Research Highlight: Rademaker, M., van Leeuwen, A., & Smallegange, I. M. (2024). Why we cannot always expect life history strategies to directly inform on sensitivity to environmental change. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.14050. Ecological studies have long delved into how organisms allocate energy between reproduction and somatic maintenance to maximize fitness. This allocation gives rise to various life-history strategies, and these strategies have been shown to predict how populations respond to environmental change, allowing us to generalize potential responses to increasing human pressures. Such predictions have, however, been made for a limited set of terrestrial taxa and typically do not explore how individual differences in life-history responses to environmental change scale to affect population-level responses. Using novel data on diverse fish species, Rademaker et al. (2024) construct models that link individual-level trade-offs in energy allocation under environmental change to population-level life-history strategies. A key finding in their study is that short-lived species are not more sensitive to environmental change-unlike results of previous studies. This study represents a new generation of work that underscores the complexity of predicting population responses to environmental shifts and suggests a need for a broader understanding of individual-level mechanisms. The results of Rademaker et al. (2024) encourage further mechanistic life-history analyses across a wider range of species and populations to validate the exciting findings and explore their implications across diverse ecological contexts.

Why we cannot always expect life history strategies to directly inform on sensitivity to environmental change

Variation in life history traits in animals and plants can often be structured along major axes of life history strategies. The position of a species along these axes can inform on their sensitivity to environmental change. For example, species with slow life histories are found to be less sensitive in their long-term population responses to environmental change than species with fast life histories. This provides a tantalizing link between sets of traits and population responses to change, contained in a highly generalizable theoretical framework. Life history strategies are assumed to reflect the outcome of life history tradeoffs that, by their very nature, act at the individual level. Examples include the tradeoff between current and future reproductive success, and allocating energy into growth versus reproduction. But the importance of such tradeoffs in structuring population-level responses to environmental change remains understudied. We aim to increase our understanding of the link between individual-level life history tradeoffs and the structuring of life history strategies across species, as well as the underlying links to population responses to environmental change. We find that the classical association between lifehistory strategies and population responses to environmental change breaks down when accounting for individual-level tradeoffs and energy allocation. Therefore, projecting population responses to environmental change should not be inferred based only on a limited set of species traits. We summarize our perspective and a way forward in a conceptual framework.

DEBBIES Dataset to study Life Histories across Ectotherms

Demographic models are used to explore how life history traits structure life history strategies across species. This study presents the DEBBIES dataset that contains estimates of eight life history traits (length at birth, puberty and maximum length, maximum reproduction rate, fraction energy allocated to respiration versus reproduction, von Bertalanffy growth rate, mortality rates) for 185 ectotherm species. The dataset can be used to parameterise dynamic energy budget integral projection models (DEB-IPMs) to calculate key demographic quantities like population growth rate and demographic resilience, but also link to conservation status or biogeographical characteristics. Our technical validation shows a satisfactory agreement between observed and predicted longevity, generation time, age at maturity across all species. Compared to existing datasets, DEBBIES accommodates (i) easy cross-taxonomical comparisons, (ii) many data-deficient species, and (iii) population forecasts to novel conditions because DEB-IPMs include a mechanistic description of the trade-off between growth and reproduction. This dataset has the potential for biologists to unlock general predictions on ectotherm population responses from only a few key life history traits.

Investigating tritrophic interactions using bioenergetic demographic models

A central debate in ecology has been the long-running discussion on the role of apex predators in affecting the abundance and dynamics of their prey. In terrestrial systems, research has primarily relied on correlational approaches, due to the challenge of implementing robust experiments with replication and appropriate controls. A consequence of this is that we largely suffer from a lack of mechanistic understanding of the population dynamics of interacting species, which can be surprisingly complex. Mechanistic models offer an opportunity to examine the causes and consequences of some of this complexity. We present a bioenergetic mechanistic model of a tritrophic system where the primary vegetation resource follows a seasonal growth function, and the herbivore and carnivore species are modeled using two integral projection models (IPMs) with body mass as the phenotypic trait. Within each IPM, the demographic functions are structured according to bioenergetic principles, describing how animals acquire and transform resources into body mass, energy reserves, and breeding potential. We parameterize this model to reproduce the population dynamics of grass, elk, and wolves in northern Yellowstone National Park (USA) and investigate the impact of wolf reintroduction on the system. Our model generated predictions that closely matched the observed population sizes of elk and wolf in Yellowstone prior to and following wolf reintroduction. The introduction of wolves into our basal grass-elk bioenergetic model resulted in a population of 99 wolves and a reduction in elk numbers by 61% (from 14,948 to 5823) at equilibrium. In turn, vegetation biomass increased by approximately 25% in the growing season and more than threefold in the nongrowing season. The addition of wolves to the model caused the elk population to switch from being food-limited to being predator-limited and had a stabilizing effect on elk numbers across different years. Wolf predation also led to a shift in the phenotypic composition of the elk population via a small increase in elk average body mass. Our model represents a novel approach to the study of predator-prey interactions, and demonstrates that explicitly considering and linking bioenergetics, population demography and body mass phenotypes can provide novel insights into the mechanisms behind complex ecosystem processes.

Modeling the effects of ration on individual growth of Oncorhynchus tshawytscha under controlled conditions

Fed aquaculture is one of the fastest-growing and most valuable food production industries in the world. The efficiency with which farmed fish convert feed into biomass influences both environmental impact and economic revenue. Salmonid species, such as king salmon (Oncorhynchus tshawytscha), exhibit high levels of plasticity in vital rates such as feed intake and growth rates. Accurate estimations of individual variability in vital rates are important for production management. The use of mean trait values to evaluate feeding and growth performance can mask individual-level differences that potentially contribute to inefficiencies. Here, the authors apply a cohort integral projection model (IPM) framework to investigate individual variation in growth performance of 1625 individually tagged king salmon fed one of three distinct rations of 60%, 80%, and 100% satiation and tracked over a duration of 276 days. To capture the observed sigmoidal growth of individuals, they compared a nonlinear mixed-effects (logistic) model to a linear model used within the IPM framework. Ration significantly influenced several aspects of growth, both at the individual and at the cohort level. Mean final body mass and mean growth rate increased with ration; however, variance in body mass and feed intake also increased significantly over time. Trends in mean body mass and individual body mass variation were captured by both logistic and linear models, suggesting the linear model to be suitable for use in the IPM. The authors also observed that higher rations resulted in a decreasing proportion of individuals reaching the cohort's mean body mass or larger by the end of the experiment. This suggests that, in the present experiment, feeding to satiation did not produce the desired effects of efficient, fast, and uniform growth in juvenile king salmon. Although monitoring individuals through time is challenging in commercial aquaculture settings, recent technological advances combined with an IPM approach could provide new scope for tracking growth performance in experimental and farmed populations. Using the IPM framework might allow the exploration of other size-dependent processes affecting vital rate functions, such as competition and mortality.

Joint survival modelling for multiple species exposed to toxicants

In environmental risk assessment (ERA), the multitude of compounds and taxa demands cross-species extrapolation to cover the variability in sensitivity to toxicants. However, only the impact of a single compound to a single species is addressed by the general unified threshold model of survival (GUTS). The reduced GUTS is the recommended model to analyse lethal toxic effects in regulatory aquatic ERA. GUTS considers toxicokinetics and toxicodynamics. Two toxicodynamic approaches are considered: Stochastic death (SD) assumes that survival decreases with an increasing internalized amount of the toxicant. Individual tolerance (IT) assumes that individuals vary in their tolerance to toxic exposure. Existing theory suggests that the product of the threshold z_w and killing rate b_w (both SD toxicodynamic parameters) are constant across species or compounds if receptors and target sites are shared. We extend that theory and show that the shape parameter β of the loglogistic threshold distribution in IT is also constant. To verify the predicted relationships, we conducted three tests using toxicity studies for eight arthropods exposed to the insecticide flupyradifurone. We confirmed previous verifications of the relation- between SD parameters, and the newly established relation for the IT parameter β. We enhanced GUTS to jointly model survival for multiple species with shared receptors and pathways by incorporating the relations among toxicodynamic parameters described above. The joint GUTS exploits the shared parameter relations and therefore constrains parameter uncertainty for each of the separate species. Particularly for IT, the joint GUTS more precisely predicted risk to the separate species than the standard single species GUTS under environmentally realistic exposure. We suggest that joint GUTS modelling can improve cross-species extrapolation in regulatory ERA by increasing the reliability of risk estimates and reducing animal testing. Furthermore, the shared toxicodynamic response provides potential to reduce complexity of ecosystem models.

Optimal energy allocation trade-off driven by size-dependent physiological and demographic responses to warming

Body size-dependent physiological effects of temperature influence individual growth, reproduction, and survival, which govern animal population responses to global warming. Considerable knowledge has been established on how such effects can affect population growth and size structure, but less is known of their potential role in temperature-driven adaptation in life-history traits. In this study, we ask how warming affects the optimal allocation of energy between growth and reproduction and disentangle the underlying fitness trade-offs. To this end, we develop a novel dynamic energy budget integral projection model (DEB-IPM), linking individuals' size- and temperature-dependent consumption and maintenance via somatic growth, reproduction, and size-dependent energy allocation to emergent population responses. At the population level, we calculate the long-term population growth rate (fitness) and stable size structure emerging from demographic processes. Applying the model to an example of pike (Esox lucius), we find that optimal energy allocation to growth decreases with warming. Furthermore, we demonstrate how growth, fecundity, and survival contribute to this change in optimal allocation. Higher energy allocation to somatic growth at low temperatures increases fitness through survival of small individuals and through the reproduction of larger individuals. In contrast, at high temperatures, increased allocation to reproduction is favored because warming induces faster somatic growth of small individuals and increased fecundity but reduced growth and higher mortality of larger individuals. Reduced optimum allocation to growth leads to further reductions in body size and an increasingly truncated population size structure with warming. Our study demonstrates how, by incorporating general physiological mechanisms driving the temperature dependence of life-history traits, the DEB-IPM framework is useful for investigating the adaptation of size-structured organisms to warming.

Using Food Webs and Metabolic Theory to Monitor, Model, and Manage Atlantic Salmon—A Keystone Species Under Threat

Populations of Atlantic salmon are crashing across most of its natural range: understanding the underlying causes and predicting these collapses in time to intervene effectively are urgent ecological and socioeconomic priorities. Current management techniques rely on phenomenological analyses of demographic population time-series and thus lack a mechanistic understanding of how and why populations may be declining. New multidisciplinary approaches are thus needed to capitalize on the long-term, large-scale population data that are currently scattered across various repositories in multiple countries, as well as marshaling additional data to understand the constraints on the life cycle and how salmon operate within the wider food web. Here, we explore how we might combine data and theory to develop the mechanistic models that we need to predict and manage responses to future change. Although we focus on Atlantic salmon—given the huge data resources that already exist for this species—the general principles developed here could be applied and extended to many other species and ecosystems.

Endogenous Lifecycle Models for Chemical Risk Assessment

Despite over 50 years of research on the use of population models in chemical risk assessment, their practical utility has remained elusive. A novel application and interpretation of ecotoxicological models, Endogenous Lifecycle Models (ELM), is proposed that offers some of the benefits sought from population models, at much lower cost of design, parametrization, and verification. ELMs capture the endogenous lifecycle processes of growth, development, survival, and reproduction and integrate these to estimate and predict expected fitness. Two measures of fitness are proposed as natural model predictions in the context of chemical risk assessment, lifetime reproductive success, and the expected annual propagation of genetic descendants, including self (intrinsic fitness). Six characteristics of the ELM approach are reviewed and illustrated with two ELM examples, the first for a general passerine lifecycle and the second for bald eagle (Haliaeetus leucocephalus). Throughout, the focus is on development of robust qualitative model predictions that depend as little as possible on specific parameter values. Thus, ELMs sacrifice precision to optimize generality in understanding the effects of chemicals across the diversity of avian lifecycles. Notably, the ELM approach integrates naturally with the adverse outcome pathway framework; this integration can be employed as a midtier risk assessment tool when lower tier analyses suggest potential risk.

Persistence and size of seasonal populations on a consumer-resource relationship depends on the allocation strategy toward life-history functions

The long-term ecological dynamics of a population inhabiting a seasonal environment is analyzed using a semi-discrete or impulsive system to represent the consumer-resource interaction. The resource corresponds to an incoming energy flow for consumers that is allocated to reproduction as well as to maintenance in each non-reproductive season. The energy invested in these life-history functions is used in reproductive events, determining the size of the offspring in each reproductive season. Two long-term dynamic patterns are found, resulting in either the persistence or the extinction of the population of consumers. In addition, our model indicates that only one energy allocation strategy provides an optimal combination between individual consumption and long-term population size. The current study contributes to the understanding of how the individual-level and the population-level are interrelated, exhibiting the importance of incorporating phenotypic traits in population dynamics.

Quantifying impacts of plastic debris on marine wildlife identifies ecological breakpoints

Quantifying sublethal effects of plastics ingestion on marine wildlife is difficult, but key to understanding the ontogeny and population dynamics of affected species. We developed a method that overcomes the difficulties by modelling individual ontogeny under reduced energy intake and expenditure caused by debris ingestion. The predicted ontogeny is combined with a population dynamics model to identify ecological breakpoints: cessation of reproduction or negative population growth. Exemplifying this approach on loggerhead turtles, we find that between 3% and 25% of plastics in digestive contents causes a 2.5-20% reduction in perceived food abundance and total available energy, resulting in a 10-15% lower condition index and 10% to 88% lower total seasonal reproductive output compared to unaffected turtles. The reported plastics ingestion is insufficient to impede sexual maturation, but population declines are possible. The method is readily applicable to other species impacted by debris ingestion.

The Role of Vector Trait Variation in Vector-Borne Disease Dynamics

Many important endemic and emerging diseases are transmitted by vectors that are biting arthropods. The functional traits of vectors can affect pathogen transmission rates directly and also through their effect on vector population dynamics. Increasing empirical evidence shows that vector traits vary significantly across individuals, populations, and environmental conditions, and at time scales relevant to disease transmission dynamics. Here, we review empirical evidence for variation in vector traits and how this trait variation is currently incorporated into mathematical models of vector-borne disease transmission. We argue that mechanistically incorporating trait variation into these models, by explicitly capturing its effects on vector fitness and abundance, can improve the reliability of their predictions in a changing world. We provide a conceptual framework for incorporating trait variation into vector-borne disease transmission models, and highlight key empirical and theoretical challenges. This framework provides a means to conceptualize how traits can be incorporated in vector borne disease systems, and identifies key areas in which trait variation can be explored. Determining when and to what extent it is important to incorporate trait variation into vector borne disease models remains an important, outstanding question.

The physiology of movement

Movement, from foraging to migration, is known to be under the influence of the environment. The translation of environmental cues to individual movement decision making is determined by an individual's internal state and anticipated to balance costs and benefits. General body condition, metabolic and hormonal physiology mechanistically underpin this internal state. These physiological determinants are tightly, and often genetically linked with each other and hence central to a mechanistic understanding of movement. We here synthesise the available evidence of the physiological drivers and signatures of movement and review (1) how physiological state as measured in its most coarse way by body condition correlates with movement decisions during foraging, migration and dispersal, (2) how hormonal changes underlie changes in these movement strategies and (3) how these can be linked to molecular pathways. We reveale that a high body condition facilitates the efficiency of routine foraging, dispersal and migration. Dispersal decision making is, however, in some cases stimulated by a decreased individual condition. Many of the biotic and abiotic stressors that induce movement initiate a physiological cascade in vertebrates through the production of stress hormones. Movement is therefore associated with hormone levels in vertebrates but also insects, often in interaction with factors related to body or social condition. The underlying molecular and physiological mechanisms are currently studied in few model species, and show -in congruence with our insights on the role of body condition- a central role of energy metabolism during glycolysis, and the coupling with timing processes during migration. Molecular insights into the physiological basis of movement remain, however, highly refractory. We finalise this review with a critical reflection on the importance of these physiological feedbacks for a better mechanistic understanding of movement and its effects on ecological dynamics at all levels of biological organization.

Cross-level considerations for explaining selection pressures and the maintenance of genetic variation in condition-dependent male morphs

Condition-dependent expression of alternative male morphologies (AMMs) exists in many arthropods. Understanding their coexistence requires answering (at least) two questions: (i) what are the ecological selection pressures that maintain condition-dependent plasticity of AMM expression, and (ii) what maintains the associated genetic variation? Focusing on acarid mites, we show that the questions should not be conflated. We argue how, instead, answers should be sought by testing phenotype-level (question 1) or genotype-level (question 2) hypotheses. We illustrate that energy allocation restrictions and physiological trade-offs are likely to play a crucial role in AMM expression in acarid mites. We thus conclude that these aspects require specific attention in identifying selection pressures maintaining condition-dependent plasticity, and evolutionary processes that maintain genetic variation in condition-dependent phenotypic plasticity.

Predicting population responses to environmental change from individual-level mechanisms: towards a standardized mechanistic approach

Animal populations will mediate the response of global biodiversity to environmental changes. Population models are thus important tools for both understanding and predicting animal responses to uncertain future conditions. Most approaches, however, are correlative and ignore the individual-level mechanisms that give rise to population dynamics. Here, we assess several existing population modelling approaches and find limitations to both 'correlative' and 'mechanistic' models. We advocate the need for a standardized mechanistic approach for linking individual mechanisms (physiology, behaviour, and evolution) to population dynamics in spatially explicit landscapes. Such an approach is potentially more flexible and informative than current population models. Key to realizing this goal, however, is overcoming current data limitations, the development and testing of eco-evolutionary theory to represent interactions between individual mechanisms, and standardized multi-dimensional environmental change scenarios which incorporate multiple stressors. Such progress is essential in supporting environmental decisions in uncertain future conditions.

The Exergy Footprint as a Sustainability Indicator: An Application to the Neanderthal–Sapiens Competition in the Late Pleistocene

A thermodynamic analysis of population dynamics and of sustainability provides rigor to many important issues. In this work, the “system society” is analysed in connection with the “system environment” using an exergy metric, and the method includes an internalization of the externalities (capital, labour, environmental effects) conducted on the basis of a “system + environment” balance. In this perspective, this study investigates the Late Pleistocene extinction of the Homo neanderthalensis, which took place in a geologically short time and in the presence of a competing species, the Homo sapiens. The case in study is not trivial, and its choice not casual: in those times, the only factor that could lead to an advantage of one group over the other was their respective resource use intensity. A specific indicator, the exergy footprint (EF), is here applied to measure the total amount of primary resources required to produce a certain (material or immaterial) commodity, including the resources needed for the physical survival of the individuals. On the basis of the available data, the results of a steady-state analysis show that the EF of the Neanderthal was higher than that of the Sapiens, and that with both species sharing the same ecological niche in a time of dwindling resources, the less frugal of the two was also more fragile in an evolutionary sense.

Effects of environmental change and early-life stochasticity on Pacific bluefin tuna population growth

Species conservation and fisheries management require approaches that relate environmental conditions to population-level dynamics, especially because environmental conditions shift due to climate change. We combined an individual-level physiological model and a conceptually simple matrix population model to develop a novel tool that relates environmental change to population dynamics, and used this tool to analyze effects of environmental changes and early-life stochasticity on Pacific bluefin tuna (PBT) population growth. We found that (i) currently, PBT population experiences a positive growth rate, (ii) somewhat surprisingly, stochasticity in early life survival increases this growth rate, (iii) sexual maturation age strongly depends on food and temperature, (iv) current fishing pressure, though high, is tolerable as long as the environment is such that PBT mature in less than 9 years of age (maturation age of up to 10 is possible in some environments), (v) PBT population growth rate is much more susceptible to changes in juvenile survival than changes in total reproductive output or adult survival. These results suggest that, to be effective, fishing regulations need to (i) focus on smaller tuna (i.e., juveniles and young adults), and (ii) mitigate adverse effects of climate change by taking into the account how future environments may affect the population growth.

A functional trait approach to identifying life history patterns in stochastic environments

Temporal variation in demographic processes can greatly impact population dynamics. Perturbations of statistical coefficients that describe demographic rates within matrix models have, for example, revealed that stochastic population growth rates (log(λ s)) of fast life histories are more sensitive to temporal autocorrelation of environmental conditions than those of slow life histories. Yet, we know little about the mechanisms that drive such patterns. Here, we used a mechanistic, functional trait approach to examine the functional pathways by which a typical fast life history species, the macrodetrivore Orchestia gammarellus, and a typical slow life history species, the reef manta ray Manta alfredi, differ in their sensitivity to environmental autocorrelation if (a) growth and reproduction are described mechanistically by functional traits that adhere to the principle of energy conservation, and if (b) demographic variation is determined by temporal autocorrelation in food conditions. Opposite to previous findings, we found that O. gammarellus log(λ s) was most sensitive to the frequency of good food conditions, likely because reproduction traits, which directly impact population growth, were most influential to log(λ s). Manta alfredi log(λs ) was instead most sensitive to temporal autocorrelation, likely because growth parameters, which impact population growth indirectly, were most influential to log(λ s). This differential sensitivity to functional traits likely also explains why we found that O. gammarellus mean body size decreased (due to increased reproduction) but M. alfredi mean body size increased (due to increased individual growth) as food conditions became more favorable. Increasing demographic stochasticity under constant food conditions decreased O. gammarellus mean body size and increased log(λ s) due to increased reproduction, whereas M. alfredi mean body and log(λ s) decreased, likely due to decreased individual growth. Our findings signify the importance of integrating functional traits into demographic models as this provides mechanistic understanding of how environmental and demographic stochasticity affects population dynamics in stochastic environments.

Trait-based predictions and responses from laboratory mite populations to harvesting in stochastic environments

Predictions on population responses to perturbations are often derived from trait-based approaches like integral projection models (IPMs), but are rarely tested. IPMs are constructed from functions that describe survival, growth and reproduction in relation to the traits of individuals and their environment. Although these functions comprise biologically non-informative statistical coefficients within standard IPMs, model parameters of the recently developed dynamic energy budget IPM (DEB-IPM) are life-history traits like "length at maturation" and "maximum reproduction rate". Testing predictions from mechanistic IPMs against empirical observations can therefore provide functional insights into the links between individual life history, the environment and population dynamics. Here, we compared the population dynamics of the bulb mite (Rhizoglyphus robini) predicted by a DEB-IPM with those observed in an experiment where populations experienced daily food rations that were either positively correlated over time (red noise), negatively (blue noise) or uncorrelated (white noise). We also selectively harvested large adults in half of these populations. The model failed to generate detailed predictions of population structure as juvenile numbers were overestimated; likely because juvenile-adult interference competition was underestimated. The model performed well at the population level as, for both harvested and unharvested populations, simulations matched the observed, long-term stochastic growth rate λ_s . We next generalised the model to investigate how stochastic change affects mite λ_s , which correlated well with the frequency f of experiencing periods of good environment, but, due to the relationship between f and noise colour ρ, did not correlate well with shifts in ρ. The sensitivity of λ_s to perturbations in life-history parameters depended on the type of stochastic change, as well as population growth. Our findings show that responses to differential mortality depend on individual life-history traits, environmental characteristics and population growth. As long-term climate change causes ever greater environmental fluctuations, trait-based approaches will be increasingly important in predicting population responses to change. We therefore conclude by illustrating what questions can be examined with mechanistic trait-based models like the DEB-IPM, the answers to which will advance our knowledge of the functional links between individual traits, the environment and population dynamics.

Have We Outgrown the Existing Models of Growth?

Theories of growth have a long history in biology. Two major branches of theory (mechanistic and phenomenological) describe the dynamics of growth and explain variation in the size of organisms. Both theory branches usually assume that reproductive output scales proportionately with body size, in other words that reproductive output is isometric. A meta-analysis of hundreds of marine fishes contradicts this assumption, larger mothers reproduce disproportionately more in 95% of species studied, and patterns in other taxa suggest that reproductive hyperallometry is widespread. We argue here that reproductive hyperallometry represents a profound challenge to mechanistic theories of growth in particular, and that they should be revised accordingly. We suspect that hyperallometric reproduction drives growth trajectories in ways that are largely unanticipated by current theories.

Review: Using physiologically based models to predict population responses to phytochemicals by wild vertebrate herbivores

To understand how foraging decisions impact individual fitness of herbivores, nutritional ecologists must consider the complex in vivo dynamics of nutrient-nutrient interactions and nutrient-toxin interactions associated with foraging. Mathematical modeling has long been used to make foraging predictions (e.g. optimal foraging theory) but has largely been restricted to a single currency (e.g. energy) or using simple indices of nutrition (e.g. fecal nitrogen) without full consideration of physiologically based interactions among numerous co-ingested phytochemicals. Here, we describe a physiologically based model (PBM) that provides a mechanistic link between foraging decisions and demographic consequences. Including physiological mechanisms of absorption, digestion and metabolism of phytochemicals in PBMs allows us to estimate concentrations of ingested and interacting phytochemicals in the body. Estimated phytochemical concentrations more accurately link intake of phytochemicals to changes in individual fitness than measures of intake alone. Further, we illustrate how estimated physiological parameters can be integrated with the geometric framework of nutrition and into integral projection models and agent-based models to predict fitness and population responses of vertebrate herbivores to ingested phytochemicals. The PBMs will improve our ability to understand the foraging decisions of vertebrate herbivores and consequences of those decisions and may help identify key physiological mechanisms that underlie diet-based ecological adaptations.

The AmP project: Comparing species on the basis of dynamic energy budget parameters

We developed new methods for parameter estimation-in-context and, with the help of 125 authors, built the AmP (Add-my-Pet) database of Dynamic Energy Budget (DEB) models, parameters and referenced underlying data for animals, where each species constitutes one database entry. The combination of DEB parameters covers all aspects of energetics throughout the full organism’s life cycle, from the start of embryo development to death by aging. The species-specific parameter values capture biodiversity and can now, for the first time, be compared between animals species. An important insight brought by the AmP project is the classification of animal energetics according to a family of related DEB models that is structured on the basis of the mode of metabolic acceleration, which links up with the development of larval stages. We discuss the evolution of metabolism in this context, among animals in general, and ray-finned fish, mollusks and crustaceans in particular. New DEBtool code for estimating DEB parameters from data has been written. AmPtool code for analyzing patterns in parameter values has also been created. A new web-interface supports multiple ways to visualize data, parameters, and implied properties from the entire collection as well as on an entry by entry basis. The DEB models proved to fit data well, the median relative error is only 0.07, for the 1035 animal species at 2018/03/12, including some extinct ones, from all large phyla and all chordate orders, spanning a range of body masses of 16 orders of magnitude. This study is a first step to include evolutionary aspects into parameter estimation, allowing to infer properties of species for which very little is known.

We discovered that parameters of Dynamic Energy Budget (DEB) models can be estimated from a set of simple data on animal life history aspects, growth and reproduction, if treated in combination. Apart from goodness-of-fit as an estimation criterion, relations with parameter values of other species are important, since DEB parameters have a clear physiological interpretation and a good fit for the wrong reasons is always a risk to consider. We developed and optimized methods for this type of parameter estimation-in-context and organized the results of over 1000 animal species in the open-access Add-my-Pet (AmP) database, to which 125 authors contributed so far. We also developed software package AmPtool to compare parameter values in the collection, that builds on DEBtool to assist applications of DEB theory. A family of related DEB models, structured with respect to the modes of metabolic acceleration, captures biodiversity, including various life stages. We discuss some features of the family structure of DEB models in an evolutionary context. The AmP collection has a great potential for research on the role of biodiversity in ecosystem structure and functioning, which will grow with the size of the database.