Summary1. Integral projection models (IPMs) provide a powerful approach to investigate ecological and rapid evolutionary change in quantitative life-history characteristics and population dynamics. IPMs are constructed from functions that describe the demographic rates -survival, growth and reproduction -in relation to the characteristics of individuals and their environment. Currently, however, demographic rates are estimated using phenomenological regression models that lack a mechanistic representation of the biological processes that give rise to observed demographic variation. This lack of mechanistic underpinning limits the ability of the model to predict future dynamics under novel environmental conditions because the model ingredients pertain to current environmental conditions only. 2. Here, we use dynamic energy budget (DEB) theory to construct DEB-IPMs based on a mechanistic representation of individual life-history trajectories. We derive the demographic functions describing growth and reproduction from a simple DEB growth model. The functions describing mortality and the association between parent and offspring characteristics do not follow DEB theory and hence are estimated from individual-level observations. 3. We apply the DEB-IPM to two contrasting systems: the small, fast-reproducing bulb mite Rhizoglyphus robini and the large, slow-reproducing reef manta ray Manta alfredi. In both cases, predictions of population growth rate, lifetime reproductive success and generation time agree with empirical observations. In case of the bulb mite, predictions and observations even agree across different feeding conditions. 4. If the DEB energetics model is accepted as describing growth and reproduction, DEB-IPMs can be parameterised using easy-to-collect life cycle information (growth rate, length at birth, maturation and old age) making them suitable for data-deficient species. Because species differ only in these DEB parameters, comparative studies of character and population dynamics between species are straightforward, particularly since DEB-IPMs can be extended to include population feedback on resources, of which we give an example. Most crucially, because DEB theory specifies growth and reproduction rates as explicitly dependent on environmental conditions such as food availability or temperature, DEB-IPMs provide a mechanistic platform to investigate the biological processes that determine joint change in phenotypic characters, life-history traits, population size and community structure.

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Biodiversity loss and anthropogenic alterations to species communities are impacting disease emergence events. These trends may be related through mechanisms in which biodiversity either increases (amplification) or decreases (dilution) diseases among hosts. Biodiversity effects can be direct, when contacts with competent hosts are replaced by sink hosts, or indirect through regulation of abundances and depend on the disease-competence of the added host. Here, we introduce a multi-host compartmental disease model, weighting host competence by their evolutionary history. We explore two scenarios: (1) where the probability of transmission depends on the evolutionary distance between the transmitting and recipient hosts, and (2) where transmission depends on the evolutionary distance from the receiving host to the primary host of the pathogen. Using simulations, and estimating the host community outbreak potential (R0), we show how differences in phylogenetic structure can switch host communities from diluting to amplifying a disease, even when species richness is unchanged. Common ecophylogenetic metrics of community structure that capture variation in the pairwise distances among hosts, are able to explain >90% of the variation in R0across simulations. Our study provides a simple illustration of how host evolutionary histories can drive disease dynamics.

With the decrease of biodiversity worldwide coinciding with an increase in disease outbreaks, investigating this link is more important then ever before. This review outlines the different modelling methods commonly used for pathogen transmission in animal host systems. There are a multitude of ways a pathogen can invade and spread through a host population. The assumptions of the transmission model used to capture disease propagation determines the outbreak potential, the net reproductive success (R0). This review offers an insight into the assumptions and motivation behind common transmission mechanisms and introduces a general framework with which contact rates, the most important parameter in disease dynamics, determines the transmission method. By using a general function introduced here and this general transmission model framework, we provide a guide for future disease ecologists for how to pick the contact function that best suites their system. Additionally, this manuscript attempts to bridge the gap between mathematical disease modelling and the controversially and heavily debated disease-diversity relationship, by expanding the summarized models to multiple hosts systems and explaining the role of host diversity in disease transmission. By outlining the mechanisms of transmission into a stepwise process, this review will serve as a guide to model pathogens in multi-host systems. We will further describe these models it in the greater context of host diversity and its effect on disease outbreaks, by introducing a novel method to include host species’ evolutionary history into the framework.