To Prosper, Live Long: Understanding the Sources of Reproductive Skew and Extreme Reproductive Success in Structured Populations.

AbstractIn many species, a few individuals produce most of the next generation. How much of this reproductive skew is driven by variation among individuals in fixed traits, how much by external factors, and how much by random chance? And what does it take to have truly exceptional lifetime reproductive output (LRO)? In the past, we and others have partitioned the variance of LRO as a proxy for reproductive skew. Here we explain how to partition LRO skewness itself into contributions from fixed trait variation, four forms of "demographic luck" (birth state, fecundity luck, survival trajectory luck, and growth trajectory luck), and two kinds of "environmental luck" (birth environment and environment trajectory). Each of these is further partitioned into contributions at different ages. We also determine what we can infer about individuals with exceptional LRO. We find that reproductive skew is largely driven by random variation in lifespan, and exceptional LRO generally results from exceptional lifespan. Other kinds of luck frequently bring skewness down rather than increasing it. In populations where fecundity varies greatly with environmental conditions, getting a good year at the right time can be an alternate route to exceptional LRO, so that LRO is less predictive of lifespan.

The natural history of luck: A synthesis study of structured population models.

Chance pervades life. In turn, life histories are described by probabilities (e.g. survival and breeding) and averages across individuals (e.g. mean growth rate and age at maturity). In this study, we explored patterns of luck in lifetime outcomes by analysing structured population models for a wide array of plant and animal species. We calculated four response variables: variance and skewness in both lifespan and lifetime reproductive output (LRO), and partitioned them into contributions from different forms of luck. We examined relationships among response variables and a variety of life history traits. We found that variance in lifespan and variance in LRO were positively correlated across taxa, but that variance and skewness were negatively correlated for both lifespan and LRO. The most important life history trait was longevity, which shaped variance and skew in LRO through its effects on variance in lifespan. We found that luck in survival, growth, and fecundity all contributed to variance in LRO, but skew in LRO was overwhelmingly due to survival luck. Rapidly growing populations have larger variances in LRO and lifespan than shrinking populations. Our results indicate that luck-induced genetic drift may be most severe in recovering populations of species with long mature lifespan and high iteroparity.

A Time for Every Purpose: Using Time-Dependent Sensitivity Analysis to Help Understand and Manage Dynamic Ecological Systems.

AbstractSensitivity analysis is often used to help understand and manage ecological systems by assessing how a constant change in vital rates or other model parameters might affect the management outcome. This allows the manager to identify the most favorable course of action. However, realistic changes are often localized in time-for example, a short period of culling leads to a temporary increase in the mortality rate over the period. Hence, knowing when to act may be just as important as knowing what to act on. In this article, we introduce the method of time-dependent sensitivity analysis (TDSA) that simultaneously addresses both questions. We illustrate TDSA using three case studies: transient dynamics in static disease transmission networks, disease dynamics in a reservoir species with seasonal life history events, and endogenously driven population cycles in herbivorous invertebrate forest pests. We demonstrate how TDSA often provides useful biological insights, which are understandable on hindsight but would not have been easily discovered without the help of TDSA. However, as a caution, we also show how TDSA can produce results that mainly reflect uncertain modeling choices and are therefore potentially misleading. We provide guidelines to help users maximize the utility of TDSA while avoiding pitfalls.

Beyond simple adaptation: Incorporating other evolutionary processes and concepts into eco-evolutionary dynamics.

Studies of eco-evolutionary dynamics have integrated evolution with ecological processes at multiple scales (populations, communities and ecosystems) and with multiple interspecific interactions (antagonistic, mutualistic and competitive). However, evolution has often been conceptualised as a simple process: short-term directional adaptation that increases population growth. Here we argue that diverse other evolutionary processes, well studied in population genetics and evolutionary ecology, should also be considered to explore the full spectrum of feedback between ecological and evolutionary processes. Relevant but underappreciated processes include (1) drift and mutation, (2) disruptive selection causing lineage diversification or speciation reversal and (3) evolution driven by relative fitness differences that may decrease population growth. Because eco-evolutionary dynamics have often been studied by population and community ecologists, it will be important to incorporate a variety of concepts in population genetics and evolutionary ecology to better understand and predict eco-evolutionary dynamics in nature.

Mechanistic Models of Trophic Interactions: Opportunities for Species Richness and Challenges for Modern Coexistence Theory.

AbstractMany potential mechanisms promote species coexistence, but we know little about their relative importance. To compare multiple mechanisms, we modeled a two-trophic planktonic food web based on mechanistic species interactions and empirically measured species traits. We simulated thousands of possible communities under realistic and altered interaction strengths to assess the relative importance of three potential drivers of phytoplankton and zooplankton species richness: resource-mediated coexistence mechanisms, predator-prey interactions, and trait trade-offs. Next, we computed niche and fitness differences of competing zooplankton to obtain a deeper understanding of how these mechanisms determine species richness. We found that predator-prey interactions were the most important driver of phytoplankton and zooplankton species richness and that large zooplankton fitness differences were associated with low species richness, but zooplankton niche differences were not associated with species richness. However, for many communities we could not apply modern coexistence theory to compute niche and fitness differences of zooplankton because of conceptual issues with the invasion growth rates arising from trophic interactions. We therefore need to expand modern coexistence theory to fully investigate multitrophic-level communities.

Predicting and Controlling Spillover in Multispecies Disease Transmission Networks: Steady-State Analysis.

AbstractIn multispecies disease systems, pathogen spillover from a "reservoir community" can maintain disease in a "sink community" where it would otherwise die out. We develop and analyze models for spillover and disease spread in sink communities, focusing on questions of control: which species or transmission links are the most important to target to reduce the disease impact on a species of concern? Our analysis focuses on steady-state disease prevalence, assuming that the timescale of interest is long compared with that of disease introduction and establishment in the sink community. We identify three regimes as the sink community R0 scales from 0 to 1. Up to R0≈0.3, overall infection patterns are dominated by direct exogenous infections and one-step subsequent transmission. For R0≈1, infection patterns are characterized by dominant eigenvectors of a force-of-infection matrix. In between, additional network details can be important; we derive and apply general sensitivity formulas that identify particularly important links and species.

A time for every purpose: using time-dependent sensitivity analysis to help understand and manage dynamic ecological systems.

Sensitivity analysis is often used to help understand and manage ecological systems, by assessing how a constant change in vital rates or other model parameters might affect the management outcome. This allows the manager to identify the most favorable course of action. However, realistic changes are often localized in time-for example, a short period of culling leads to a temporary increase in the mortality rate over the period. Hence, knowing when to act may be just as important as knowing what to act upon. In this article, we introduce the method of time-dependent sensitivity analysis (TDSA) that simultaneously addresses both questions. We illustrate TDSA using three case studies: transient dynamics in static disease transmission networks, disease dynamics in a reservoir species with seasonal life-history events, and endogenously-driven population cycles in herbivorous invertebrate forest pests. We demonstrate how TDSA often provides useful biological insights, which are understandable on hindsight but would not have been easily discovered without the help of TDSA. However, as a caution, we also show how TDSA can produce results that mainly reflect uncertain modeling choices and are therefore potentially misleading. We provide guidelines to help users maximize the utility of TDSA while avoiding pitfalls.

Plant defense synergies and antagonisms affect performance of specialist herbivores of common milkweed.

As a general rule, plants defend against herbivores with multiple traits. The defense synergy hypothesis posits that some traits are more effective when co-expressed with others compared to their independent efficacy. However, this hypothesis has rarely been tested outside of phytochemical mixtures, and seldom under field conditions. We tested for synergies between multiple defense traits of common milkweed (Asclepias syriaca) by assaying the performance of two specialist chewing herbivores on plants in natural populations. We employed regression and a novel application of random forests to identify synergies and antagonisms between defense traits. We found the first direct empirical evidence for two previously hypothesized defense synergies in milkweed (latex by secondary metabolites, latex by trichomes) and identified numerous other potential synergies and antagonisms. Our strongest evidence for a defense synergy was between leaf mass per area and low nitrogen content; given that these "leaf economic" traits typically covary in milkweed, a defense synergy could reinforce their co-expression. We report that each of the plant defense traits showed context-dependent effects on herbivores, and increased trait expression could well be beneficial to herbivores for some ranges of observed expression. The novel methods and findings presented here complement more mechanistic approaches to the study of plant defense diversity and provide some of the best evidence to date that multiple classes of plant defense synergize in their impact on insects. Plant defense synergies against highly specialized herbivores, as shown here, are consistent with ongoing reciprocal evolution between these antagonists.

Snared in an Evil Time: How Age-Dependent Environmental and Demographic Variability Contribute to Variance in Lifetime Outcomes.

AbstractTo what degree is lifetime success determined by innate individual quality versus external events and random chance, whether success is measured by lifetime reproductive output, life span, years that a tree spends in the canopy, or some other measure? And how do external events and chance interact with development (survival and growth) to drive success? To answer these questions, we extend our earlier age partitioning of luck in lifetime outcomes in two ways: we incorporate effects of external environmental variation, and we subdivide demographic luck into contributions from survival and growth. Applying our methods to four case studies, we find that luck in survival, in growth, or in environmental variation can all be the dominant driver of success, depending on life history, but variation in individual quality remains a lesser driver. Luck in its various forms is most important at very early ages and again close to the time when individuals typically first begin to be successful (e.g., entering the canopy, reaching reproductive maturity), but different forms of luck peak at different times. For example, a favorable year can boost a tree into the canopy, while luck in survival is required to take full advantage of that fortunate event.

Pathogen transport amplifies or dilutes disease transmission depending on the host dose-response relationship.

Pathogen transport by biotic or abiotic processes (e.g. mechanical vectors, wind, rain) can increase disease transmission by creating more opportunities for host exposure. But transport without replication has an inherent trade-off, that creating new venues for exposure decreases the average pathogen abundance at each venue. The host dose-response relationship is therefore required to correctly assess infection risk. We model and analyse two examples-biotic mechanical vectors in plant-pollinator networks, and abiotic-facilitated long-distance pathogen dispersal-to illustrate how oversimplifying the dose-response relationship can lead to incorrect epidemiological predictions. When the minimum infective dose is high, mechanical vectors amplify disease transmission less than suggested by simple compartment models, and may even dilute transmission. When long-distance dispersal leads to infrequent large exposures, models that assume a linear force of infection can substantially under-predict the speed of epidemic spread. Our work highlights an important general interplay between dose-response relationships and pathogen transport.

Collective behaviour can stabilize ecosystems.

Collective behaviour is common in bacteria, plants and animals, and therefore occurs across ecosystems, from biofilms to cities. With collective behaviour, social interactions among individuals propagate to affect the behaviour of groups, whereas group-level responses in turn affect individual behaviour. These cross-scale feedback loops between individuals, populations and their environments can provide fitness benefits, such as the efficient exploitation of uncertain resources, as well as costs, such as increased resource competition. Although the social mechanics of collective behaviour are increasingly well-studied, its role in ecosystems remains poorly understood. Here we introduce collective movement into a model of consumer-resource dynamics to demonstrate that collective behaviour can attenuate consumer-resource cycles and promote species coexistence. We focus on collective movement as a particularly well-understood example of collective behaviour. Adding collective movement to canonical unstable ecological scenarios causes emergent social-ecological feedback, which mitigates conditions that would otherwise result in extinction. Collective behaviour could play a key part in the maintenance of biodiversity.

Host-pathogen immune feedbacks can explain widely divergent outcomes from similar infections.

A long-standing question in infection biology is why two very similar individuals, with very similar pathogen exposures, may have very different outcomes. Recent experiments have found that even isogenic Drosophila melanogaster hosts, given identical inoculations of some bacterial pathogens at suitable doses, can experience very similar initial bacteria proliferation but then diverge to either a lethal infection or a sustained chronic infection with much lower pathogen load. We hypothesized that divergent infection outcomes are a natural result of mutual negative feedbacks between pathogens and the host immune response. Here, we test this hypothesis in silico by constructing process-based dynamic models for bacterial population growth, host immune induction and the feedbacks between them, based on common mechanisms of immune system response. Mathematical analysis of a minimal conceptual model confirms our qualitative hypothesis that mutual negative feedbacks can magnify small differences among hosts into life-or-death differences in outcome. However, explaining observed features of chronic infections requires an extension of the model to include induced pathogen modifications that shield themselves from host immune responses at the cost of reduced proliferation rate. Our analysis thus generates new, testable predictions about the mechanisms underlying bimodal infection outcomes.

A practical guide to selecting models for exploration, inference, and prediction in ecology.

Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions.

Time and Chance: Using Age Partitioning to Understand How Luck Drives Variation in Reproductive Success.

AbstractOver the course of individual lifetimes, luck usually explains a large fraction of the between-individual variation in life span or lifetime reproductive output (LRO) within a population, while variation in individual traits or "quality" explains much less. To understand how, where in the life cycle, and through which demographic processes luck trumps trait variation, we show how to partition by age the contributions of luck and trait variation to LRO variance and how to quantify three distinct components of luck. We apply these tools to several empirical case studies. We find that luck swamps effects of trait variation at all ages, primarily because of randomness in individual state dynamics ("state trajectory luck"). Luck early in life is most important. Very early state trajectory luck generally determines whether an individual ever breeds, likely by ensuring that they are not dead or doomed quickly. Less early luck drives variation in success among those breeding at least once. Consequently, the importance of luck often has a sharp peak early in life or it has two peaks. We suggest that ages or stages where the importance of luck peaks are potential targets for interventions to benefit a population of concern, different from those identified by eigenvalue elasticity analysis.

Technical Comment on Pande et al. (2020): Why invasion analysis is important for understanding coexistence.

Pande et al. (2020) point out that persistence time can decrease even as invader growth rates (IGRs) increase, which potentially undermines modern coexistence theory. However, because persistence time increases rapidly with system size only when IGR > 0, to understand how any real community persists, we should first identify the mechanisms producing positive IGR.

Individual Specialization and Multihost Epidemics: Disease Spread in Plant-Pollinator Networks.

Many parasites infect multiple species and persist through a combination of within- and between-species transmission. Multispecies transmission networks are typically constructed at the species level, linking two species if any individuals of those species interact. However, generalist species often consist of specialized individuals that prefer different subsets of available resources, so individual- and species-level contact networks can differ systematically. To explore the epidemiological impacts of host specialization, we build and study a model for pollinator pathogens on plant-pollinator networks, in which individual pollinators have dynamic preferences for different flower species. We find that modeling and analysis that ignore individual host specialization can predict die-off of a disease that is actually strongly persistent and can badly over- or underpredict steady-state disease prevalence. Effects of individual preferences remain substantial whenever mean preference duration exceeds half of the mean time from infection to recovery or death. Similar results hold in a model where hosts foraging in different habitats have different frequencies of contact with an environmental reservoir for the pathogen. Thus, even if all hosts have the same long-run average behavior, dynamic individual differences can profoundly affect disease persistence and prevalence.

Consumer-resource dynamics is an eco-evolutionary process in a natural plankton community.

When traits affecting species interactions evolve rapidly, ecological dynamics can be altered while they occur. These eco-evolutionary dynamics have been documented repeatedly in laboratory and mesocosm experiments. We show here that they are also important for understanding community functioning in a natural ecosystem. Daphnia is a major planktonic consumer influencing seasonal plankton dynamics in many lakes. It is also sensitive to succession in its phytoplankton food, from edible algae in spring to relatively inedible cyanobacteria in summer. We show for Daphnia mendotae in Oneida Lake, New York, United States, that within-year ecological change in phytoplankton (from spring diatoms, cryptophytes and greens to summer cyanobacteria) resulted in consumers evolving increasing tolerance to cyanobacteria over time. This evolution fed back on ecological seasonal changes in population abundance of this major phytoplankton consumer. Oneida Lake is typical of mesotrophic lakes broadly, suggesting that eco-evolutionary consumer-resource dynamics is probably common.

Why So Variable: Can Genetic Variance in Flowering Thresholds Be Maintained by Fluctuating Selection?

We use integral projection models (IPMs) and individual-based simulations to study the evolution of genetic variance in two monocarpic plant systems. Previous approaches combining IPMs with an adaptive dynamics-style invasion analysis predicted that genetic variability in the size threshold for flowering will not be maintained, which conflicts with empirical evidence. We ask whether this discrepancy can be resolved by making more realistic assumptions about the underlying genetic architecture, assuming a multilocus quantitative trait in an outcrossing diploid species. To do this, we embed the infinitesimal model of quantitative genetics into an IPM for a size-structured cosexual plant species. The resulting IPM describes the joint dynamics of individual size and breeding value of the evolving trait. We apply this general framework to the monocarpic perennials Oenothera glazioviana and Carlina vulgaris. The evolution of heritable variation in threshold size is explored in both individual-based models (IBMs) and IPMs, using a mutation rate modifier approach. In the Oenothera model, where the environment is constant, there is selection against producing genetically variable offspring. In the Carlina model, where the environment varies between years, genetically variable offspring provide a selective advantage, allowing the maintenance of genetic variability. The contrasting predictions of adaptive dynamics and quantitative genetics models for the same system suggest that fluctuating selection may be more effective at maintaining genetic variation than previously thought.