The American Naturalist

The immune system is the primary defense against parasites. With the ever-increasing rate of disease, epidemiologic models considering geographic variation in immune responses could prove useful. Despite increasing interest in the macroecology of parasitism and infectious diseases, we know little about the macroecology of immune responses. Host characteristics, parasite exposure, and environmental factors can all affect immunity, but how these factors interact to shape spatial variation in the strength of immune responses remains unexplored. We captured odonates (dragonflies and damselflies) and their conspicuous ectoparasitic mites across a geographic area spanning the temperate and boreal forest biomes in eastern Canada. We then conducted immune response bioassays on 1,237 individuals from 63 odonate species. We used linear regressions and structural equation models to relate immune responses to host body size, parasite load, pH, temperature, and precipitation while accounting for evolutionary relationships among host species. We found significant differences in the strength of immune response among host individuals, and this variation was best explained by climatic conditions, specifically decreasing with precipitation and, to a lesser degree, temperature. While host species significantly differed in immune response strength, we found no effect of host body size, evolutionary relationships among hosts, or parasitism on immune response. Our study investigating the drivers of immune response across dozens of species spread across two biomes is the most comprehensive to date. Climatic conditions have a strong influence on host immune response, regardless of host characteristics or parasitism rates. In this specific case, strong immune responses were associated with low levels of annual precipitation, which could relate to the role of cuticular melanin content in desiccation resistance, and the melanin-based encapsulation response being a byproduct of this adaptation. A spatially-explicit understanding of the biological processes affecting immunity could improve epidemiological models of disease risk that inform disease management globally.

Empirical work suggests that spatial sorting--a mechanism of evolutionary change fueled by spatial assortment of phenotypes--may lead to phenotypic shifts on ecological timescales. However, we currently lack theoretical tools to measure the strength of spatial sorting, as we do to measure the strength of natural selection. To address this gap, we present a quantitative genetics model and identify an evolutionary parameter in the model to measure the strength of spatial sorting. This parameter, the standardized sorting gradient, is structurally akin to the standardized selection gradient, commonly used to measure the strength of natural selection. To show the utility of our approach, we analyzed wing-morphology data of soapberry bugs (Jadera haematoloma) recolonizing flooded habitats extirpated by a hurricane. We found that the estimated strength of spatial sorting ranked in the top ten percentile of standardized selection gradients documented in the scientific literature. Our results underscore that, like natural selection, spatial sorting, too, can yield rapid evolution after extreme events.

Observed group sizes rarely match the size that would maximize what each member gets from belonging. We propose a two-part theory in which group size is regulated by two related conflicts: insider-outsider conflict over admission, and within-group conflict as crowding, competition, and social tensions intensify with size. Three strategies are available: admission, exclusion, and fission. The first part shows that even when exclusion is unavailable, fission dynamics alone drive group size away from the optimum in both directions, with the pattern set by how prospective joiners encounter groups and by the geometry of fission. When joiners compare groups across a shared landscape and fission is asymmetric, the standing distribution is bimodal: supra-optimal large groups coexisting with a sub-optimal mode of small groups, the pattern characteristic of fission-fusion societies. The second part promotes exclusion and fission to active decisions: incumbents weigh the per-capita cost of accommodating entry ({beta}) against the costs of coordinated exclusion (c +{gamma} N*) and fissioning (F). A single inequality, {beta} > c +{gamma} N*, partitions populations into two regimes: where it holds, exclusion is viable and groups lock at the optimum size; where it fails, groups grow past the optimum and cycle through recurrent fission. Modal group size, fission frequency, and exclusion behavior together identify which regime governs a population -- a set of predictions applicable across fishes, social insects, birds, and mammals including primates and human foragers.

How behaviors vary among individuals and covary with other behaviors has been a major topic of interest over the last two decades. Unfortunately, proposed theoretical and conceptual frameworks explaining the seemingly ubiquitous observation of behavioral (co)variation have rarely successfully generalized. Two observations perhaps explain this failure: First, phenotypic correlations between behaviors are more strongly influenced by correlated and reversible plastic changes in behavior than by "behavioral syndromes". Second, while trait correlations are frequently assumed to arise via trade-offs, the observed pattern of correlations is not consistent with simple pair-wise trade-offs. A possible resolution to the apparent inconsistency between observed correlations and a role for trade-offs is provided by state-behavior feedbacks. This is critical because the inconsistency between data and theory represents a major failure in our understanding of behavioral evolution. These two primary observations emphasize the importance of an increased research focus on correlated reversible plasticity in behavior--frequently estimated and then disregarded as within-individual covariances. LAY SUMMARYCorrelations between behaviors are common but observed patterns of these correlations are, at least superficially, inconsistent with expectations of trade-offs. This mismatch is potentially resolved via feedbacks between behaviors and energy availability, suggesting important new research directions. DATA AND MODEL AVAILABILITYModel code, as well as the data associated with Figures 2 & 3, are available at github.com/DochtermannLab/FeedbacksModel. Both code and data will be made available at Dryad if accepted. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/453877v2_fig2.gif" ALT="Figure 2"> View larger version (19K): org.highwire.dtl.DTLVardef@f3814forg.highwire.dtl.DTLVardef@ae702dorg.highwire.dtl.DTLVardef@46be9borg.highwire.dtl.DTLVardef@8be692_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 2.C_FLOATNO Relationship of within-individual behavioral correlations (rW) with (A) among-individual correlations (rA) and (B) genetic correlations (rG). The sign and magnitude of rA and rG are highly concordant with rW across behaviors and taxa. Diagonal lines indicate 1:1 relationship. Horizontal and vertical dashed lines divide plots into sign mismatches (top left, bottom right) and sign matches (top right, bottom left). Within-individual correlations were the same sign as among-individual and genetic correlations in 62% and 79% of cases respectively. Data in A are from Brommer and Class (2017). Data in B are from Dochtermann (2011). C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/453877v2_fig3.gif" ALT="Figure 3"> View larger version (19K): org.highwire.dtl.DTLVardef@ce931dorg.highwire.dtl.DTLVardef@1c8798org.highwire.dtl.DTLVardef@10a418eorg.highwire.dtl.DTLVardef@55e6ae_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 3.C_FLOATNO (A) Simple model structure combining Sih et al.s (2015) feedback model with Houles (1991) y-model to include feedbacks between behavior and state. TO (the trade-off parameter) is the proportion of state energy (S1 at time t) allocated to behavior B1, making 1-TO the energy allocated to B2. {lambda} is the feedback strength and the conversion rate of a behavior (B1 or B2) into energy that can be used at a later time. (B) Magnitude of among-(purple) and within-individual (green) correlations under feedbacks ({lambda}) of different strength. Shaded regions indicate those feedback strengths that produce correlations of the same sign. Repeatability is not set a priori and is instead an emergent property of the model. Individuals within a population expressed behaviors according to A over ten time steps, with 250 individuals per population. Fifty populations were simulated at each of five levels of feebacks ({lambda} in B). rA and rW were estimated using the MCMCglmm package in R (Hadfield 2010). C_FIG

Foragers exploiting heterogeneous habitats must make strategic movement decisions in order to maximize fitness. Foraging theory has produced very general formalizations of the optimal patch-leaving decisions rational individuals should make. One is Charnovs Marginal Value Theorem (MVT), which models the sequential visit of habitat patches and their spatial distribution. The MVT has a simple intuitive graphical interpretation in terms of gain functions and travel times. However, it considers only energy gains, and the effect of predation risk on the time allocation strategy is notoriously lacking. An important development that includes predation risk was Browns economic treatment of optimal patch leaving decisions, the basis of giving-up density (GUD) theory, often cited as an extension of the MVT. However, it is a more abstract result that does not have the specificities or graphical appeal of the MVT. Although both successful, the two theories are cited by distinct communities and are seldom connected in texbooks. Here we formally introduce the risk-MVT (rMVT), a generalization of the MVT that can incorporate most types of predation risks. We show that Browns GUD-theory is equivalent to a rMVT, but applies for one type of predation risk only. The rMVT retains the structure and graphical simplicity of the MVT, but implies a shift from residence time to expected dose of risk (micromort units, as used in decision analysis) as the domain over which rates of gain are computed and maximized. Applications of the rMVT show that different types of risk can yield opposite responses of optimal strategies to an increase in the risk level, and predict differential responses of behaviours observed in experimental versus natural conditions. The risk-MVT can also be used to predict the optimal level of risk taking, or "optimal boldness", and suggests that individuals should generally be bolder in riskier habitats.

Predicting ecological outcomes of hybridizing species remains challenging because of complex interactions between genetic and ecological processes. Previous studies have mainly focused on exclusion scenarios driven by genetic mechanisms, whereas few have explored the impact of competitive interactions among parent species and their hybrids on generating multiple coexistence regimes. Here, we develop a population dynamics model that explicitly couples hybridization genetics with population dynamics driven by competitive interactions among two parent species and their hybrids. Our analysis reveals that backcrossing can fundamentally reshape coexistence regimes, generating five alternative outcomes: (i) alternative stable states of coexistence of both parent species and hybrids, (ii) frequency-independent coexistence, (iii) frequency-dependent coexistence, (iv) competitive exclusion of a rarer species, and (v) extinction of all populations. These outcomes arise from the balance between two key processes: regeneration of parental genotypes through hybrid x hybrid mating and competitive exclusion mediated by hybrids. Thus, hybridization can generate multiple stable coexistence regimes even when species would otherwise be excluded. This eco-genetic mechanism provides a unifying theoretical basis for predicting the ecological consequences of hybridization, highlighting the crucial role of competitive interactions in determining coexistence outcomes.

Resource choice behaviour has enormous fitness consequences and can drive niche expansion. However, individual behavioural choices are often mediated by context, determined by past experience. Are such context-dependent behaviours adaptive? Using Tribolium castaneum (the red flour beetle), we demonstrate that context-dependent oviposition choice reflects distinct, context-specific local fitness peaks. Manipulating female egg allocation in a habitat containing optimal and suboptimal resource patches, we measured offspring fitness to generate fitness landscapes as a function of all possible oviposition behaviours (i.e., combinations of fecundity and resource preference). Females from different age and competition contexts exhibit distinct behaviours which optimize different fitness components that are linked in a tradeoff. With increasing age and prior exposure to competition, they produce few but fast-developing offspring that are advantageous under high resource competition. In contrast, young naive females produce many slow-developing offspring, beneficial under weak competition. Systematically mapping complete context-dependent fitness landscapes is thus critical to infer behavioural optimality and offers predictive power in novel contexts. Preprint available at - https://www.biorxiv.org/content/10.1101/2021.05.27.445916v1.full Citation - Vrinda Ravi Kumar, Gaurav Agavekar, Deepa Agashe; bioRxiv 2021.05.27.445916; doi: https://doi.org/10.1101/2021.05.27.445916

1O_LITraditionally, we expect that self-incompatibility alleles (S-alleles), which prevent self-fertilization, should benefit from negative-frequency dependent selection and rise to high frequency when introduced to a new population through gene flow. However, the most taxonomically widespread form of self-incompatibility, the ribonuclease-based system ancestral to the core eudicots, functions through nonself-recognition, which drastically alters the process of S-allele diversification. C_LIO_LIWe analyze a model of S-allele evolution in two populations connected by migration, focusing on comparisons among the fates of S-alleles originally unique to each population and those shared among populations. C_LIO_LIWe find that both shared and unique S-alleles originating from the population with more unique S-alleles were usually fitter than S-alleles from the population with fewer. Resident S-alleles were often driven extinct and replaced by migrant S-alleles, though this outcome could be averted by pollen limitation or biased migration. C_LIO_LINonself-recognition-based self-incompatibility will usually either disfavor introgression of S-alleles or result in the whole-sale replacement of S-alleles from one population with those from another. C_LI

In patch- or habitat-structured populations different processes can lead to diversity at different scales. While spatial heterogeneity generates spatially disruptive selection favoring variation between patches, local competition can lead to locally disruptive selection promoting variation within patches. So far, almost all theory has studied these two processes in isolation. Here, we use mathematical modelling to investigate how resource variation within and between habitats influences the evolution of variation in a consumer population where individuals compete in finite patches connected by dispersal. We find that locally and spatially disruptive selection typically act in concert, favoring polymorphism under a significantly wider range of conditions than when in isolation. But when patches are small and dispersal between them is low, kin competition inhibits the emergence of polymorphism, especially when driven by local competition. We further use our model to clarify what comparisons between trait and neutral genetic differentiation (Qst/Fst comparisons) can tell about the nature of selection. Overall, our results help understand the interaction between two major drivers of diversity: locally and spatially disruptive selection; and how this interaction is modulated by the unavoidable effects of kin selection under limited dispersal.

What determines how much resistance hosts evolve? One might intuit that hosts evolve higher resistance when parasites are more abundant. However, the opposite pattern can arise due to costs of resistance. Here we illustrate with mathematical, experimental, and field approaches how ecological context can increase parasite abundance and select for lower resistance. Resistance is futile when all host genotypes become sufficiently infected. To make this argument, we first analyzed an eco-evolutionary model of parasites, hosts, and hosts resources. We determined eco-evolutionary outcomes for resistance (mathematically, transmission rate) and densities along gradients that drive epidemic size. When epidemic drivers are high, hosts evolve lower resistance, amplifying epidemics and decreasing host density. Experimental mesocosms qualitatively agreed. In the experiment, higher supply of nutrients drove larger epidemics of survival-reducing fungal parasites. Evolving zooplankton hosts were less resistant at high nutrients than at low. Less resistance, in turn, was associated with higher infection prevalence and lower host density. We also analyzed the size of naturally occurring epidemics, finding a broad, bimodal distribution of epidemic sizes consistent with the eco-evolutionary model. Together, our three approaches supported predictions that high epidemic drivers lead to evolution of lower resistance which drives higher prevalence and lower host density.

When only some hosts are protected from disease vectors, disease spread may be inhibited through a net reduction in vector visits or amplified as vectors redirect their attention to unprotected hosts. Two factors that determine which outcome prevails are host microbiota that alter vector host-seeking behavior and natural enemies that redistribute or suppress vector populations. Because both shape the frequency and distribution of vector visits, they are essential for understanding how individual-level protection scales to population-level disease dynamics. Yet, how these processes interact across scales remains poorly understood. Pea aphids are major virus vectors in pea crops and are commonly managed using parasitoid wasps. Recent evidence suggests that epiphytic bacteria in the genus Pseudomonas can also repel or kill pea aphids, yet whether Pseudomonas complements or undermines parasitoid-based vector control remains unknown. We used a mathematical model to show when and why Pseudomonas complements versus undermines biocontrol of aphid-vectored virus outbreaks. The effect of Pseudomonas on virus outbreaks depends most strongly on how successful parasitoids are at tracking aphid densities: When parasitoids effectively track aphids, Pseudomonas inhibits virus outbreaks by reducing aphid densities. With poor parasitoid tracking of aphids, Pseudomonas-induced aphid mortality generates spatial variability in aphid densities that slows parasitoid population growth. The net result is amplified crowding in plants not protected by Pseudomonas, increasing winged aphid production and accelerating viral spread. Counterintuitively, the more effective Pseudomonas is at killing aphids, the more strongly it generates spatial variability and promotes virus spread. The only other factor that can change the direction of Pseudomonas effects on virus outbreaks is whether the virus starts on a Pseudomonas-protected plant, which can cause Pseudomonas to inhibit virus outbreaks when it would otherwise promote them. Our results show how community and spatial context dictate whether microbiota protective to individual hosts will accelerate viral outbreaks.

Many cases of animal-mediated dispersal are non-random, with the animals altering their movement pattern in response to the local species composition of the organisms that the vectoring animals disperse. Yet, this dispersal-community feedback has received little attention in metacommunity ecology. We use a mathematical model to show that dispersal-community feedback can promote regional species coexistence. As a well-characterized system, our model focuses on nectar-inhabiting bacteria and yeast that are dispersed by pollinators and affected by priority effects within flowers once dispersed. Model analysis suggests that bacteria and yeast coexist regionally only when their occurrence in flowers influences the frequency of flower visits by pollinators. This microbe-pollinator feedback creates positive density dependence in each plant, causing competitive exclusion at the plant scale, but spatial partitioning across multiple plants, realizing coexistence at this scale. Our finding highlights dispersal-community feedback as an overlooked potential mechanism of species coexistence.

Cross-scale integration remains a persistent challenge in ecology. Mechanistic network models have advanced this integration by linking individual behavior to community dynamics. Their complexity, however, often limits exploration to numerical simulations, which tend to be insufficient for fully unveiling the fundamental rules governing system behavior. Extracting these rules requires moving beyond numerical observation to establish exact, analytical constraints. Here, a complete mathematical analysis of a mechanistically detailed plant-pollinator model is presented. This cross-scale analysis decouples transient and equilibrium dynamics, proving that pollination strictly gates plant persistence while recruitment competition caps equilibrium abundance. The precise behavioral mechanisms scaling up to determine network stability are determined: nestedness stabilizes communities by generating floral reward gradients that guide adaptive foraging, whereas connectance destabilizes by eroding these rescue pathways. Additionally, native community persistence and biological invasions are conceptually unified; a single, multi-scale reward threshold (R*) is shown to govern both native survival and alien establishment. These analytical derivations are distilled into conceptual frameworks and visual summaries accessible for empiricists interested in theory and conceptual unification. By translating numerical observations into rigorous, trait-grounded proofs, this analysis demonstrates that complex, cross-scale networks are tractable, revealing the precise conditions under which communities assemble, persist, and collapse.

In many animal species, individuals acquire knowledge from others that enhances their survival and reproduction. However, among the many available cultural exemplars, not all provide reliable information. Consequently, individuals tend to choose their exemplars selectively. One widespread pattern is a preference for older individuals, who may have accumulated valuable knowledge through life. Yet empirical studies also show that individuals frequently learn from age peers, suggesting that copying elders is not universally optimal. The ecological and social conditions that favor learning from elders rather than peers, therefore, remain unclear. Here, we investigate the evolutionary drivers of age-biased exemplar choice in age-structured populations where individuals accumulate knowledge over their lifespan. We develop a model that captures the coevolution of exemplar age choice and age-specific investments in social learning, individual learning, and the use of acquired knowledge for energy extraction. We show that selection promotes a progressive shift from social to individual learning and from learning to energy extraction with age. Exemplar age choice, in turn, evolves through a trade-off between targeting knowledgeable individuals and accessible ones. This trade-off leads young learners to learn preferentially from relatively young exemplars, who are common and still able to provide substantial amounts of novel knowledge, given learners limited knowledge at early ages. As individuals age, encountering exemplars with substantially novel knowledge becomes increasingly difficult. Consequently, as they age, individuals are expected to shift toward learning from older individuals, who possess more knowledge. Population, environment, and knowledge characteristics can shift this balance, generating a wide range of strategies from learning primarily from peers to consistently targeting the oldest individuals. In particular, learning from age peers is favored in populations with strong within-cohort interaction structure, high mortality, or high encounter rates, in unstable environments with rapid knowledge loss, and when knowledge is easily acquired or transmitted.

Dormancy is usually understood as a strategy for coping with extrinsically variable environments, but intrinsic population fluctuations also create conditions where dormancy is adaptive. By analyzing simple population models, we show that, very generally, population fluctuations favor the evolution of dormancy, but dormancy stabilizes population dynamics. This sets up a feedback loop that can enable the coexistence of alternative dormancy strategies. Over longer timescales, we show that evolution of dormancy to an evolutionary stable state can drive populations to the edge of stability, where dynamics are only weakly stabilized. We briefly consider how these conclusions are likely to apply in more complex community contexts. Our results suggest that chaos and high-amplitude population cycles are highly vulnerable to invasion and subsequent stabilization by dormancy, potentially explaining their rarity. At the same time, the propensity of ecological dynamics to fluctuate may be an underappreciated driver of the evolution of dormancy.

Evolutionary conflicts are pervasive in nature and have the potential to drive antagonistic coevolution of conflict-related traits. However, when such conflicts are weak or idiosyncratic, phenotypic signatures of coevolutionary arms races may be absent. Here, we ask whether variation in group-living traits among pine-sawfly species in the genus Neodiprion is consistent with a history of parent-offspring conflict. To address this question, we compile data on adult female clutch size, larval aggregation behavior, and larval group size for a monophyletic group of 19 eastern North American Neodiprion species from field observations, laboratory assays, and published descriptions. We then evaluate the extent to which each trait exhibits phylogenetic signal and, based on these results, examine correlations between group-size traits both with and without phylogenetic correction. Although female oviposition behavior and larval grouping behavior varies among species and variation in these traits is decoupled from phylogeny, we find no evidence of antagonistic coevolution between these traits. Furthermore, while larvae are physically capable of dispersal, female clutch size is a strong predictor of larval colony size, indicating that larvae do not substantially alter initial group size after hatching. Thus, although theoretical work demonstrates the potential for parent-offspring conflict over group size in animals that lack parental care, our data suggest that this type of conflict is not likely to be a long-term driver of phenotypic evolution.

Symbiotic relationships affect the fitness and organismal function of virtually all organisms. In many cases, the fitness effects of symbiosis may be beneficial or harmful depending on the environment. The hosts of such symbionts are favored to acquire them only when the symbiont is beneficial. However, it is not clear whether such selection favors vertical or horizontal transmission, both, or neither. To address this question, we model the evolution of transmission mode in a conditional mutualism experiencing spatial and temporal environmental variation. We find that when symbionts affect host lifespan, but not fecundity, horizontal transmission can contain them to beneficial environments. Vertical transmission can produce symbiont containment when the environmental state is synchronized across locations. We also find an emergent trade-off between horizontal and vertical transmission, suggesting that physiological constraints are not required for the evolution of limits on the total amount of transmission.

Variation in social traits and behaviours is widespread in nature and can be maintained by selection. Many social traits influence fitness indirectly by modifying shared environments that are transmitted across generations, a process known as ecological inheritance. Here, we investigate how variation in environmentally mediated social behaviour, such as helping to improve shared resources, can emerge and persist in spatially subdivided populations where locally modified environments are transmitted across generations. Using mathematical and computational modelling, we show that ecological inheritance, when combined with limited dispersal, readily leads to the stable coexistence of two types with opposite environmental legacies: helpers, who improve the local environment for the future at a personal cost, and environmental free-riders, who benefit without contributing to the detriment of future generations. In turn, this polymorphism generates lasting spatial heterogeneity in environmental quality and, consequently, in survival and reproduction--particularly under isolation-by-distance, creating stable clusters of high- and low-quality habitats across an otherwise homogeneous landscape. These findings reveal how ecological inheritance and spatial structure interact to stabilise polymorphism, potentially driving long-term behavioural, ecological, and fitness variation across diverse biological systems.

Coevolution of competitors can lead to niche partitioning promoting coexistence or to heightened conflicts promoting competitive exclusion. If both are possible, when should coevolution favor coexistence versus exclusion? We investigated this question with a general eco-evolutionary model in which species can reduce the interspecific competition they experience through evolutionary investments in two types of competitive traits: partitioning traits that promote coexistence and conflict traits that promote exclusion. We found that communities were generally mixed, consisting of species investing in both trait types or mixtures of species specializing in one type. For each species, its competitors abundances and investments determined its experienced competition, and stronger competition begot greater competitive trait investment. Species investing in conflict traits strengthened competition for other species both directly and indirectly, whereas partitioning traits only weakened competition via direct effects. Conflict traits were therefore the stronger driver of community-wide investments in all traits. However, species investing most in conflict traits experienced less competition, so they ultimately evolved least investment, making them most likely to be excluded by the next invader. Thus, coevolution may provide an open door for species that play nice and a revolving door of exclusion for those that do not.

Population genetic models excel at identifying the conditions for polymorphisms based on balancing selection but typically disregard the ecological processes that yield particular values of selection coefficients. We model a system that combines antagonistic pleiotropy, dominance reversal and heterozygote advantage: the wood tiger moth Arctia plantaginis, where alternative haplotypes at a major-effect locus determine male hindwing coloration. Yellow offers better protection against predators, while white is often associated with better mating success. The effects of mortality and reproductive success overlap in time because protandrous males can mate as long as they are alive, but they need to avoid predation for several days before the bulk of females emerge. We show that protandry aids polymorphism maintenance whenever the second-fittest genotype (after the heterozygote) is the poorly surviving but mating advantaged homozygote, while increased protandry harms polymorphism when the second-best fitness is that of the survival advantaged morph. Ecologically plausible protandry times predict that dominance reversal does not have to be strong for polymorphism to be maintained. Our study highlights the importance of timing traits in maintaining polymorphisms in Lepidoptera and showcases the benefits of deriving fitness explicitly in place of abstract selection coefficients that lack temporal components within the life cycle.