Biodiversity and productivity in eastern US forests.

Despite experimental and observational studies demonstrating that biodiversity enhances primary productivity, the best metric for predicting productivity at broad geographic extents-functional trait diversity, phylogenetic diversity, or species richness-remains unknown. Using >1.8 million tree measurements from across eastern US forests, we quantified relationships among functional trait diversity, phylogenetic diversity, species richness, and productivity. Surprisingly, functional trait and phylogenetic diversity explained little variation in productivity that could not be explained by tree species richness. This result was consistent across the entire eastern United States, within ecoprovinces, and within data subsets that controlled for biomass or stand age. Metrics of functional trait and phylogenetic diversity that were independent of species richness were negatively correlated with productivity. This last result suggests that processes that determine species sorting and packing are likely important for the relationships between productivity and biodiversity. This result also demonstrates the potential confusion that can arise when interdependencies among different diversity metrics are ignored. Our findings show the value of species richness as a predictive tool and highlight gaps in knowledge about linkages between functional diversity and ecosystem functioning.

The factors that favor adaptive habitat construction versus non-adaptive environmental conditioning.

Adaptive habitat construction is a process by which individuals alter their environment so as to increase their (inclusive) fitness. Such alterations are a subset of the myriad ways that individuals condition their environment. We present an individual-based model of habitat construction to explore what factors might favor selection when the benefits of environmental alterations are shared by individuals of the same species. Our results confirm the predictions of inclusive fitness and group selection theory and expectations based on previous models that construction will be more favored when its benefits are more likely to be directed to self or near kin. We found that temporal variation had no effect on the evolution of construction. For spatial heterogeneity, construction was disfavored when the spatial pattern of movement did not match the spatial pattern of environmental heterogeneity, especially when there was spatial heterogeneity in the optimal amount of construction. Under those conditions, very strong selection was necessary to favor genetic differentiation of construction propensity among demes. We put forth a constitutive theory for the evolution of adaptive habitat construction that unifies our model with previous verbal and quantitative models into a formal conceptual framework.

Do I build or do I move? Adaptation by habitat construction versus habitat choice.

Trait adaptation to a heterogeneous environment can occur through six modes: genetic differentiation of those traits, a jack-of-all-trades phenotypic uniformity, diversified bet-hedging, phenotypic plasticity, habitat choice, and habitat construction. A key question is what circumstances favor one mode over another, and how they might interact if a system can express more than one mode at a time. We examined the joint evolution of habitat choice and habitat construction using individual-based simulations. We manipulated when during the life cycle construction occurred and the fitness value of construction. We found that for our model habitat construction was nearly always favored over habitat choice, especially if construction happened after dispersal. Because of the ways that the various modes of adaptation interact with each other, there is no simple answer as to which will be favored; it depends on details of the biology and ecology of a given system.

The evolution of habitat construction with and without phenotypic plasticity.

Habitat construction and phenotypic plasticity are alternative responses to variable environments. We explored evolution along an environmental gradient of habitat construction alone and in combination with phenotypic plasticity using individual-based simulations that manipulated the fitness benefit of construction and whether construction maintained or eliminated that gradient. Construction was favored when its benefits were more likely to flow to the immediate offspring of the constructing individuals. Habitat construction and phenotypic plasticity traded off against each other or plasticity was selected against, depending on how the optimum environment varied and with the fitness value of construction. When selection favored differences in the amount of construction along the environmental gradient, genetic differentiation for habitat construction increased as the fitness value of construction increased. The degree to which each adaptive response was likely to evolve also depended on the precise ordering of life history events. Adaptive habitat construction does not always occur and may be selected against.

Severe limitations of the FEve metric of functional evenness and some alternative metrics.

The metric of functional evenness FEve is an example of how approaches to conceptualizing and measuring functional variability may go astray. This index has several critical conceptual and practical drawbacks: Different values of the FEve index for the same community can be obtained if the species have unequal species abundances; this result is highly likely if most of the traits are categorical.Very minor differences in even one pairwise distance can result in very different values of FEve.FEve uses only a fraction of the information contained in the matrix of species distances. Counterintuitively, this can cause very similar FEve scores for communities with substantially different patterns of species dispersal in trait space.FEve is a valid metric only if all species have exactly the same abundances. However, the meaning of FEve in such an instance is unclear as the purpose of the metric is to measure the variability of abundances in trait space. We recommend not using the FEve metric in studies of functional variability. Given the wide usage of FEve index over the last decade, the validity of the conclusions based on those estimates is in question. Instead, we suggest three alternative metrics that combine variability in species distances in trait space with abundance in various ways. More broadly, we recommend that researchers think about which community properties (e.g., trait distances of a focus species to the nearest neighbor or all other species, variability of pairwise interactions between species) they want to measure and pick from among the appropriate metrics.

The genetics of phenotypic plasticity. XVII. Response to climate change.

The world is changing at a rapid rate, threatening extinction for a large part of the world's biota. One potential response to those altered conditions is to evolve so as to be able to persist in place. Such evolution includes not just traits themselves, but also the phenotypic plasticity of those traits. We used individual-based simulations to explore the potential of an evolving phenotypic plasticity to increase the probability of persistence in the response to either a step change or continual, directional change in the environment accompanied by within-generation random environmental fluctuations. Populations could evolve by altering both their nonplastic and plastic genetic components. We found that phenotypic plasticity enhanced survival and adaptation if that plasticity was not costly. If plasticity was costly, for it to be beneficial the phenotypic magnitude of plasticity had to be great enough in the initial generations to overcome those costs. These results were not sensitive to either the magnitude of the within-generation correlation between the environment of development and the environment of selection or the magnitude of the environmental fluctuations, except for very small phenotypic magnitudes of plasticity. So, phenotypic plasticity has the potential to enhance survival; however, more data are needed on the ubiquity of trait plasticity, the extent of costs of plasticity, and the rate of mutational input of genetic variation for plasticity.

The genetics of phenotypic plasticity. XVI. Interactions among traits and the flow of information.

Although the environment varies, adaptive trait plasticity is uncommon, which can be due to either costs or limitations. Currently there is little evidence for costs of plasticity; limitations are a more promising explanation, including information reliability. A possible cause for a decrease in information reliability is the channeling of environmental information through one trait that then affects the phenotype of a second trait, the information path. Using an individual-based simulation model, I explored the ways in which configurations of trait interactions and patterns of environmental variation in space and time affect the evolution of phenotypic plasticity. I found that genotypes and phenotypes evolved to shorten and simplify the information path from the environment to fitness. A shortened path was characterized by a decrease in the amount of plasticity for traits that had a less direct connection between the environment of development and fitness. A simplified path was characterized by a decrease in the amount of plasticity for traits that had multiple paths between the environment and their phenotype. These results suggest that an eighth proposition be added to the theory of the evolution of phenotypic plasticity: Trait plasticity will evolve to minimize the information path between the environment and fitness.

Correction to: Phylogenetic Insight into Zika and Emerging Viruses for a Perspective on Potential Hosts.

The article Phylogenetic Insight into Zika and Emerging Viruses for a Perspective on Potential Hosts, written by Diana S. Weber, Karen A. Alroy, and Samuel M. Scheiner, was originally published Online First without open access.

The genetics of phenotypic plasticity. XV. Genetic assimilation, the Baldwin effect, and evolutionary rescue.

We used an individual-based simulation model to examine the role of phenotypic plasticity on persistence and adaptation to two patterns of environmental variation, a single, abrupt step change and continual, linear change. Our model tested the assumptions and predictions of the theory of genetic assimilation, explored the evolutionary dynamics of the Baldwin effect, and provided expectations for the evolutionary response to climate change. We found that genetic assimilation as originally postulated is not likely to occur because the replacement of plasticity by fixed genetic effects takes much longer than the environment is likely to remain stable. On the other hand, trait plasticity as an enhancement to continual evolutionary change may be an important evolutionary mechanism as long as plasticity has little or no costs. Whether or not plasticity helps or hinders evolutionary rescue following a step change in the environment depends on whether plasticity is costly. For linear environmental change, noncostly plasticity always decreases extinction rates, while costly plasticity can create a fitness drag and increase the chance of extinction. Thus, with changing climates plasticity can enhance adaptation and prevent extinction under some conditions, but not others.

The components of biodiversity, with a particular focus on phylogenetic information.

We present a framework for biodiversity metrics that organizes the growing panoply of metrics. Our framework distinguishes metrics based on the type of information-abundance, phylogeny, function-and two common properties-magnitude and variability. Our new metrics of phylogenetic diversity are based on a partition of the total branch lengths of a cladogram into the proportional share of each species, including: a measure of divergence which standardizes the amount of evolutionary divergence by species richness and time depth of the cladogram; a measure of regularity which is maximal when the tree is perfectly symmetrical so that all species have the same proportional branch lengths; a measure that combines information on the magnitude and variability of abundance with phylogenetic variability, and a measure of phylogenetically weighted effective mean abundance; and indicate how those metrics can be decomposed into α and ß components. We illustrate the utility of these new metrics using empirical data on the bat fauna of Manu, Peru. Divergence was greatest in lowland rainforest and at the transition between cloud and elfin forests, and least in upper elfin forests and in cloud forests. In contrast, regularity was greatest in lowland rainforest, dipping to its smallest values in mid-elevation cloud forests, and then increasing in high elevation elfin forests. These patterns indicate that the first species to drop out with increasing elevation are ones that are closely related to other species in the metacommunity. Measures of the effective number of phylogenetically independent or distinct species decreased very rapidly with elevation, and ß-diversity was larger. In contrast, a comparison of feeding guilds shows a different effect of phylogenetic patterning. Along the elevational gradient, each guild generally loses some species from each clade-rather than entire clades-explaining the maintenance of functional diversity as phylogenetic diversity decreases.

Phylogenetic Insight into Zika and Emerging Viruses for a Perspective on Potential Hosts.

Global viral diversity is substantial, but viruses that contribute little to the public health burden or to agricultural damage receive minimal attention until a seemingly unimportant virus becomes a threat. The Zika virus (ZIKV) illustrated this, as there was limited information and awareness of the virus when it was identified as a public health emergency in February 2016. Predicting which virus may pose a future threat is difficult. This is in part because significant knowledge gaps in the basic biology and ecology of an emerging virus can impede policy development, delay decision making, and hinder public health action. We suggest using a phylogenetic framework of pathogens and their infected host species for insight into which animals may serve as reservoirs. For example, examining flaviviruses closely related to ZIKV, the phylogenetic framework indicates New World monkeys are the most likely candidates to be potential reservoirs for ZIKV. Secondarily, mammals that are in close proximity to humans should be considered because of the increased opportunity for pathogen exchange. The increase in human-mediated environmental change is accelerating the probability of another previously overlooked virus becoming a significant concern. By investing in basic science research and organizing our knowledge into an evolutionary framework, we will be better prepared to respond to the next emerging infectious disease.

Habitat Choice and Temporal Variation Alter the Balance between Adaptation by Genetic Differentiation, a Jack-of-All-Trades Strategy, and Phenotypic Plasticity.

Confronted with variable environments, species adapt in several ways, including genetic differentiation, a jack-of-all-trades strategy, or phenotypic plasticity. Adaptive habitat choice favors genetic differentiation and local adaptation over a generalist, jack-of-all-trades strategy. Models predict that, absent plasticity costs, variable environments generally favor phenotypic plasticity over genetic differentiation and being a jack-of-all-trades generalist. It is unknown how habitat choice might affect the evolution of plasticity. Using an individual-based simulation model, I explored the interaction of choice and plasticity. With only spatial variation, habitat choice promotes genetic differentiation over a jack-of-all-trades strategy or phenotypic plasticity. In the absence of plasticity, temporal variation favors a jack-of-all-trades strategy over choice-mediated genetic differentiation; when plasticity is an option, it is favored. This occurs because habitat choice creates a feedback between genetic differentiation and dispersal rates. As demes become better adapted to their local environments, the effective dispersal rate decreases, because more individuals have very high fitness and so choose not to disperse, reinforcing local stabilizing selection and negating selection for plasticity. Temporal variation breaks that feedback. These results point to a potential data paradox: systems with habitat choice may have the lowest actual movement rates. The potential for adaptive habitat choice may be very common, but its existence may reduce observed dispersal rates enough that we do not recognize systems where it may be present, warranting further exploration of likely systems.

The Genetics of Phenotypic Plasticity. XIV. Coevolution.

Plastic changes in organisms' phenotypes can result from either abiotic or biotic effectors. Biotic effectors create the potential for a coevolutionary dynamic. Through the use of individual-based simulations, we examined the coevolutionary dynamic of two species that are phenotypically plastic. We explored two modes of biotic and abiotic interactions: ecological interactions that determine the form of natural selection and developmental interactions that determine phenotypes. Overall, coevolution had a larger effect on the evolution of phenotypic plasticity than plasticity had on the outcome of coevolution. Effects on the evolution of plasticity were greater when the fitness-maximizing coevolutionary outcomes were antagonistic between the species pair (predator-prey interactions) than when those outcomes were augmenting (competitive or mutualistic). Overall, evolution in the context of biotic interactions reduced selection for plasticity even when trait development was responding to just the abiotic environment. Thus, the evolution of phenotypic plasticity must always be interpreted in the full context of a species' ecology. Our results show how the merging of two theory domains--coevolution and phenotypic plasticity--can deepen our understanding of both and point to new empirical research.

Evaluation of an integrated framework for biodiversity with a new metric for functional dispersion.

Growing interest in understanding ecological patterns from phylogenetic and functional perspectives has driven the development of metrics that capture variation in evolutionary histories or ecological functions of species. Recently, an integrated framework based on Hill numbers was developed that measures three dimensions of biodiversity based on abundance, phylogeny and function of species. This framework is highly flexible, allowing comparison of those diversity dimensions, including different aspects of a single dimension and their integration into a single measure. The behavior of those metrics with regard to variation in data structure has not been explored in detail, yet is critical for ensuring an appropriate match between the concept and its measurement. We evaluated how each metric responds to particular data structures and developed a new metric for functional biodiversity. The phylogenetic metric is sensitive to variation in the topology of phylogenetic trees, including variation in the relative lengths of basal, internal and terminal branches. In contrast, the functional metric exhibited multiple shortcomings: (1) species that are functionally redundant contribute nothing to functional diversity and (2) a single highly distinct species causes functional diversity to approach the minimum possible value. We introduced an alternative, improved metric based on functional dispersion that solves both of these problems. In addition, the new metric exhibited more desirable behavior when based on multiple traits.

A conceptual framework for organismal biology: linking theories, models, and data.

Implicit or subconscious theory is especially common in the biological sciences. Yet, theory plays a variety of roles in scientific inquiry. First and foremost, it determines what does and does not count as a valid or interesting question or line of inquiry. Second, theory determines the background assumptions within which inquiries are pursued. Third, theory provides linkages among disciplines. For these reasons, it is important and useful to develop explicit theories for biology. A general theory of organisms is developed, which includes 10 fundamental principles that apply to all organisms, and 6 that apply to multicellular organisms only. The value of a general theory comes from its utility to help guide the development of more specific theories and models. That process is demonstrated by examining two domains: ecoimmunology and development. For the former, a constitutive theory of ecoimmunology is presented, and used to develop a specific model that explains energetic trade-offs that may result from an immunological response of a host to a pathogen. For the latter, some of the issues involved in trying to devise a constitutive theory that covers all of development are explored, and a more narrow theory of phenotypic novelty is presented. By its very nature, little of a theory of organisms will be new. Rather, the theory presented here is a formal expression of nearly two centuries of conceptual advances and practice in research. Any theory is dynamic and subject to debate and change. Such debate will occur as part of the present, initial formulation, as the ideas presented here are refined. The very process of debating the form of the theory acts to clarify thinking. The overarching goal is to stimulate debate about the role of theory in the study of organisms, and thereby advance our understanding of them.

The genetics of phenotypic plasticity. XIII. Interactions with developmental instability.

In a heterogeneous environment, natural selection on a trait can lead to a variety of outcomes, including phenotypic plasticity and bet-hedging through developmental instability. These outcomes depend on the magnitude and pattern of that heterogeneity and the spatial and temporal distribution of individuals. However, we do not know if and how those two outcomes might interact with each other. I examined the joint evolution of plasticity and instability through the use of an individual-based simulation in which each could be genetically independent or pleiotropically linked. When plasticity and instability were determined by different loci, the only effect on the evolution of plasticity was the elimination of plasticity as a bet-hedging strategy. In contrast, the effects on the evolution of instability were more substantial. If conditions were such that the population was likely to evolve to the optimal reaction norm, then instability was disfavored. Instability was favored only when the lack of a reliable environmental cue disfavored plasticity. When plasticity and instability were determined by the same loci, instability acted as a strong limitation on the evolution of plasticity. Under some conditions, selection for instability resulted in maladaptive plasticity. Therefore, before testing any models of plasticity or instability evolution, or interpreting empirical patterns, it is important to know the ecological, life history, developmental, and genetic contexts of trait phenotypic plasticity and developmental instability.

Bet-hedging as a complex interaction among developmental instability, environmental heterogeneity, dispersal, and life-history strategy.

One potential evolutionary response to environmental heterogeneity is the production of randomly variable offspring through developmental instability, a type of bet-hedging. I used an individual-based, genetically explicit model to examine the evolution of developmental instability. The model considered both temporal and spatial heterogeneity alone and in combination, the effect of migration pattern (stepping stone vs. island), and life-history strategy. I confirmed that temporal heterogeneity alone requires a threshold amount of variation to select for a substantial amount of developmental instability. For spatial heterogeneity only, the response to selection on developmental instability depended on the life-history strategy and the form and pattern of dispersal with the greatest response for island migration when selection occurred before dispersal. Both spatial and temporal variation alone select for similar amounts of instability, but in combination resulted in substantially more instability than either alone. Local adaptation traded off against bet-hedging, but not in a simple linear fashion. I found higher-order interactions between life-history patterns, dispersal rates, dispersal patterns, and environmental heterogeneity that are not explainable by simple intuition. We need additional modeling efforts to understand these interactions and empirical tests that explicitly account for all of these factors.

The genetics of phenotypic plasticity. XII. Temporal and spatial heterogeneity.

To understand empirical patterns of phenotypic plasticity, we need to explore the complexities of environmental heterogeneity and how it interacts with cue reliability. I consider both temporal and spatial variation separately and in combination, the timing of temporal variation relative to development, the timing of movement relative to selection, and two different patterns of movement: stepping-stone and island. Among-generation temporal heterogeneity favors plasticity, while within-generation heterogeneity can result in cue unreliability. In general, spatial variation more strongly favors plasticity than temporal variation, and island migration more strongly favors plasticity than stepping-stone migration. Negative correlations among environments between the time of development and selection can result in seemingly maladaptive reaction norms. The effects of higher dispersal rates depend on the life history stage when dispersal occurs and the pattern of environmental heterogeneity. Thus, patterns of environmental heterogeneity can be complex and can interact in unforeseen ways to affect cue reliability. Proper interpretation of patterns of trait plasticity requires consideration of the ecology and biology of the organism. More information on actual cue reliability and the ecological and developmental context of trait plasticity is needed.