Resource use differences of two coexisting chironomid species at localized scales.

Competing species may show positive correlations in abundance through time and space if they rely on a shared resource. Such positive correlations might obscure resource partitioning that facilitates competitor coexistence. Here, we examine the potential for resource partitioning between two ecologically similar midge species (Diptera: Chironomidae) in Lake Mývatn, Iceland. Tanytarsus gracilentus and Chironomus islandicus show large, roughly synchronized population fluctuations, implying potential reliance on a shared fluctuating resource and thereby posing the question of how these species coexist at high larval abundances. We first considered spatial partitioning of larvae. Abundances of both species were positively correlated in space; thus, spatial partitioning across different sites in the lake did not appear to be strong. We then inferred differences in dietary resources with stable carbon isotopes. T. gracilentus larvae had significantly higher δ13C values than C. islandicus, suggesting interspecific differences in resource use. Differences in resource selectivity, tube-building behavior, and feeding styles may facilitate resource partitioning between these species. Relative to surface sediments, T. gracilentus had higher δ13C values, suggesting that they selectively graze on 13C-enriched resources such as productive algae from the surface of their tubes. In contrast, C. islandicus had lower δ13C values than surface sediments, suggesting reliance on 13C-depleted resources that may include detrital organic matter and associated microbes that larvae selectively consume from the sediment surface or within their burrow walls. Overall, our study illustrates that coexisting and ecologically similar species may show positive correlations in space and time while using different resources at fine spatial scales.

Shared Features Underlying Compact Genomes and Extreme Habitat Use in Chironomid Midges.

Nonbiting midges (family Chironomidae) are found throughout the world in a diverse array of aquatic and terrestrial habitats, can often tolerate harsh conditions such as hypoxia or desiccation, and have consistently compact genomes. Yet we know little about the shared molecular basis for these attributes and how they have evolved across the family. Here, we address these questions by first creating high-quality, annotated reference assemblies for Tanytarsus gracilentus (subfamily Chironominae, tribe Tanytarsini) and Parochlus steinenii (subfamily Podonominae). Using these and other publicly available assemblies, we created a time-calibrated phylogenomic tree for family Chironomidae with outgroups from order Diptera. We used this phylogeny to test for features associated with compact genomes, as well as examining patterns of gene family evolution and positive selection that may underlie chironomid habitat tolerances. Our results suggest that compact genomes evolved in the common ancestor of Chironomidae and Ceratopogonidae and that this occurred mainly through reductions in noncoding regions (introns, intergenic sequences, and repeat elements). Significantly expanded gene families in Chironomidae included biological processes that may relate to tolerance of stressful environments, such as temperature homeostasis, carbohydrate transport, melanization defense response, and trehalose transport. We identified several positively selected genes in Chironomidae, notably sulfonylurea receptor, CREB-binding protein, and protein kinase D. Our results improve our understanding of the evolution of small genomes and extreme habitat use in this widely distributed group.

Dispersal stabilizes coupled ecological and evolutionary dynamics in a host-parasitoid system.

When ecological and evolutionary dynamics occur on comparable timescales, persistence of the ensuing eco-evolutionary dynamics requires both ecological and evolutionary stability. This unites key questions in ecology and evolution: How do species coexist, and what maintains genetic variation in a population? In this work, we investigated a host-parasitoid system in which pea aphid hosts rapidly evolve resistance to Aphidius ervi parasitoids. Field data and mathematical simulations showed that heterogeneity in parasitoid dispersal can generate variation in parasitism-mediated selection on hosts through time and space. Experiments showed how evolutionary trade-offs plus moderate host dispersal across this selection mosaic cause host-parasitoid coexistence and maintenance of genetic variation in host resistance. Our results show how dispersal can stabilize both the ecological and evolutionary components of eco-evolutionary dynamics.

Disentangling the drivers of decadal body size decline in an insect population.

While climate warming is widely predicted to reduce body size of ectotherms, evidence for this trend is mixed. Body size depends not only on temperature but also on other factors, such as food quality and intraspecific competition. Because temperature trends or other long-term environmental factors may affect population size and food sources, attributing trends in average body size to temperature requires the separation of potentially confounding effects. We evaluated trends in the body size of the midge Tanytarsus gracilentus and potential drivers (water temperature, population size, and food quality) between 1977 and 2015 at Lake Mývatn, Iceland. Although temperatures increased at Mývatn over this period, there was only a slight (non-significant) decrease in midge adult body size, contrary to theoretical expectations. Using a state-space model including multiple predictors, body size was negatively associated with both water temperature and midge population abundance, and it was positively associated with 13 C enrichment of midges (an indicator of favorable food conditions). The magnitude of these effects were similar, such that simultaneous changes in temperature, abundance, and carbon stable isotopic signature could counteract each other in the long-term body size trend. Our results illustrate how multiple factors, all of which could be influenced by global change, interact to affect average ectotherm body size.

What Can Evolutionary History Tell Us about the Functioning of Ecological Communities? The ASN Presidential Debate.

AbstractIn January 2018, Sharon Strauss, then president of the American Society of Naturalists, organized a debate on the following topic: does evolutionary history inform the current functioning of ecological communities? The debaters-Ives, Lau, Mayfield, and Tobias-presented pro and con arguments, caricatured in standard debating format. Numerous examples show that both recent microevolutionary and longer-term macroevolutionary history are important to the ecological functioning of communities. On the other hand, many other examples illustrate that the evolutionary history of communities or community members does not influence ecological function, or at least not very much. This article aims to provide a provocative discussion of the consistent and conflicting patterns that emerge in the study of contemporary and historical evolutionary influences on community function, as well as to identify questions for further study. It is intended as a thought-provoking exercise to explore this complex field, specifically addressing (1) key assumptions and how they can lead us astray and (2) issues that need additional study. The debaters all agree that evolutionary history can inform us about at least some aspects of community function. The underlying question at the root of the debate, however, is how the fields of ecology and evolution can most profitably collaborate to provide a deeper and broader understanding of ecological communities.

Differences in COVID-19 cyclicity and predictability among U.S. counties and states reflect the effectiveness of protective measures.

During the COVID-19 pandemic, many quantitative approaches were employed to predict the course of disease spread. However, forecasting faces the challenge of inherently unpredictable spread dynamics, setting a limit to the accuracy of all models. Here, we analyze COVID-19 data from the USA to explain variation among jurisdictions in disease spread predictability (that is, the extent to which predictions are possible), using a combination of statistical and simulation models. We show that for half the counties and states the spread rate of COVID-19, r(t), was predictable at most 9 weeks and 8 weeks ahead, respectively, corresponding to at most 40% and 35% of an average cycle length of 23 weeks and 26 weeks. High predictability was associated with high cyclicity of r(t) and negatively associated with R0 values from the pandemic's onset. Our statistical evidence suggests the following explanation: jurisdictions with a severe initial outbreak, and where individuals and authorities took strong and sustained protective measures against COVID-19, successfully curbed subsequent waves of disease spread, but at the same time unintentionally decreased its predictability. Decreased predictability of disease spread should be viewed as a by-product of positive and sustained steps that people take to protect themselves and others.

Beyond "greening" and "browning": Trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation.

Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002-2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned.

Demographic Basis of Spatially Structured Fluctuations in a Threespine Stickleback Metapopulation.

AbstractUncovering the demographic basis of population fluctuations is a central goal of population biology. This is particularly challenging for spatially structured populations, which require disentangling synchrony in demographic rates from coupling via movement between locations. In this study, we fit a stage-structured metapopulation model to a 29-year time series of threespine stickleback abundance in the heterogeneous and productive Lake Mývatn, Iceland. The lake comprises two basins (North and South) connected by a channel through which the stickleback disperse. The model includes time-varying demographic rates, allowing us to assess the potential contributions of recruitment and survival, spatial coupling via movement, and demographic transience to the population's large fluctuations in abundance. Our analyses indicate that recruitment was only modestly synchronized between the two basins, whereas survival probabilities of adults were more strongly synchronized, contributing to cyclic fluctuations in the lake-wide population size with a period of approximately 6 years. The analyses further show that the two basins were coupled through movement, with the North Basin subsidizing the South Basin and playing a dominant role in driving the lake-wide dynamics. Our results show that cyclic fluctuations of a metapopulation can be explained in terms of the combined effects of synchronized demographic rates and spatial coupling.

Reconstructing midge consumer-resource dynamics using carbon stable isotope signatures of archived specimens.

Population cycles can be caused by consumer-resource interactions. Confirming the role of consumer-resource interactions, however, can be challenging due to an absence of data for the resource candidate. For example, interactions between midge larvae and benthic algae likely govern the high-amplitude population fluctuations of Tanytarsus gracilentus in Lake Mývatn, Iceland, but there are no records of benthic resources concurrent with adult midge population counts. Here, we investigate consumer population dynamics using the carbon stable isotope signatures of archived T. gracilentus specimens collected from 1977 to 2015, under the assumption that midge δ13 C values reflect those of resources they consumed as larvae. We used the time series for population abundance and δ13 C to estimate interactions between midges and resources while accounting for measurement error and possible preservation effects on isotope values. Results were consistent with consumer-resource interactions: high δ13 C values preceded peaks in the midge population, and δ13 C values tended to decline after midges reached high abundance. One interpretation of this dynamic coupling is that midge isotope signatures reflect temporal variation in benthic algal δ13 C values, which we expected to mirror primary production. Following from this explanation, high benthic production (enriched δ13 C values) would contribute to increased midge abundance, and high midge abundance would result in declining benthic production (depleted δ13 C values). An additional and related explanation is that midges deplete benthic algal abundance once they reach peak densities, causing midges to increase their relative reliance on other resources including detritus and associated microorganisms. Such a shift in resource use would be consistent with the subsequent decline in midge δ13 C values. Our study adds evidence that midge-resource interactions drive T. gracilentus fluctuations and demonstrates a novel application of stable isotope time-series data to understand consumer population dynamics.

Statistical tests for non-independent partitions of large autocorrelated datasets.

Large sets of autocorrelated data are common in fields such as remote sensing and genomics. For example, remote sensing can produce maps of information for millions of pixels, and the information from nearby pixels will likely be spatially autocorrelated. Although there are well-established statistical methods for testing hypotheses using autocorrelated data, these methods become computationally impractical for large datasets. • The method developed here makes it feasible to perform F-tests, likelihood ratio tests, and t-tests for large autocorrelated datasets. The method involves subsetting the dataset into partitions, analyzing each partition separately, and then combining the separate tests to give an overall test. • The separate statistical tests on partitions are non-independent, because the points in different partitions are not independent. Therefore, combining separate analyses of partitions requires accounting for the non-independence of the test statistics among partitions. • The methods can be applied to a wide range of data, including not only purely spatial data but also spatiotemporal data. For spatiotemporal data, it is possible to estimate coefficients from time-series models at different spatial locations and then analyze the spatial distribution of the estimates. The spatial analysis can be simplified by estimating spatial autocorrelation directly from the spatial autocorrelation among time series.

Phylogenetic conservatism explains why plants are more likely to produce fleshy fruits in the tropics.

Plant functional traits often show strong latitudinal trends. To explain these trends, studies have often focused on environmental variables, correlations with other traits that themselves show latitudinal trends, and phylogenetic conservatism. However, few studies have systematically disentangled the relative contributions of these factors. Using a dataset consisting of 9,370 plant species from Southwest China, we investigated factors affecting fruit type (fleshy vs. dry): plant growth form, environmental constraints (summarized by climate region), and phylogenetic conservatism. Growth form and climate region are often cited in the literature as important explanations for the higher proportion of fleshy fruited species in the tropics. Nonetheless, in our analyses using partial R2 , growth form and climate region explained only 1.7% and 0.3%, respectively, of the variance in fruit type in a model including phylogeny, while phylogenetic conservatism explained 79.5%. Furthermore, phylogenetic conservatism was evenly distributed along the phylogeny, implying that fruit type reflects both ancient and recent phylogenetic relationships. Our findings illustrate the value of parsing out the contributions of explanatory variables and phylogeny to the variance in species' traits. Methods using phylogenies that calculate partial R2 give a more informative tool than traditional methods to explore the phylogenetic patterns of functional traits.

Trade-Offs (and Constraints) in Organismal Biology.

AbstractTrade-offs and constraints are inherent to life, and studies of these phenomena play a central role in both organismal and evolutionary biology. Trade-offs can be defined, categorized, and studied in at least six, not mutually exclusive, ways. (1) Allocation constraints are caused by a limited resource (e.g., energy, time, space, essential nutrients), such that increasing allocation to one component necessarily requires a decrease in another (if only two components are involved, this is referred to as the Y-model, e.g., energy devoted to size versus number of offspring). (2) Functional conflicts occur when features that enhance performance of one task decrease performance of another (e.g., relative lengths of in-levers and out-levers, force-velocity trade-offs related to muscle fiber type composition). (3) Shared biochemical pathways, often involving integrator molecules (e.g., hormones, neurotransmitters, transcription factors), can simultaneously affect multiple traits, with some effects being beneficial for one or more components of Darwinian fitness (e.g., survival, age at first reproduction, fecundity) and others detrimental. (4) Antagonistic pleiotropy describes genetic variants that increase one component of fitness (or a lower-level trait) while simultaneously decreasing another. (5) Ecological circumstances (or selective regime) may impose trade-offs, such as when foraging behavior increases energy availability yet also decreases survival. (6) Sexual selection may lead to the elaboration of (usually male) secondary sexual characters that improve mating success but handicap survival and/or impose energetic costs that reduce other fitness components. Empirical studies of trade-offs often search for negative correlations between two traits that are the expected outcomes of the trade-offs, but this will generally be inadequate if more than two traits are involved and especially for complex physiological networks of interacting traits. Moreover, trade-offs often occur only in populations that are experiencing harsh environmental conditions or energetic challenges at the extremes of phenotypic distributions, such as among individuals or species that have exceptional athletic abilities. Trade-offs may be (partially) circumvented through various compensatory mechanisms, depending on the timescale involved, ranging from acute to evolutionary. Going forward, a pluralistic view of trade-offs and constraints, combined with integrative analyses that cross levels of biological organization and traditional boundaries among disciplines, will enhance the study of evolutionary organismal biology.

Ecosystem engineering alters density-dependent feedbacks in an aquatic insect population.

Ecosystem engineers have large impacts on the communities in which they live, and these impacts may feed back to populations of engineers themselves. In this study, we assessed the effect of ecosystem engineering on density-dependent feedbacks for midges in Lake Mývatn, Iceland. The midge larvae reside in the sediment and build silk tubes that provide a substrate for algal growth, thereby elevating benthic primary production. Benthic algae are in turn the primary food source for the midge larvae, setting the stage for the effects of engineering to feed back to the midges themselves. Using a field mesocosm experiment manipulating larval midge densities, we found a generally positive but nonlinear relationship between density and benthic production. Furthermore, adult emergence increased with the primary production per midge larva. By combining these two relationships in a simple model, we found that the positive effect of midges on benthic production weakened negative density dependence at low to intermediate larval densities. However, this benefit disappeared at high densities when midge consumption of primary producers exceeded their positive effects on primary production through ecosystem engineering. Our results illustrate how ecosystem engineering can alter density-dependent feedbacks for engineer populations.

Estimating and explaining the spread of COVID-19 at the county level in the USA.

The basic reproduction number, R0, determines the rate of spread of a communicable disease and therefore gives fundamental information needed to plan public health interventions. Using mortality records, we estimated the rate of spread of COVID-19 among 160 counties and county-aggregates in the USA at the start of the epidemic. We show that most of the high among-county variance is explained by four factors (R2 = 0.70): the timing of outbreak, population size, population density, and spatial location. For predictions of future spread, population density and spatial location are important, and for the latter we show that SARS-CoV-2 strains containing the G614 mutation to the spike gene are associated with higher rates of spread. Finally, the high predictability of R0 allows extending estimates to all 3109 counties in the conterminous 48 states. The high variation of R0 argues for public health policies enacted at the county level for controlling COVID-19.

Coevolution, diversification and alternative states in two-trophic communities.

Single-trait eco-evolutionary models of arms races between consumers and their resource species often show inhibition rather than promotion of community diversification. In contrast, modelling arms races involving multiple traits, we found that arms races can promote diversification when trade-off costs among traits make simultaneous investment in multiple traits either more beneficial or more costly. Coevolution between resource and consumer species generates an adaptive landscape for each, with the configuration giving predictable suites of consumer and resource species. Nonetheless, the adaptive landscape contains multiple alternative stable states, and which stable community is reached depends on small stochastic differences occurring along evolutionary pathways. Our results may solve a puzzling conflict between eco-evolutionary theory that predicts community diversification via consumer-resource interactions will be rare, and empirical research that has uncovered real cases. Furthermore, our results suggest that these real cases might be just a subset of alternative stable communities.

Self-perpetuating ecological-evolutionary dynamics in an agricultural host-parasite system.

Ecological and evolutionary processes may become intertwined when they operate on similar time scales. Here we show ecological-evolutionary dynamics between parasitoids and aphids containing heritable symbionts that confer resistance against parasitism. In a large-scale field experiment, we manipulated the aphid's host plant to create ecological conditions that either favoured or disfavoured the parasitoid. The result was rapid evolutionary divergence of aphid resistance between treatment populations. Consistent with ecological-evolutionary dynamics, the resistant aphid populations then had reduced parasitism and increased population growth rates. We fit a model to quantify costs (reduced intrinsic rates of increase) and benefits of resistance. We also performed genetic assays on 5 years of field samples that showed persistent but highly variable frequencies of aphid clones containing protective symbionts; these patterns were consistent with simulations from the model. Our results show (1) rapid evolution that is intertwined with ecological dynamics and (2) variation in selection that prevents traits from becoming fixed, which together generate self-perpetuating ecological-evolutionary dynamics.

Inbreeding reduces long-term growth of Alpine ibex populations.

Many studies document negative inbreeding effects on individuals, and conservation efforts to preserve rare species routinely employ strategies to reduce inbreeding. Despite this, there are few clear examples in nature of inbreeding decreasing the growth rates of populations, and the extent of population-level effects of inbreeding in the wild remains controversial. Here, we take advantage of a long-term dataset of 26 reintroduced Alpine ibex (Capra ibex ibex) populations spanning nearly 100 years to show that inbreeding substantially reduced per capita population growth rates, particularly for populations in harsher environments. Populations with high average inbreeding (F ≈ 0.2) had population growth rates reduced by 71% compared with populations with no inbreeding. Our results show that inbreeding can have long-term demographic consequences even when environmental variation is large and deleterious alleles may have been purged during bottlenecks. Thus, efforts to guard against inbreeding effects in populations of endangered species have not been misplaced.

The potential role of intrinsic processes in generating abrupt and quasi-synchronous tree declines during the Holocene.

Multiple abrupt and sometimes near-synchronous declines in tree populations have been detected in the temperate forests of eastern North America and Europe during the Holocene. Traditional approaches to understanding these declines focus on searching for climatic or other broad-scale extrinsic drivers. These approaches include multi-proxy studies that match reconstructed changes in tree abundance to reconstructed changes in precipitation or temperature. Although these correlative approaches are informative, they neglect the potential role of intrinsic processes, such as competition and dispersal, in shaping tree community dynamics. We developed a simple process-based community model that includes competition among tree species, density-dependent survival, and dispersal to investigate how these processes might generate abrupt changes in tree abundances even when extrinsic climatic factors do not themselves change abruptly. Specifically, a self-reinforcing (i.e., positive) feedback between abundance and survival can produce abrupt changes in tree abundance in the absence of long-term climatic changes. Furthermore, spatially correlated, short-term environmental variation and seed dispersal can increase the synchrony of abrupt changes. Using the well-studied, late-Holocene crash of Tsuga canadensis (eastern hemlock) populations as an empirical case study, we find that our model generates abrupt and quasi-synchronized crashes qualitatively similar to the observed hemlock patterns. Other tree taxa vary in the frequency and clustering of abrupt change and the proportion of increases and decreases. This complexity argues for caution in interpreting abrupt changes in species abundances as indicative of abrupt climatic changes. Nonetheless, some taxa show patterns that the model cannot produce: observed abrupt declines in hemlock abundance are more synchronized than abrupt increases, whereas the degree of synchronization is the same for abrupt decreases and increases in the model. Our results show that intrinsic processes can be significant contributing factors in abrupt tree population changes and highlight the diagnostic value of analyzing entire time series rather than single events when testing hypotheses about abrupt changes. Thus, intrinsic processes should be considered along with extrinsic drivers when seeking to explain rapid changes in community composition.

R$

Many researchers want to report an $R^{2}$ to measure the variance explained by a model. When the model includes correlation among data, such as phylogenetic models and mixed models, defining an $R^{2}$ faces two conceptual problems. (i) It is unclear how to measure the variance explained by predictor (independent) variables when the model contains covariances. (ii) Researchers may want the $R^{2}$ to include the variance explained by the covariances by asking questions such as "How much of the data is explained by phylogeny?" Here, I investigated three $R^{2}$s for phylogenetic and mixed models. $R^{2}_{resid}$ is an extension of the ordinary least-squares $R^{2}$ that weights residuals by variances and covariances estimated by the model; it is closely related to $R^{2}_{glmm}$ presented by Nakagawa and Schielzeth (2013. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4:133-142). $R^{2}_{pred}$ is based on predicting each residual from the fitted model and computing the variance between observed and predicted values. $R^{2}_{lik}$ is based on the likelihood of fitted models, and therefore, reflects the amount of information that the models contain. These three $R^{2}$s are formulated as partial $R^{2}$s, making it possible to compare the contributions of predictor variables and variance components (phylogenetic signal and random effects) to the fit of models. Because partial $R^{2}$s compare a full model with a reduced model without components of the full model, they are distinct from marginal $R^{2}$s that partition additive components of the variance. I assessed the properties of the $R^{2}$s for phylogenetic models using simulations for continuous and binary response data (phylogenetic generalized least squares and phylogenetic logistic regression). Because the $R^{2}$s are designed broadly for any model for correlated data, I also compared $R^{2}$s for linear mixed models and generalized linear mixed models. $R^{2}_{resid}$, $R^{2}_{pred}$, and $R^{2}_{lik}$ all have similar performance in describing the variance explained by different components of models. However, $R^{2}_{pred}$ gives the most direct answer to the question of how much variance in the data is explained by a model. $R^{2}_{resid}$ is most appropriate for comparing models fit to different data sets, because it does not depend on sample sizes. And $R^{2}_{lik}$ is most appropriate to assess the importance of different components within the same model applied to the same data, because it is most closely associated with statistical significance tests.