Life history complementarity and the maintenance of biodiversity.

Life history, the schedule of when and how fast organisms grow, die and reproduce, is a critical axis along which species differ from each other1-4. In parallel, competition is a fundamental mechanism that determines the potential for species coexistence5-8. Previous models of stochastic competition have demonstrated that large numbers of species can persist over long timescales, even when competing for a single common resource9-12, but how life history differences between species increase or decrease the possibility of coexistence and, conversely, whether competition constrains what combinations of life history strategies complement each other remain open questions. Here we show that specific combinations of life history strategy optimize the persistence times of species competing for a single resource before one species overtakes its competitors. This suggests that co-occurring species would tend to have such complementary life history strategies, which we demonstrate using empirical data for perennial plants.

Universal abundance fluctuations across microbial communities, tropical forests, and urban populations.

The growth of complex populations, such as microbial communities, forests, and cities, occurs over vastly different spatial and temporal scales. Although research in different fields has developed detailed, system-specific models to understand each individual system, a unified analysis of different complex populations is lacking; such an analysis could deepen our understanding of each system and facilitate cross-pollination of tools and insights across fields. Here, we use a shared framework to analyze time-series data of the human gut microbiome, tropical forest, and urban employment. We demonstrate that a single, three-parameter model of stochastic population dynamics can reproduce the empirical distributions of population abundances and fluctuations in all three datasets. The three parameters characterizing a species measure its mean abundance, deterministic stability, and stochasticity. Our analysis reveals that, despite the vast differences in scale, all three systems occupy a similar region of parameter space when time is measured in generations. In other words, although the fluctuations observed in these systems may appear different, this difference is primarily due to the different physical timescales associated with each system. Further, we show that the distribution of temporal abundance fluctuations is described by just two parameters and derive a two-parameter functional form for abundance fluctuations to improve risk estimation and forecasting.

Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes.

Microbiome engineering offers the potential to leverage microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloging community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanisms are included and which are omitted. Here, we resolve both issues by introducing a mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the identification of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just [Formula: see text]1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.

A Spatial Signal of Niche Differentiation in Tropical Forests.

AbstractExplaining diversity in tropical forests remains a challenge in community ecology. Theory tells us that species differences can stabilize communities by reducing competition, while species similarities can promote diversity by reducing fitness differences and thus prolonging the time to competitive exclusion. Combined, these processes may lead to clustering of species such that species are niche differentiated across clusters and share a niche within each cluster. Here, we characterize this partial niche differentiation in a tropical forest in Panama by measuring spatial clustering of woody plants and relating these clusters to local soil conditions. We find that species were spatially clustered and the clusters were associated with specific concentrations of soil nutrients, reflecting the existence of nutrient niches. Species were almost twice as likely to recruit in their own nutrient niche. A decision tree algorithm showed that local soil conditions correctly predicted the niche of the trees with up to 85% accuracy. Iron, zinc, phosphorus, manganese, and soil pH were among the best predictors of species clusters.

Genomic insights into isolation of the threatened Florida crested caracara (Caracara plancus).

We conducted a population genomic study of the crested caracara (Caracara plancus) using samples (n = 290) collected from individuals in Florida, Texas, and Arizona, United States. Crested caracaras are non-migratory raptors ranging from the southern tip of South America to the southern United States, including a federally protected relict population in Florida long thought to have been isolated since the last ice age. Our objectives were to evaluate genetic diversity and population structure of Florida's apparently isolated population and to evaluate taxonomic relationships of crested caracaras at the northern edge of their range. Using DNA purified from blood samples, we conducted double-digest restriction site associated DNA sequencing and sequenced the mitochondrial ND2 gene. Analyses of population structure using over 9,000 SNPs suggest that two major clusters are best supported, one cluster including only Florida individuals and the other cluster including Arizona and Texas individuals. Both SNPs and mitochondrial haplotypes reveal the Florida population to be highly differentiated genetically from Arizona and Texas populations, whereas, Arizona and Texas populations are moderately differentiated from each other. The Florida population's mitochondrial haplotypes form a separate monophyletic group, while Arizona and Texas populations share mitochondrial haplotypes. Results of this study provide substantial genetic evidence that Florida's crested caracaras have experienced long-term isolation from caracaras in Arizona and Texas and thus, represent a distinct evolutionary lineage possibly warranting distinction as an Evolutionarily Significant Unit (ESU) or subspecies. This study will inform conservation strategies focused on long-term survival of Florida's distinct, panmictic population.

Stability criteria for the consumption and exchange of essential resources.

Models of consumer effects on a shared resource environment have helped clarify how the interplay of consumer traits and resource supply impact stable coexistence. Recent models generalize this picture to include the exchange of resources alongside resource competition. These models exemplify the fact that although consumers shape the resource environment, the outcome of consumer interactions is context-dependent: such models can have either stable or unstable equilibria, depending on the resource supply. However, these recent models focus on a simplified version of microbial metabolism where the depletion of resources always leads to consumer growth. Here, we model an arbitrarily large system of consumers governed by Liebig's law, where species require and deplete multiple resources, but each consumer's growth rate is only limited by a single one of these resources. Resources that are taken up but not incorporated into new biomass are leaked back into the environment, possibly transformed by intracellular reactions, thereby tying the mismatch between depletion and growth to cross-feeding. For this set of dynamics, we show that feasible equilibria can be either stable or unstable, again depending on the resource environment. We identify special consumption and production networks which protect the community from instability when resources are scarce. Using simulations, we demonstrate that the qualitative stability patterns derived analytically apply to a broader class of network structures and resource inflow profiles, including cases where multiple species coexist on only one externally supplied resource. Our stability criteria bear some resemblance to classic stability results for pairwise interactions, but also demonstrate how environmental context can shape coexistence patterns when resource limitation and exchange are modeled directly.

Emergent neutrality in consumer-resource dynamics.

Neutral theory assumes all species and individuals in a community are ecologically equivalent. This controversial hypothesis has been tested across many taxonomic groups and environmental contexts, and successfully predicts species abundance distributions across multiple high-diversity communities. However, it has been critiqued for its failure to predict a broader range of community properties, particularly regarding community dynamics from generational to geological timescales. Moreover, it is unclear whether neutrality can ever be a true description of a community given the ubiquity of interspecific differences, which presumably lead to ecological inequivalences. Here we derive analytical predictions for when and why non-neutral communities of consumers and resources may present neutral-like outcomes, which we verify using numerical simulations. Our results, which span both static and dynamical community properties, demonstrate the limitations of summarizing distributions to detect non-neutrality, and provide a potential explanation for the successes of neutral theory as a description of macroecological pattern.

Adherence in chronic hepatitis B: associations between medication possession ratio and adverse viral outcomes.

BACKGROUND: Antiviral therapy for chronic hepatitis B (CHB) is effective and can substantially reduce the risk of progressive liver disease and hepatocellular carcinoma but is often administered for an indefinite duration. Adherence has been shown in clinical trials to maximize the benefit of therapy and prevent the development of resistance, however the optimal threshold for predicting clinical outcomes has not been identified. The aim of this study was to analyse adherence using the medication possession ration (MPR) and its relation to virological outcomes in a large multi-centre hospital outpatient population, and guide development of an evidence-based threshold for optimal adherence. METHODS: Pharmacy and pathology records of patients dispensed CHB antiviral therapy from 4 major hospitals in Melbourne between 2010 and 2013 were extracted and analysed to determine their MPR and identify instances of unfavourable viral outcomes. Viral outcomes were classified categorically, with unfavourable outcomes including HBV DNA remaining detectable after 2 years treatment or experiencing viral breakthrough. The association between MPR and unfavourable outcomes was assessed according to various thresholds using ROC analysis and time-to-event regression. RESULTS: Six hundred forty-two individuals were included in the analysis. Median age was 46.6 years, 68% were male, 77% were born in Asia, and the median time on treatment was 27.5 months. The majority had favourable viral outcomes (91.06%), with most having undetectable HBV DNA at the end of the study period. The most common unfavourable outcome was a rise of < 1 log in HBV DNA (6.54% of the total), while 2.49% of participants experienced viral breakthrough. Adherence was linearly associated with favourable outcomes, with increasing risk of virological breakthrough as MPR fell. Decreasing the value of MPR, at which a cut-point was taken, was associated with a progressively larger reduction in the rate of unfavourable event; from a 60% reduction under a cut-point of 1.00 to a 79% reduction when the MPR cut-point was set at 0.8. CONCLUSION: Lower adherence as measured using the MPR was strongly associated with unfavourable therapeutic outcomes, including virological failure. Optimising adherence is therefore important for preventing viral rebound and potential complications such as antiviral resistance. The evidence of dose-response highlights the need for nuanced interventions.

Environmental migrants, structural injustice, and moral responsibility.

Climate change and environmental problems will force or induce millions of people to migrate. In this article, I describe environmental migration and articulate some of the ethical issues. To begin, I give an account of these migrants that overcomes misleading dichotomies. Then, I focus attention on two important ethical issues: justice and responsibility. Although we are all at risk of becoming environmental migrants, we are not equally at risk. Our risk depends on our temporal position, geographical location, social position, and the kind of society in which we live. We all contribute to environmental problems, but we do not contribute equally. About 11% of the world population is responsible for 50% of carbon emissions. These inequalities raise issues of justice because many of the people who are at high risk have contributed little to the problems. Since the issues of justice are relatively clear and compelling, I focus more attention on issues of responsibility. I use Iris Marion Young's account of responsibility for structural injustice to address four key questions about moral responsibility and environmental migration.

Translucent windows: how uncertainty in competitive interactions impacts detection of community pattern.

Traits can provide a window into the mechanisms that maintain coexistence among competing species. Recent theory suggests that competitive interactions will lead to groups, or clusters, of species with similar traits. However, theoretical predictions typically assume complete knowledge of the map between competition and measured traits. These assumptions limit the plausible application of these patterns for inferring competitive interactions in nature. Here, we relax these restrictions and find that the clustering pattern is robust to contributions of unknown or unobserved niche axes. However, it may not be visible unless measured traits are close proxies for niche strategies. We conclude that patterns along single niche axes may reveal properties of interspecific competition in nature, but detecting these patterns requires natural history expertise firmly tying traits to niches.

Born to Run? Quantifying the Balance of Prior Bias and New Information in Prey Escape Decisions.

Animal behaviors can often be challenging to model and predict, though optimality theory has improved our ability to do so. While many qualitative predictions of behavior exist, accurate quantitative models, tested by empirical data, are often lacking. This is likely due to variation in biases across individuals and variation in the way new information is gathered and used. We propose a modeling framework based on a novel interpretation of Bayes's theorem to integrate optimization of energetic constraints with both prior biases and specific sources of new information gathered by individuals. We present methods for inferring distributions of prior biases within populations rather than assuming known priors, as is common in Bayesian approaches to modeling behavior, and for evaluating the goodness of fit of overall model descriptions. We apply this framework to predict optimal escape during predator-prey encounters, based on prior biases and variation in what information prey use. Using this approach, we collected and analyzed data characterizing white-tailed deer (Odocoileus virginianus) escape behavior in response to human approaches. We found that distance to predator alone was not sufficient to predict deer flight response and show that the inclusion of additional information is necessary. We also compared differences in the inferred distributions of prior biases across different populations and discuss the possible role of human activity in influencing these distributions.

Backbones of evolutionary history test biodiversity theory for microbes.

Identifying the ecological and evolutionary mechanisms that determine biological diversity is a central question in ecology. In microbial ecology, phylogenetic diversity is an increasingly common and relevant means of quantifying community diversity, particularly given the challenges in defining unambiguous species units from environmental sequence data. We explore patterns of phylogenetic diversity across multiple bacterial communities drawn from different habitats and compare these data to evolutionary trees generated using theoretical models of biodiversity. We have two central findings. First, although on finer scales the empirical trees are highly idiosyncratic, on coarse scales the backbone of these trees is simple and robust, consistent across habitats, and displays bursts of diversification dotted throughout. Second, we find that these data demonstrate a clear departure from the predictions of standard neutral theories of biodiversity and that an alternative family of generalized models provides a qualitatively better description. Together, these results lay the groundwork for a theoretical framework to connect ecological mechanisms to observed phylogenetic patterns in microbial communities.

Reinterpreting maximum entropy in ecology: a null hypothesis constrained by ecological mechanism.

Simplified mechanistic models in ecology have been criticised for the fact that a good fit to data does not imply the mechanism is true: pattern does not equal process. In parallel, the maximum entropy principle (MaxEnt) has been applied in ecology to make predictions constrained by just a handful of state variables, like total abundance or species richness. But an outstanding question remains: what principle tells us which state variables to constrain? Here we attempt to solve both problems simultaneously, by translating a given set of mechanisms into the state variables to be used in MaxEnt, and then using this MaxEnt theory as a null model against which to compare mechanistic predictions. In particular, we identify the sufficient statistics needed to parametrise a given mechanistic model from data and use them as MaxEnt constraints. Our approach isolates exactly what mechanism is telling us over and above the state variables alone.

Species-abundance distributions under colored environmental noise.

Natural communities at all spatiotemporal scales are subjected to a wide variety of environmental pressures, resulting in random changes in the demographic rates of species populations. Previous analyses have examined the effects of such environmental variance on the long-term growth rate and time to extinction of single populations, but studies of its effects on the diversity of communities remain scarce. In this study, we construct a new master-equation model incorporating demographic and environmental variance and use it to examine how statistical patterns of diversity, as encapsulated by species-abundance distributions (SADs), are altered by environmental variance. Unlike previous diffusion models with environmental variance uncorrelated in time (white noise), our model allows environmental variance to be correlated at different timescales (colored noise), thus facilitating representation of phenomena such as yearly and decadal changes in climate. We derive an exact analytical expression for SADs predicted by our model together with a close approximation, and use them to show that the main effect of adding environmental variance is to increase the proportion of abundant species, thus flattening the SAD relative to the log-series form found in the neutral case. This flattening effect becomes more prominent when environmental variance is more correlated in time and has greater effects on species' demographic rates, holding all other factors constant. Furthermore, we show how our model SADs are consistent with those from diffusion models near the white noise limit. The mathematical techniques we develop are catalysts for further theoretical work exploring the consequences of environmental variance for biodiversity.

A Case Study of Improving Yield Prediction and Sulfur Deficiency Detection Using Optical Sensors and Relationship of Historical Potato Yield with Weather Data in Maine.

In Maine, potato yield is consistent, 38 t·ha-1, for last 10 years except 2016 (44 t·ha-1) which confirms that increasing the yield and quality of potatoes with current fertilization practices is difficult; hence, new or improvised agronomic methods are needed to meet with producers and industry requirements. Normalized difference vegetative index (NDVI) sensors have shown promise in regulating N as an in season application; however, using late N may stretch out the maturation stage. The purpose of the research was to test Trimble GreenSeeker® (TGS) and Holland Scientific Crop Circle™ ACS-430 (HCCACS-430) wavebands to predict potato yield, before the second hilling (6-8 leaf stage). Ammonium sulfate, S containing N fertilizer, is not advised to be applied on acidic soils but accounts for 60-70% fertilizer in Maine's acidic soils; therefore, sensors are used on sulfur deficient site to produce sensor-bound S application guidelines before recommending non-S-bearing N sources. Two study sites investigated for this research include an S deficient site and a regular spot with two kinds of soils. Six N treatments, with both calcium ammonium nitrate and ammonium nitrate, under a randomized complete block design with four replications, were applied at planting. NDVI readings from both sensors were obtained at V8 leaf stages (8 leaf per plant) before the second hilling. Both sensors predict N and S deficiencies with a strong interaction with an average coefficient of correlation (r²) ~45. However, HCCACS-430 was observed to be more virtuous than TGS. The correlation between NDVI (from both sensors) and the potato yield improved using proprietor-proxy leaf area index (PPLAI) from HCCACS-430, e.g., r² value of TGS at Easton site improve from 48 to 60. Weather data affected marketable potato yield (MPY) significantly from south to north in Maine, especially precipitation variations that could be employed in the N recommendations at planting and in season application. This case study addresses a substantial need to revise potato N recommendations at planting and develop possible in season N recommendation using ground based active optical (GBAO) sensors.

Maintenance of biodiversity on islands.

MacArthur and Wilson's theory of island biogeography predicts that island species richness should increase with island area. This prediction generally holds among large islands, but among small islands species richness often varies independently of island area, producing the so-called 'small-island effect' and an overall biphasic species-area relationship (SAR). Here, we develop a unified theory that explains the biphasic island SAR. Our theory's key postulate is that as island area increases, the total number of immigrants increases faster than niche diversity. A parsimonious mechanistic model approximating these processes reproduces a biphasic SAR and provides excellent fits to 100 archipelago datasets. In the light of our theory, the biphasic island SAR can be interpreted as arising from a transition from a niche-structured regime on small islands to a colonization-extinction balance regime on large islands. The first regime is characteristic of classic deterministic niche theories; the second regime is characteristic of stochastic theories including the theory of island biogeography and neutral theory. The data furthermore confirm our theory's key prediction that the transition between the two SAR regimes should occur at smaller areas, where immigration is stronger (i.e. for taxa that are better dispersers and for archipelagos that are less isolated).

Comparing process-based and constraint-based approaches for modeling macroecological patterns.

Ecological patterns arise from the interplay of many different processes, and yet the emergence of consistent phenomena across a diverse range of ecological systems suggests that many patterns may in part be determined by statistical or numerical constraints. Differentiating the extent to which patterns in a given system are determined statistically, and where it requires explicit ecological processes, has been difficult. We tackled this challenge by directly comparing models from a constraint-based theory, the Maximum Entropy Theory of Ecology (METE) and models from a process-based theory, the size-structured neutral theory (SSNT). Models from both theories were capable of characterizing the distribution of individuals among species and the distribution of body size among individuals across 76 forest communities. However, the SSNT models consistently yielded higher overall likelihood, as well as more realistic characterizations of the relationship between species abundance and average body size of conspecific individuals. This suggests that the details of the biological processes contain additional information for understanding community structure that are not fully captured by the METE constraints in these systems. Our approach provides a first step towards differentiating between process- and constraint-based models of ecological systems and a general methodology for comparing ecological models that make predictions for multiple patterns.

Reproducing static and dynamic biodiversity patterns in tropical forests: the critical role of environmental variance.

Ecological communities are subjected to stochasticity in the form of demographic and environmental variance. Stochastic models that contain only demographic variance (neutral models) provide close quantitative fits to observed species-abundance distributions (SADs) but substantially underestimate observed temporal species-abundance fluctuations. To provide a holistic assessment of whether models with demographic and environmental variance perform better than neutral models, the fit of both to SADs and temporal species-abundance fluctuations at the same time has to be tested quantitatively. In this study, we quantitatively test how closely a model with demographic and environmental variance reproduces total numbers of species, total abundances, SADs and temporal species-abundance fluctuations for two tropical forest tree communities, using decadal data from long-term monitoring plots and considering individuals larger than two size thresholds for each community. We find that the model can indeed closely reproduce these static and dynamic patterns of biodiversity in the two communities for the two size thresholds, with better overall fits than corresponding neutral models. Therefore, our results provide evidence that stochastic models incorporating demographic and environmental variance can simultaneously capture important static and dynamic biodiversity patterns arising in tropical forest communities.

Antepartum dalteparin versus no antepartum dalteparin for the prevention of pregnancy complications in pregnant women with thrombophilia (TIPPS): a multinational open-label randomised trial.

BACKGROUND: Thrombophilias are common disorders that increase the risk of pregnancy-associated venous thromboembolism and pregnancy loss and can also increase the risk of placenta-mediated pregnancy complications (severe pre-eclampsia, small-for-gestational-age infants, and placental abruption). We postulated that antepartum dalteparin would reduce these complications in pregnant women with thrombophilia. METHODS: In this open-label randomised trial undertaken in 36 tertiary care centres in five countries, we enrolled consenting pregnant women with thrombophilia at increased risk of venous thromboembolism or with previous placenta-mediated pregnancy complications. Eligible participants were randomly allocated in a 1:1 ratio to either antepartum prophylactic dose dalteparin (5000 international units once daily up to 20 weeks' gestation, and twice daily thereafter until at least 37 weeks' gestation) or to no antepartum dalteparin (control group). Randomisation was done by a web-based randomisation system, and was stratified by country and gestational age at randomisation day with a permuted block design (block sizes 4 and 8). At randomisation, site pharmacists (or delegates) received a randomisation number and treatment allocation (by fax and/or e-mail) from the central web randomisation system and then dispensed study drug to the local coordinator. Patients and study personnel were not masked to treatment assignment, but the outcome adjudicators were masked. The primary composite outcome was independently adjudicated severe or early-onset pre-eclampsia, small-for-gestational-age infant (birthweight <10th percentile), pregnancy loss, or venous thromboembolism. We did intention-to-treat and on-treatment analyses. This trial is registered with ClinicalTrials.gov, number NCT00967382, and with Current Controlled Trials, number ISRCTN87441504. FINDINGS: Between Feb 28, 2000, and Sept 14, 2012, 292 women consented to participate and were randomly assigned to the two groups. Three women were excluded after randomisation because of ineligibility (two in the antepartum dalteparin group and one in the control group), leaving 146 women assigned to antepartum dalteparin and 143 assigned to no antepartum dalteparin. Some patients crossed over to the other group during treatment, and therefore for on-treatment and safety analysis there were 143 patients in the dalteparin group and 141 in the no dalteparin group. Dalteparin did not reduce the incidence of the primary composite outcome in both intention-to-treat analysis (dalteparin 25/146 [17·1%; 95% CI 11·4-24·2%] vs no dalteparin 27/143 [18·9%; 95% CI 12·8-26·3%]; risk difference -1·8% [95% CI -10·6% to 7·1%)) and on-treatment analysis (dalteparin 28/143 [19·6%] vs no dalteparin 24/141 [17·0%]; risk difference +2·6% [95% CI -6·4 to 11·6%]). In safety analysis, the occurrence of major bleeding did not differ between the two groups. However, minor bleeding was more common in the dalteparin group (28/143 [19·6%]) than in the no dalteparin group (13/141 [9·2%]; risk difference 10·4%, 95% CI 2·3-18·4; p=0·01). INTERPRETATION: Antepartum prophylactic dalteparin does not reduce the occurrence of venous thromboembolism, pregnancy loss, or placenta-mediated pregnancy complications in pregnant women with thrombophilia at high risk of these complications and is associated with an increased risk of minor bleeding. FUNDING: Canadian Institutes of Health Research, Heart and Stroke Foundation of Canada, and Pharmacia and UpJohn.