Why ex post peer review encourages high-risk research while ex ante review discourages it.

Peer review is an integral component of contemporary science. While peer review focuses attention on promising and interesting science, it also encourages scientists to pursue some questions at the expense of others. Here, we use ideas from forecasting assessment to examine how two modes of peer review-ex ante review of proposals for future work and ex post review of completed science-motivate scientists to favor some questions instead of others. Our main result is that ex ante and ex post peer review push investigators toward distinct sets of scientific questions. This tension arises because ex post review allows investigators to leverage their own scientific beliefs to generate results that others will find surprising, whereas ex ante review does not. Moreover, ex ante review will favor different research questions depending on whether reviewers rank proposals in anticipation of changes to their own personal beliefs or to the beliefs of their peers. The tension between ex ante and ex post review puts investigators in a bind because most researchers need to find projects that will survive both. By unpacking the tension between these two modes of review, we can understand how they shape the landscape of science and how changes to peer review might shift scientific activity in unforeseen directions.

Resonance in Physiologically Structured Population Models.

Ecologists have long sought to understand how the dynamics of natural populations are affected by the environmental variation those populations experience. A transfer function is a useful tool for this purpose, as it uses linearization theory to show how the frequency spectrum of the fluctuations in a population's abundance relates to the frequency spectrum of environmental variation. Here, we show how to derive and to compute the transfer function for a continuous-time model of a population that is structured by a continuous individual-level state variable such as size. To illustrate, we derive, compute, and analyze the transfer function for a size-structured population model of stony corals with open recruitment, parameterized for a common Indo-Pacific coral species complex. This analysis identifies a sharp multi-decade resonance driven by space competition between existing coral colonies and incoming recruits. The resonant frequency is most strongly determined by the rate at which colonies grow, and the potential for resonant oscillations is greatest when colony growth is only weakly density-dependent. While these resonant oscillations are unlikely to be a predominant dynamical feature of degraded reefs, they suggest dynamical possibilities for marine invertebrates in more pristine waters. The size-structured model that we analyze is a leading example of a broader class of physiologically structured population models, and the methods we present should apply to a wide variety of models in this class.

Stony coral populations are more sensitive to changes in vital rates in disturbed environments.

Reef-building corals, like many long-lived organisms, experience environmental change as a combination of separate but concurrent processes, some of which are gradual yet long-lasting, while others are more acute but short-lived. For corals, some chronic environmental stressors, such as rising temperature and ocean acidification, are thought to induce gradual changes in colonies' vital rates. Meanwhile, other environmental changes, such as the intensification of tropical cyclones, change the disturbance regime that corals experience. Here, we use a physiologically structured population model to explore how chronic environmental stressors that impact the vital rates of individual coral colonies interact with the intensity and magnitude of disturbance to affect coral population dynamics and cover. We find that, when disturbances are relatively benign, intraspecific density dependence driven by space competition partially buffers coral populations against gradual changes in vital rates. However, the impact of chronic stressors is amplified in more highly disturbed environments, because disturbance weakens the buffering effect of space competition. We also show that coral cover is more sensitive to changes in colony growth and mortality than to external recruitment, at least in open populations, and that space competition and size structure mediate the extent and pace of coral population recovery following a large-scale mortality event. Understanding the complex interplay among chronic environmental stressors, mass-mortality events, and population size structure sharpens our ability to manage and to restore coral-reef ecosystems in an increasingly disturbed future.

Regulation of OmpA Translation and Shigella dysenteriae Virulence by an RNA Thermometer.

RNA thermometers are cis-acting riboregulators that mediate the posttranscriptional regulation of gene expression in response to environmental temperature. Such regulation is conferred by temperature-responsive structural changes within the RNA thermometer that directly result in differential ribosomal binding to the regulated transcript. The significance of RNA thermometers in controlling bacterial physiology and pathogenesis is becoming increasingly clear. This study combines in silico, molecular genetics, and biochemical analyses to characterize both the structure and function of a newly identified RNA thermometer within the ompA transcript of Shigella dysenteriae First identified by in silico structural predictions, genetic analyses have demonstrated that the ompA RNA thermometer is a functional riboregulator sufficient to confer posttranscriptional temperature-dependent regulation, with optimal expression observed at the host-associated temperature of 37°C. Structural studies and ribosomal binding analyses have revealed both increased exposure of the ribosomal binding site and increased ribosomal binding to the ompA transcript at permissive temperatures. The introduction of site-specific mutations predicted to alter the temperature responsiveness of the ompA RNA thermometer has predictable consequences for both the structure and function of the regulatory element. Finally, in vitro tissue culture-based analyses implicate the ompA RNA thermometer as a bona fide S. dysenteriae virulence factor in this bacterial pathogen. Given that ompA is highly conserved among Gram-negative pathogens, these studies not only provide insight into the significance of riboregulation in controlling Shigella virulence, but they also have the potential to facilitate further understanding of the physiology and/or pathogenesis of a wide range of bacterial species.

Drivers of elevational richness peaks, evaluated for trees in the east Himalaya.

Along elevational gradients, species richness often peaks at intermediate elevations and not the base. Here we refine and test eight hypotheses to evaluate causes of a richness peak in trees of the eastern Himalaya. In the field, we enumerated trees in 50 plots of size 0.1 ha each at eight zones along an elevational gradient and compared richness patterns with interpolation of elevational ranges of species from a thorough review of literature, including floras from the plains of India. The maximum number of species peaks at similar elevations in the two data sets (at 500 m in the field sampling and between 500 m and 1,000 m in range interpolation); concordance between the methods implies that statistical artefacts are unlikely to explain the peak in the data. We reject most hypotheses (e.g., area, speciation rate, mixing of distinct floras). We find support for a model in which climate (actual evapotranspiration [AET] or its correlates) sets both the number of species and each species optimum, coupled with a geometric constraint. We consider that AET declines with elevation, but an abrupt change in the association of AET with geographical distance into the plains means that the location of highest AET, at the base of the mountain, receives range overlaps from fewer species than the location just above the base. We formalize this explanation with a mathematical model to show how this can generate the observed low-elevation richness peak.

Contest models highlight inherent inefficiencies of scientific funding competitions.

Scientific research funding is allocated largely through a system of soliciting and ranking competitive grant proposals. In these competitions, the proposals themselves are not the deliverables that the funder seeks, but instead are used by the funder to screen for the most promising research ideas. Consequently, some of the funding program's impact on science is squandered because applying researchers must spend time writing proposals instead of doing science. To what extent does the community's aggregate investment in proposal preparation negate the scientific impact of the funding program? Are there alternative mechanisms for awarding funds that advance science more efficiently? We use the economic theory of contests to analyze how efficiently grant proposal competitions advance science, and compare them with recently proposed, partially randomized alternatives such as lotteries. We find that the effort researchers waste in writing proposals may be comparable to the total scientific value of the research that the funding supports, especially when only a few proposals can be funded. Moreover, when professional pressures motivate investigators to seek funding for reasons that extend beyond the value of the proposed science (e.g., promotion, prestige), the entire program can actually hamper scientific progress when the number of awards is small. We suggest that lost efficiency may be restored either by partial lotteries for funding or by funding researchers based on past scientific success instead of proposals for future work.

Evolution of host preference in anthropophilic mosquitoes.

BACKGROUND: Insecticide-treated bed nets (ITNs) have played a large role in reducing the burden of malaria. There is concern however regarding the potential of the mass distributions and use of ITNs to select for insecticide and behavioural resistance in mosquito populations. A key feature of the vectorial capacity of the major sub-Saharan African malaria vector Anopheles gambiae sensu stricto (s.s.) is its tendency to feed almost exclusively on humans. Here, an evolutionary model is used to investigate the potential for ITNs to select for increased zoophily in this highly anthropophilic species and how this is influenced by ecological and operational conditions. RESULTS: The evolution of a single trait, namely the tendency to accept cattle as hosts, is modelled in mosquito populations which initially only bite humans. Thus, the conditions under which a resource specialist would broaden its diet and become a generalist are investigated. The results indicate that in the absence of insecticide-treated nets, host specialization in mosquitoes is either driven toward human specialization (when humans are more abundant than alternative hosts), or displays evolutionary bistability. The latter implies that the evolutionary endpoint relies on the initial trait value of the population. Bed nets select for increased zoophily while in use. When ITNs are removed, whether or not the population reverts to anthropophagic or zoophagic behaviour depends on whether the intervention had been maintained sufficiently long to drive the population past the evolutionarily unstable point. CONCLUSIONS: The use of ITNs is likely to select for an increase in the biting preference for cattle. Bed nets may thus alter the population composition of major vector species in a manner that has positive epidemiological ramifications. Whether populations are set on a trajectory toward increased zoophily following the cessation of intense bed net usage in an area depends on the composition of host communities as well as operational conditions. This has potential implications for bed net campaigns, particularly with an eye toward scaling down interventions following interruption of transmission. Further research on malaria mosquito feeding behaviour is warranted to explore the conditions under which such adaptive shifts may actually occur in the field.

Does knee malalignment predict the efficacy of realignment therapy for patients with knee osteoarthritis?

BACKGROUND: Realignment therapies, including knee braces, foot orthoses and shoes are prescribed to patients with medial knee osteoarthritis (OA) with the goal of unloading the medial tibiofemoral (TF) compartment. It is uncertain whether realignment therapies have different effects in those with knee malalignment. We studied whether the efficacy of realignment therapy for pain and function in persons with medial TF OA is predicted by the severity of the baseline knee malalignment. METHODS: The baseline characteristics of 48 participants with moderate to severe medial knee OA were collected. Participants' pain and function were measured using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale before and after 12 weeks of realignment therapy using a valgus unloader knee brace plus bilateral neutral foot orthoses and motion control shoes. Anatomical axis (AA) was measured on weight-bearing knee radiographs by a blinded reader and knee malalignment was categorized as either varus malaligned (moderate or severe) or neutral according to the AA angle. We assessed for differences in response to treatment according to alignment category. General linear statistical models were generated to determine which of the measured alignment variables and covariates predicted change in the pain outcome. RESULTS: Anatomical axis knee alignment was not a significant predictor of pain or function change with active treatment. Baseline WOMAC scores were the best predictor of change in WOMAC (P < 0.01 and P = 0.06 for pain and function, respectively). CONCLUSIONS: Baseline knee alignment did not predict the efficacy of 12 weeks realignment therapy in participants with medial tibiofemoral OA. [Correction added on 27 August 2015, after first online publication: 'did predict' has been corrected to 'did not predict' in the conclusions of the abstract section.].

Publication bias and the canonization of false facts.

Science is facing a "replication crisis" in which many experimental findings cannot be replicated and are likely to be false. Does this imply that many scientific facts are false as well? To find out, we explore the process by which a claim becomes fact. We model the community's confidence in a claim as a Markov process with successive published results shifting the degree of belief. Publication bias in favor of positive findings influences the distribution of published results. We find that unless a sufficient fraction of negative results are published, false claims frequently can become canonized as fact. Data-dredging, p-hacking, and similar behaviors exacerbate the problem. Should negative results become easier to publish as a claim approaches acceptance as a fact, however, true and false claims would be more readily distinguished. To the degree that the model reflects the real world, there may be serious concerns about the validity of purported facts in some disciplines.

A General, Synthetic Model for Predicting Biodiversity Gradients from Environmental Geometry.

Latitudinal and elevational biodiversity gradients fascinate ecologists, and have inspired dozens of explanations. The geometry of the abiotic environment is sometimes thought to contribute to these gradients, yet evaluations of geometric explanations are limited by a fragmented understanding of the diversity patterns they predict. This article presents a mathematical model that synthesizes multiple pathways by which environmental geometry can drive diversity gradients. The model characterizes species ranges by their environmental niches and limits on range sizes and places those ranges onto the simplified geometries of a sphere or cone. The model predicts nuanced and realistic species-richness gradients, including latitudinal diversity gradients with tropical plateaus and mid-latitude inflection points and elevational diversity gradients with low-elevation diversity maxima. The model also illustrates the importance of a mid-environment effect that augments species richness at locations with intermediate environments. Model predictions match multiple empirical biodiversity gradients, depend on ecological traits in a testable fashion, and formally synthesize elements of several geometric models. Together, these results suggest that previous assessments of geometric hypotheses should be reconsidered and that environmental geometry may play a deeper role in driving biodiversity gradients than is currently appreciated.

Estimating index of refraction from polarimetric hyperspectral imaging measurements.

Current material identification techniques rely on estimating reflectivity or emissivity which vary with viewing angle. As off-nadir remote sensing platforms become increasingly prevalent, techniques robust to changing viewing geometries are desired. A technique leveraging polarimetric hyperspectral imaging (P-HSI), to estimate complex index of refraction, N̂(ν̃), an inherent material property, is presented. The imaginary component of N̂(ν̃) is modeled using a small number of "knot" points and interpolation at in-between frequencies ν̃. The real component is derived via the Kramers-Kronig relationship. P-HSI measurements of blackbody radiation scattered off of a smooth quartz window show that N̂(ν̃) can be retrieved to within 0.08 RMS error between 875 cm-1 ≤ ν̃ ≤ 1250 cm-1. P-HSI emission measurements of a heated smooth Pyrex beaker also enable successful N̂(ν̃) estimates, which are also invariant to object temperature.

Within-Host Niche Differences and Fitness Trade-offs Promote Coexistence of Plant Viruses.

Pathogens live in diverse, competitive communities, yet the processes that maintain pathogen diversity remain elusive. Here, we use a species-rich, well-studied plant virus system, the barley yellow dwarf viruses, to examine the mechanisms that regulate pathogen diversity. We empirically parameterized models of three viruses, their two aphid vectors, and one perennial grass host. We found that high densities of both aphids maximized virus diversity and that competition limited the coexistence of two closely related viruses. Even limited ability to simultaneously infect (coinfect) host individuals strongly promoted virus coexistence; preventing coinfection led to priority effects. Coinfection generated stabilizing niche differences by allowing viruses to share hosts. However, coexistence also required trade-offs between vector generalist and specialist life-history strategies. Our predicted outcomes broadly concur with previous field observations. These results show how competition within individual hosts and vectors may lead to unexpected population-level outcomes between pathogens, including coexistence, competitive exclusion, and priority effects, and how contemporary coexistence theory can help to predict these outcomes.

Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models.

Pre-planting factors have been associated with the late-season severity of Stagonospora nodorum blotch (SNB), caused by the fungal pathogen Parastagonospora nodorum, in winter wheat (Triticum aestivum). The relative importance of these factors in the risk of SNB has not been determined and this knowledge can facilitate disease management decisions prior to planting of the wheat crop. In this study, we examined the performance of multiple regression (MR) and three machine learning algorithms namely artificial neural networks, categorical and regression trees, and random forests (RF), in predicting the pre-planting risk of SNB in wheat. Pre-planting factors tested as potential predictor variables were cultivar resistance, latitude, longitude, previous crop, seeding rate, seed treatment, tillage type, and wheat residue. Disease severity assessed at the end of the growing season was used as the response variable. The models were developed using 431 disease cases (unique combinations of predictors) collected from 2012 to 2014 and these cases were randomly divided into training, validation, and test datasets. Models were evaluated based on the regression of observed against predicted severity values of SNB, sensitivity-specificity ROC analysis, and the Kappa statistic. A strong relationship was observed between late-season severity of SNB and specific pre-planting factors in which latitude, longitude, wheat residue, and cultivar resistance were the most important predictors. The MR model explained 33% of variability in the data, while machine learning models explained 47 to 79% of the total variability. Similarly, the MR model correctly classified 74% of the disease cases, while machine learning models correctly classified 81 to 83% of these cases. Results show that the RF algorithm, which explained 79% of the variability within the data, was the most accurate in predicting the risk of SNB, with an accuracy rate of 93%. The RF algorithm could allow early assessment of the risk of SNB, facilitating sound disease management decisions prior to planting of wheat.

Environmental influences on mosquito foraging and integrated vector management can delay the evolution of behavioral resistance.

Along with the scaled-up distribution of long-lasting insecticidal nets for malaria control has become concern about insecticide resistance. A related concern regards the evolution of host-seeking periodicity from the nocturnal to the crepuscular periods of the day. Why we observe such shifts in some areas but not others and which methods could prove useful in managing such behavioral resistance remain open questions. We developed a foraging model to explore whether environmental conditions affect the evolution of behavioral resistance. We looked at the role of the abundance of blood hosts and nectar sources and investigated the potential of attractive toxic sugar baits for integrated control. Higher encounter rates with hosts and nectar sources allowed behaviorally resistant populations to persist at higher levels of bed net coverage. Whereas higher encounter rates with nectar increased the threshold where resistance emerged, higher encounter rates of hosts lowered this threshold. Adding sugar baits lowered the coverage level of bed nets required to eliminate the vector population. In certain environments, using lower bed net coverage levels together with toxic sugar baits may delay or prevent the evolution of behavioral resistance. Designing sustainable control strategies will depend on an understanding of vector behavior expressed in local environmental conditions.

HYPERSPECTRAL ANALYSIS FOR STANDOFF DETECTION OF DIMETHYL METHYLPHOSPHONATE ON BUILDING MATERIALS.

Detecting organophosphates in indoor settings can greatly benefit from more efficient and faster methods of surveying large surface areas than conventional approaches, which sample small surface areas followed by extraction and analysis. This study examined a standoff detection technique utilizing hyperspectral imaging for analysis of building materials in near-real time. In this proof-of-concept study, dimethyl methylphosphonate (DMMP) was applied to stainless steel and laminate coupons and spectra were collected during active illumination. Absorbance bands at approximately 1275 cm-1 and 1050 cm-1 were associated with phosphorus-oxygen double bond (P=O) and phosphorus-oxygen-carbon (P-O-C) bond stretches of DMMP, respectively. The magnitude of these bands increased linearly (r2 = 0.93) with DMMP across the full absorbance spectrum, between ν1 = 877 cm-1 to ν2 = 1262 cm-1. Comparisons between bare and contaminated surfaces on stainless steel using the spectral contrast angle technique indicated that the bare samples showed no sign of contamination, with large uniformly distributed contrast angles of 45°-55°, while the contaminated samples had smaller spectral contact angles of < 20° in the contaminated region and > 40° in the uncontaminated region. The laminate contaminated region exhibited contact angles of < 25°. To the best of our knowledge, this is the first report to demonstrate that hyperspectral imaging can be used to detect DMMP on building materials, with detection levels similar to concentrations expected for some organophosphate deposition scenarios.

Controls over native perennial grass exclusion and persistence in California grasslands invaded by annuals.

Despite obvious impacts of nonnative species in many ecosystems, the long-term outcome of competition between native and exotic species often remains unclear. Demographic models can resolve the outcome of competition between native and exotic species and provide insight into conditions favoring exclusion vs. coexistence. California grasslands are one of the most heavily invaded ecosystems in North America. Although California native perennial bunchgrasses are thought to be restricted to a fraction of their original abundance, the eventual outcome of competition with invasive European annual grasses at a local scale (competitive exclusion, stable persistence, or priority effects) remains unresolved. Here, we used a two-species discrete time population growth model to predict the outcome of competition between exotic annual and native perennial grasses in California, and to determine the demographic traits responsible for the outcome. The model is parameterized with empirical data from several field experiments. We found that, once introduced, annual grasses persist stably with little uncertainty. Although perennial grasses are competitively excluded on average, the most likely range of model predictions also includes stable coexistence with annual grasses. As for many other perennial plants, native bunchgrass population growth is highly sensitive to the survival of adults. Management interventions that improve perennial adult survival are likely to be more effective than those that reduce exotic annual seed production or establishment, reduce competition, or increase perennial seedling establishment. Further empirical data on summer survival of bunchgrass adults and competitive effects of annuals on perennials would most improve model predictions because they contribute most to the uncertainty in the predicted outcome for the perennial grass. This work demonstrates how demographic approaches can clarify the outcome of competition between native and exotic species, identify key targets for future empirical work, and predict the effectiveness of management interventions. Such studies are critical both for understanding the impacts of invasion and for targeting management responses that maximize the benefit to native species.

The differential effects of increasing frequency and magnitude of extreme events on coral populations.

Extreme events, which have profound ecological consequences, are changing in both frequency and magnitude with climate change. Because extreme temperatures induce coral bleaching, we can explore the relative impacts of changes in frequency and magnitude of high temperature events on coral reefs. Here, we combined climate projections and a dynamic population model to determine how changing bleaching regimes influence coral persistence. We additionally explored how coral traits and competition with macroalgae mediate changes in bleaching regimes. Our results predict that severe bleaching events reduce coral persistence more than frequent bleaching. Corals with low adult mortality and high growth rates are successful when bleaching is mild, but bleaching resistance is necessary to persist when bleaching is severe, regardless of frequency. The existence of macroalgae-dominated stable states reduces coral persistence and changes the relative importance of coral traits. Building on previous studies, our results predict that management efforts may need to prioritize protection of "weaker" corals with high adult mortality when bleaching is mild, and protection of "stronger" corals with high bleaching resistance when bleaching is severe. In summary, future reef projections and conservation targets depend on both local bleaching regimes and biodiversity.

Stability of Caribbean coral communities quantified by long-term monitoring and autoregression models.

Tropical coral reefs exemplify ecosystems imperiled by environmental change. Anticipating the future of reef ecosystems requires understanding how scleractinian corals respond to the multiple environmental disturbances that threaten their survival. We analyzed the stability of coral reefs at three habitats at different depths along the south shore of St. John, U.S. Virgin Islands, using multivariate autoregression (MAR) models and two decades of monitoring data. We quantified several measures of ecosystem stability, including the magnitude of typical stochastic fluctuations, the rate of recovery following disturbance, and the sensitivity of coral cover to hurricanes and elevated sea temperature. Our results show that, even within a -4 km shore, coral communities in different habitats display different stability properties, and that the stability of each habitat corresponds with the habitat's known synecology. Two Orbicella-dominated habitats are less prone to annual stochastic fluctuations than coral communities in shallower water, but they recover slowly from disturbance, and one habitat has suffered recent losses in scleractinian cover that will not be quickly reversed. In contrast, a shallower, low-coral-cover habitat is subject to greater stochastic fluctuations, but rebounds more quickly from disturbance and is more robust to hurricanes and seawater warming. In some sense, the shallower community is more stable, although the stability arguably arises from having little coral cover left. Our results sharpen understanding of recent changes in coral communities at these habitats, provide a more detailed understanding of how these habitats may change in future environments, and illustrate how MAR models can be used to assess stability of communities founded upon long-lived species.