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One strand of modern coexistence theory (MCT) partitions invader growth rates (IGR) to quantify how different mechanisms contribute to species coexistence, highlighting fluctuation‐dependent mechanisms. A general conclusion from the classical analytic MCT theory is that coexistence mechanisms relying on temporal variation (such as the temporal storage effect) are generally less effective at promoting coexistence than mechanisms relying on spatial or spatiotemporal variation (primarily growth‐density covariance). However, the analytic theory assumes continuous population density, and IGRs are calculated for infinitesimally rare invaders that have infinite time to find their preferred habitat and regrow, without ever experiencing intraspecific competition. Here we ask if the disparity between spatial and temporal mechanisms persists when individuals are, instead, discrete and occupy finite amounts of space. We present a simulation‐based approach to quantifying IGRs in this situation, building on our previous approach for spatially non‐varying habitats. As expected, we found that spatial mechanisms are weakened; unexpectedly, the contribution to IGR from growth‐density covariance could even become negative, opposing coexistence. We also found shifts in which demographic parameters had the largest effect on the strength of spatial coexistence mechanisms. Our substantive conclusions are statements about one model, across parameter ranges that we subjectively considered realistic. Using the methods developed here, effects of individual discreteness should be explored theoretically across a broader range of conditions, and in models parameterized from empirical data on real communities. [ABSTRACT FROM AUTHOR]

The survival of animals is impacted by landscapes of spatially varying mortality factors including habitat type, predation risk or harvest risk, among others. Characterization of these spatial mortality processes is important for managing animal populations and their habitats, yet this information has proved challenging to capture. Advances in telemetry now make it possible to monitor tagged individuals' locations through time for a wide range of taxa, providing opportunity to assess movement and mortality simultaneously through spatial time‐to‐event data. Existing time‐to‐event modelling frameworks have largely ignored spatially varying mortality processes or have treated an animal's location as fixed at a regional level.Here we present a general spatial survival model for analysing time‐to‐event data arising from animal telemetry. Our model has a flexible Cox regression form and can estimate the effects of conventional non‐spatial risk factors (size, seasonality, etc.), spatial covariates (e.g. habitat type) and spatial variation in risk not explained by covariates on mortality. We show how to fit our model using Bayesian methods and demonstrate applications of our model with several simulated examples and two animal telemetry case studies.Our model produced consistent and unbiased parameter estimates throughout simulations with a variety of spatial and non‐spatial hazards. In the first case study, data from 147 tagged caribou in British Columbia, Canada, revealed a spatially heterogeneous mortality landscape with caribou survival varying by elevation, likely in response to space use by predators. Our second case study involved a dataset of 390 acoustically tagged Atlantic cod in a southern Norwegian fjord where a marine protected area (MPA) was established during the study. We found that the MPA led to a shift from mostly fishing mortality to mostly natural mortality within the fjord and that these risks had markedly different spatial hazard patterns.Our spatial time‐to‐event model can make use of data from a variety of widely used telemetry technologies to characterize landscapes of mortality risks for different taxa. Our work provides new opportunities to inform the spatial ecology and management of fish and wildlife populations. [ABSTRACT FROM AUTHOR]

Chance pervades life. In turn, life histories are described by probabilities (e.g. survival and breeding) and averages across individuals (e.g. mean growth rate and age at maturity). In this study, we explored patterns of luck in lifetime outcomes by analysing structured population models for a wide array of plant and animal species. We calculated four response variables: variance and skewness in both lifespan and lifetime reproductive output (LRO), and partitioned them into contributions from different forms of luck. We examined relationships among response variables and a variety of life history traits. We found that variance in lifespan and variance in LRO were positively correlated across taxa, but that variance and skewness were negatively correlated for both lifespan and LRO. The most important life history trait was longevity, which shaped variance and skew in LRO through its effects on variance in lifespan. We found that luck in survival, growth, and fecundity all contributed to variance in LRO, but skew in LRO was overwhelmingly due to survival luck. Rapidly growing populations have larger variances in LRO and lifespan than shrinking populations. Our results indicate that luck‐induced genetic drift may be most severe in recovering populations of species with long mature lifespan and high iteroparity. [ABSTRACT FROM AUTHOR]

Strategies to control ongoing biological invasions are often developed by modelling the invasive species' population and aiming to reduce its abundance. However, if the ultimate objective is to protect and restore native species, focussing solely on the invader may not be optimal because it does not account for (i) species interactions that can cause the invader's impacts to depend nonlinearly on its abundance, (ii) collateral damages to native species incurred due to nonspecific removal methods or (iii) native‐invader trait differences.To identify an invader suppression strategy that maximizes average native population size, we applied optimal control theory to a two‐species model of a native species threatened by an invasive competitor. We examined trade‐offs between iterative physical removals that selectively target invaders and intensifiable chemical control that is nonselective but has higher efficacy.We found that while iterative removals were capable of supporting large native populations when applied continuously, cost could be prohibitively high. In contrast, when favourable native‐invader trait differences enabled native species to re‐establish more quickly than invaders, intensifiable methods could achieve substantial restoration benefits at lower cost by focussing removal effort into periodic, high‐efficacy events.In a metapopulation, removals that rotated among spatial patches were optimal when the native species had higher dispersal, whereas synchronous removals were preferred when native recovery was initiated locally and the invader could disperse.For a case study in Hawaiian streams, we compared how effective two alternative methods of removing invasive live‐bearing fishes (poeciliids) might be at restoring the endemic freshwater goby Sicyopterus stimpsoni. We found that rotenone (a piscicidal chemical) offered superior benefits when the control budget was small and efficacy was high, but that electrofishing (use of electricity to manually collect target fish) was better with larger budgets and in many lower‐efficacy scenarios.Synthesis and applications. Our findings demonstrate that, by accounting for species interactions and collateral damage, invasive species control strategies can be optimized in light of species traits. Choices about the timing, locations and types of removal events present opportunities to increase the efficiency with which invasive species suppression benefits native species. [ABSTRACT FROM AUTHOR]

Sensitivity analysis of ecological and epidemiological models is often used to identify the best targets of opportunity for control or management, for example, which subpopulations should be prioritised for vaccination or protection. However, effectiveness of a management action depends not just on which system component is targeted but also on when an action is taken. Traditional methods of model sensitivity analysis apply to time‐invariant parameter perturbations, which limits their ability to address questions about the timing of management interventions. A semianalytic method for performing time‐dependent sensitivity analysis (TDSA) has been recently introduced to address this need (Ng et al., in press). However, some of the steps typically require substantial time and effort.We have developed an R package, tdsa, that automates all steps required for TDSA by using numerical rather than analytic differentiation of model terms to evaluate the sensitivity equations. By avoiding analytic differentiation, the package substantially reduces user effort and risk of human error and increases the generality of TDSA by making it possible to analyse nonparametric or equation‐free models.The results of TDSA may be sensitive to assumptions of the system model. By reducing the effort needed to perform TDSA, the tdsa package facilitates comparison of results under different model assumptions, hence allowing for more robust conclusions. We illustrate this using an example involving the transmission of Morogoro virus in the Natal multimammate mouse, under different assumptions about how the contact rate depends on mouse population density.By automating most of the mathematical and programming steps of the semianalytical approach, the tdsa package makes TDSA accessible to a wide range of users with varying quantitative expertise. [ABSTRACT FROM AUTHOR]

Studies of eco‐evolutionary dynamics have integrated evolution with ecological processes at multiple scales (populations, communities and ecosystems) and with multiple interspecific interactions (antagonistic, mutualistic and competitive). However, evolution has often been conceptualised as a simple process: short‐term directional adaptation that increases population growth. Here we argue that diverse other evolutionary processes, well studied in population genetics and evolutionary ecology, should also be considered to explore the full spectrum of feedback between ecological and evolutionary processes. Relevant but underappreciated processes include (1) drift and mutation, (2) disruptive selection causing lineage diversification or speciation reversal and (3) evolution driven by relative fitness differences that may decrease population growth. Because eco‐evolutionary dynamics have often been studied by population and community ecologists, it will be important to incorporate a variety of concepts in population genetics and evolutionary ecology to better understand and predict eco‐evolutionary dynamics in nature. [ABSTRACT FROM AUTHOR]

Community assembly is often treated as deterministic, converging on one or at most a few possible stable endpoints. However, in nature, we typically observe continuous change in community composition, which is often ascribed to environmental change. But continuous changes in community composition can also arise in deterministic, time‐invariant community models, especially food web models. Our goal was to determine why some models produce continuous assembly and others do not. We investigated a simple two‐trophic‐level community model to show that continuous assembly is driven by the relative niche width of the trophic levels. If predators have a larger niche width than prey, community assembly converges to a stable equilibrium. Conversely, if predators have a smaller niche width than prey, then community composition never stabilizes. Evidence that food webs need not reach a stable equilibrium has important implications, as many ecological theories of community ecology based on equilibria may be difficult to apply to such food webs. [ABSTRACT FROM AUTHOR]

Matrix population models are frequently built and used by ecologists to analyse demography and elucidate the processes driving population growth or decline. Life Table Response Experiments (LTREs) are comparative analyses that decompose the realized difference or variance in population growth rate (λ) into contributions from the differences or variances in the vital rates (i.e. the matrix elements). Since their introduction, LTREs have been based on approximations and have not included biologically relevant interaction terms.We used the functional analysis of variance framework to derive an exact LTRE method, which calculates the exact response of λ to the difference or variance in a given vital rate, for all interactions among vital rates—including higher‐order interactions neglected by the classical methods. We used the publicly available COMADRE and COMPADRE databases to perform a meta‐analysis comparing the results of exact and classical LTRE methods. We analysed 186 and 1487 LTREs for animal and plant matrix population models, respectively.We found that the classical methods often had small errors, but that very high errors were possible. Overall error was related to the difference or variance in the matrices being analysed, consistent with the Taylor series basis of the classical method. Neglected interaction terms accounted for most of the errors in fixed design LTRE, highlighting the importance of two‐way interaction terms. For random design LTRE, errors in the contribution terms present in both classical and exact methods were comparable to errors due to neglected interaction terms. In most examples we analysed, evaluating exact contributions up to three‐way interaction terms was sufficient for interpreting 90% or more of the difference or variance in λ.Relative error, previously used to evaluate the accuracy of classical LTREs, is not a reliable metric of how closely the classical and exact methods agree. Error compensation between estimated contribution terms and neglected contribution terms can lead to low relative error despite faulty biological interpretation. Trade‐offs or negative covariances among matrix elements can lead to high relative error despite accurate biological interpretation. Exact LTRE provides reliable and accurate biological interpretation, and the R package exactLTRE makes the exact method accessible to ecologists. [ABSTRACT FROM AUTHOR]

As a general rule, plants defend against herbivores with multiple traits. The defense synergy hypothesis posits that some traits are more effective when co‐expressed with others compared to their independent efficacy. However, this hypothesis has rarely been tested outside of phytochemical mixtures, and seldom under field conditions. We tested for synergies between multiple defense traits of common milkweed (Asclepias syriaca) by assaying the performance of two specialist chewing herbivores on plants in natural populations. We employed regression and a novel application of random forests to identify synergies and antagonisms between defense traits. We found the first direct empirical evidence for two previously hypothesized defense synergies in milkweed (latex by secondary metabolites, latex by trichomes) and identified numerous other potential synergies and antagonisms. Our strongest evidence for a defense synergy was between leaf mass per area and low nitrogen content; given that these "leaf economic" traits typically covary in milkweed, a defense synergy could reinforce their co‐expression. We report that each of the plant defense traits showed context‐dependent effects on herbivores, and increased trait expression could well be beneficial to herbivores for some ranges of observed expression. The novel methods and findings presented here complement more mechanistic approaches to the study of plant defense diversity and provide some of the best evidence to date that multiple classes of plant defense synergize in their impact on insects. Plant defense synergies against highly specialized herbivores, as shown here, are consistent with ongoing reciprocal evolution between these antagonists. [ABSTRACT FROM AUTHOR]

Our study found evidence that D. plexippus mortality was increased by a synergy between latex production and trichome density, and between latex and concentrations of toxic cardenolide compounds. For common milkweed, observations of herbivore behavior and plant physiology have led to hypotheses of synergies between latex and trichome density, and between latex and toxic cardenolides. [Extracted from the article]

Tuberculosis (TB) is a leading cause of morbidity and mortality from a single infectious agent, despite being preventable and curable. Early and accurate diagnosis of active TB is critical to both enhance patient care, improve patient outcomes, and break Mycobacterium tuberculosis (Mtb) transmission cycles. In 2020 an estimated 9.9 million people fell ill from Mtb, but only a little over half (5.8 million) received an active TB diagnosis and treatment. The World Health Organization has proposed target product profiles for biomarker‐ or biosignature‐based diagnostics using point‐of‐care tests from easily accessible specimens such as urine or blood. Here we review and summarize progress made in the development of pathogen‐ and host‐based biomarkers for active TB diagnosis. We describe several unique patient populations that have posed challenges to development of a universal diagnostic TB biomarker, such as people living with HIV, extrapulmonary TB, and children. We also review additional limitations to widespread validation and utilization of published biomarkers. We conclude with proposed solutions to enhance TB diagnostic biomarker validation and uptake. [ABSTRACT FROM AUTHOR]

The usual theoretical condition for coexistence is that each species in a community can increase when it is rare (mutual invasibility). Traditional coexistence theory implicitly assumes that the invading species is common enough that we can ignore demographic stochasticity but rare enough that it does not compete with itself, even after it has reached a stationary spatial distribution. However, short‐distance dispersal of discrete individuals leads to locally dense population clusters, and existing theory breaks down. We have an intuition that when we account for invader–invader competition, shorter‐range dispersal should reduce the invader's ability to escape competition, but exactly how does this translate into lower population growth? And how will invader discreteness affect outcomes? We need a way of partitioning the contributions to coexistence, but current modern coexistence theory (MCT) does not apply under these conditions. Here we present a computationally based partitioning method to quantify the contributions to coexistence from different mechanisms, as in MCT. We also build up an intuition for how invader clumping and discreteness will affect these contributions by analyzing a case study, a lattice‐based spatial lottery model. We first consider fluctuation‐dependent coexistence, partitioning the contributions of variable environment, variable competition, demographic stochasticity, and their correlations and interactions. Our second example examines fluctuation‐independent coexistence maintained by a fecundity–survival trade‐off, and partitions the contributions to coexistence from interspecific differences in fecundity, in mortality, and in dispersal. We find that demographic stochasticity harms an invader, but only slightly. Localized invader dispersal, on the other hand, can have a strong effect. When invaders are more clumped, they compete with each other more intensely when rare, so they too become limited by environment‐competition covariance. More invader clumping also means that variation in competition changes from helping the invader to harming it. More broadly, invader clumping is likely to weaken any coexistence mechanism that relies on the invader escaping competition from the resident, because invader clumping means that the resident is no longer the only source of competition. [ABSTRACT FROM AUTHOR]

We have argued (and the authors of D21 may well agree) that the more important dichotomy is traditional binning versus statistical modeling for model parameterization; for continuous traits, what D21 call DVR versus CVR. What D21 say about the equivalence of MPM and IPM (D21, p. 6) fails to distinguish the model itself from the numerical solution methods. The authors of D21 have shown, and we do not dispute, that a binning-based matrix model can be as effective as an IPM for size-structured populations if it becomes a whole lot more like an IPM by using narrow, evenly spaced size classes instead of the historically predominant wide, uneven classes. The most troubling sentence in Doak et al. (2021, hereafter D21) is this: "Finally, some of us (Doak, Morris, Garcia pers. obs.) have seen an increasing tendency of reviewers and editors to dichotomize these two approaches, considering matrix models as out of fashion.". [Extracted from the article]

Pathogen transport by biotic or abiotic processes (e.g. mechanical vectors, wind, rain) can increase disease transmission by creating more opportunities for host exposure. But transport without replication has an inherent trade‐off, that creating new venues for exposure decreases the average pathogen abundance at each venue. The host dose‐response relationship is therefore required to correctly assess infection risk. We model and analyse two examples—biotic mechanical vectors in plant–pollinator networks, and abiotic‐facilitated long‐distance pathogen dispersal—to illustrate how oversimplifying the dose‐response relationship can lead to incorrect epidemiological predictions. When the minimum infective dose is high, mechanical vectors amplify disease transmission less than suggested by simple compartment models, and may even dilute transmission. When long‐distance dispersal leads to infrequent large exposures, models that assume a linear force of infection can substantially under‐predict the speed of epidemic spread. Our work highlights an important general interplay between dose‐response relationships and pathogen transport. [ABSTRACT FROM AUTHOR]

Objective: We aimed to determine the prevalence and find the risk factors associated with latent tuberculosis infection (LTBI) among the household contacts (HHC) of pulmonary TB patients. Methods: This cohort study was conducted from 2014 to 2019. Pretested standardised questionnaires and tools were used for data collection. The prevalence of LTBI among HHCs of TB patients was summarised as proportion with 95% confidence interval (CI). Mixed‐effects generalised linear modelling function (meglm) in STATA with family Poisson and log link was performed to find the factors associated with LTBI. Results: In total, 1523 HHC of pulmonary TB patients were included in the study. Almost all HHC shared their residence with the index case (IC) for more than a year; 25% shared the same bed with the IC. The prevalence of LTBI among the HHC of TB patients was 52.6% (95% CI: 50.1–55.1%). In an adjusted model, we found that among HHC belonging to the age group of 19–64 years (aIRR = 1.2; 95% CI: 1.1–1.3; p‐value: 0.02), to the age group >65 years (aIRR = 1.4, 95% CI: 1.1–1.9, p‐value: 0.02) and sharing the same bed with the IC (aIRR = 1.2, 95% CI: 1.1–1.3, p value: 0.04) were independent determinants of LTBI among the HHC. Conclusion: One in two household contacts of TB patients have latent tuberculosis infection. This underscores the need of targeted contact screening strategies, effective contact tracing and testing using standardised methods in high TB burden settings. [ABSTRACT FROM AUTHOR]

Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions. [ABSTRACT FROM AUTHOR]

Pande et al. (2020) point out that persistence time can decrease even as invader growth rates (IGRs) increase, which potentially undermines modern coexistence theory. However, because persistence time increases rapidly with system size only when IGR > 0, to understand how any real community persists, we should first identify the mechanisms producing positive IGR. [ABSTRACT FROM AUTHOR]

Understanding long‐term coexistence of numerous competing species is a longstanding challenge in ecology. Progress requires determining which processes and species differences are most important for coexistence when multiple processes operate and species differ in many ways. Modern coexistence theory (MCT), formalised by Chesson, holds out the promise of doing that, but empirical applications remain scarce. We argue that MCT's mathematical complexity and subtlety have obscured the simplicity and power of its underlying ideas and hindered applications. We present a general computational approach that extends our previous solution for the storage effect to all of standard MCT's spatial and temporal coexistence mechanisms, and also process‐defined mechanisms amenable to direct study such as resource partitioning, indirect competition, and life history trade‐offs. The main components are a method to partition population growth rates into contributions from different mechanisms and their interactions, and numerical calculations in which some mechanisms are removed and others retained. We illustrate how our approach handles features that have not been analysed in the standard framework through several case studies: competing diatom species under fluctuating temperature, plant–soil feedbacks in grasslands, facilitation in a beach grass community, and niche differences with independent effects on recruitment, survival and growth in sagebrush steppe. [ABSTRACT FROM AUTHOR]

In both plant and animal systems, size can determine whether an individual survives and grows under different environmental conditions. However, it is unclear whether and when size‐dependent responses to exogenous environmental fluctuations affect population dynamics. Size‐by‐environment interactions create pathways for environmental fluctuations to influence population dynamics by allowing for negative covariation between sizes within vital rates (e.g. small and large individuals have negatively covarying survival rates) and/or size‐dependent variability in a vital rate (e.g. survival of large individuals varies less than small individuals through time). Whether these phenomena affect population dynamics depends on how they are mediated by elasticities (they must affect the sizes and vital rates that matter) and their projected impacts will depend on model functional form (the impact of reduced variance depends on the relationship between the environment and vital rate). We demonstrate these ideas with an analysis of fifteen species from five semiarid plant communities. We find that size‐by‐environment interactions are common but do not impact long‐term population dynamics. Size‐by‐environment interactions may yet be important for other species. Our approach can be applied to species in other ecosystems to determine if and how size‐by‐environment interactions allow them to cope with, or exploit, fluctuating environments. [ABSTRACT FROM AUTHOR]

Hotspots of disease transmission can strongly influence pathogen spread. Bee pathogens may be transmitted via shared floral use, but the role of plant species and floral trait variation in shaping transmission dynamics is almost entirely unexplored. Given the importance of pathogens for the decline of several bee species, understanding whether and how plant species and floral traits affect transmission could give us important tools for predicting which plant species may be hotspots for disease spread. We assessed variation in transmission via susceptibility (probability of infection) and mean intensity (cell count of infected bees) of the trypanosomatid gut pathogen Crithidia bombi to uninfected Bombus impatiens workers foraging on 14 plant species, and assessed the role of floral traits, bee size and foraging behavior on transmission. We also conducted a manipulative experiment to determine how the number of open flowers affected transmission on three plant species, Penstemon digitalis, Monarda didyma, and Lythrum salicaria. Plant species differed fourfold in the overall mean abundance of Crithidia in foraging bumble bees (mean including infected and uninfected bees). Across plant species, bee susceptibility and mean intensity increased with the number of reproductive structures per inflorescence (buds, flowers and fruits); smaller bees and those that foraged longer were also more susceptible. Trait‐based models were as good or better than species‐based models at predicting susceptibility and mean intensity based on AIC values. Surprisingly, floral size and morphology did not significantly predict transmission across species. In the manipulative experiment, more open flowers increased mean pathogen abundance fourfold in Monarda, but had no effect in the other two plant species. Our results suggest that variation among plant species, through their influence on pathogen transmission, may shape bee disease dynamics. Given widespread investment in pollinator‐friendly plantings to support pollinators, understanding how plant species affect disease transmission is important for recommending plant species that optimize pollinator health. [ABSTRACT FROM AUTHOR]

Abstract: Stable coexistence requires intraspecific limitations to be stronger than interspecific limitations. The greater the difference between intra‐ and interspecific limitations, the more stable the coexistence, and the weaker the competitive release any species should experience following removal of competitors. We conducted a removal experiment to test whether a previously estimated model, showing surprisingly weak interspecific competition for four dominant species in a sagebrush steppe, accurately predicts competitive release. Our treatments were (1) removal of all perennial grasses and (2) removal of the dominant shrub, Artemisia tripartita. We regressed survival, growth, and recruitment on the locations, sizes, and species identities of neighboring plants, along with an indicator variable for removal treatment. If our “baseline” regression model, which accounts for local plant–plant interactions, accurately explains the observed responses to removals, then the removal coefficient should be non‐significant. For survival, the removal coefficients were never significantly different from zero, and only A. tripartita showed a (negative) response to removals at the recruitment stage. For growth, the removal treatment effect was significant and positive for two species, Poa secunda and Pseudoroegneria spicata, indicating that the baseline model underestimated interspecific competition. For all three grass species, population models based on the vital rate regressions that included removal effects projected 1.4‐ to 3‐fold increases in equilibrium population size relative to the baseline model (no removal effects). However, we found no evidence of higher response to removal in quadrats with higher pretreatment cover of A. tripartita, or by plants experiencing higher pre‐treatment crowding by A. tripartita, raising questions about the mechanisms driving the positive response to removal. While our results show the value of combining observations with a simple removal experiment, more tightly controlled experiments focused on underlying mechanisms may be required to conclusively validate or reject predictions from phenomenological models. [ABSTRACT FROM AUTHOR]

Household contact studies, a mainstay of tuberculosis transmission research, often assume that tuberculosis-infected household contacts of an index case were infected within the household. However, strain genotyping has provided evidence against this assumption. Understanding the household versus community infection dynamic is essential for designing interventions. The misattribution of infection sources can also bias household transmission predictor estimates. We present a household-community transmission model that estimates the probability of community infection, that is, the probability that a household contact of an index case was actually infected from a source outside the home and simultaneously estimates transmission predictors. We show through simulation that our method accurately predicts the probability of community infection in several scenarios and that not accounting for community-acquired infection in household contact studies can bias risk factor estimates. Applying the model to data from Vitória, Brazil, produced household risk factor estimates similar to two other standard methods for age and sex. However, our model gave different estimates for sleeping proximity to index case and disease severity score. These results show that estimating both the probability of community infection and household transmission predictors is feasible and that standard tuberculosis transmission models likely underestimate the risk for two important transmission predictors. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The storage effect has become a core concept in community ecology, explaining how environmental fluctuations can promote coexistence and maintain biodiversity. However, limitations of existing theory have hindered empirical applications: the need for detailed mathematical analysis whenever the study system requires a new model, and restricted theory for structured populations. We present a new approach that overcomes both these limitations. We show how temporal storage effect can be quantified by Monte Carlo simulations in a wide range of models for competing species. We use the lottery model and a generic integral projection model (IPM) to introduce ideas, and present two empirical applications: (1) algal species in a chemostat with variable temperature, showing that the storage effect can operate without a long-lived life stage and (2) a sagebrush steppe community IPM. Our results highlight the need for careful modelling of nonlinearities so that conclusions are not driven by unrecognised model constraints. [ABSTRACT FROM AUTHOR]

Threats to several of the world's great animal migrations necessitate a research agenda focused on identifying drivers of their population dynamics. The monarch butterfly is an iconic species whose continental migratory population in eastern North America has been declining precipitously. Recent analyses have linked the monarch decline to reduced abundance of milkweed host plants in the USA caused by increased use of genetically modified herbicide-resistant crops. To identify the most sensitive stages in the monarch's annual multi-generational migration, and to test the milkweed limitation hypothesis, we analyzed 22 years of citizen science records from four monitoring programs across North America. We analyzed the relationships between butterfly population indices at successive stages of the annual migratory cycle to assess demographic connections and to address the roles of migrant population size versus temporal trends that reflect changes in habitat or resource quality. We find a sharp annual population decline in the first breeding generation in the southern USA, driven by the progressively smaller numbers of spring migrants from the overwintering grounds in Mexico. Monarch populations then build regionally during the summer generations. Contrary to the milkweed limitation hypothesis, we did not find statistically significant temporal trends in stage-to-stage population relationships in the mid-western or northeastern USA. In contrast, there are statistically significant negative temporal trends at the overwintering grounds in Mexico, suggesting that monarch success during the fall migration and re-establishment strongly contributes to the butterfly decline. Lack of milkweed, the only host plant for monarch butterfly caterpillars, is unlikely to be driving the monarch's population decline. Conservation efforts therefore require additional focus on the later phases in the monarch's annual migratory cycle. We hypothesize that lack of nectar sources, habitat fragmentation, continued degradation at the overwintering sites, or other threats to successful fall migration are critical limiting factors for declining monarchs. [ABSTRACT FROM AUTHOR]

Despite major advances in quantitative approaches to natural resource management, there has been resistance to using these tools in the actual practice of managing ecological populations. Given a managed system and a set of assumptions, translated into a model, optimization methods can be used to solve for the most cost-effective management actions. However, when the underlying assumptions are not met, such methods can potentially lead to decisions that harm the environment and economy. Managers who develop decisions based on past experience and judgment, without the aid of mathematical models, can potentially learn about the system and develop flexible management strategies. However, these strategies are often based on subjective criteria and equally invalid and often unstated assumptions. Given the drawbacks of both methods, it is unclear whether simple quantitative models improve environmental decision making over expert opinion. In this study, we explore how well students, using their experience and judgment, manage simulated fishery populations in an online computer game and compare their management outcomes to the performance of model-based decisions. We consider harvest decisions generated using four different quantitative models: (1) the model used to produce the simulated population dynamics observed in the game, with the values of all parameters known (as a control), (2) the same model, but with unknown parameter values that must be estimated during the game from observed data, (3) models that are structurally different from those used to simulate the population dynamics, and (4) a model that ignores age structure. Humans on average performed much worse than the models in cases 1-3, but in a small minority of scenarios, models produced worse outcomes than those resulting from students making decisions based on experience and judgment. When the models ignored age structure, they generated poorly performing management decisions, but still outperformed students using experience and judgment 66% of the time. [ABSTRACT FROM AUTHOR]

Government agencies develop invasive species management programmes assuming early detection is key to successful management. Some theoretical studies confirm this intuition, while others, which restrict sampling effort to be constant in time, suggest managers are investing too heavily in sampling to detect new local invader populations., We explore whether these optimal constant-effort strategies underplay the importance of early surveillance and determine how much changing sampling effort through time reduces total management costs., Using optimal control theory to calculate time-dependent surveillance policies that minimize the total cost of sampling, eradication, and damage by the invasive, we find that the best strategies often use intense early sampling, followed by reduced sampling effort., Intense early sampling can drastically reduce costs compared with constant-effort strategies if propagule pressure from outside the managed area is low. However, if new infestations arise from frequent independent introductions from an outside source, constant strategies are cost-effective., Synthesis and applications. For invasive species that are initially present, not frequently reintroduced into the managed area, and for which surveillance is reasonably cost-effective, we recommend a simple three-phase management programme that deploys intense early surveillance until the majority of undetected populations have been discovered, followed by medium effort until most of the heavily infested areas have been cleared of the invader and finally low long-term effort to prevent infestations caused by future introductions and spread from populations that escaped surveillance. This programme captures the majority of the economic benefits from varying sampling effort continuously through time. [ABSTRACT FROM AUTHOR]

Rationale Provision of colorectal cancer ( CRC) screening in primary care is suboptimal; failure to observe screening guidelines poses unnecessary risks to patients and doctors. Aims and objectives Implement a population management system for CRC screening; evaluate impact on compliance with evidence-based guidelines. Methods Design A quasi-experimental, prospective quality improvement study design using pre-post-analyses with concurrent controls. Setting Six suites within an academic primary care practice. Participants 5320 adults eligible for CRC screening treated by 70 doctors. Intervention In three intervention suites, doctors reviewed real-time rosters of patients due for CRC screening and chose practice delegate outreach or default reminder letter. Delegates tracked overdue patients, made outreach calls, facilitated test ordering, obtained records and documented patient deferral, exclusion or decline. In three control suites, doctors followed usual preventive care practices. Main outcome measures CRC screening compliance (including documented decline, deferral or exclusion) and CRC screening completion rates over 5 months. Results At baseline, there was no significant difference in CRC screening compliance ( I: 80.4% and C: 79.6%, P = 0.439) and CRC screening completion rates ( I: 78.3% and C: 77.3%, P = 0.398) between intervention and control groups. Post-intervention, compliance rates ( I: 88.1% and C: 80.5%, P < 0.01) and completion rates ( I: 81.0% and C: 78.1%, P < 0.05) were significantly higher in the intervention group. Conclusions A population management system using closed-loop communication may improve CRC screening compliance and completion rates within academic primary care practices. Team-based care using well-designed IT systems can enable sharing of patient care responsibilities and improve patient outcomes. [ABSTRACT FROM AUTHOR]

In observational demographic data, the number of measured factors that could potentially drive demography (such as daily weather records between two censuses) can easily exceed the number of independent observations. Thus, identifying the important drivers requires alternatives to standard model selection and variable selection methods., Spline methods that estimate smooth functions over continuous domains (such as space or time) have the potential to resolve high-dimensional problems in ecological systems. We consider two examples that are important for many plant populations: competition with neighbours that vary in size and distance from the focal individual and climate variables during a window of time before a response (growth, survival, etc.) is measured., For competition covariates, we use a simulation study based on empirical data to show that a monotone spline estimate of competition kernels via approximate AIC returns very accurate estimates. We then apply the method to long-term, mapped quadrat data on the four dominant species in an Idaho (US) sagebrush steppe community., For climate predictors and their temporal lags, we use simulated data sets to compare functional smoothing methods with competing linear (LASSO) or machine learning (random forests) methods. Given sufficient data, functional smoothing methods outperformed the other two methods., Functional smoothing methods can advance data-driven population modelling by providing alternatives to specifying competition kernels a priori and to arbitrarily aggregating continuous environmental covariates. However, there are important open questions related to modelling of nonlinear climate responses and size × climate interactions. [ABSTRACT FROM AUTHOR]

Patterns of survival and reproduction determine fitness, and there is a rich body of theory linking demography with evolution. For example, selection analysis can be used to predict changes in trait values, and by approximating selection, equations describing trait dynamics derived and evolutionary endpoints predicted. Here, we provide an overview of how these methods can be used to understand evolutionary dynamics and selection in a structured population modelled by an integral projection model (IPM)., General expressions for selection are given, and we show how these can be approximated using eigenvalue sensitivities. Approximations for the dynamics of both the trait mean and variance are presented and compared to simulation results (IPMs and individual-based models) for monocarpic perennials., We describe how selection can be decomposed into components related to survival and reproduction and into components resulting from changes in demography versus changes in population structure. We show that, in an empirically based IPM, effects of changes in population structure can be a major component of the overall selection pressure. Most studies of selection in natural populations have focused entirely on selection due to changes in demography, so our results may help explain why directional selection is often predicted but no evolutionary response is observed., The endpoints of evolution, what we expect to see in nature, are explored using ideas from adaptive dynamics theory. The key methods are based on evolutionarily stable strategies (ESS) and convergence stability. Efficient methods for finding an ESS are described. We then extend these ideas to function-valued traits, where it is assumed that an entire function can evolve, rather than a few parameters defining it., By combining theory for IPMs with ideas from several different fields, we show how the dynamics of ecologically complex, evolving systems can be understood. [ABSTRACT FROM AUTHOR]

Collective behaviour can allow populations to have emergent responses to uncertain environments, driven by simple interactions among nearby individuals. High-throughput ethological studies, where individual behaviour is closely observed in each member of a population (typically in the laboratory or by simulation), have revealed that collective behaviour in populations requires only rudimentary cognitive abilities in individuals and could therefore represent a widespread adaptation to life in an uncertain world. However, the ecological significance of collective behaviour is not yet well understood, as most studies to date have been confined to specialized situations that allow intensive monitoring of individual behaviour., Here, we describe a way to screen for collective behaviour in ecological data that is sampled at a coarser resolution than the underlying behavioural processes. We develop and test the method in the context of a well-studied model for collective movement in a noisy environmental gradient. The large-scale distribution patterns associated with collective behaviour are difficult to distinguish from the aggregated responses of independent individuals in this setting because independent individuals also align to track the gradient. However, we show that collective idiosyncratic deviations from the mean gradient direction have high predictive value for detecting collective behaviour. We describe a method of testing for these deviations using the average normalized velocity of the population., We demonstrate the method using data from satellite tracking collars on the migration patterns of caribou ( Rangifer tarandus), recovering evidence that collective behaviour is a key driver of caribou migration patterns. We find moreover that the relative importance of collective behaviour fluctuates seasonally, concurrent with the timing of migration and reproduction., Collective behaviour is a potentially widespread dynamic property of populations that can, in some cases, be detected in coarsely sampled ecological data. [ABSTRACT FROM AUTHOR]

Plant induced resistance to herbivory affects the spatial distribution of herbivores, as well as their performance. In recent years, theories regarding the benefit to plants of induced resistance have shifted from ideas of optimal resource allocation towards a more eclectic set of theories that consider spatial and temporal plant variability and the spatial distribution of herbivores among plants. However, consensus is lacking on whether induced resistance causes increased herbivore aggregation or increased evenness, as both trends have been experimentally documented., We created a spatial individual-based model that can describe many plant-herbivore systems with induced resistance, in order to analyse how different aspects of induced resistance might affect herbivore distribution, and the total damage to a plant population, during a growing season., We analyse the specific effects on herbivore aggregation of informed herbivore movement (preferential movement to less-damaged plants) and of information transfer between plants about herbivore attacks, in order to identify mechanisms driving both aggregation and evenness. We also investigate how the resulting herbivore distributions affect the total damage to plants and aggregation of damage., Even, random and aggregated herbivore distributions can all occur in our model with induced resistance. Highest levels of aggregation occurred in the models with informed herbivore movement, and the most even distributions occurred when the average number of herbivores per plant was low. With constitutive resistance, only random distributions occur. Damage to plants was spatially correlated, unless plants recover very quickly from damage; herbivore spatial autocorrelation was always weak., Our model and results provide a simple explanation for the apparent conflict between experimental results, indicating that both increased aggregation and increased evenness of herbivores can result from induced resistance. We demonstrate that information transfer from plants to herbivores, and from plants to neighbouring plants, can both be major factors in determining non-random herbivore distributions. [ABSTRACT FROM AUTHOR]

Temporal fluctuations in vital rates such as survival, growth or reproduction alter long-term population dynamics and can change the dynamics from invasion and population persistence to extinction. Projections of population dynamics made in the absence of such fluctuations may consequently be misleading. However, data for estimation of yearly fluctuations in demographic parameters are often limited. Accordingly, the current diverse range of statistical and demographic modelling strategies used for stochastic population modelling may influence predictions., We used simulations to explore the effects of different methods of parameter estimation on projections of population dynamics obtained using stochastic integral projection models (IPMs). The simulations were built from data on a monocarpic thistle, Carlina vulgaris, and an ungulate, Soay sheep, Ovis aries; these populations are subject to yearly fluctuation in vital rates facilitating the exploration of the effects of different methods of model construction on the properties of stochastic IPMs. Specifically, we looked at effects on the stochastic growth rate, log λ s, and the mean and variance in the one-step population growth rate ( Nt+1/ Nt)., Our analyses showed that none of the tested approaches resulted in large biases in the estimation of log λ s. However, when realistic study durations (e.g. 12 years) were used for statistical modelling, the confidence intervals around the λ s estimates remained large. Estimation of the variance in one-step population growth rates, on the other hand, was strongly sensitive to the method employed, and the overestimation and underestimation of the variance were also influenced by the life history of the organism., Our findings highlight the need to consider the influences of statistical and demographic modelling approaches when population dynamics have significant temporal stochasticity, as in population viability analyses and evolutionary predictions of bet hedging. [ABSTRACT FROM AUTHOR]

Consumer-resource interactions are fundamental components of ecological communities. Classic features of consumer-resource models are that temporal dynamics are often cyclic, with a ¼-period lag between resource and consumer population peaks. However, there are few published empirical examples of this pattern. Here, we show that many published examples of consumer-resource cycling show instead patterns indicating eco-evolutionary dynamics. When prey evolve along a trade-off between defence and competitive ability, two-species consumer-resource cycles become longer and antiphase (half-period lag, so consumer maxima coincide with minima of the resource species). Using stringent criteria, we identified 21 two-species consumer-resource time series, published between 1934 and 1997, suitable to investigate for eco-evolutionary dynamics. We developed a statistical method to probe for a transition from classic to eco-evolutionary cycles, and find evidence for eco-evolutionary type cycles in about half of the studies. We show that rapid prey evolution is the most likely explanation for the observed patterns. [ABSTRACT FROM AUTHOR]

In order to understand how changes in individual performance (growth, survival or reproduction) influence population dynamics and evolution, ecologists are increasingly using parameterized mathematical models. , For continuously structured populations, where some continuous measure of individual state influences growth, survival or reproduction, integral projection models ( IPMs) are commonly used., We provide a detailed description of the steps involved in constructing an IPM, explaining how to: (i) translate your study system into an IPM; (ii) implement your IPM; and (iii) diagnose potential problems with your IPM. We emphasize how the study organism's life cycle, and the timing of censuses, together determine the structure of the IPM kernel and important aspects of the statistical analysis used to parameterize an IPM using data on marked individuals., An IPM based on population studies of Soay sheep is used to illustrate the complete process of constructing, implementing and evaluating an IPM fitted to sample data., We then look at very general approaches to parameterizing an IPM, using a wide range of statistical techniques (e.g. maximum likelihood methods, generalized additive models, nonparametric kernel density estimators). Methods for selecting models for parameterizing IPMs are briefly discussed., We conclude with key recommendations and a brief overview of applications that extend the basic model. The online Supporting Information provides commented R code for all our analyses. [ABSTRACT FROM AUTHOR]

Ecologists now recognize that ecologically important traits sometimes undergo large heritable changes on the same time-scale as ecological change ('rapid' or 'contemporary' evolution). We know much less about the meanings of 'sometimes' (how often?) and 'important' (how important?). I review some of what I and my collaborators have learned about rapid evolution and suggest some of its implications., Rapid evolution is common: when conditions change, traits evolve. But the rate of change is hard to predict, even across a set of published laboratory predator-prey experiments, and very similar trait changes can result from very different molecular mechanisms, even within one species., Evolutionary rescue (adaptive response to a detrimental environmental change) is common, but not always effective. The full range is observed, from little to almost complete compensation for environmental change., Cases of limited compensation show that strong selection is necessary, but not sufficient for rapid adaptation. The response of the copepod Onychodiaptomus sanguineus to fluctuating predation shows that the time-scale of environmental variation is important. The response of Daphnia galeata to eutrophication shows that life-history trade-offs and constraints can prevent evolution from buffering the entire life history against environmental changes., Eco-evolutionary dynamics are harder to predict when traits affecting multiple interactions evolve. Theory predicted, and experiments confirmed, a much larger range of possible evolutionary outcomes when a prey species evolves defences against two predators rather than one. These and other recent findings suggest that the devil is in the details: many subtle factors, such as how trade-offs depend on resource availability, can have large effects on eco-evolutionary dynamics., Ecologists are increasingly engaged in predicting the ecological effects of human impacts such as species introductions and climate change. Forecasting evolutionary responses is part of that challenge. One implication of the 'newest synthesis' is that rapid adaptation to changing conditions is nothing new, so the past and present can tell us a lot about the future. If prediction is our goal, then developing predictive theory for eco-evolutionary dynamics, and testing it in tractable model systems, should be a priority. [ABSTRACT FROM AUTHOR]

We explore how adding complexity to a typical predator-prey interaction affects temporal dynamics. Intraguild predation webs contain competition, predation, and omnivory in a system of three species where theory and empirical results can be compared. We studied a planktonic microcosm community in which an alga is consumed by a flagellate and by a rotifer that also consumes the flagellate. Previously published theory predicts that phase lags between the species are the outcome of a "tug of war" among the intraguild-predation links: rotifers...algae, flagellates...algae, and rotifers...flageIlates. We observed sustained oscilltions with abundance peaks that corresponded exactly to theoretical predictions in all replicates: peaks of the rotifers and flagellates fell on either side of a quarter-period lag behind the prey (algae) peaks, with the peak of the intermediate predator (flagellates) preceding that of the top predator (rotifers). The phase lags in these experiments suggest that temporal variation in flagellate growth rate is primarily driven by variation in the intensity of its consumption by rotifers, rather than by variation in the density of its algal prey. This system illustrates how interaction strength affects the pattern of intraguild predation cycles and provides an opportunity to explore how evolution of interaction strength may affect those dynamics. [ABSTRACT FROM AUTHOR]

Predation acts as a selective pressure, driving prey adaptation. Predators can also evolve counter-defenses to increase the likelihood of successful attack. Investment in - either trait can be costly, leading to a trade-off between the traits and other fitness components. Costs for defense have been shown experimentally to depend on environmental factors such as resource availability. This suggests that costs can increase with population size, rather than remaining constant as models often assume. Using a quantitative trait model with predator-prey coevolution, we investigate how both population, and trait dynamics are affected by density-dependent prey defense cost ("variable cost") vs. density-independent cost ("fixed cost"). We assume predator counter-defense cost is always density independent. We also investigate the effect of relative speeds of prey and predator evolution on population and trait dynamics, by varying a parameter that determines each population's additive genetic variance. For both models, increasing the speed of predator evolution always has a stabilizing effect on the dynamics, while increasing the speed of prey evolution (within a biologically reasonable range) is largely destabilizing. Within the plausible range of prey evolution speed, variable cost of prey defense is more stabilizing than fixed cost. Our results suggest that density-dependent costs of defense can have important effects on predator-prey dynamics, even when evolution is relatively slow. Our results might also help to explain why many real populations do not display predator-prey cycles. [ABSTRACT FROM AUTHOR]

Integral projection models (IPMs) are increasingly being applied to study size-structured populations. Here we call attention to a potential problem in their construction that can have important consequences for model results. IPMs are implemented using an approximating matrix and bounded size range. Individuals near the size limits can be unknowingly “evicted” from the model because their predicted future size is outside the range. We provide simple measures for the magnitude of eviction and the sensitivity of the population growth rate (λ) to eviction, allowing modelers to assess the severity of the problem in their IPM. For IPMs of three plant species, we found that eviction occurred in all cases and caused underestimation of the population growth rate (λ) relative to eviction-free models; it is likely that other models are similarly affected. Models with frequent eviction should be modified because eviction is only possible when size transitions are badly mis-specified. We offer several solutions to eviction problems, but we emphasize that the modeler must choose the most appropriate solution based on an understanding of why eviction occurs in the first place. We recommend testing IPMs for eviction problems and resolving them, so that population dynamics are modeled more accurately. [ABSTRACT FROM AUTHOR]

1. Trap cropping, the use of alternative host plants to reduce pest damage to a focal cash crop or other managed plant population, can be a sustainable strategy for pest control, but in practice it has often failed to reach management goals. Of the few successful trap cropping examples at a commercial scale, nearly all have included supplemental management strategies that reduce pest dispersal off the trap crop. In contrast, the trap cropping literature has focused extensively on trap plant attractiveness. 2. To test whether the dispersal of insects off trap plants is as important as the anecdotal evidence suggests, we developed a simple model to understand how a trap plant's spatial configuration within a field, its attractiveness and its ability to retain pests affect pest density on a target cash crop. 3. The model predicts that when trap crop retention is low, trap cropping is ineffective, and small increases in retention offer little improvement. However, when retention is high, small differences in retention dramatically affect trap cropping efficacy. In contrast, when the attractiveness of a trap crop is high, further increases in attractiveness have little effect on trap cropping efficacy. 4. Placing trap plants close together is most often detrimental to pest management because it leaves large portions of the field without nearby traps. However, planting the trap crop in rows often does not clump the landscape enough to cause this detrimental effect. 5. Synthesis and applications. The predictions from our model confirm the anecdotal evidence that trap cropping failures may be attributed to a focus on attraction at the expense of retention. A very high retention rate is required for effective reduction of pest densities. Therefore, additional practices that prevent insects from dispersing back into the cash crop may be essential for effective trap cropping designs. These techniques include trap vacuuming, trap harvesting, sticky traps, planting a high proportion of trap plants or applications of pesticides or natural enemies to the trap crop. [ABSTRACT FROM AUTHOR]

Ecology Letters (2012) 15: 492-501 Abstract Feedbacks between ecological and evolutionary change may play important roles in community and ecosystem functioning, but a complete eco-evolutionary feedback loop has not been demonstrated at the community level, and we know little about molecular mechanisms underlying this kind of eco-evolutionary dynamics. In predator-prey (rotifer-alga) microcosms, cyclical changes in predator abundance generated fluctuating selection for a heritable prey defence trait, cell clumping. Predator population growth was affected more by prey evolution than by changes in prey abundance, and changes in predator abundance drove further prey evolution, completing the feedback loop. Within a predator-prey cycle, genes up-regulated as clumping declined were down-regulated as clumping increased, and vice-versa. Genes changing most in expression tended to be associated with defence or its cost. Expression patterns of individual genes differed greatly between consecutive cycles (often reversing direction), suggesting that a particular phenotype may be produced by several (perhaps many) different gene transcription pathways. [ABSTRACT FROM AUTHOR]

Summary 1. A change in a climate variable may alter a species' abundance not only through a direct effect on that species' vital rates, but also through 'indirect' effects mediated by species interactions. While recent work has highlighted cases in which indirect effects overwhelm the direct effects of climate, we lack robust generalizations to predict the strength of indirect effects. 2. For communities dominated by non-trophic interactions, we propose that the potential for indirect effects of climate change declines with the strength of stabilizing niche differences. 3. We tested this hypothesis by analysing an empirically parameterized four species population model. We quantified negative frequency dependence in population growth rates as a measure of stabilizing niche differences and projected the sensitivity of each species to direct and indirect effects of climate perturbations. 4. Consistent with our hypothesis, species' sensitivities to indirect effects decreased rapidly with increasing stabilization by niche differences. 5. Synthesis. Information about niche differences can identify species sensitive to indirect effects of climate change and determine when multispecies forecasting approaches are necessary. However, practical application of this generalization will require methods to predict niche differences from easily collected data. [ABSTRACT FROM AUTHOR]

Appraise the feasibility of interchanging conventional components of a fixed dental prosthesis (FDP) with those of Cresco in two different early loading protocols. In five centers patients with an edentulous, fully healed maxilla were recruited to partake in a three-arm blinded randomized-controlled trial (RCT). Each patient received 5/6 implants using a single-stage surgery approach to support a 10/12-unit FDP. The implants used were SLA solid screw two-part implants. In test groups 1 and 2 components from Cresco were used and the implants loaded 10 days or 6-8 weeks post-implant placement. Group 3 received their FDP fabricated with conventional components 6-8 weeks post-implant placement. Patients were followed up 3 years. Of 36 patients, 30 remained after 3 years. The adjusted means and ranges of changes in crestal bone levels were −0.65, −0.5 and −0.4 mm in groups 1, 2 and 3, respectively. The change from baseline was statistically significant in all treatment groups. Adjusting for the difference in implant depth, there was an expected additional change in bone level of −0.29 mm by each 1 mm the implant was placed deeper. There was no significant difference between the 6-8 weeks post-implant placement loading Cresco group vs. the control group or between the two Cresco groups. The vertical placement has more effect on bone loss than the fabrication technique used for the suprastructure and whether the implants were loaded after 10 days vs. 6-8 weeks. : Jokstad A, Ellner S, Gussgard A. Comparison of two early loading protocols in full arch reconstructions in the edentulous maxilla using the Cresco prosthetic system: a three-arm parallel group randomized-controlled trial. Clin. Oral Impl. Res. , 2011; 455-463. doi: 10.1111/j.1600-0501.2010.02156.x [ABSTRACT FROM AUTHOR]

Little is known about how epizootics in natural populations affect vital rates and population structure, or about the process of recovery after an outbreak subsides. We investigated the effects of aspergillosis, an infectious disease caused by the fungal pathogen Aspergillus sydowii, on the demography of a gorgonian coral, Gorgonia ventalina. Caribbean sea fans were affected by a seven-year epizootic, marked by an initial period in 1994 of high infection prevalence, high mortality rates, and almost complete reproductive failure of infected fans. Post epizootic, in 2005, host populations were relatively healthy, with low disease prevalence. Using longitudinal data from populations on coral reefs in the Florida Keys (USA) and the Yucatán Peninsula (Mexico), we documented changes in the epidemiology of sea fan aspergillosis over the course of the epizootic. We developed an "integral projection model" that scales disease impacts from individual to population levels using direct estimates of vital rates. Within-colony lesion growth rate and host mortality were higher during the peak of the epizootic. Effects on individuals and populations changed substantially post-epizootic; recruitment increased, mortality of infected adults decreased, and the size dependence of infection was reduced. Elasticity analysis indicated that population growth is more sensitive to changes in the growth and survival of established colonies than to recruitment, due to slow colony growth and the longevity and fecundity of large adults. Disease prevalence in our monitored populations decreased from 50% in 1997 to <10% by 2003 and <1% in 2007 and was accompanied by very high mortality during the early stages of the epizootic. The population model suggested that host evolution (due to selection for higher disease resistance through differential mortality) could proceed quickly enough to explain the observed changes in prevalence and in the size independence of infection risk. Our model indicates that the time required for population recovery following an outbreak is largely determined by the percentage of healthy tissue lost from the population. However, recovery following an especially severe outbreak (i.e., 80% or more tissue loss) is much faster if the affected population receives an external supply of recruits from unaffected areas. [ABSTRACT FROM AUTHOR]

Plant survival, growth, and flowering are size dependent in many plant populations but also vary among individuals of the same size. This individual variation, along with variation in dispersal caused by differences in, e.g., seed release height, seed characteristics, and wind speed, is a key determinant of the spread rate of species through homogeneous landscapes. Here we develop spatial integral projection models (SIPMs) that include both demography and dispersal with continuous state variables. The advantage of this novel approach over discrete-stage spread models is that the effect of variation in plant size and size-dependent vital rates can be studied at much higher resolution. Comparing Neubert- Caswell matrix models to SIPMs allowed us to assess the importance of including individual variation in the models. As a test case we parameterized a SIPM with previously published data on the invasive monocarpic thistle Carduus nutans in New Zealand. Spread rate (c*) estimates were 34% lower than for standard spatial matrix models and stabilized with as few as seven evenly distributed size classes. The SIPM allowed us to calculate spread rate elasticities. over the range of plant sizes, showing the size range of seedlings that contributed most to c* through their survival, growth and reproduction. The annual transitions of these seedlings were also the most important ones for local population growth (λ). However, seedlings that reproduced within a year contributed relatively more to λ than to c*. In contrast, plants that grow over several years to reach a large size and produce many more seeds, contributed relatively more to λ than to c*. We show that matrix models pick up some of these details, while other details disappear within wide size classes. Our results show that SIPMs integrate various sources of variation much better than discrete-stage matrix models. Simpler, heuristic models, however, remain very valuable in studies where the main goal is to investigate the general impact of a life history stage on population dynamics. We conclude with a discussion of future extensions of SIPMs, including incorporation of continuous time and environmental drivers. [ABSTRACT FROM AUTHOR]

Ecology Letters (2010) 13: 1019–1029 Despite decades of research documenting niche differences between species, we lack a quantitative understanding of their effect on coexistence in natural communities. We perturbed an empirical sagebrush steppe community model to remove the demographic effect of niche differences and quantify their impact on coexistence. With stabilizing mechanisms operating, all species showed positive growth rates when rare, generating stable coexistence. Fluctuation-independent mechanisms contributed more than temporal variability to coexistence and operated more strongly on recruitment than growth or survival. As expected, removal of stabilizing niche differences led to extinction of all inferior competitors. However, complete exclusion required 300–400 years, indicating small fitness differences among species. Our results show an ‘excess’ of niche differences: stabilizing mechanisms were not only strong enough to maintain diversity but were much stronger than necessary given the small fitness differences. The diversity of this community cannot be understood without consideration of niche differences. [ABSTRACT FROM AUTHOR]

Ecology Letters (2010) 13: 989–997 Adaptive variation in the traits determining ecological interactions can lead to evolution so rapid that ecological dynamics change course while in progress (i.e., ‘eco-evolutionary dynamics’). However, little is known about how the qualitative properties of eco-evolutionary dynamics (e.g., cycling, equilibrium, etc.) are affected by the amount of heritable variation present. Here, we show that a change in the range of variation in a heritable prey defense trait determines what dynamics are observed in an experimental predator–prey system. We combine modelling and laboratory experiments to show that initial defense trait variation determines whether populations exhibit eco-evolutionary cycles in which heritable variation is maintained, or converge to an equilibrium at which the prey population becomes monomorphic. Our results show how small changes in the amount of adaptive genetic variance initially present can radically alter eco-evolutionary dynamics, and can ultimately determine whether heritable variation is maintained or lost. [ABSTRACT FROM AUTHOR]

Regulation of the immune response during active tuberculosis (TB) has been partly deciphered. In pulmonary TB there is transient systemic immunosuppression due to overexpression of transforming growth factor beta and interleukin-10. This is superimposed on a primary T-cell defect. Locally there is intense inflammation (lung, pleural fluid) with overexpression of immunosuppressive factors (bronchoalveolar lavage) and extensive apoptosis. These observations suggest that immune therapies should be aimed at neutralizing the negative regulatory factors rather than accentuating an already intense immune response. Also a partially effective vaccine carries the potential risk of exacerbating disease. Clin Trans Sci 2010; Volume 3: 23–28 [ABSTRACT FROM AUTHOR]

The zoonotic pathogen Bartonella henselae ( Bh) can lead to vasoproliferative tumour lesions in the skin and inner organs known as bacillary angiomatosis and bacillary peliosis. The knowledge on the molecular and cellular mechanisms involved in this pathogen-triggered angiogenic process is confined by the lack of a suitable animal model and a physiologically relevant cell culture model of angiogenesis. Here we employed a three-dimensional in vitro angiogenesis assay of collagen gel-embedded endothelial cell (EC) spheroids to study the angiogenic properties of Bh. Spheroids generated from Bh-infected ECs displayed a high capacity to form sprouts, which represent capillary-like projections into the collagen gel. The VirB/VirD4 type IV secretion system and a subset of its translocated Bartonella effector proteins (Beps) were found to profoundly modulate this Bh-induced sprouting activity. BepA, known to protect ECs from apoptosis, strongly promoted sprout formation. In contrast, BepG, triggering cytoskeletal rearrangements, potently inhibited sprouting. Hence, the here established in vitro model of Bartonella- induced angiogenesis revealed distinct and opposing activities of type IV secretion system effector proteins, which together with a VirB/VirD4-independent effect may control the angiogenic activity of Bh during chronic infection of the vasculature. [ABSTRACT FROM AUTHOR]