Strong “bottom‐up” influences on small mammal populations: State‐space model analyses from long‐term studies

“Bottom‐up” influences, that is, masting, plus population density and climate, commonly influence woodland rodent demography. However, “top‐down” influences (predation) also intervene. Here, we assess the impacts of masting, climate, and density on rodent populations placed in the context of what is known about “top‐down” influences. To explain between‐year variations in bank vole Myodes glareolus and wood mouse Apodemus sylvaticus population demography, we applied a state‐space model to 33 years of catch‐mark‐release live‐trapping, winter temperature, and precise mast‐collection data. Experimental mast additions aided interpretation. Rodent numbers in European ash Fraxinus excelsior woodland were estimated (May/June, November/December). December–March mean minimum daily temperature represented winter severity. Total marked adult mice/voles (and juveniles in May/June) provided density indices validated against a model‐generated population estimate; this allowed estimation of the structure of a time‐series model and the demographic impacts of the climatic/biological variables. During two winters of insignificant fruit‐fall, 6.79 g/m² sterilized ash seed (as fruit) was distributed over an equivalent woodland similarly live‐trapped. September–March fruit‐fall strongly increased bank vole spring reproductive rate and winter and summer population growth rates; colder winters weakly reduced winter population growth. September–March fruit‐fall and warmer winters marginally increased wood mouse spring reproductive rate and September–December fruit‐fall weakly elevated summer population growth. Density dependence significantly reduced both species' population growth. Fruit‐fall impacts on demography still appeared after a year. Experimental ash fruit addition confirmed its positive influence on bank vole winter population growth with probable moderation by colder temperatures. The models show the strong impact of masting as a “bottom‐up” influence on rodent demography, emphasizing independent masting and weather influences; delayed effects of masting; and the importance of density dependence and its interaction with masting. We conclude that these rodents show strong “bottom‐up” and density‐dependent influences on demography moderated by winter temperature. “Top‐down” influences appear weak and need further investigation.     

Model‐free feedback design for a mixed cancer therapy

In this article, a model‐free feedback control design is proposed for the drug administration in mixed cancer therapy. This strategy is very attractive because of the important issue of parameter uncertainties unavoidable when dealing with biological models. The proposed feedback scheme use past measurements to update an on‐line simplified model. The control design is then based on model predictive control in which a suitable switching is performed between two different cost functions. The effectiveness of the proposed model‐free control strategy is validated using a recently developed model (unknown to the controller) governing the cancer growth on a cells population level under combined immune and chemotherapy and using real human data. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Semi‐Markov Models with Phase‐Type Sojourn Distributions

Summary Continuous‐time multistate models are widely used for categorical response data, particularly in the modeling of chronic diseases. However, inference is difficult when the process is only observed at discrete time points, with no information about the times or types of events between observation times, unless a Markov assumption is made. This assumption can be limiting as rates of transition between disease states might instead depend on the time since entry into the current state. Such a formulation results in a semi‐Markov model. We show that the computational problems associated with fitting semi‐Markov models to panel‐observed data can be alleviated by considering a class of semi‐Markov models with phase‐type sojourn distributions. This allows methods for hidden Markov models to be applied. In addition, extensions to models where observed states are subject to classification error are given. The methodology is demonstrated on a dataset relating to development of bronchiolitis obliterans syndrome in post‐lung‐transplantation patients.     

A Bayesian Chi‐Squared Goodness‐of‐Fit Test for Censored Data Models

Summary We propose a Bayesian chi‐squared model diagnostic for analysis of data subject to censoring. The test statistic has the form of Pearson's chi‐squared test statistic and is easy to calculate from standard output of Markov chain Monte Carlo algorithms. The key innovation of this diagnostic is that it is based only on observed failure times. Because it does not rely on the imputation of failure times for observations that have been censored, we show that under heavy censoring it can have higher power for detecting model departures than a comparable test based on the complete data. In a simulation study, we show that tests based on this diagnostic exhibit comparable power and better nominal Type I error rates than a commonly used alternative test proposed by Akritas (1988, Journal of the American Statistical Association 83, 222–230). An important advantage of the proposed diagnostic is that it can be applied to a broad class of censored data models, including generalized linear models and other models with nonidentically distributed and nonadditive error structures. We illustrate the proposed model diagnostic for testing the adequacy of two parametric survival models for Space Shuttle main engine failures.     

High‐resolution structures of collagen‐like peptides [(Pro‐Pro‐Gly)4‐Xaa‐Yaa‐Gly‐(Pro‐Pro‐Gly)4]: Implications for triple‐helix hydration and Hyp(X) puckering

Structures of (Pro‐Pro‐Gly)₄‐Xaa‐Yaa‐Gly‐(Pro‐Pro‐Gly)₄ (ppg9‐XYG) where (Xaa, Yaa) = (Pro, Hyp), (Hyp, Pro) or (Hyp, Hyp) were analyzed at high resolution using synchrotron radiation. Molecular and crystal structures of these peptides are very similar to those of the (Pro‐Pro‐Gly)₉ peptide. The results obtained in this study, together with those obtained from related compounds, indicated the puckering propensity of the Hyp in the X position: (1) Hyp(X) residues involved in the Hyp(X):Pro(Y) stacking pairs prefer the down‐puckering conformation, as in ppg9‐OPG, and ppg9‐OOG; (2) Hyp(X) residues involved in the Hyp(X):Hyp(Y) stacking pairs prefer the up‐puckering conformation if there is no specific reason to adopt the down‐puckering conformation. Water molecules in these peptide crystals are classified into two groups, the 1st and 2nd hydration waters. Water molecules in the 1st hydration group have direct hydrogen bonds with peptide oxygen atoms, whereas those in the 2nd hydration group do not. Compared with globular proteins, the number of water molecules in the 2nd hydration shell of the ppg9‐XYG peptides is very large, likely due to the unique rod‐like molecular structure of collagen model peptides. In the collagen helix, the amino acid residues in the X and Y positions must protrude outside of the triple helix, which forces even the hydrophobic side chains, such as Pro, to be exposed to the surrounding water molecules. Therefore, most of the waters in the 2nd hydration shell are covering hydrophobic Pro side chains by forming clathrate structures. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 361–372, 2009. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com     

A Robust Test for Two‐Stage Design in Genome‐Wide Association Studies

Summary A two‐stage design is cost‐effective for genome‐wide association studies (GWAS) testing hundreds of thousands of single nucleotide polymorphisms (SNPs). In this design, each SNP is genotyped in stage 1 using a fraction of case–control samples. Top‐ranked SNPs are selected and genotyped in stage 2 using additional samples. A joint analysis, combining statistics from both stages, is applied in the second stage. Follow‐up studies can be regarded as a two‐stage design. Once some potential SNPs are identified, independent samples are further genotyped and analyzed separately or jointly with previous data to confirm the findings. When the underlying genetic model is known, an asymptotically optimal trend test (TT) can be used at each analysis. In practice, however, genetic models for SNPs with true associations are usually unknown. In this case, the existing methods for analysis of the two‐stage design and follow‐up studies are not robust across different genetic models. We propose a simple robust procedure with genetic model selection to the two‐stage GWAS. Our results show that, if the optimal TT has about 80% power when the genetic model is known, then the existing methods for analysis of the two‐stage design have minimum powers about 20% across the four common genetic models (when the true model is unknown), while our robust procedure has minimum powers about 70% across the same genetic models. The results can be also applied to follow‐up and replication studies with a joint analysis.     

Inverse‐probability‐of‐treatment weighted estimation of causal parameters in the presence of error‐contaminated and time‐dependent confounders

Inverse‐probability‐of‐treatment weighted (IPTW) estimation has been widely used to consistently estimate the causal parameters in marginal structural models, with time‐dependent confounding effects adjusted for. Just like other causal inference methods, the validity of IPTW estimation typically requires the crucial condition that all variables are precisely measured. However, this condition, is often violated in practice due to various reasons. It has been well documented that ignoring measurement error often leads to biased inference results. In this paper, we consider the IPTW estimation of the causal parameters in marginal structural models in the presence of error‐contaminated and time‐dependent confounders. We explore several methods to correct for the effects of measurement error on the estimation of causal parameters. Numerical studies are reported to assess the finite sample performance of the proposed methods.     

Accounting for tagging‐to‐harvest mortality in a Brownie tag‐recovery model by incorporating radio‐telemetry data

The Brownie tag‐recovery model is useful for estimating harvest rates but assumes all tagged individuals survive to the first hunting season; otherwise, mortality between time of tagging and the hunting season will cause the Brownie estimator to be negatively biased. Alternatively, fitting animals with radio transmitters can be used to accurately estimate harvest rate but may be more costly. We developed a joint model to estimate harvest and annual survival rates that combines known‐fate data from animals fitted with transmitters to estimate the probability of surviving the period from capture to the first hunting season, and data from reward‐tagged animals in a Brownie tag‐recovery model. We evaluated bias and precision of the joint estimator, and how to optimally allocate effort between animals fitted with radio transmitters and inexpensive ear tags or leg bands. Tagging‐to‐harvest survival rates from >20 individuals with radio transmitters combined with 50–100 reward tags resulted in an unbiased and precise estimator of harvest rates. In addition, the joint model can test whether transmitters affect an individual's probability of being harvested. We illustrate application of the model using data from wild turkey, Meleagris gallapavo, to estimate harvest rates, and data from white‐tailed deer, Odocoileus virginianus, to evaluate whether the presence of a visible radio transmitter is related to the probability of a deer being harvested. The joint known‐fate tag‐recovery model eliminates the requirement to capture and mark animals immediately prior to the hunting season to obtain accurate and precise estimates of harvest rate. In addition, the joint model can assess whether marking animals with radio transmitters affects the individual's probability of being harvested, caused by hunter selectivity or changes in a marked animal's behavior.     

Many‐to‐one Comparisons in Stratified Designs

Cheung and Holland (1992) extended Dunnett's procedure for comparing all active treatments with a control simultaneously within each of r groups while maintaining the Type I error rate at some designated level α allowing different sample sizes for each of the group‐treatment categories. This paper shows that exact percentage points can be easily calculated with current available statistical software (SAS). This procedure is compared to resampling techniques and a Bonferroni corrected Dunnett‐within‐group procedure by means of a simulation study.     

Modelling climate‐change‐induced nonlinear thresholds in cephalopod population dynamics

A significant global challenge lies in our current inability to anticipate, and therefore prepare for, critical ecological thresholds (i.e. tipping points in ecosystems). This deficit stems largely from an inadequate understanding of the many complex interactions between species and the environment at the ecosystem level, and the paucity of mechanistic models relating environment to population dynamics at the species level. In marine ecosystems, abundant, short‐lived and fast‐growing species such as anchovies or squids, consistently function as ‘keystone’ groups whose population dynamics affect entire ecosystems. Increasing exploitation coupled with climate change impacts has the potential to affect these ecological groups and consequently, the entire marine ecosystem. There are currently very few models that predict the impact of climate change on these keystone groups. Here we use a combination of individual‐based bioenergetics and stage‐structured population models to characterize the fundamental capacity of cephalopods to respond to climate change. We demonstrate the potential for, and mechanisms behind, two unfavourable climate‐change‐induced thresholds in future population dynamics. Although one threshold was the direct consequence of a decrease in incubation time caused by ocean warming, the other threshold was linked to survivorship, implying the possibility of management through a modification of fishing mortality. Additional substantive changes in phenology were also predicted, with a possible loss in population resilience. Our results demonstrate the feasibility of predicting complex nonlinear dynamics with a reasonably simplistic mechanistic model, and highlight the necessity of developing such approaches for other species if attempts to moderate the impact of climate change on natural resources are to be effective.     

Hotspots of uncertainty in land‐use and land‐cover change projections: a global‐scale model comparison

Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.     

A goodness‐of‐fit test for occupancy models with correlated within‐season revisits

Occupancy modeling is important for exploring species distribution patterns and for conservation monitoring. Within this framework, explicit attention is given to species detection probabilities estimated from replicate surveys to sample units. A central assumption is that replicate surveys are independent Bernoulli trials, but this assumption becomes untenable when ecologists serially deploy remote cameras and acoustic recording devices over days and weeks to survey rare and elusive animals. Proposed solutions involve modifying the detection‐level component of the model (e.g., first‐order Markov covariate). Evaluating whether a model sufficiently accounts for correlation is imperative, but clear guidance for practitioners is lacking. Currently, an omnibus goodness‐of‐fit test using a chi‐square discrepancy measure on unique detection histories is available for occupancy models (MacKenzie and Bailey, Journal of Agricultural, Biological, and Environmental Statistics, 9, 2004, 300; hereafter, MacKenzie–Bailey test). We propose a join count summary measure adapted from spatial statistics to directly assess correlation after fitting a model. We motivate our work with a dataset of multinight bat call recordings from a pilot study for the North American Bat Monitoring Program. We found in simulations that our join count test was more reliable than the MacKenzie–Bailey test for detecting inadequacy of a model that assumed independence, particularly when serial correlation was low to moderate. A model that included a Markov‐structured detection‐level covariate produced unbiased occupancy estimates except in the presence of strong serial correlation and a revisit design consisting only of temporal replicates. When applied to two common bat species, our approach illustrates that sophisticated models do not guarantee adequate fit to real data, underscoring the importance of model assessment. Our join count test provides a widely applicable goodness‐of‐fit test and specifically evaluates occupancy model lack of fit related to correlation among detections within a sample unit. Our diagnostic tool is available for practitioners that serially deploy survey equipment as a way to achieve cost savings.     

Crystal structure of the collagen model peptide (Pro‐Pro‐Gly)4‐Hyp‐Asp‐Gly‐(Pro‐Pro‐Gly)4 at 1.0 Å resolution

The single‐crystal structure of the collagen‐like peptide (Pro‐Pro‐Gly)₄‐Hyp‐Asp‐Gly‐(Pro‐Pro‐Gly)₄, was analyzed at 1.02 Å resolution. The overall average helical twist (θ = 49.6°) suggests that this peptide adopts a 7/2 triple‐helical structure and that its conformation is very similar to that of (Gly‐Pro‐Hyp)₉, which has the typical repeating sequence in collagen. High‐resolution studies on other collagen‐like peptides have shown that imino acid‐rich sequences preferentially adopt a 7/2 triple‐helical structure (θ = 51.4°), whereas imino acid‐lean sequences adopt relaxed conformations (θ < 51.4°). The guest Gly‐Hyp‐Asp sequence in the present peptide, however, has a large helical twist (θ = 61.1°), whereas that of the host Pro‐Pro‐Gly sequence is small (θ = 46.7°), indicating that the relationship between the helical conformation and the amino acid sequence of such peptides is complex. In the present structure, a strong intermolecular hydrogen bond between two Asp residues on the A and B strands might induce the large helical twist of the guest sequence; this is compensated by a reduced helical twist in the host, so that an overall 7/2‐helical symmetry is maintained. The Asp residue in the C strand might interact electrostatically with the N‐terminus of an adjacent molecule, causing axial displacement, reminiscent of the D‐staggered structure in fibrous collagens. © 2013 Wiley Periodicals, Inc. Biopolymers 99: 436–447, 2013.     

Nickel‐Rich and Lithium‐Rich Layered Oxide Cathodes: Progress and Perspectives

Ni‐rich layered oxides and Li‐rich layered oxides are topics of much research interest as cathodes for Li‐ion batteries due to their low cost and higher discharge capacities compared to those of LiCoO₂ and LiMn₂O₄. However, Ni‐rich layered oxides have several pitfalls, including difficulty in synthesizing a well‐ordered material with all Ni³⁺ ions, poor cyclability, moisture sensitivity, a thermal runaway reaction, and formation of a harmful surface layer caused by side reactions with the electrolyte. Recent efforts towards Ni‐rich layered oxides have centered on optimizing the composition and processing conditions to obtain controlled bulk and surface compositions to overcome the capacity fade. Li‐rich layered oxides also have negative aspects, including oxygen loss from the lattice during first charge, a large first cycle irreversible capacity loss, poor rate capability, side reactions with the electrolyte, low tap density, and voltage decay during extended cycling. Recent work on Li‐rich layered oxides has focused on understanding the surface and bulk structures and eliminating the undesirable properties. Followed by a brief introduction, an account of recent developments on the understanding and performance gains of Ni‐rich and Li‐rich layered oxide cathodes is provided, along with future research directions.