Large old trees increase growth under shifting climatic constraints: Aligning tree longevity and individual growth dynamics in primary mountain spruce forests

In a world of accelerating changes in environmental conditions driving tree growth, tradeoffs between tree growth rate and longevity could curtail the abundance of large old trees (LOTs), with potentially dire consequences for biodiversity and carbon storage. However, the influence of tree-level tradeoffs on forest structure at landscape scales will also depend on disturbances, which shape tree size and age distribution, and on whether LOTs can benefit from improved growing conditions due to climate warming. We analyzed temporal and spatial variation in radial growth patterns from ~5000 Norway spruce (Picea abies [L.] H. Karst) live and dead trees from the Western Carpathian primary spruce forest stands. We applied mixed-linear modeling to quantify the importance of LOT growth histories and stand dynamics (i.e., competition and disturbance factors) on lifespan. Finally, we assessed regional synchronization in radial growth variability over the 20th century, and modeled the effects of stand dynamics and climate on LOTs recent growth trends. Tree age varied considerably among forest stands, implying an important role of disturbance as an age constraint. Slow juvenile growth and longer period of suppressed growth prolonged tree lifespan, while increasing disturbance severity and shorter time since last disturbance decreased it. The highest age was not achieved only by trees with continuous slow growth, but those with slow juvenile growth followed by subsequent growth releases. Growth trend analysis demonstrated an increase in absolute growth rates in response to climate warming, with late summer temperatures driving the recent growth trend. Contrary to our expectation that LOTs would eventually exhibit declining growth rates, the oldest LOTs (>400 years) continuously increase growth throughout their lives, indicating a high phenotypic plasticity of LOTs for increasing biomass, and a strong carbon sink role of primary spruce forests under rising temperatures, intensifying droughts, and increasing bark beetle outbreaks.     

Dominant vs. codominant genetic markers in the estimation of male mating success

Although a major component of fitness, male reproductive success is generally extremely difficult to estimate. As a result, genetic methods and maximum likelihood models have been developed to estimate male parentage, but all are limited in practice by the degree of genetic variation observable. Scoring individuals phenotypically at a large number of random loci exhibiting dominance (e.g. RAPD markers) may provide a means of detecting sufficient genetic variation. Dominance, however, represents a loss of information and therefore greater variation in the estimate of paternity. A mixture model describing mating in a population is presented to quantify the trade-off between marker types when estimates of male fertility are sought. A sample size 1.5-2.0 times greater is required for dominant markers under some conditions to obtain the same confidence in fertility estimates as for codominant markers, although with large sample sizes the fertility estimates are similar for either marker type. Since the number of dominant DN A markers is not limited in the same manner as is the number of codominant protein markers, one's confidence in the estimates can be increased above that possible from proteins by surveying additional loci. However, for a fixed sample size a trade-off exists between the number of progeny assayed per female and the number of loci surveyed. In many cases more progeny per female provide better estimates of fertility than more loci.     

Upper thermal limits predict herpetofaunal responses to forest edge and cover

Amphibians and reptiles are sensitive to changes in the thermal environment, which varies considerably in human-modified landscapes. Although it is known that thermal traits of species influence their distribution in modified landscapes, how herpetofauna respond specifically to shifts in ambient temperature along forest edges remains unclear. This may be because most studies focus on local-scale metrics of edge exposure, which only account for a single edge or habitat patch. We predicted that accounting for the combined effect of multiple habitat edges in a landscape would best explain herpetofaunal response to thermally mediated edge effects. We (1) surveyed herpetofauna at two lowland, fragmented forest sites in central Colombia, (2) measured the critical thermal maximum (CT_max) of the species sampled, (3) measured their edge exposure at both local and landscape scales, and (4) created a thermal profile of the landscape itself. We found that species with low CT_max occurred both further from forest edges and in areas of denser vegetation, but were unaffected by the landscape-scale configuration of habitat edges. Variation in the thermal landscape was driven primarily by changes in vegetation density. Our results suggest that amphibians and reptiles with low CT_max are limited by both canopy gaps and proximity to edge, making them especially vulnerable to human modification of tropical forest. Abstract in Spanish is available with online material.     

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study.     

Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.     

Coherent modeling of longitudinal causal effects on binary outcomes

Analyses of biomedical studies often necessitate modeling longitudinal causal effects. The current focus on personalized medicine and effect heterogeneity makes this task even more challenging. Toward this end, structural nested mean models (SNMMs) are fundamental tools for studying heterogeneous treatment effects in longitudinal studies. However, when outcomes are binary, current methods for estimating multiplicative and additive SNMM parameters suffer from variation dependence between the causal parameters and the noncausal nuisance parameters. This leads to a series of difficulties in interpretation, estimation, and computation. These difficulties have hindered the uptake of SNMMs in biomedical practice, where binary outcomes are very common. We solve the variation dependence problem for the binary multiplicative SNMM via a reparameterization of the noncausal nuisance parameters. Our novel nuisance parameters are variation independent of the causal parameters, and hence allow for coherent modeling of heterogeneous effects from longitudinal studies with binary outcomes. Our parameterization also provides a key building block for flexible doubly robust estimation of the causal parameters. Along the way, we prove that an additive SNMM with binary outcomes does not admit a variation independent parameterization, thereby justifying the restriction to multiplicative SNMMs.     

Functional data analysis with covariate-dependent mean and covariance structures

Functional data analysis has emerged as a powerful tool in response to the ever-increasing resources and efforts devoted to collecting information about response curves or anything that varies over a continuum. However, limited progress has been made with regard to linking the covariance structures of response curves to external covariates, as most functional models assume a common covariance structure. We propose a new functional regression model with covariate-dependent mean and covariance structures. Particularly, by allowing variances of random scores to be covariate-dependent, we identify eigenfunctions for each individual from the set of eigenfunctions that govern the variation patterns across all individuals, resulting in high interpretability and prediction power. We further propose a new penalized quasi-likelihood procedure that combines regularization and B-spline smoothing for model selection and estimation and establish the convergence rate and asymptotic normality of the proposed estimators. The utility of the developed method is demonstrated via simulations, as well as an analysis of the Avon Longitudinal Study of Parents and Children concerning parental effects on the growth curves of their offspring, which yields biologically interesting results.     

THE MEASUREMENT OF SELECTION ON QUANTITATIVE TRAITS: BIASES DUE TO ENVIRONMENTAL COVARIANCES BETWEEN TRAITS AND FITNESS

The use of regression techniques for estimating the direction and magnitude of selection from measurements on phenotypes has become widespread in field studies. A potential problem with these techniques is that environmental correlations between fitness and the traits examined may produce biased estimates of selection gradients. This report demonstrates that the phenotypic covariance between fitness and a trait, used as an estimate of the selection differential in estimating selection gradients, has two components: a component induced by selection itself and a component due to the effect of environmental factors on fitness. The second component is shown to be responsible for biases in estimates of selection gradients. The use of regressions involving genotypic and breeding values instead of phenotypic values can yield estimates of selection gradients that are not biased by environmental covariances. Statistical methods for estimating the coefficients of such regressions, and for testing for biases in regressions involving phenotypic values, are described.     

Nesting activity,breeding success and colony size for the Wedge-tailed Shearwater Puffinus pacificus on Heron Island

Abstract The Capricorn Group of islands in Australia's Great Barrier Reef Marine Park sustains one of the world's largest breeding populations of the Wedge-tailed Shearwater Puffinus pacificus. Heron Island, a 13.5 ha coral cay which supports tourist and research station leases as well as a national park, is the third largest nesting island in the group. Sample censuses of breeding burrows were conducted each year between 1985 and 1990 and a further survey was completed in 1993. These returned estimates of between 13 264±1387 and 16 337±1545 active burrows (Y±SE). Burrow densities within each of the habitats monitored showed no significant trends between years, although there were large differences in burrow density between habitats. There were roughly the same number of burrows in the developed (west) and national park (east) halves of the cay. A miniature video camera system (burrowscope), which allowed nesting chambers at the ends of burrows to be inspected, was used in 1989, 1990 and 1993. This demonstrated that around half the burrows were occupied by incubating birds. Variations were found in the distribution of incubating birds between habitats, although this did not remain constant between the years. In the 1993 season, breeding activity was traced from the burrow establishment to fledging stage. Fifty-one per cent of burrows were used for breeding (eggs laid), 77% of eggs hatched and 80% of chicks produced a fledgling. Overall breeding success for the island was estimated at 61%. In 1993 the area designated as Buildings was found to have significantly lower hatching success compared with natural habitats. Most mortality occurred at the egg stage; however, in the Fringe habitat, mortality was highest at the chick stage. Previous surveys have estimated the breeding population from burrow counts. It now appears that only about 30% of such burrows produce fledglings.