Trait vs. phylogenetic diversity as predictors of competition and community composition in herbivorous marine amphipods

Field studies of community assembly patterns increasingly use phylogenetic relatedness as a surrogate for traits. Recent experiments appear to validate this approach by showing effects of correlated trait and phylogenetic distances on coexistence. However, traits governing resource use in animals are often labile. To test whether feeding trait or phylogenetic diversity can predict competition and production in communities of grazing amphipods, we manipulated both types of diversity independently in mesocosms. We found that increasing the feeding trait diversity of the community increased the number of species coexisting, reduced dominance and changed food availability. In contrast, phylogenetic diversity had no effect, suggesting that whatever additional ecological information it represents was not relevant in this context. Although community phylogenetic structure in the field may result from multiple traits with potential for phylogenetic signal, phylogenetic effects on species interactions in controlled experiments may depend on the lability of fewer key traits.     

Life history parameters and diet of Risso's dolphins,Grampus griseus,from southeastern South Africa

The life history of Risso's dolphins (Grampus griseus) remains poorly known and data from strandings can help provide important information. Data from 126 Risso's dolphins stranded or bycaught along the southeastern coastline of South Africa between 1958 and 2017 were analyzed in relation to their sex, age structure, and diet. Mean estimated length at birth was 146.9 cm, while maximum length was 325 cm for males and 313 cm for females; small sample sizes precluded detailed examination of sexual dimorphism. Age estimates for 33 individuals (14 males, 17 females, 2 unknown sex) indicated a maximum age of 13 years (males) and 17 years (females), respectively; the oldest animal was 19 years (unknown sex). Mean length and age at attainment of sexual maturity were estimated at 280 cm and 7.1 years in males and at 282 cm and 7.7 years in females. Stomach contents from 27 individuals showed that diets of immature and mature males and females overlapped and consisted predominantly of cephalopods. Reported strandings decreased between 2000 and 2017, possibly due to a lack of reporting associated with a ban on driving on beaches or related to the collapse of the local “chokka” squid (Loligo reynaudii) fishery in 2014–2015.     

One-step targeted maximum likelihood estimation for time-to-event outcomes

Researchers in observational survival analysis are interested in not only estimating survival curve nonparametrically but also having statistical inference for the parameter. We consider right-censored failure time data where we observe n independent and identically distributed observations of a vector random variable consisting of baseline covariates, a binary treatment at baseline, a survival time subject to right censoring, and the censoring indicator. We assume the baseline covariates are allowed to affect the treatment and censoring so that an estimator that ignores covariate information would be inconsistent. The goal is to use these data to estimate the counterfactual average survival curve of the population if all subjects are assigned the same treatment at baseline. Existing observational survival analysis methods do not result in monotone survival curve estimators, which is undesirable and may lose efficiency by not constraining the shape of the estimator using the prior knowledge of the estimand. In this paper, we present a one-step Targeted Maximum Likelihood Estimator (TMLE) for estimating the counterfactual average survival curve. We show that this new TMLE can be executed via recursion in small local updates. We demonstrate the finite sample performance of this one-step TMLE in simulations and an application to a monoclonal gammopathy data.     

Sequential adaptive variables and subject selection for GEE methods

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active-learning methods found in the machine-learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.     

Genetic mark-recapture analysis of winter faecal pellets allows estimation of population size in Sage Grouse Centrocercus urophasianus

The Sage Grouse Centrocercus urophasianus is a species of conservation concern throughout its range in western North America. Since the 1950s, the high count of males at leks has been used as an index for monitoring populations. However, the relationship between this lek-count index and population size is unclear, and its reliability for assessing population trends has been questioned. We used non-invasive genetic mark-recapture analysis of faecal and feather samples to estimate pre-breeding population size for the Parachute-Piceance-Roan, a small, geographically isolated population of Sage Grouse in western Colorado, during two consecutive winters from 2012 to 2014. We estimated total pre-breeding population size as 335 (95% confidence interval (CI): 287–382) in the first winter and 745 (95% CI: 627–864) in the second, an approximate doubling in abundance between years. Although we also observed a large increase in the spring lek-count index between those years, high male count data poorly represented mark-recapture estimates of male abundance in each year. Our data suggest that lek counts are useful for detecting the direction and magnitude of large changes in Sage Grouse abundance over time but they may not reliably reflect small changes in abundance that may be relevant to small populations of conservation concern.     

Field evaluation of abundance estimates under binomial and multinomial N-mixture models

Assessing and modelling abundance from animal count data is a very common task in ecology and management. Detection is arguably never perfect, but modern hierarchical models can incorporate detection probability and yield abundance estimates that are corrected for imperfect detection. Two variants of these models rely on counts of unmarked individuals, or territories (binomial N-mixture models, or binmix), and on detection histories based on territory-mapping data (multinomial N-mixture models or multimix). However, calibration studies which evaluate these two N-mixture model approaches are needed. We analysed conventional territory-mapping data (three surveys in 2014 and four in 2015) using both binmix and multimix models to estimate abundance for two common avian cavity-nesting forest species (Great Tit Parus major and Eurasian Blue Tit Cyanistes caeruleus). In the same study area, we used two benchmarks: occupancy data from a dense nestbox scheme and total number of detected territories. To investigate variance in estimates due to the territory assignment, three independent ornithologists conducted territory assignments. Nestbox occupancy yields a minimum number of territories, as some natural cavities may have been used, and binmix model estimates were generally higher than this benchmark. Estimates using the multimix model were slightly more precise than binmix model estimates. Depending on the person assigning the territories, the multimix model estimates became quite different, either overestimating or underestimating the ‘truth’. We conclude that N-mixture models estimated abundance reliably, even for our very small sample sizes. Territory-mapping counts depended on territory assignment and this carried over to estimates under the multimix model. This limitation has to be taken into account when abundance estimates are compared between sites or years. Whenever possible, accounting for such hidden heterogeneity in the raw data of bird surveys, via including a ‘territory editor’ factor, is recommended. Distributing the surveys randomly (in time and space) to editors may also alleviate this problem.     

Estimation of a monotone percentile residual life function under random censorship

In this paper, we introduce a new estimator of a percentile residual life function with censored data under a monotonicity constraint. Specifically, it is assumed that the percentile residual life is a decreasing function. This assumption is useful when estimating the percentile residual life of units, which degenerate with age. We establish a law of the iterated logarithm for the proposed estimator, and its ‐equivalence to the unrestricted estimator. The asymptotic normal distribution of the estimator and its strong approximation to a Gaussian process are also established. We investigate the finite sample performance of the monotone estimator in an extensive simulation study. Finally, data from a clinical trial in primary biliary cirrhosis of the liver are analyzed with the proposed methods. One of the conclusions of our work is that the restricted estimator may be much more efficient than the unrestricted one.     

A new genus of Aeolothripidae (Thysanoptera) from New Zealand and New Caledonia

Desmidothrips n.gen. is described from New Zealand, with walkerae n.sp. as type‐species. Aeolothrips inauditus Bianchi from New Caledonia is transferred to this new genus. Desmidothrips is related to the Australian genus Desmothrips, and is the only member of the Aeolothripidae native to New Zealand.