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To produce viable seeds, plants must simultaneously allocate mass to other reproductive tissues; however, it remains unclear how big these investments are, relative to seeds, and how much the balance of investment across diverse species reflects broader variation in their life history strategies. In particular, species are known to vary in their seed mass, and the resources they invest in pollen attraction versus seed provisioning. We quantified overall reproductive investment and its partitioning among different reproductive tissues for 14 woody perennial species in a recurrent-fire heath community. Integrating two lineages of evolutionary theory led to the prediction that relative investment in different reproductive tissues would be correlated with a species' seed mass, and the data supported this. Species with larger seeds were found to mature a smaller proportion of ovules, to expend more of pre-zygotic investment on discarded tissues, and to invest more in seed provisioning compared to pollen attraction. These patterns were similar when investment is assessed as nitrogen and phosphorus content instead of biomass. A little more than half of this correlation was phylogenetically conservative, reflecting the tendency for many species in the locally abundant and species-rich family Proteaceae to have large seeds, low seed set and relatively lower investment in pollen attraction. The total biomass of accessory tissues – reproduction-related mass not directly invested in the seed – ranged from 95.8 to 99.8% of total investment for species in this study. Counting only seeds thus substantially underestimates total reproductive investment. Many studies have established that the seed mass of a species positions it along a colonization-competition life-history spectrum. Here we have shown that relative investment in pollen-attraction versus provisioning tissues and in successful versus discarded ovules are also associated with seed mass. The seed mass spectrum among angiosperms is therefore connected with a spectrum of reproductive allocation strategies.

Functional data are smooth, often continuous, random curves, which can be seen as an extreme case of multivariate data with infinite dimensionality. Just as component-wise inference for multivariate data naturally performs feature selection, subset-wise inference for functional data performs domain selection. In this paper, we present a unified testing framework for domain selection on populations of functional data. In detail, p-values of hypothesis tests performed on point-wise evaluations of functional data are suitably adjusted for providing a control of the family-wise error rate (FWER) over a family of subsets of the domain. We show that several state-of-the-art domain selection methods fit within this framework and differ from each other by the choice of the family over which the control of the FWER is provided. In the existing literature, these families are always defined a priori. In this work, we also propose a novel approach, coined threshold-wise testing, in which the family of subsets is instead built in a data-driven fashion. The method seamlessly generalizes to multidimensional domains in contrast to methods based on a-priori defined families. We provide theoretical results with respect to consistency and control of the FWER for the methods within the unified framework. We illustrate the performance of the methods within the unified framework on simulated and real data examples, and compare their performance with other existing methods.

Nonparametric bagging clustering methods are studied and compared to identify latent structures from a sequence of dependent categorical data observed along a one-dimensional (discrete) time domain. The frequency of the observed categories is assumed to be generated by a (slowly varying) latent signal, according to latent state-specific probability distributions. The bagging clustering methods use random tessellations (partitions) of the time domain and clustering of the category frequencies of the observed data in the tessellation cells to recover the latent signal, within a bagging framework. New and existing ways of generating the tessellations and clustering are discussed and combined into different bagging clustering methods. Edge tessellations and adaptive tessellations are the new proposed ways of forming partitions. Composite methods are also introduced, that are using (automated) decision rules based on entropy measures to choose among the proposed bagging clustering methods. The performance of all the methods is compared in a simulation study. From the simulation study it can be concluded that local and global entropy measures are powerful tools in improving the recovery of the latent signal, both via the adaptive tessellation strategies (local entropy) and in designing composite methods (global entropy). The composite methods are robust and overall improve performance, in particular the composite method using adaptive (edge) tessellations.

The recent sports science literature conveys a growing interest in robust statistical methods to analyze smooth, regularly-sampled functional data. This paper focuses on the inferential problem of identifying the parts of a functional domain where two population means differ. We considered four approaches recently used in sports science: interval-wise testing (IWT), statistical parametric mapping (SPM), statistical nonparametric mapping (SnPM) and the Benjamini-Hochberg (BH) procedure for false discovery control. We applied these procedures to both six representative sports science datasets, and also to systematically varied simulated datasets which replicated ten signal- and/or noise-relevant parameters that were identified in the experimental datasets. We observed generally higher IWT and BH sensitivity for five of the six experimental datasets. BH was the most sensitive procedure in simulation, but also had relatively high false positive rates (generally > 0.1) which increased sharply (> 0.3) in certain extreme simulation scenarios including highly rough data. SPM and SnPM were more sensitive than IWT in simulation except for (1) high roughness, (2) high nonstationarity, and (3) highly nonuniform smoothness. These results suggest that the optimum procedure is both signal and noise-dependent. We conclude that: (1) BH is most sensitive but also susceptible to high false positive rates, (2) IWT, SPM and SnPM appear to have relatively inconsequential differences in terms of domain identification sensitivity, except in cases of extreme signal/noise characteristics, where IWT appears to be superior at identifying a greater portion of the true signal.

One of the main concerns of silviculture and forest management focuses on finding fast, cost-efficient and non-destructive ways of measuring wood properties in standing trees. This paper presents an R package \verb+fiberLD+ that provides functions for estimating tree fiber length distributions in the standing tree based on increment core samples. The methods rely on increment core data measured by means of an optical fiber analyzer (OFA) or measured by microscopy. Increment core data analyzed by OFAs consist of the cell lengths of both cut and uncut fibers (tracheids) and fines (such as ray parenchyma cells) without being able to identify which cells are cut or if they are fines or fibers. The microscopy measured data consist of the observed lengths of the uncut fibers in the increment core. A censored version of a mixture of the fine and fiber length distributions is proposed to fit the OFA data, under distributional assumptions. Two choices for the assumptions of the underlying density functions of the true fiber (fine) lengths of those fibers (fines) that at least partially appear in the increment core are considered, such as the generalized gamma and the log normal densities. Maximum likelihood estimation is used for estimating the model parameters for both the OFA analyzed data and the microscopy measured data.

We propose a new nonparametric clustering method for dependent functional data, the double clustering bagging Voronoi method. It consists of two levels of clustering. Given a spatial lattice of points, a function is observed at each grid point. In the first‐level clustering, features of the functional data are clustered. The second‐level clustering takes dependence into account, by grouping local representatives, built from the resulting first‐level clusters, using a bagging Voronoi strategy. Depending on the distance measure used, features of the functions may be included in the second‐step clustering, making the method flexible and general. Combined with the clustering method, a multiresolution approach is proposed that searches for stable clusters at different spatial scales, aiming to capture latent structures. This provides a powerful and computationally efficient tool to cluster dependent functional data at different spatial scales, here illustrated by a simulation study. The introduced methodology is applied to varved lake sediment data, aiming to reconstruct winter climate regimes in northern Sweden at different time resolutions over the past 6,000 years.

Allocation of carbohydrates between competing organs is fundamental to plant development, growth and productivity. Carbohydrates are synthesized in mature leaves and distributed via the phloem vasculature to developing buds where they are consumed to produce new biomass. The distribution and mass-allocation processes within the plant remain poorly understood and may involve complex feedbacks between different plant functions, with implications for the emergent structure of the plant. Here, we investigate how the order in which dormant buds are flushed affects the development of tree size and reproductive output during the first 20 years of growth in full light and shaded canopy environments. We report the following findings: (i) Bud-flushing strategies strongly affect the temporal dynamics of height, mass and the size of reproduction pool, as well as the resulting architectures. (ii) Bud-flushing strategies affect tree growth by altering the rate of growth and final size of trees. (iii) No single bud-flushing strategy performs best when both the size and allocation for reproduction of the resulting trees are compared. However, we observe that the strategy that optimizes the net carbon gain for the entire tree architecture always results in a high reproduction output. (iv) Branch turnover and meristem regeneration enhance the performance of certain strategies with respect to the measured quantities. These results highlight the importance of employing generic models of architecture (i.e., non-species-specific) to identify general mechanisms of carbon allocation and the spatial distribution of newly formed biomass in growing trees.