Warm springs alter timing but not total growth of temperate deciduous trees.

As the climate changes, warmer spring temperatures are causing earlier leaf-out1-3 and commencement of CO2 uptake1,3 in temperate deciduous forests, resulting in a tendency towards increased growing season length3 and annual CO2 uptake1,3-7. However, less is known about how spring temperatures affect tree stem growth8,9, which sequesters carbon in wood that has a long residence time in the ecosystem10,11. Here we show that warmer spring temperatures shifted stem diameter growth of deciduous trees earlier but had no consistent effect on peak growing season length, maximum growth rates, or annual growth, using dendrometer band measurements from 440 trees across two forests. The latter finding was confirmed on the centennial scale by 207 tree-ring chronologies from 108 forests across eastern North America, where annual ring width was far more sensitive to temperatures during the peak growing season than in the spring. These findings imply that any extra CO2 uptake in years with warmer spring temperatures4,5 does not significantly contribute to increased sequestration in long-lived woody stem biomass. Rather, contradicting projections from global carbon cycle models1,12, our empirical results imply that warming spring temperatures are unlikely to increase woody productivity enough to strengthen the long-term CO2 sink of temperate deciduous forests.

The forestecology R package for fitting and assessing neighborhood models of the effect of interspecific competition on the growth of trees.

Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking.We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross-validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model.We demonstrate the package's functionality using data from the Smithsonian Conservation Biology Institute's large forest dynamics plot, part of the ForestGEO global network of research sites. Given ForestGEO's data collection protocols and data formatting standards, the package was designed with cross-site compatibility in mind. We highlight the importance of spatial cross-validation when interpreting model results.The package features (a) tidyverse-like structure whereby verb-named functions can be modularly "piped" in sequence, (b) functions with standardized inputs/outputs of simple features sf package class, and (c) an S3 object-oriented implementation of the Bayesian linear regression model. These three facts allow for clear articulation of all the steps in the sequence of analysis and easy wrangling and visualization of the geospatial data. Furthermore, while the package only has Bayesian linear regression implemented, the package was designed with extensibility to other methods in mind.

A permutation test and spatial cross-validation approach to assess models of interspecific competition between trees.

Measuring species-specific competitive interactions is key to understanding plant communities. Repeat censused large forest dynamics plots offer an ideal setting to measure these interactions by estimating the species-specific competitive effect on neighboring tree growth. Estimating these interaction values can be difficult, however, because the number of them grows with the square of the number of species. Furthermore, confidence in the estimates can be overestimated if any spatial structure of model errors is not considered. Here we measured these interactions in a forest dynamics plot in a transitional oak-hickory forest. We analytically fit Bayesian linear regression models of annual tree radial growth as a function of that tree's species, its size, and its neighboring trees. We then compared these models to test whether the identity of a tree's neighbors matters and if so at what level: based on trait grouping, based on phylogenetic family, or based on species. We used a spatial cross-validation scheme to better estimate model errors while avoiding potentially over-fitting our models. Since our model is analytically solvable we can rapidly evaluate it, which allows our proposed cross-validation scheme to be computationally feasible. We found that the identity of the focal and competitor trees mattered for competitive interactions, but surprisingly, identity mattered at the family rather than species-level.

sall1 and sall4 repress pou5f3 family expression to allow neural patterning, differentiation, and morphogenesis in Xenopus laevis.

The embryonic precursor of the vertebrate central nervous system, the neural plate, is patterned along the anterior-posterior axis and shaped by morphogenetic movements early in development. We previously identified the genes sall1 and sall4, known regulators of pluripotency in other contexts, as transcriptional targets of developmental signaling pathways that regulate neural development. Here, we demonstrate that these two genes are required for induction of posterior neural fates, the cell shape changes that contribute to neural tube closure, and later neurogenesis. Upon sall1 or sall4 knockdown, defects are associated with the failure of the neural plate to differentiate. Consistent with this, sall-deficient neural tissue exhibits an aberrant upregulation of pou5f3 family genes, the Xenopus homologs of the mammalian stem cell maintenance factor Pou5f1 (Oct4). Furthermore, overexpression of pou5f3 genes in Xenopus causes defects in neural patterning, morphogenesis, and differentiation that phenocopy those observed in sall1 and sall4 morphants. In all, this work shows that both sall1 and sall4 act to repress pou5f3 family gene expression in the neural plate, thereby allowing vertebrate neural development to proceed.

A Bayesian Method for Cluster Detection with Application to Brain and Breast Cancer in Puget Sound.

Cluster detection is an important public health endeavor, and in this article, we describe and apply a recently developed Bayesian method. Commonly used approaches are based on so-called scan statistics and suffer from a number of difficulties, which include how to choose a level of significance and how to deal with the possibility of multiple clusters. The basis of our model is to partition the study region into a set of areas that are either "null" or "non-null," the latter corresponding to clusters (excess risk) or anticlusters (reduced risk). We demonstrate the Bayesian method and compare with a popular existing approach, using data on breast, brain, lung, prostate, and colorectal cancer, in the Puget Sound region of Washington State.