Functional traits and environment jointly determine the spatial scaling of population stability in North American birds.

Understanding the spatial scaling of population stability is critical for informing conservation strategies. A recently proposed metric for quantifying how population stability varies across scales is the invariability-area relationship (IAR), but the underlying drivers shaping IARs remain unclear. Using 15-year records of 249 bird species in 1035 survey transects in North America, we derived the IAR for each species by calculating population temporal invariability at different spatial scales (i.e., number of routes) and investigated how species IARs were influenced by functional traits and environmental factors. We found that species with faster life history traits and reduced flight efficiency had higher IAR intercepts (i.e., locally more stable), whereas migratory species exhibited higher IAR slopes (i.e., a faster gain of stability with increasing spatial scale). In addition, spatial correlation in temperature and vegetation structure synchronized bird population dynamics over space and thus decreased IAR slopes. By demonstrating the joint influence of functional traits and environmental factors on bird population stability across scales, our results highlight the need for dynamic conservation strategies tailored to particular types of species in an era of global environmental changes.

How complementarity and selection affect the relationship between ecosystem functioning and stability.

The biotic mechanisms underlying ecosystem functioning and stability have been extensively-but separately-explored in the literature, making it difficult to understand the relationship between functioning and stability. In this study, we used community models to examine how complementarity and selection, the two major biodiversity mechanisms known to enhance ecosystem biomass production, affect ecosystem stability. Our analytic and simulation results show that although complementarity promotes stability, selection impairs it. The negative effects of selection on stability operate through weakening portfolio effects and selecting species that have high productivity but low tolerance to perturbations ("risk-prone" species). In contrast, complementarity enhances stability by increasing portfolio effects and reducing the relative abundance of risk-prone species. Consequently, ecosystem functioning and stability exhibit either a synergy, if complementarity effects prevail, or trade-off, if selection effects prevail. Across species richness levels, ecosystem functioning and stability tend to be positively related, but negative relationships can occur when selection co-varies with richness. Our findings provide novel insights for understanding the functioning-stability relationship, with potential implications for both ecological research and ecosystem management.

The complete mitochondrial genome of Soriculus nigrescens (Soricomorpha: Soricinae).

The Himalayan Shrew (Soriculus nigrescens) Gray, 1842 belongs to the subfamily Soricinae, which is distributed in southwest China, Nepal, India, and Bhutan. This species is classified as Least Concern on The IUCN Red List. In this study, we sequenced the complete mitochondrial genome of S. nigrescens. This mitogenome is 17,284 bp in length and contains a set of 13 protein-coding genes (PCGs), two ribosomal RNA genes (rRNA), 22 transfer RNA genes (tRNA), one origin of L strand replication, and one control region. In order to explore the molecular phylogenetics evolution of Soricinae, the nucleotide sequence data of 13 PCGs of S. nigrescens and other 17 Insectivores were used for the phylogenetic analysis.

Separate neural systems for behavioral change and for emotional responses to failure during behavioral inhibition.

To analyze the involvement of different brain regions in behavioral inhibition and impulsiveness, differences in activation were investigated in fMRI data from a response inhibition task, the stop-signal task, in 1709 participants. First, areas activated more in stop-success (SS) than stop-failure (SF) included the lateral orbitofrontal cortex (OFC) extending into the inferior frontal gyrus (ventrolateral prefrontal cortex, BA 47/12), and the dorsolateral prefrontal cortex (DLPFC). Second, the anterior cingulate and anterior insula (AI) were activated more on failure trials, specifically in SF versus SS. The interaction between brain region and SS versus SF activations was significant (P = 5.6 * 10-8 ). The results provide new evidence from this "big data" investigation consistent with the hypotheses that the lateral OFC is involved in the stop-related processing that inhibits the action; that the DLPFC is involved in attentional processes that influence task performance; and that the AI and anterior cingulate are involved in emotional processes when failure occurs. The investigation thus emphasizes the role of the human lateral OFC BA 47/12 in changing behavior, and inhibiting behavior when necessary. A very similar area in BA47/12 is involved in changing behavior when an expected reward is not obtained, and has been shown to have high functional connectivity in depression. Hum Brain Mapp 38:3527-3537, 2017. © 2017 Wiley Periodicals, Inc.

LEARNING LOCAL DIRECTED ACYCLIC GRAPHS BASED ON MULTIVARIATE TIME SERIES DATA.

Multivariate time series (MTS) data such as time course gene expression data in genomics are often collected to study the dynamic nature of the systems. These data provide important information about the causal dependency among a set of random variables. In this paper, we introduce a computationally efficient algorithm to learn directed acyclic graphs (DAGs) based on MTS data, focusing on learning the local structure of a given target variable. Our algorithm is based on learning all parents (P), all children (C) and some descendants (D) (PCD) iteratively, utilizing the time order of the variables to orient the edges. This time series PCD-PCD algorithm (tsPCD-PCD) extends the previous PCD-PCD algorithm to dependent observations and utilizes composite likelihood ratio tests (CLRTs) for testing the conditional independence. We present the asymptotic distribution of the CLRT statistic and show that the tsPCD-PCD is guaranteed to recover the true DAG structure when the faithfulness condition holds and the tests correctly reject the null hypotheses. Simulation studies show that the CLRTs are valid and perform well even when the sample sizes are small. In addition, the tsPCD-PCD algorithm outperforms the PCD-PCD algorithm in recovering the local graph structures. We illustrate the algorithm by analyzing a time course gene expression data related to mouse T-cell activation.