Using density surface models to estimate spatio-temporal changes in population densities and trend

Precise measures of population abundance and trend are needed for species conservation; these are most difficult to obtain for rare and rapidly changing populations. We compare uncertainty in densities estimated from spatio–temporal models with that from standard design-based methods. Spatio–temporal models allow us to target priority areas where, and at times when, a population may most benefit. Generalised additive models were fitted to a 31-year time series of point-transect surveys of an endangered Hawaiian forest bird, the Hawai‘i ‘ākepa Loxops coccineus. This allowed us to estimate bird densities over space and time. We used two methods to quantify uncertainty in density estimates from the spatio–temporal model: the delta method (which assumes independence between detection and distribution parameters) and a variance propagation method. With the delta method we observed a 52% decrease in the width of the design-based 95% confidence interval (CI), while we observed a 37% decrease in CI width when propagating the variance. We mapped bird densities as they changed across space and time, allowing managers to evaluate management actions. Integrating detection function modelling with spatio–temporal modelling exploits survey data more efficiently by producing finer-grained abundance estimates than are possible with design-based methods as well as producing more precise abundance estimates. Model-based approaches require switching from making assumptions about the survey design to assumptions about bird distribution. Such a switch warrants consideration. In this case the model-based approach benefits conservation planning through improved management efficiency and reduced costs by taking into account both spatial shifts and temporal changes in population abundance and distribution.     

Substrate binding to Src: A new perspective on tyrosine kinase substrate recognition from NMR and molecular dynamics

Most signal transduction pathways in humans are regulated by protein kinases through phosphorylation of their protein substrates. Typical eukaryotic protein kinases are of two major types: those that phosphorylate‐specific sequences containing tyrosine (~90 kinases) and those that phosphorylate either serine or threonine (~395 kinases). The highly conserved catalytic domain of protein kinases comprises a smaller N lobe and a larger C lobe separated by a cleft region lined by the activation loop. Prior studies find that protein tyrosine kinases recognize peptide substrates by binding the polypeptide chain along the C‐lobe on one side of the activation loop, while serine/threonine kinases bind their substrates in the cleft and on the side of the activation loop opposite to that of the tyrosine kinases. Substrate binding structural studies have been limited to four families of the tyrosine kinase group, and did not include Src tyrosine kinases. We examined peptide‐substrate binding to Src using paramagnetic‐relaxation‐enhancement NMR combined with molecular dynamics simulations. The results suggest Src tyrosine kinase can bind substrate positioning residues C‐terminal to the phosphoacceptor residue in an orientation similar to serine/threonine kinases, and unlike other tyrosine kinases. Mutagenesis corroborates this new perspective on tyrosine kinase substrate recognition. Rather than an evolutionary split between tyrosine and serine/threonine kinases, a change in substrate recognition may have occurred within the TK group of the human kinome. Protein tyrosine kinases have long been therapeutic targets, but many marketed drugs have deleterious off‐target effects. More accurate knowledge of substrate interactions of tyrosine kinases has the potential for improving drug selectivity.     

Colonization history of Galapagos giant tortoises: Insights from mitogenomes support the progression rule

Galapagos giant tortoises (Chelonoidis spp.) are a group of large, long-lived reptiles that includes 14 species, 11 of which are extant and threatened by human activities and introductions of non-native species. Here, we evaluated the phylogenetic relationships of all extant and two extinct species (Chelonoidis abingdonii from the island of Pinta and Chelonoidis niger from the island of Floreana) using Bayesian and maximum likelihood analysis of complete or nearly complete mitochondrial genomes. We also provide an updated phylogeographic scenario of their colonization of the Galapagos Islands using chrono-phylogenetic and biogeographic approaches. The resulting phylogenetic trees show three major groups of species: one from the southern, central, and western Galapagos Islands; the second from the northwestern islands; and the third group from the northern, central, and eastern Galapagos Islands. The time-calibrated phylogenetic and ancestral area reconstructions generally align with the geologic ages of the islands. The divergence of the Galapagos giant tortoises from their South American ancestor likely occurred in the upper Miocene. Their diversification on the Galapagos adheres to the island progression rule, starting in the Pleistocene with the dispersal of the ancestral form from the two oldest islands (San Cristóbal and Española) to Santa Cruz, Santiago, and Pinta, followed by multiple colonizations from different sources within the archipelago. Our work provides an example of how to reconstruct the history of endangered taxa in spite of extinctions and human-mediated dispersal events and provides a framework for evaluating the contribution of colonization and in situ speciation to the diversity of other Galapagos lineages.     

The complex response of free and bound amino acids to water stress during the seed setting stage in Arabidopsis

Free amino acids (FAAs) and protein‐bound amino acids (PBAAs) in seeds play an important role in seed desiccation, longevity, and germination. However, the effect that water stress has on these two functional pools, especially when imposed during the crucial seed setting stage is unclear. To better understand these effects, we exposed Arabidopsis plants at the seed setting stage to a range of water limitation and water deprivation conditions and then evaluated physiological, metabolic, and proteomic parameters, with special focus on FAAs and PBAAs. We found that in response to severe water limitation, seed yield decreased, while seed weight, FAA, and PBAA content per seed increased. Nevertheless, the composition of FAAs and PBAAs remained unaltered. In response to severe water deprivation, however, both seed yield and weight were reduced. In addition, major alterations were observed in both FAA and proteome compositions, which indicated that both osmotic adjustment and proteomic reprogramming occurred in these naturally desiccation‐tolerant organs. However, despite the major proteomic alteration, the PBAA composition did not change, suggesting that the proteomic reprogramming was followed by a proteomic rebalancing. Proteomic rebalancing has not been observed previously in response to stress, but its occurrence under stress strongly suggests its natural function. Together, our data show that the dry seed PBAA composition plays a key role in seed fitness and therefore is rigorously maintained even under severe water stress, while the FAA composition is more plastic and adaptable to changing environments, and that both functional pools are distinctly regulated.     

New Caledonian crows plan for specific future tool use

The ability to plan for future events is one of the defining features of human intelligence. Whether non-human animals can plan for specific future situations remains contentious: despite a sustained research effort over the last two decades, there is still no consensus on this question. Here, we show that New Caledonian crows can use tools to plan for specific future events. Crows learned a temporal sequence where they were (a) shown a baited apparatus, (b) 5 min later given a choice of five objects and (c) 10 min later given access to the apparatus. At test, these crows were presented with one of two tool–apparatus combinations. For each combination, the crows chose the right tool for the right future task, while ignoring previously useful tools and a low-value food item. This study establishes that planning for specific future tool use can evolve via convergent evolution, given that corvids and humans shared a common ancestor over 300 million years ago, and offers a route to mapping the planning capacities of animals.     

Confidently identifying the correct K value using the ΔK method: When does K = 2?

Populations delineated based on genetic data are commonly used for wildlife conservation and management. Many studies use the program structure combined with the ΔK method to identify the most probable number of populations (K). We recently found K = 2 was identified more often when studies used ΔK compared to studies that did not. We suggested two reasons for this: hierarchical population structure leads to underestimation, or the ΔK method does not evaluate K = 1 causing an overestimation. The present contribution aims to develop a better understanding of the limits of the method using one, two and three population simulations across migration scenarios. From these simulations we identified the “best K” using model likelihood and ΔK. Our findings show that mean probability plots and ΔK are unable to resolve the correct number of populations once migration rate exceeds 0.005. We also found a strong bias towards selecting K = 2 using the ΔK method. We used these data to identify the range of values where the ΔK statistic identifies a value of K that is not well supported. Finally, using the simulations and a review of empirical data, we found that the magnitude of ΔK corresponds to the level of divergence between populations. Based on our findings, we suggest researchers should use the ΔK method cautiously; they need to report all relevant data, including the magnitude of ΔK, and an estimate of connectivity for the research community to assess whether meaningful genetic structure exists within the context of management and conservation.     

The contribution of corals to reef structural complexity in Kāne‘ohe Bay

Coral Reefs - The structural complexity of coral reefs provides important ecosystem functions, such as wave attenuation for coastal protection, surfaces for coral growth, and habitat for other...     

Mice with dysfunctional TGF-β signaling develop altered intestinal microbiome and colorectal cancer resistant to 5FU

Emerging data show a rise in colorectal cancer (CRC) incidence in young men and women that is often chemoresistant. One potential risk factor is an alteration in the microbiome. Here, we investigated the role of TGF-β signaling on the intestinal microbiome and the efficacy of chemotherapy for CRC induced by azoxymethane and dextran sodium sulfate in mice. We used two genotypes of TGF-β-signaling-deficient mice (Smad4^+/? and Smad4^+/?Sptbn1^+/?), which developed CRC with similar phenotypes and had similar alterations in the intestinal microbiome. Using these mice, we evaluated the intestinal microbiome and determined the effect of dysfunctional TGF-β signaling on the response to the chemotherapeutic agent 5-Fluoro-uracil (5FU) after induction of CRC. Using shotgun metagenomic sequencing, we determined gut microbiota composition in mice with CRC and found reduced amounts of beneficial species of Bacteroides and Parabacteroides in the mutants compared to the wild-type (WT) mice. Furthermore, the mutant mice with CRC were resistant to 5FU. Whereas the abundances of E. boltae, B.dorei, Lachnoclostridium sp., and Mordavella sp. were significantly reduced in mice with CRC, these species only recovered to basal amounts after 5FU treatment in WT mice, suggesting that the alterations in the intestinal microbiome resulting from compromised TGF-β signaling impaired the response to 5FU. These findings could have implications for inhibiting the TGF-β pathway in the treatment of CRC or other cancers.     

Bayesian modeling of skewed X inactivation in genetically diverse mice identifies a novel Xce allele associated with copy number changes

Female mammals are functional mosaics of their parental X-linked gene expression due to X chromosome inactivation (XCI). This process inactivates one copy of the X chromosome in each cell during embryogenesis and that state is maintained clonally through mitosis. In mice, the choice of which parental X chromosome remains active is determined by the X chromosome controlling element (Xce), which has been mapped to a 176-kb candidate interval. A series of functional Xce alleles has been characterized or inferred for classical inbred strains based on biased, or skewed, inactivation of the parental X chromosomes in crosses between strains. To further explore the function structure basis and location of the Xce, we measured allele-specific expression of X-linked genes in a large population of F1 females generated from Collaborative Cross (CC) strains. Using published sequence data and applying a Bayesian “Pólya urn” model of XCI skew, we report two major findings. First, inter-individual variability in XCI suggests mouse epiblasts contain on average 20–30 cells contributing to brain. Second, CC founder strain NOD/ShiLtJ has a novel and unique functional allele, Xce^g, that is the weakest in the Xce allelic series. Despite phylogenetic analysis confirming that NOD/ShiLtJ carries a haplotype almost identical to the well-characterized C57BL/6J (Xce^b), we observed unexpected patterns of XCI skewing in females carrying the NOD/ShiLtJ haplotype within the Xce. Copy number variation is common at the Xce locus and we conclude that the observed allelic series is a product of independent and recurring duplications shared between weak Xce alleles.