Insights from modeling studies on how climate change affects invasive alien species geography

Climate change and biological invasions are threatening biodiversity and ecosystem services worldwide. It has now been widely acknowledged that climate change will affect biological invasions. A large number of studies have investigated predicted shifts and other changes in the geographic ranges of invasive alien species related to climate change using modeling approaches. Yet these studies have provided contradictory evidence, and no consensus has been reached. We conducted a systematic review of 423 modeling case studies included in 71 publications that have examined the predicted effects of climate change on those species. We differentiate the approaches used in these studies and synthesize their main results. Our results reaffirm the major role of climate change as a driver of invasive alien species distribution in the future. We found biases in the literature both regarding the taxa, toward plants and invertebrates, and the areas of the planet investigated. Despite these biases, we found for the plants and vertebrates studied that climate change will more frequently contribute to a decrease in species range size than an increase in the overall area occupied. This is largely due to oceans preventing terrestrial invaders from spreading poleward. In contrast, we found that the ranges of invertebrates and pathogens studied are more likely to increase following climate change. An important caveat to these findings is that researchers have rarely considered the effects of climate change on transport, introduction success, or the resulting impacts. We recommend closing these research gaps, and propose additional avenues for future investigations, as well as opportunities and challenges for managing invasions under climate change.     

Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi‐model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi‐model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.     

Knockout of 5‐lipoxygenase prevents dexamethasone‐induced tau pathology in 3xTg mice

Emerging evidence suggests that dysregulation stress hormones, such as glucocorticoids, in aged persons put them at a higher risk to develop Alzheimer's disease (AD). However, the mechanisms underlying such vulnerability remain to be unraveled. Pharmacologic inhibition of 5‐lipoxygenase (5LO), an active player in AD pathogenesis whose protein level increases with aging in the human, has been shown to blunt glucocorticoid‐mediated amyloid β (Ab) formation in vitro. In this article, we investigated the role of this pathway in modulating the development of the corticosteroid‐dependent AD‐like phenotype in the triple transgenic mice (3xTg). Dexamethasone was administered for 1 week to 3xTg or 3xTg genetically deficient for 5LO (3xTg/5LO^?/?) mice, and its effect on memory, amyloid‐β and tau levels, and metabolism assessed. At the end of the treatment, we observed that dexamethasone did not induce changes in behavior. Compared with controls, treated mice did not show significant alterations in brain soluble Aβ levels. While total tau protein levels were unmodified in all groups, we found that dexamethasone significantly increased tau phosphorylation at S396, as recognized by the antibody PHF‐13, which was specifically associated with an increase in the GSK3β activity. Additionally, dexamethasone‐treated mice had a significant increase in the tau insoluble fraction and reduction in the postsynaptic protein PDS‐95. By contrast, these modifications were blunted in the 3xTg/5LO^?/? mice. Our findings highlight the functional role that 5LO plays in stress‐induced AD tau pathology and support the hypothesis that pharmacologic inhibition of this enzyme could be a useful tool for individuals with this risk factor.     

Finite elements/Taguchi method based procedure for the identification of the geometrical parameters significantly affecting the biomechanical behavior of a lumbar disc

The aim of this work is to show a quick and simple procedure able to identify the geometrical parameters of the intervertebral disc that strongly affect the behavior of the FEM model. First, we allocated a selection criterion for the minimum number of geometrical parameters that describe, with a good degree of approximation, a healthy human vertebra. Next, we carried out a sensitivity analysis using the ‘Taguchi orthogonal array’ to arrive at a quick identification of the parameters that strongly affect the behavior of the Fem model.     

Transcriptional landscape of alternative splicing during peripheral nerve injury

Alternative splicing (AS) regulates a variety of biological activities in numerous tissues and organs, including the nervous system. However, the existence and specific roles of AS events during peripheral nerve repair and regeneration remain largely undetermined. In the current study, by mapping splice-crossing sequence reads, we identified AS events and relevant spliced genes in rat sciatic nerve stumps following sciatic nerve crush. AS-related genes at 1, 4, 7, and 14 days post nerve crush were compared with those at 0 day to discover alternatively spliced genes induced by sciatic nerve crush. These injury-induced alternatively spliced genes were then categorized to diseases and biological functions, genetic networks, and canonical signaling pathways. Bioinformatic analysis indicated that these alternatively spliced genes were mainly correlated to immune response, cellular growth, and cellular function maintenance. Our study elucidated AS events following peripheral nerve injury and might help deepen our understanding of the molecular mechanisms underlying peripheral nerve regeneration.     

MolMod – an open access database of force fields for molecular simulations of fluids

The MolMod database is presented, which is openly accessible at http://molmod.boltzmann-zuse.de and contains intermolecular force fields for over 150 pure fluids at present. It was developed and is maintained by the Boltzmann-Zuse Society for Computational Molecular Engineering (BZS). The set of molecular models in the MolMod database provides a coherent framework for molecular simulations of fluids. The molecular models in the MolMod database consist of Lennard-Jones interaction sites, point charges, and point dipoles and quadrupoles, which can be equivalently represented by multiple point charges. The force fields can be exported as input files for the simulation programmes ms2 and ls1 mardyn, GROMACS, and LAMMPS. To characterise the semantics associated with the numerical database content, a force field nomenclature is introduced that can also be used in other contexts in materials modelling at the atomistic and mesoscopic levels. The models of the pure substances that are included in the database were generally optimised such as to yield good representations of experimental data of the vapour–liquid equilibrium with a focus on the vapour pressure and the saturated liquid density. In many cases, the models also yield good predictions of caloric, transport, and interfacial properties of the pure fluids. For all models, references to the original works in which they were developed are provided. The models can be used straightforwardly for predictions of properties of fluid mixtures using established combination rules. Input errors are a major source of errors in simulations. The MolMod database contributes to reducing such errors.     

The MLA6 coiled-coil, NBS-LRR protein confers AvrMla6-dependent resistance specificity to Blumeria graminis f. sp. hordei in barley and wheat

The barley Mla locus confers multiple resistance specificities to the obligate fungal biotroph, Blumeria (= Erysiphe) graminis f. sp. hordei. Interspersed within the 240 kb Mla complex are three families of resistance gene homologs (RGHs). Probes from the Mla-RGH1 family were used to identify three classes of cDNAs. The first class is predicted to encode a full-length CC-NBS-LRR protein and the other two classes contain alternatively spliced, truncated variants. Utilizing a cosmid that contains a gene corresponding to the full-length candidate cDNA, two single-cell expression assays were used to demonstrate complementation of AvrMla6-dependent, resistance specificity to B. graminis in barley and wheat. The first of these assays was also used to substantiate previous genetic data that the Mla6 allele requires the signaling pathway component, Rar1, for function. Computational analysis of MLA6 and the Rar1-independent, MLA1 protein reveals 91.2% identity and shows that the LRR domain is subject to diversifying selection. Our findings demonstrate that highly related CC-NBS-LRR proteins encoded by alleles of the Mla locus can dictate similar powdery mildew resistance phenotypes yet still require distinct downstream signaling components.     

Geographic range determinants of two commercially important marine molluscs

Aim We modelled the spatial abundance patterns of two abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814) inhabiting inshore rocky reefs to better understand the importance of current sea surface temperature (SST) (among other predictors) and, ultimately, the effect of future climate change, on marine molluscs. Location Southern Australia. Methods We used an ensemble species distribution modelling approach that combined likelihood‐based generalized linear models and boosted regression trees. For each modelling technique, a two‐step procedure was used to predict: (1) the current probability of presence, followed by (2) current abundance conditional on presence. The resulting models were validated using an independent, spatially explicit dataset of abalone abundance patterns in Victoria. Results For both species, the presence of reef was the main driver of abalone occurrence, while SST was the main driver of spatial abundance patterns. Predictive maps at c. 1‐km resolution showed maximal abundance on shallow coastal reefs characterized by mild winter SSTs for both species. Main conclusions Sea surface temperature was a major driver of abundance patterns for both abalone species, and the resulting ensemble models were used to build fine‐resolution predictive range maps (c. 1 km) that incorporate measures of habitat suitability and quality in support of resource management. By integrating this output with structured spatial population models, a more robust understanding of the potential impacts of threatening human processes such as climate change can be established.