Drivers of future alien species impacts: An expert‐based assessment

Understanding the likely future impacts of biological invasions is crucial yet highly challenging given the multiple relevant environmental, socio‐economic and societal contexts and drivers. In the absence of quantitative models, methods based on expert knowledge are the best option for assessing future invasion trajectories. Here, we present an expert assessment of the drivers of potential alien species impacts under contrasting scenarios and socioecological contexts through the mid‐21st century. Based on responses from 36 experts in biological invasions, moderate (20%–30%) increases in invasions, compared to the current conditions, are expected to cause major impacts on biodiversity in most socioecological contexts. Three main drivers of biological invasions—transport, climate change and socio‐economic change—were predicted to significantly affect future impacts of alien species on biodiversity even under a best‐case scenario. Other drivers (e.g. human demography and migration in tropical and subtropical regions) were also of high importance in specific global contexts (e.g. for individual taxonomic groups or biomes). We show that some best‐case scenarios can substantially reduce potential future impacts of biological invasions. However, rapid and comprehensive actions are necessary to use this potential and achieve the goals of the Post‐2020 Framework of the Convention on Biological Diversity.     

Climate‐induced changes in the distribution of freshwater fish: observed and predicted trends

1. Climate change could be one of the main threats faced by aquatic ecosystems and freshwater biodiversity. Improved understanding, monitoring and forecasting of its effects are thus crucial for researchers, policy makers and biodiversity managers. 2. Here, we provide a review and some meta‐analyses of the literature reporting both observed and predicted climate‐induced effects on the distribution of freshwater fish. After reviewing three decades of research, we summarise how methods in assessing the effects of climate change have evolved, and whether current knowledge is geographically or taxonomically biased. We conducted multispecies qualitative and quantitative analyses to find out whether the observed responses of freshwater fish to recent changes in climate are consistent with those predicted under future climate scenarios. 3. We highlight the fact that, in recent years, freshwater fish distributions have already been affected by contemporary climate change in ways consistent with anticipated responses under future climate change scenarios: the range of most cold‐water species could be reduced or shift to higher altitude or latitude, whereas that of cool‐ and warm‐water species could expand or contract. 4. Most evidence about the effects of climate change is underpinned by the large number of studies devoted to cold‐water fish species (mainly salmonids). Our knowledge is still incomplete, however, particularly due to taxonomic and geographic biases. 5. Observed and expected responses are well correlated among families, suggesting that model predictions are supported by empirical evidence. The observed effects are of greater magnitude and show higher variability than the predicted effects, however, indicating that other drivers of changes may be interacting with climate and seriously affecting freshwater fish. 6. Finally, we suggest avenues of research required to address current gaps in what we know about the climate‐induced effects on freshwater fish distribution, including (i) the need for more long‐term data analyses, (ii) the assessment of climate‐induced effects at higher levels of organisation (e.g. assemblages), (iii) methodological improvements (e.g. accounting for uncertainty among projections and species’ dispersal abilities, combining both distributional and empirical approaches and including multiple non‐climatic stressors) and (iv) systematic confrontation of observed versus predicted effects across multi‐species assemblages and at several levels of biological organisation (i.e. populations and assemblages).     

Model‐based demographic inference of introgression history in European whitefish species pairs'

Parallel phenotypic differentiation is generally attributed to parallel adaptive divergence as an evolutionary response to similar environmental contrasts. Such parallelism may actually originate from several evolutionary scenarios ranging from repeated parallel divergence caused by divergent selection to a unique divergence event followed by gene flow. Reconstructing the evolutionary history underlying parallel phenotypic differentiation is thus fundamental to understand the relative contribution of demography and selection on genomic divergence during speciation. In this study, we investigate the divergence history of replicate European whitefish (Coregonus lavaretus), limnetic and benthic species pairs from two lakes in Norway and two lakes in Switzerland. Demographic models accounting for semi‐permeability and linked selection were fitted to the unfolded joint allele frequency spectrum built from genome‐wide SNPs and compared to each other in each species pair. We found strong support for a model of asymmetrical post‐glacial secondary contact between glacial lineages in all four lakes. Moreover, our results suggest that heterogeneous genomic differentiation has been shaped by the joint action of linked selection accelerating lineage sorting during allopatry, and heterogeneous migration eroding divergence at different rates along the genome following secondary contact. Our analyses reveal how the interplay between demography, selection and historical contingency has influenced the levels of diversity observed in previous whitefish phylogeographic studies. This study thus provides new insights into the historical demographic and selective processes that shaped the divergence associated with ecological speciation in European whitefish.     

Transferability of trait‐based species distribution models

The need for reliable prediction of species distributions dependent upon traits has been hindered by a lack of model transferability testing. We tested the predictive capacity of trait‐SDMs by fitting hierarchical generalised linear models with three trait and four environmental predictors for 20 eucalypt taxa in a reference region. We used these models to predict occurrence for a much larger set of taxa and target areas (82 taxa across 18 target regions) in south‐eastern Australia. Median predictive performance for new species in target regions was 0.65 (area under receiver operating curve) and 1.24 times random (area under precision recall curve). Prediction in target regions did not worsen with increasing geographic, environmental or community compositional distance from the reference region, and was improved with reliable trait–environment relationships. Transfer testing also identified trait–environment relationships that did not transfer. These results give confidence that traits and transfer testing can assist in the hard problem of predicting environmental responses for new species, environmental conditions and regions.     

Bio‐ORACLE: a global environmental dataset for marine species distribution modelling

Aim The oceans harbour a great diversity of organisms whose distribution and ecological preferences are often poorly understood. Species distribution modelling (SDM) could improve our knowledge and inform marine ecosystem management and conservation. Although marine environmental data are available from various sources, there are currently no user‐friendly, high‐resolution global datasets designed for SDM applications. This study aims to fill this gap by assembling a comprehensive, uniform, high‐resolution and readily usable package of global environmental rasters. Location Global, marine. Methods We compiled global coverage data, e.g. satellite‐based and in situ measured data, representing various aspects of the marine environment relevant for species distributions. Rasters were assembled at a resolution of 5 arcmin (c. 9.2 km) and a uniform landmask was applied. The utility of the dataset was evaluated by maximum entropy SDM of the invasive seaweed Codium fragile ssp. fragile. Results We present Bio‐ORACLE (ocean rasters for analysis of climate and environment), a global dataset consisting of 23 geophysical, biotic and climate rasters. This user‐friendly data package for marine species distribution modelling is available for download at http://www.bio‐oracle.ugent.be . The high predictive power of the distribution model of C. fragile ssp. fragile clearly illustrates the potential of the data package for SDM of shallow‐water marine organisms. Main conclusions The availability of this global environmental data package has the potential to stimulate marine SDM. The high predictive success of the presence‐only model of a notorious invasive seaweed shows that the information contained in Bio‐ORACLE can be informative about marine distributions and permits building highly accurate species distribution models.     

Phylogeographic incongruence of codistributed amphibian species based on small differences in geographic distribution

Codistributed species may display either congruent phylogeographic patterns, indicating similar responses to a series of shared climatic and geologic events, or discordant patterns, indicating independent responses. This study compares the phylogeographic patterns of two similarly distributed salamander species within the Pacific Northwest of the United States: Cope's giant salamander (Dicamptodon copei) and Van Dyke's salamander (Plethodon vandykei). Previous studies of P. vandykei support two reciprocally monophyletic lineages corresponding to coastal populations, located from the Olympic Mountains to the mouth of the Columbia River, and inland populations within the Cascade Mountains. We hypothesized that D. copei would have a congruent phylogeographic pattern to P. vandykei due to similarity in distribution and dependence upon similar stream and stream-side habitats. We test this hypothesis by estimating the phylogeny of D. copei using approximately 1800bp of mitochondrial DNA and comparing it to that of P. vandykei. Sympatric populations of D. copei and of P. vandykei display an identical phylogeographic pattern, suggesting similar responses within their shared distribution. Populations of D. copei occurring outside the range of P. vandykei displayed high levels of genetic divergence from those sympatric to P. vandykei. Overall, phylogeographic patterns between the two species were ultimately incongruent due to the high divergence of these allopatric populations. These results provide an example of codistributed species displaying overall incongruent phylogeographic patterns while simultaneously displaying congruent patterns within portions of their shared geographic distribution. This pattern demonstrates that a simple dichotomy of congruent and incongruent phylogeographic patterns of codistributed species may be too simplistic and that more complex intermediate patterns can result even from minor differences in species' ranges.     

Bioluminescence‐based assays for assessing eco‐ and genotoxicity of airborne emissions

Ecotoxicological tests (or bioassays) are controlled, reproducible tests where ecological responses are determined quantitatively. Due to numerous difficulties arising when airborne emissions are sampled, relatively few ecotoxicological assays have been applied. Aerosol particles are generally collected on a filter, which limits the quantity of the sample, thus also limiting the range of available test organisms. Bacterial bioassays require low sample quantity, and make a good choice when eco‐ or genotoxicity of the sample are to be determined. Of bacterial assays, the bioluminescence inhibition test has been proven applicable for assessing toxicity of airborne contaminants. In this paper diverse test protocols and their modifications are reviewed. Copyright © 2016 John Wiley & Sons, Ltd.     

Formalized reproduction of an expert‐based phytosociological classification: A case study of subalpine tall‐forb vegetation

Abstract. Delimitation of vegetation units in phytosociology is traditionally based on expert knowledge. Applications of expert‐based classifications are often inconsistent because criteria for assigning relevés to vegetation units are seldom given explicitly. Still, there is, e.g. in nature conservation, an increasing need for a consistent application of vegetation classification using computer expert systems for unit identification. We propose a procedure for formalized reproduction of an expert‐based vegetation classification, which is applicable to large phytosociological data sets. This procedure combines Bruelheide's Cocktail method with a similarity‐based assignment of relevés to constancy columns of a vegetation table. As a test of this method we attempt to reproduce the expert‐based phytosociological classification of subalpine tall‐forb vegetation of the Czech Republic which has been made by combination of expert judgement and stepwise numerical classification of 718 relevés by TWINSPAN. Applying the Cocktail method to a geographically stratified data set of 21794 relevés of all Czech vegetation types, we defined groups of species with the statistical tendency of joint occurrences in vegetation. Combinations of 12 of these species groups by logical operators AND, OR and AND NOT yielded formal definitions of 14 of 16 associations which had been accepted in the expert‐based classification. Application of these formal definitions to the original data set of 718 relevés resulted in an assignment of 376 relevés to the associations. This assignment agreed well with the original expert‐based classification. Relevés that remained un‐assigned because they had not met the requirements of any of the formal definitions, were subsequently assigned to the associations by calculating similarity to relevé groups that had already been assigned to the associations. A new index, based on frequency and fidelity, was proposed for calculating similarity. The agreement with the expert‐based classification achieved by the formal definitions was still improved after applying the similarity‐based assignment. Results indicate that the expert‐based classification can be successfully formalized and converted into a computer expert system.     

Model‐based investigation of intracellular processes determining antibody Fc‐glycosylation under mild hypothermia

Despite the positive effects of mild hypothermic conditions on monoclonal antibody (mAb) productivity (q_mAb) during mammalian cell culture, the impact of reduced culture temperature on mAb Fc‐glycosylation and the mechanism behind changes in the glycan composition are not fully established. The lack of knowledge about the regulation of dynamic intracellular processes under mild hypothermia restricts bioprocess optimization. To address this issue, a mathematical model that quantitatively describes Chinese hamster ovary (CHO) cell behavior and metabolism, mAb synthesis and mAb N‐linked glycosylation profile before and after the induction of mild hypothermia is constructed. Results from this study show that the model is capable of representing experimental results well in all of the aspects mentioned above, including the N‐linked glycosylation profile of mAb produced under mild hypothermia. Most importantly, comparison between model simulation results for different culture temperatures suggests the reduced rates of nucleotide sugar donor production and galactosyltransferase (GalT) expression to be critical contributing factors that determine the variation in Fc‐glycan profiles between physiological and mild hypothermic conditions in stable CHO transfectants. This is then confirmed using experimental measurements of GalT expression levels, thereby closing the loop between the experimental and the computational system. The identification of bottlenecks within CHO cell metabolism under mild hypothermic conditions will aid bioprocess optimization, for example, by tailoring feeding strategies to improve NSD production, or manipulating the expression of specific glycosyltransferases through cell line engineering. Biotechnol. Bioeng. 2017;114: 1570–1582. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals Inc.     

Controlling the time evolution of mAb N‐linked glycosylation ‐ Part II: Model‐based predictions

N‐linked glycosylation is known to be a crucial factor for the therapeutic efficacy and safety of monoclonal antibodies (mAbs) and many other glycoproteins. The nontemplate process of glycosylation is influenced by external factors which have to be tightly controlled during the manufacturing process. In order to describe and predict mAb N‐linked glycosylation patterns in a CHO‐S cell fed‐batch process, an existing dynamic mathematical model has been refined and coupled to an unstructured metabolic model. High‐throughput cell culture experiments carried out in miniaturized bioreactors in combination with intracellular measurements of nucleotide sugars were used to tune the parameter configuration of the coupled models as a function of extracellular pH, manganese and galactose addition. The proposed modeling framework is able to predict the time evolution of N‐linked glycosylation patterns during a fed‐batch process as a function of time as well as the manipulated variables. A constant and varying mAb N‐linked glycosylation pattern throughout the culture were chosen to demonstrate the predictive capability of the modeling framework, which is able to quantify the interconnected influence of media components and cell culture conditions. Such a model‐based evaluation of feeding regimes using high‐throughput tools and mathematical models gives rise to a more rational way to control and design cell culture processes with defined glycosylation patterns. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1135–1148, 2016     

Model‐Based Quality Assessment and Base‐Calling for Second‐Generation Sequencing Data

Summary Second‐generation sequencing (sec‐gen) technology can sequence millions of short fragments of DNA in parallel, making it capable of assembling complex genomes for a small fraction of the price and time of previous technologies. In fact, a recently formed international consortium, the 1000 Genomes Project, plans to fully sequence the genomes of approximately 1200 people. The prospect of comparative analysis at the sequence level of a large number of samples across multiple populations may be achieved within the next five years. These data present unprecedented challenges in statistical analysis. For instance, analysis operates on millions of short nucleotide sequences, or reads—strings of A,C,G, or T's, between 30 and 100 characters long—which are the result of complex processing of noisy continuous fluorescence intensity measurements known as base‐calling. The complexity of the base‐calling discretization process results in reads of widely varying quality within and across sequence samples. This variation in processing quality results in infrequent but systematic errors that we have found to mislead downstream analysis of the discretized sequence read data. For instance, a central goal of the 1000 Genomes Project is to quantify across‐sample variation at the single nucleotide level. At this resolution, small error rates in sequencing prove significant, especially for rare variants. Sec‐gen sequencing is a relatively new technology for which potential biases and sources of obscuring variation are not yet fully understood. Therefore, modeling and quantifying the uncertainty inherent in the generation of sequence reads is of utmost importance. In this article, we present a simple model to capture uncertainty arising in the base‐calling procedure of the Illumina/Solexa GA platform. Model parameters have a straightforward interpretation in terms of the chemistry of base‐calling allowing for informative and easily interpretable metrics that capture the variability in sequencing quality. Our model provides these informative estimates readily usable in quality assessment tools while significantly improving base‐calling performance.     

Mapping mountain vegetation using species distribution modeling,image‐based texture analysis,and object‐based classification

Objective: The objective of this study was to map vegetation composition across a 24 000 ha watershed. Location: The study was conducted on the western slope of the Sierra Nevada mountain range of California, USA. Methods: Automated image segmentation was used to delineate image objects representing vegetation patches of similar physiognomy and structure. Image objects were classified using a decision tree and data sources extracted from individual species distribution models, Landsat spectral data, and life form cover estimates derived from aerial image‐based texture variables. Results: A total of 12 plant communities were mapped with an overall accuracy of 75% and a χ‐value of 0.69. Species distribution model inputs improved map accuracy by approximately 15% over maps derived solely from image data. Automated mapping of existing vegetation distributions, based solely on predictive distribution model results, proved to be more accurate than mapping based on Landsat data, and equivalent in accuracy to mapping based on all image data sources. Conclusions: Results highlight the importance of terrain, edaphic, and bioclimatic variables when mapping vegetation communities in complex terrain. Mapping errors stemmed from the lack of spectral discernability between vegetation classes, and the inability to account for the confounding effects of land use history and disturbance within a static distribution modeling framework.     

Species distribution modeling and machine learning in assessing the potential distribution of freshwater zooplankton in Northern Italy

Species distribution models (SDM's) are powerful tools used to describe species suitable habitats and spatial occurrences and many statistical methods and algorithms are available to model the spatial distribution of a target species. Here we explore a species distribution model framework combined with machine learning algorithms to describe the distribution of two freshwater zooplankton species Daphnia longispina (Cladocera) and Eucyclops serrulatus (Copepods) in a system of 283 shallow and ephemeral freshwater habitats in the Northern Italian Appennines. For each species, we model the habitat suitability by comparing one regression-based model, one generalized linear model (GLM) and two machine learning algorithms: random forest (RF) and artificial neural network (ANN) with one hidden layer. We used a total of 27 predictor variables. The modeling framework was used considering a scenario of future climate change in order to evaluate potential shifts in spatial distribution of the zooplankton species. For both species, the supervised machine learning algorthn (ANN) produced the highest mean values for all the performance metrics. For D. longispina and E. serrulatus, the two most important variables ranked by the shap analysis and global sensitivity and uncertainty analysis (GSUA) were temperature seasonality and precipitation of the warmest quarter. Both species, in a future climatic change scenario, are expected to shift their distribution mainly toward lower northern altitudes with an overall expansion of 7% with respect to the past/present climatic conditions. However, the spatial expansion of D. longispina and E. serrulatus was qualitatively different. In agricultural and natural areas, the expansion of E. serrulatus was greater than that of D. longispina but, in natural areas, the expansion of E. serrulatus was counterbalanced by a greater spatial contraction than that of D. longispina. As hypothesized, direct and indirect anthropogenic pressures may affect the predicted potential shift and expansion of the zooplankton species.