Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

© 2017 Hageer et al. Background. Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear.Weevaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods. This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results. The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions. Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants

Intrauterine Growth and Offspring Neurodevelopmental Traits: A Mendelian Randomization Analysis of the Norwegian Mother, Father and Child Cohort Study (MoBa)

This is the final version. Available on open access from the American Medical Association via the DOI in this recordData Sharing Statement: See Supplement 3.IMPORTANCE: Conventional epidemiological analyses have suggested that lower birth weight is associated with later neurodevelopmental difficulties; however, it is unclear whether this association is causal. OBJECTIVE: To investigate the relationship between intrauterine growth and offspring neurodevelopmental difficulties. DESIGN, SETTING, AND PARTICIPANTS: MoBa is a population-based pregnancy cohort that recruited pregnant women from June 1999 to December 2008 included approximately 114 500 children, 95 200 mothers, and 75 200 fathers. Observational associations between birth weight and neurodevelopmental difficulties were assessed with a conventional epidemiological approach. Mendelian randomization analyses were performed to investigate the potential causal association between maternal allele scores for birth weight and offspring neurodevelopmental difficulties conditional on offspring allele scores. EXPOSURES: Birth weight and maternal allele scores for birth weight (derived from genetic variants robustly associated with birth weight) were the exposures in the observational and mendelian randomization analyses, respectively. MAIN OUTCOMES AND MEASURES: Clinically relevant maternal ratings of offspring neurodevelopmental difficulties at 6 months, 18 months, 3 years, 5 years, and 8 years of age assessing language and motor difficulties, inattention and hyperactivity-impulsivity, social communication difficulties, and repetitive behaviors. RESULTS: The conventional epidemiological sample included up to 46 970 offspring, whereas the mendelian randomization sample included up to 44 134 offspring (median offspring birth year, 2005 [range, 1999-2009]; mean [SD] maternal age at birth, 30.1 [4.5] years; mean [SD] paternal age at birth, 32.5 [5.1] years). The conventional epidemiological analyses found evidence that birth weight was negatively associated with several domains at multiple offspring ages (outcome of autism-related trait scores: Social Communication Questionnaire [SCQ]-full at 3 years, β = -0.046 [95% CI, -0.057 to -0.034]; SCQ-Restricted and Repetitive Behaviors subscale at 3 years, β = -0.049 [95% CI, -0.060 to -0.038]; attention-deficit/hyperactivity disorder [ADHD] trait scores: Child Behavior Checklist [CBCL]-ADHD subscale at 18 months, β = -0.035 [95% CI, -0.045 to -0.024]; CBCL-ADHD at 3 years, β = -0.032 [95% CI, -0.043 to -0.021]; CBCL-ADHD at 5 years, β = -0.050 [95% CI, -0.064 to -0.037]; Rating Scale for Disruptive Behavior Disorders [RS-DBD]-ADHD at 8 years, β = -0.036 [95% CI, -0.049 to -0.023]; RS-DBD-Inattention at 8 years, β = -0.037 [95% CI, -0.050 to -0.024]; RS-DBD-Hyperactive-Impulsive Behavior at 8 years, β = -0.027 [95% CI, -0.040 to -0.014]; Conners Parent Rating Scale-Revised [Short Form] at 5 years, β = -0.041 [95% CI, -0.054 to -0.028]; motor scores: Ages and Stages Questionnaire-Motor Difficulty [ASQ-MOTOR] at 18 months, β = -0.025 [95% CI, -0.035 to -0.015]; ASQ-MOTOR at 3 years, β = -0.029 [95% CI, -0.040 to -0.018]; and Child Development Inventory-Gross and Fine Motor Skills at 5 years, β = -0.028 [95% CI, -0.042 to -0.015]). Mendelian randomization analyses did not find any evidence for an association between maternal allele scores for birth weight and offspring neurodevelopmental difficulties. CONCLUSIONS AND RELEVANCE: This study found that the maternal intrauterine environment, as proxied by maternal birth weight genetic variants, is unlikely to be a major determinant of offspring neurodevelopmental outcomes.Research Council of NorwayWellcome TrustNational Institute for Health and Care Research (NIHR)QUEX Accelerator grantAustralian Government Research Training ProgramAustralian Research Council (ARC)South East Norway Regional Health AuthorityNils Norman mindefondAustralian National Health and Medical Research Council (NHMRC

The implications of the United Nations Paris Agreement on climate change for globally significant biodiversity areas

Climate change is already affecting species and their distributions. Distributional range changes have occurred and are projected to intensify for many widespread plants and animals, creating associated risks to many ecosystems. Here, we estimate the climate change-related risks to the species in globally significant biodiversity conservation areas over a range of climate scenarios, assessing their value as climate refugia. In particular, we quantify the aggregated benefit of countries’ emission reduction pledges (Intended Nationally Determined Contributions and Nationally Determined Contributions under the Paris Agreement), and also of further constraining global warming to 2 °C above pre-industrial levels, against an unmitigated scenario of 4.5 °C warming. We also quantify the contribution that can be made by using smart spatial conservation planning to facilitate some levels of autonomous (i.e. natural) adaptation to climate change by dispersal. We find that without mitigation, on average 33% of each conservation area can act as climate refugium (or 18% if species are unable to disperse), whereas if warming is constrained to 2 °C, the average area of climate refuges doubles to 67% of each conservation area (or, without dispersal, more than doubles to 56% of each area). If the country pledges are fulfilled, an intermediate estimate of 47–52% (or 31–38%, without dispersal) is obtained. We conclude that the Nationally Determined Contributions alone have important but limited benefits for biodiversity conservation, with larger benefits accruing if warming is constrained to 2 °C. Greater benefits would result if warming was constrained to well below 2 °C as set out in the Paris Agreement

Multidisciplinary investigations of the diets of two post-medieval populations from London using stable isotopes and microdebris analysis

This paper presents the first multi-tissue study of diet in post-medieval London using both the stable light isotope analysis of carbon and nitrogen and analysis of microdebris in dental calculus. Dietary intake was explored over short and long timescales. Bulk bone collagen was analysed from humans from the Queen’s Chapel of the Savoy (QCS) (n = 66) and the St Barnabas/St Mary Abbots (SB) (n = 25). Incremental dentine analysis was performed on the second molar of individual QCS1123 to explore childhood dietary intake. Bulk hair samples (n = 4) were sampled from adults from QCS, and dental calculus was analysed from four other individuals using microscopy. In addition, bone collagen from a total of 46 animals from QCS (n = 11) and the additional site of Prescot Street (n = 35) was analysed, providing the first animal dietary baseline for post-medieval London. Overall, isotopic results suggest a largely C3-based terrestrial diet for both populations, with the exception of QCS1123 who exhibited values consistent with the consumption of C4 food sources throughout childhood and adulthood. The differences exhibited in δ15Ncoll across both populations likely reflect variations in diet due to social class and occupation, with individuals from SB likely representing wealthier individuals consuming larger quantities of animal and marine fish protein. Microdebris analysis results were limited but indicate the consumption of domestic cereals. This paper demonstrates the utility of a multidisciplinary approach to investigate diet across long and short timescales to further our understanding of variations in social status and mobility

Projected Loss of a Salamander Diversity Hotspot as a Consequence of Projected Global Climate Change

Background: Significant shifts in climate are considered a threat to plants and animals with significant physiological limitations and limited dispersal abilities. The southern Appalachian Mountains are a global hotspot for plethodontid salamander diversity. Plethodontids are lungless ectotherms, so their ecology is strongly governed by temperature and precipitation. Many plethodontid species in southern Appalachia exist in high elevation habitats that may be at or near their thermal maxima, and may also have limited dispersal abilities across warmer valley bottoms. Methodology/Principal Findings: We used a maximum-entropy approach (program Maxent) to model the suitable climatic habitat of 41 plethodontid salamander species inhabiting the Appalachian Highlands region (33 individual species and eight species included within two species complexes). We evaluated the relative change in suitable climatic habitat for these species in the Appalachian Highlands from the current climate to the years 2020, 2050, and 2080, using both the HADCM3 and the CGCM3 models, each under low and high CO 2 scenarios, and using two-model thresholds levels (relative suitability thresholds for determining suitable/unsuitable range), for a total of 8 scenarios per species. Conclusion/Significance: While models differed slightly, every scenario projected significant declines in suitable habitat within the Appalachian Highlands as early as 2020. Species with more southern ranges and with smaller ranges had larger projected habitat loss. Despite significant differences in projected precipitation changes to the region, projections did no

Impacts of climate change on plant diseases – opinions and trends

There has been a remarkable scientific output on the topic of how climate change is likely to affect plant diseases in the coming decades. This review addresses the need for review of this burgeoning literature by summarizing opinions of previous reviews and trends in recent studies on the impacts of climate change on plant health. Sudden Oak Death is used as an introductory case study: Californian forests could become even more susceptible to this emerging plant disease, if spring precipitations will be accompanied by warmer temperatures, although climate shifts may also affect the current synchronicity between host cambium activity and pathogen colonization rate. A summary of observed and predicted climate changes, as well as of direct effects of climate change on pathosystems, is provided. Prediction and management of climate change effects on plant health are complicated by indirect effects and the interactions with global change drivers. Uncertainty in models of plant disease development under climate change calls for a diversity of management strategies, from more participatory approaches to interdisciplinary science. Involvement of stakeholders and scientists from outside plant pathology shows the importance of trade-offs, for example in the land-sharing vs. sparing debate. Further research is needed on climate change and plant health in mountain, boreal, Mediterranean and tropical regions, with multiple climate change factors and scenarios (including our responses to it, e.g. the assisted migration of plants), in relation to endophytes, viruses and mycorrhiza, using long-term and large-scale datasets and considering various plant disease control methods

Effects of the Training Dataset Characteristics on the Performance of Nine Species Distribution Models: Application to Diabrotica virgifera virgifera

Many distribution models developed to predict the presence/absence of invasive alien species need to be fitted to a training dataset before practical use. The training dataset is characterized by the number of recorded presences/absences and by their geographical locations. The aim of this paper is to study the effect of the training dataset characteristics on model performance and to compare the relative importance of three factors influencing model predictive capability; size of training dataset, stage of the biological invasion, and choice of input variables. Nine models were assessed for their ability to predict the distribution of the western corn rootworm, Diabrotica virgifera virgifera, a major pest of corn in North America that has recently invaded Europe. Twenty-six training datasets of various sizes (from 10 to 428 presence records) corresponding to two different stages of invasion (1955 and 1980) and three sets of input bioclimatic variables (19 variables, six variables selected using information on insect biology, and three linear combinations of 19 variables derived from Principal Component Analysis) were considered. The models were fitted to each training dataset in turn and their performance was assessed using independent data from North America and Europe. The models were ranked according to the area under the Receiver Operating Characteristic curve and the likelihood ratio. Model performance was highly sensitive to the geographical area used for calibration; most of the models performed poorly when fitted to a restricted area corresponding to an early stage of the invasion. Our results also showed that Principal Component Analysis was useful in reducing the number of model input variables for the models that performed poorly with 19 input variables. DOMAIN, Environmental Distance, MAXENT, and Envelope Score were the most accurate models but all the models tested in this study led to a substantial rate of mis-classification

Nitrous oxide as a function of oxygen and archaeal gene abundance in the North Pacific

Natural Environment Research Council (NERC) (NE/E01559X/1)

Assessing population genetic structure via the maximisation of genetic distance

<p>Abstract</p> <p>Background</p> <p>The inference of the hidden structure of a population is an essential issue in population genetics. Recently, several methods have been proposed to infer population structure in population genetics.</p> <p>Methods</p> <p>In this study, a new method to infer the number of clusters and to assign individuals to the inferred populations is proposed. This approach does not make any assumption on Hardy-Weinberg and linkage equilibrium. The implemented criterion is the maximisation (via a <it>simulated annealing </it>algorithm) of the averaged genetic distance between a predefined number of clusters. The performance of this method is compared with two Bayesian approaches: STRUCTURE and BAPS, using simulated data and also a real human data set.</p> <p>Results</p> <p>The simulations show that with a reduced number of markers, BAPS overestimates the number of clusters and presents a reduced proportion of correct groupings. The accuracy of the new method is approximately the same as for STRUCTURE. Also, in Hardy-Weinberg and linkage disequilibrium cases, BAPS performs incorrectly. In these situations, STRUCTURE and the new method show an equivalent behaviour with respect to the number of inferred clusters, although the proportion of correct groupings is slightly better with the new method. Re-establishing equilibrium with the randomisation procedures improves the precision of the Bayesian approaches. All methods have a good precision for <it>F</it>_{<it>ST </it>}≥ 0.03, but only STRUCTURE estimates the correct number of clusters for <it>F</it>_{<it>ST </it>}as low as 0.01. In situations with a high number of clusters or a more complex population structure, MGD performs better than STRUCTURE and BAPS. The results for a human data set analysed with the new method are congruent with the geographical regions previously found.</p> <p>Conclusion</p> <p>This new method used to infer the hidden structure in a population, based on the maximisation of the genetic distance and not taking into consideration any assumption about Hardy-Weinberg and linkage equilibrium, performs well under different simulated scenarios and with real data. Therefore, it could be a useful tool to determine genetically homogeneous groups, especially in those situations where the number of clusters is high, with complex population structure and where Hardy-Weinberg and/or linkage equilibrium are present.</p

Soil carbon loss by experimental warming in a tropical forest

Tropical soils contain one-third of the carbon stored in soils globally1, so destabilization of soil organic matter caused by the warming predicted for tropical regions this century2 could accelerate climate change by releasing additional carbon dioxide (CO2) to the atmosphere3,4,5,6. Theory predicts that warming should cause only modest carbon loss from tropical soils relative to those at higher latitudes5,7, but there have been no warming experiments in tropical forests to test this8. Here we show that in situ experimental warming of a lowland tropical forest soil on Barro Colorado Island, Panama, caused an unexpectedly large increase in soil CO2 emissions. Two years of warming of the whole soil profile by four degrees Celsius increased CO2 emissions by 55 per cent compared to soils at ambient temperature. The additional CO2 originated from heterotrophic rather than autotrophic sources, and equated to a loss of 8.2 ± 4.2 (one standard error) tonnes of carbon per hectare per year from the breakdown of soil organic matter. During this time, we detected no acclimation of respiration rates, no thermal compensation or change in the temperature sensitivity of enzyme activities, and no change in microbial carbon-use efficiency. These results demonstrate that soil carbon in tropical forests is highly sensitive to warming, creating a potentially substantial positive feedback to climate chang