Unique genetic variation at a species' rear edge is under threat from global climate change

Global climate change is having a significant effect on the distributions of a wide variety of species, causing both range shifts and population extinctions. To date, however, no consensus has emerged on how these processes will affect the range-wide genetic diversity of impacted species. It has been suggested that species that recolonized from low-latitude refugia might harbour high levels of genetic variation in rear-edge populations, and that loss of these populations could cause a disproportionately large reduction in overall genetic diversity in such taxa. In the present study, we have examined the distribution of genetic diversity across the range of the seaweed Chondrus crispus, a species that has exhibited a northward shift in its southern limit in Europe over the last 40 years. Analysis of 19 populations from both sides of the North Atlantic using mitochondrial single nucleotide polymorphisms (SNPs), sequence data from two singlecopy nuclear regions and allelic variation at eight microsatellite loci revealed unique genetic variation for all marker classes in the rear-edge populations in Iberia, but not in the rear-edge populations in North America. Palaeodistribution modelling and statistical testing of alternative phylogeographic scenarios indicate that the unique genetic diversity in Iberian populations is a result not only of persistence in the region during the last glacial maximum, but also because this refugium did not contribute substantially to the recolonization of Europe after the retreat of the ice. Consequently, loss of these rear-edge populations as a result of ongoing climate change will have a major effect on the overall genetic diversity of the species, particularly in Europe, and this could compromise the adaptive potential of the species as a whole in the face of future global warming.

Testing the timing of radiocarbon-dated events between proxy archives

For interpreting past changes on a regional or global scale, the timings of proxy-inferred events are usually aligned with data from other locations. However, too often chronological uncertainties are ignored in proxy diagrams and multisite comparisons, making it possible for researchers to fall into the trap of sucking separate events into one illusionary event (or vice versa). Here we largely solve this "suck in and smear syndrome" for radiocarbon (14C) dated sequences. In a Bayesian framework, millions of plausible age-models are constructed to quantify the chronological uncertainties within and between proxy archives. We test the technique on replicated high-resolution 14C-dated peat cores deposited during the "Little Ice Age" (c. AD 1400-1900), a period characterized by abrupt climate changes and severe 14C calibration problems. Owing to internal variability in proxy data and uncertainties in age-models, these (and possibly many more) archives are not consistent in recording decadal climate change. Through explicit statistical tests of palaeoenvironmental hypotheses, we can move forward to systematic interpretations of proxy data. However, chronological uncertainties of non-annually resolved palaeoclimate records are too large for answering decadal timescale questions.

Testing the stability of the benefit transfer function for discrete choice contingent valuation data

This paper examines the stability of the benefit transfer function across 42 recreational forests in the British Isles. A working definition of reliable function transfer is Put forward, and a suitable statistical test is provided. A novel split sample method is used to test the sensitivity of the models' log-likelihood values to the removal of contingent valuation (CV) responses collected at individual forest sites, We find that a stable function improves Our measure of transfer reliability, but not by much. We conclude that, in empirical Studies on transferability, considerations of function stability are secondary to the availability and quality of site attribute data. Modellers' can study the advantages of transfer function stability vis-a-vis the value of additional information on recreation site attributes. (c) 2008 Elsevier GmbH. All rights reserved.

TESTING FOR STATIONARITY IN HETEROGENEOUS PANEL DATA IN THE CASE OF MODEL MISSPECIFICATION

This paper investigates the performance of the tests proposed by Hadri and by Hadri and Larsson for testing for stationarity in heterogeneous panel data under model misspecification. The panel tests are based on the well known KPSS test (cf. Kwiatkowski et al.) which considers two models: stationarity around a deterministic level and stationarity around a deterministic trend. There is no study, as far as we know, on the statistical properties of the test when the wrong model is used. We also consider the case of the simultaneous presence of the two types of models in a panel. We employ two asymptotics: joint asymptotic, T, N -> infinity simultaneously, and T fixed and N allowed to grow indefinitely. We use Monte Carlo experiments to investigate the effects of misspecification in sample sizes usually used in practice. The results indicate that the assumption that T is fixed rather than asymptotic leads to tests that have less size distortions, particularly for relatively small T with large N panels (micro-panels) than the tests derived under the joint asymptotics. We also find that choosing a deterministic trend when a deterministic level is true does not significantly affect the properties of the test. But, choosing a deterministic level when a deterministic trend is true leads to extreme over-rejections. Therefore, when unsure about which model has generated the data, it is suggested to use the model with a trend. We also propose a new statistic for testing for stationarity in mixed panel data where the mixture is known. The performance of this new test is very good for both cases of T asymptotic and T fixed. The statistic for T asymptotic is slightly undersized when T is very small (

Association Testing of Previously Reported Variants in a Large Case-Control Meta-analysis of Diabetic Nephropathy

We formed the GEnetics of Nephropathy–an International Effort (GENIE) consortium to examine previously reported genetic associations with diabetic nephropathy (DN) in type 1 diabetes. GENIE consists of 6,366 similarly ascertained participants of European ancestry with type 1 diabetes, with and without DN, from the All Ireland-Warren 3-Genetics of Kidneys in Diabetes U.K. and Republic of Ireland (U.K.-R.O.I.) collection and the Finnish Diabetic Nephropathy Study (FinnDiane), combined with reanalyzed data from the Genetics of Kidneys in Diabetes U.S. Study (U.S. GoKinD). We found little evidence for the association of the EPO promoter polymorphism, rs161740, with the combined phenotype of proliferative retinopathy and end-stage renal disease in U.K.-R.O.I. (odds ratio [OR] 1.14, P = 0.19) or FinnDiane (OR 1.06, P = 0.60). However, a fixed-effects meta-analysis that included the previously reported cohorts retained a genome-wide significant association with that phenotype (OR 1.31, P = 2 × 10-9). An expanded investigation of the ELMO1 locus and genetic regions reported to be associated with DN in the U.S. GoKinD yielded only nominal statistical significance for these loci. Finally, top candidates identified in a recent meta-analysis failed to reach genome-wide significance. In conclusion, we were unable to replicate most of the previously reported genetic associations for DN, and significance for the EPO promoter association was attenuated.

Testing metabolic scaling theory using intraspecific allometries in Antarctic microarthropods

Quantitative scaling relationships among body mass, temperature and metabolic rate of organisms are still controversial, while resolution may be further complicated through the use of different and possibly inappropriate approaches to statistical analysis. We propose the application of a modelling strategy based on the theoretical approach of Akaike's information criteria and non-linear model fitting (nlm). Accordingly, we collated and modelled available data at intraspecific level on the individual standard metabolic rate of Antarctic microarthropods as a function of body mass (M), temperature (T), species identity (S) and high rank taxa to which species belong (G) and tested predictions from metabolic scaling theory (mass-metabolism allometric exponent b = 0.75, activation energy range 0.2-1.2 eV). We also performed allometric analysis based on logarithmic transformations (lm). Conclusions from lm and nlm approaches were different. Best-supported models from lm incorporated T, M and S. The estimates of the allometric scaling exponent linking body mass and metabolic rate resulted in a value of 0.696 +/- 0.105 (mean +/- 95% CI). In contrast, the four best-supported nlm models suggested that both the scaling exponent and activation energy significantly vary across the high rank taxa (Collembola, Cryptostigmata, Mesostigmata and Prostigmata) to which species belong, with mean values of b ranging from about 0.6 to 0.8. We therefore reached two conclusions: 1, published analyses of arthropod metabolism based on logarithmic data may be biased by data transformation; 2, non-linear models applied to Antarctic microarthropod metabolic rate suggest that intraspecific scaling of standard metabolic rate in Antarctic microarthropods is highly variable and can be characterised by scaling exponents that greatly vary within taxa, which may have biased previous interspecific comparisons that neglected intraspecific variability.

The BRUTUS Automatic Cryptanalytic Framework: Testing CAESAR Authenticated Encryption Candidates for Weaknesses

This report summarizes our results from security analysis covering all 57 competitions for authenticated encryption: security, applicability, and robustness (CAESAR) first-round candidates and over 210 implementations. We have manually identified security issues with three candidates, two of which are more serious, and these ciphers have been withdrawn from the competition. We have developed a testing framework, BRUTUS, to facilitate automatic detection of simple security lapses and susceptible statistical structures across all ciphers. From this testing, we have security usage notes on four submissions and statistical notes on a further four. We highlight that some of the CAESAR algorithms pose an elevated risk if employed in real-life protocols due to a class of adaptive-chosen-plaintext attacks. Although authenticated encryption with associated data are often defined (and are best used) as discrete primitives that authenticate and transmit only complete messages, in practice, these algorithms are easily implemented in a fashion that outputs observable ciphertext data when the algorithm has not received all of the (attacker-controlled) plaintext. For an implementor, this strategy appears to offer seemingly harmless and compliant storage and latency advantages. If the algorithm uses the same state for secret keying information, encryption, and integrity protection, and the internal mixing permutation is not cryptographically strong, an attacker can exploit the ciphertext–plaintext feedback loop to reveal secret state information or even keying material. We conclude that the main advantages of exhaustive, automated cryptanalysis are that it acts as a very necessary sanity check for implementations and gives the cryptanalyst insights that can be used to focus more specific attack methods on given candidates.