4 ◦C and beyond: what did this mean for biodiversity in the past?

How do the predicted climatic changes (IPCC, 2007) for the next century compare in magnitude and rate to those that Earth has previously encountered? Are there comparable intervals of rapid rates of temperature change, sea-level rise and levels of atmospheric CO2 that can be used as analogues to assess possible biotic responses to future change? Or are we stepping into the great unknown? This perspective article focuses on intervals in time in the fossil record when atmospheric CO2 concentrations increased up to 1200 ppmv, temperatures in mid- to high-latitudes increased by greater than 4 ?C within 60 years, and sea levels rose by up to 3 m higher than present. For these intervals in time, case studies of past biotic responses are presented to demonstrate the scale and impact of the magnitude and rate of such climate changes on biodiversity. We argue that although the underlying mechanisms responsible for these past changes in climate were very different (i.e. natural processes rather than anthropogenic), the rates and magnitude of climate change are similar to those predicted for the future and therefore potentially relevant to understanding future biotic response. What emerges from these past records is evidence for rapid community turnover, migrations, development of novel ecosystems and thresholds from one stable ecosystem state to another, but there is very little evidence for broad-scale extinctions due to a warming world. Based on this evidence from the fossil record, we make four recommendations for future climate-change integrated conservation strategies.

Laplace transforms of probability distributions and their inversions are easy on logarithmic scales

It is shown that, when expressing arguments in terms of their logarithms, the Laplace transform of a function is related to the antiderivative of this function by a simple convolution. This allows efficient numerical computations of moment generating functions of positive random variables and their inversion. The application of the method is straightforward, apart from the necessity to implement it using high-precision arithmetics. In numerical examples the approach is demonstrated to be particularly useful for distributions with heavy tails, Such as lognormal, Weibull, or Pareto distributions, which are otherwise difficult to handle. The computational efficiency compared to other methods is demonstrated for an M/G/1 queueing problem.

Genomic instability in Chinese hamster cells after exposure to X rays or alpha particles of different mean linear energy transfer

Evidence has accumulated that radiation induces a transmissible persistent destabilization of the genome, which mag. result in effects arising in the progeny of irradiated but surviving cells. An enhanced death rate among the progeny of cells surviving irradiation persists for many generations in the form of a reduced plating efficiency. Such delayed reproductive death is correlated with an increased occurrence of micronuclei. Since it has been suggested that radiation-induced chromosomal instability might depend on the radiation quality, we investigated the effects of alpha particles of different LET by looking at the frequency of delayed micronuclei in Chinese hamster V79 cells after cytochalasin-induced block of cell division, A dose-dependent increase in the frequency of micronuclei was found in cells assayed 1 week postirradiation or later. Also, there was a persistent increase in the frequency of dicentrics in surviving irradiated cells, Moreover, we found an increased micronucleus frequency in all of the 30 clones isolated from individual cells which had been irradiated with doses equivalent to either one, two or three alpha-particle traversals per cell nucleus, We conclude that the target for genomic instability in Chinese hamster cells must be larger than the cell nucleus. (C) 1997 by Radiation Research Society

Mean field approach for tracking similar objects

In this paper, we consider the problem of tracking similar objects. We show how a mean field approach can be used to deal with interacting targets and we compare it with Markov Chain Monte Carlo (MCMC). Two mean field implementations are presented. The first one is more general and uses particle filtering. We discuss some simplifications of the base algorithm that reduce the computation time. The second one is based on suitable Gaussian approximations of probability densities that lead to a set of self-consistent equations for the means and covariances. These equations give the Kalman solution if there is no interaction. Experiments have been performed on two kinds of sequences. The first kind is composed of a single long sequence of twenty roaming ants and was previously analysed using MCMC. In this case, our mean field algorithms obtain substantially better results. The second kind corresponds to selected sequences of a football match in which the interaction avoids tracker coalescence in situations where independent trackers fail.

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.