Hormesis - The Stimulation Of Growth By Low-Levels Of Inhibitors

Hormesis is the name given to the stimulatory effects caused by low levels of potentially toxic agents. When this phenomenon was first identified it was called the Arndt-Schulz Law or Hueppe's Rule, because it was thought to occur generally. Although this generalisation is not accepted today, there has never been more evidence in its support, justifying a re-examination of the phenomenon. Evidence from the literature shows that not only has growth hormesis been observed in a range of taxa after exposure to a variety of agents, but also that the dose-response data have a consistent form. While there are a number of separate hypotheses to explain specific instances of hormesis, the evidence presented here suggests that different examples might have a common explanation, and the possibility of a general theory is considered.

Continuous Plankton Records: General Introduction to the 1938-39 Survey

The Bulletins in this volume, except the last, deal entirely with the results of this expanded survey of 1938 and 1939. They show for the first time, month by month, the main changes in the plankton over practically the whole of the North Sea for a year and eight months. They form an important basis for comparison with the results of the post-war survey, revived on an even more extensive scale and to be described in later volumes.

A general framework for aquatic biogeochemical models

One of the most of challenging steps in the development of coupled hydrodynamic-biogeochemical models is the combination of multiple, often incompatible computer codes that describe individual physical, chemical, biological and geological processes. This “coupling” is time-consuming, error-prone, and demanding in terms of scientific and programming expertise. The open source, Fortran-based Framework for Aquatic Biogeochemical Models addresses these problems by providing a consistent set of programming interfaces through which hydrodynamic and biogeochemical models communicate. Models are coded once to connect to FABM, after which arbitrary combinations of hydrodynamic and biogeochemical models can be made. Thus, a biogeochemical model code works unmodified within models of a chemostat, a vertically structured water column, and a three-dimensional basin. Moreover, complex biogeochemistry can be distributed over many compact, self-contained modules, coupled at run-time. By enabling distributed development and user-controlled coupling of biogeochemical models, FABM enables optimal use of the expertise of scientists, programmers and end-users.

Scaling laws of ambush predator 'waiting' behaviour are tuned to a common ecology

The decisions animals make about how long to wait between activities can determine the success of diverse behaviours such as foraging, group formation or risk avoidance. Remarkably, for diverse animal species, including humans, spontaneous patterns of waiting times show random ‘burstiness’ that appears scale-invariant across a broad set of scales. However, a general theory linking this phenomenon across the animal kingdom currently lacks an ecological basis. Here, we demonstrate from tracking the activities of 15 sympatric predator species (cephalopods, sharks, skates and teleosts) under natural and controlled conditions that bursty waiting times are an intrinsic spontaneous behaviour well approximated by heavy-tailed (power-law) models over data ranges up to four orders of magnitude. Scaling exponents quantifying ratios of frequent short to rare very long waits are species-specific, being determined by traits such as foraging mode (active versus ambush predation), body size and prey preference. A stochastic–deterministic decision model reproduced the empirical waiting time scaling and species-specific exponents, indicating that apparently complex scaling can emerge from simple decisions. Results indicate temporal power-law scaling is a behavioural ‘rule of thumb’ that is tuned to species’ ecological traits, implying a common pattern may have naturally evolved that optimizes move–wait decisions in less predictable natural environments.