The Influence Of Temperature And Salinity On Energy Partitioning In The Marine Nematode Diplolaimelloides-Bruciei

Measurements of population growth, generation time, fecundity and respiration in laboratory culture have been made, in relation to temperature and salinity, for the nematode Diplolaimelloides bruciei Hopper, a species normally associated with decayed material of the marsh grass Spartina. The intrinsic rate of increase (r) is high: it is related to temperature between 5° and 25°C by a sigmoid function which is steepest between 10° and 15°C, and is maximum at 26‰ salinity. Generation time is related to temperature by a power function and is shortest at 26‰ salinity. The effect of temperature on generation time is consistent with other data for marine nematodes, and the steep slope of r against temperature is largely due to the marked effect of temperature on fecundity. A sex ratio of 2:1 in favour of males is maintained regardless of culture conditions or population density. Respiration increases exponentially with temperature between 5° and 25°C, with a very high Q10 (3.94), but is not affected by salinity. At 30°C respiration is no higher than at 25°C. A high and relatively stable production efficiency (P/A) is maintained between 10 and 30°C with a maximum of 87% at 15°C; there is a stable reproductive effort (Pr/A) of about 10%. At 5°C both these ratios are zero. Data for the harpacticoid copepod Tachidius discipes, derived from the literature, show that this too has a high and stable production efficiency, which may be a characteristic of meiofaunal species in general, but in this species efficiency is relatively high at 5°C. Many features of the energy balance in D. bruciei can be related to an opportunistic mode of life.

Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework

Understanding the exploration patterns of foragers in the wild provides fundamental insight into animal behavior. Recent experimental evidence has demonstrated that path lengths (distances between consecutive turns) taken by foragers are well fitted by a power law distribution. Numerous theoretical contributions have posited that “Lévy random walks”—which can produce power law path length distributions—are optimal for memoryless agents searching a sparse reward landscape. It is unclear, however, whether such a strategy is efficient for cognitively complex agents, from wild animals to humans. Here, we developed a model to explain the emergence of apparent power law path length distributions in animals that can learn about their environments. In our model, the agent’s goal during search is to build an internal model of the distribution of rewards in space that takes into account the cost of time to reach distant locations (i.e., temporally discounting rewards). For an agent with such a goal, we find that an optimal model of exploration in fact produces hyperbolic path lengths, which are well approximated by power laws. We then provide support for our model by showing that humans in a laboratory spatial exploration task search space systematically and modify their search patterns under a cost of time. In addition, we find that path length distributions in a large dataset obtained from free-ranging marine vertebrates are well described by our hyperbolic model. Thus, we provide a general theoretical framework for understanding spatial exploration patterns of cognitively complex foragers.

Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework

Understanding the exploration patterns of foragers in the wild provides fundamental insight into animal behavior. Recent experimental evidence has demonstrated that path lengths (distances between consecutive turns) taken by foragers are well fitted by a power law distribution. Numerous theoretical contributions have posited that “Lévy random walks”—which can produce power law path length distributions—are optimal for memoryless agents searching a sparse reward landscape. It is unclear, however, whether such a strategy is efficient for cognitively complex agents, from wild animals to humans. Here, we developed a model to explain the emergence of apparent power law path length distributions in animals that can learn about their environments. In our model, the agent’s goal during search is to build an internal model of the distribution of rewards in space that takes into account the cost of time to reach distant locations (i.e., temporally discounting rewards). For an agent with such a goal, we find that an optimal model of exploration in fact produces hyperbolic path lengths, which are well approximated by power laws. We then provide support for our model by showing that humans in a laboratory spatial exploration task search space systematically and modify their search patterns under a cost of time. In addition, we find that path length distributions in a large dataset obtained from free-ranging marine vertebrates are well described by our hyperbolic model. Thus, we provide a general theoretical framework for understanding spatial exploration patterns of cognitively complex foragers.