Effects of increases in temperature and nutrients on phytoplankton community structure and photosynthesis in the Western English Channel

Anthropogenic climate change is exerting pressures on coastal ecosystems through increases in temperature, precipitation and ocean acidification. Phytoplankton community structure and photo-physiology are therefore adapting to these conditions. Changes in phytoplankton biomass and photosynthesis in relation to temperature and nutrient concentrations were assessed using a 14 year dataset from a coastal station in the Western English Channel (WEC). Dinoflagellate and coccolithophorid biomass exhibited a positive correlation with temperature, reaching the highest biomass at between 15 and 17°C. Diatoms showed a negative correlation with temperature, with highest biomass at 10°C. Chlorophyll a (chl a) normalised light-saturated photosynthetic rates (PBm) exhibited a hyperbolic response to increasing temperature, with an initial linear increase from 8 to 11°C, and reaching a plateau from 12°C. There was however no significant positive correlation between nutrients and phytoplankton biomass or PBm, which reflects the lag time between nutrient input and phytoplankton growth at this coastal site. The major phytoplankton groups that occurred at this site occupied distinct thermal niches, which in turn modified PBm. Increasing temperature, and higher water column stratification, was major factors in the initiation of dinoflagellates blooms at this site. Dinoflagellates blooms during summer also co-varied with silicate concentration, and acted as a tracer of dissolved inorganic nitrogen and phosphate from river run-off, which were subsequently reduced during these blooms. The data implies that increasing temperature and high river runoff during summer, will promote dinoflaglellates blooms in the WEC.

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.