Encounter success of free-ranging marine predator movements across a dynamic prey landscape

Movements of wide-ranging top predators can now be studied effectively using satellite and archival telemetry. However, the motivations underlying movements remain difficult to determine because trajectories are seldom related to key biological gradients, such as changing prey distributions. Here, we use a dynamic prey landscape of zooplankton biomass in the north-east Atlantic Ocean to examine active habitat selection in the plankton-feeding basking shark Cetorhinus maximus. The relative success of shark searches across this landscape was examined by comparing prey biomass encountered by sharks with encounters by random-walk simulations of ‘model’ sharks. Movements of transmitter-tagged sharks monitored for 964 days (16754km estimated minimum distance) were concentrated on the European continental shelf in areas characterized by high seasonal productivity and complex prey distributions. We show movements by adult and sub-adult sharks yielded consistently higher prey encounter rates than 90% of random-walk simulations. Behavioural patterns were consistent with basking sharks using search tactics structured across multiple scales to exploit the richest prey areas available in preferred habitats. Simple behavioural rules based on learned responses to previously encountered prey distributions may explain the high performances. This study highlights how dynamic prey landscapes enable active habitat selection in large predators to be investigated from a trophic perspective, an approach that may inform conservation by identifying critical habitat of vulnerable species.

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.

Ecological And Metabolic Studies On Free-Living Nematodes From An Estuarine Mud-Flat

Nematodes from a mud-flat in the river Lynher estuary, Cornwall, U.K., have a population density ranging between 8 and 9 × 106 m−2 in the winter months, corresponding to a dry weight of 1·4 and 1·6 g m−2. They reach a peak abundance of 22·86 × 106 m−2 (3·4 g) in May. About 40 species are present, and the species composition remained seasonally stable over the period of study. Analysis of age-structure suggests that the major species have continuous asynchronous reproduction. Respiration rates of 16 species have been determined at 20 °C using Cartesian diver respirometry. For five species, respiration/body size regressions were obtained in the form log10R = log10a+b log10V, where R = respiration in nl O2 ind−1 h−1 and V = body volume in nl: Mesotheristus setosus (log10a = −0·04,b = 0·74), Sphaerolaimus hirsutus (log10a = 0·11, b = 0·68), Axonolaimus paraspinosus (log10a = 0·00, b = 0·79), Metachromadora vivipara (log10a = −0·59, b = 1·07), Praeacanthonchus punctatus (log10a = 0·00, b = 0·55). For the remaining 11 species, several animals were used in each diver and, by assuming b = 0·75, log10a′ values were calculated: Viscosia viscosa (log10a′ = 0·188), Innocuonema tentabundum (−0·012), Ptycholaimellus ponticus (−0·081), Odontophora setosa (−0·092), Sphaerolaimus balticus (−0·112), Dichromadora cephalata (−0·133), Atrochromadora microlaima (−0·142), Cylindrotheristus normandicus (−0·150), Terschellingialongicaudata (−0·170), Sabatieria pulchra (−0·197), Terschellingia communis (−0·277). These values are compared with recalculated values for other species from the literature. Annual respiration of the nematode community is 28·01 O2 m−2, equivalent to 11·2 g carbon metabolised. Community respiration is compared with figures from N. American saltmarshes. At 20 °C, a respiration of about 61 O2 m−2 year−1 g−1 wet weight of nematodes appears to be typical. Annual production is estimated to be 6·6 g C m−2. The correlation between feeding-group, body-size, habitat and the repiration rate of individual species is discussed.

Effect of elevated CO2 on the dynamics of particle-attached and free-living bacterioplankton communities in an Arctic fjord

In the frame of the European Project on Ocean Acidification (EPOCA), the response of an Arctic pelagic community (<3 mm) to a gradient of seawater pCO(2) was investigated. For this purpose 9 large-scale in situ mesocosms were deployed in Kongsfjorden, Svalbard (78 degrees 56.2' N, 11 degrees 53.6' E), in 2010. The present study investigates effects on the communities of particle-attached (PA; >3 mu m) and free-living (FL; <3 mu m > 0.2 mu m) bacteria by Automated Ribosomal Intergenic Spacer Analysis (ARISA) in 6 of the mesocosms, ranging from 185 to 1050 mu atm initial pCO(2), and the surrounding fjord. ARISA was able to resolve, on average, 27 bacterial band classes per sample and allowed for a detailed investigation of the explicit richness and diversity. Both, the PA and the FL bacterioplankton community exhibited a strong temporal development, which was driven mainly by temperature and phytoplankton development. In response to the breakdown of a picophytoplankton bloom, numbers of ARISA band classes in the PA community were reduced at low and medium CO2 (similar to 185-685 mu atm) by about 25 %, while they were more or less stable at high CO2 (similar to 820-1050 mu atm). We hypothesise that enhanced viral lysis and enhanced availability of organic substrates at high CO2 resulted in a more diverse PA bacterial community in the post-bloom phase. Despite lower cell numbers and extracellular enzyme activities in the post-bloom phase, bacterial protein production was enhanced in high CO2 mesocosms, suggesting a positive effect of community richness on this function and on carbon cycling by bacteria.