Diel movement patterns of the scalloped hammerhead shark (Sphyrna lewini) in relation to El Bajo Espiritu Santo: a refuging central-position social system

Summary Movement patterns of scalloped hammerhead sharks in the vicinity of El Bajo Espiritu Santo, a seamount in the Gulf of California, were determined by tracking by ultrasonic telemetry 13 sharks and marking 100 sharks. The 13 tracked sharks swam back and forth along the seamount ridge throughout the day. They did not swim in different directions to reduce swimming effort when currents changed from a parallel to a perpendicular orientation to the ridge. Sharks tracked up to 8 km away into the pelagic environment soon returned to the seamount. From such trackings and repeated observations of marked sharks over periods of several weeks, it is believed that most sharks disperse and return to the seamount in a rhythmical fashion. The separate departures of individual hammerheads in five paired trackings indicated that the sharks left the seamount either in small groups or singly. For these reasons, we argue that the social system of the scalloped hammerhead shark can be described as a refuging system.     

Optimisation algorithms for spatially constrained forest planning

We compared genetic algorithms, simulated annealing and hill climbing algorithms on spatially constrained, integrated forest planning problems. There has been growing interest in algorithms that mimic natural processes, such as genetic algorithms and simulated annealing. These algorithms use random moves to generate new solutions, and employ a probabilistic acceptance/rejection criterion that allows inferior moves within the search space. Algorithms for a genetic algorithm, simulated annealing, and random hill climbing are formulated and tested on a same-sample forest-planning problem where the adjacency rule is strictly enforced. Each method was randomly started 20 times and allowed to run for 10,000 iterations. All three algorithms identified good solutions (within 3% of the highest found), however, simulated annealing consistently produced superior solutions. Simulated annealing and random hill climbing were approximately 10 times faster than the genetic algorithm because only one solution needs to be modified at each iteration. Performance of simulated annealing was essentially independent of the starting point, giving it an important advantage over random hill climbing. The genetic algorithm was not well suited to the strict adjacency problem because considerable computation time was necessary to repair the damage caused during crossover.     

Comparison of Evapotranspiration Simulation Performance by APEX Model in Dryland and Irrigated Cropping Systems

Accurate estimation of evapotranspiration (ET) is essential to improve water use efficiency of crop production systems managed under different water regimes. The Agricultural Policy/Environmental eXtender (APEX) model was used to simulate ET using four potential ET (ET_p) methods. The objectives were to determine sensitive ET parameters in dryland and irrigated cropping systems and compare ET simulation in the two systems using multiple performance criteria. Measured ET and crop yield data from lysimeter fields located in the United States Department of Agriculture‐Agricultural Research Service Bushland, Texas were used for evaluation. The number of sensitive parameters was higher for dryland (11–14) than irrigated cropping systems (6–8). Only four input parameters: soil evaporation plant cover factor, root growth soil strength, maximum rain intercept, and rain intercept coefficient were sensitive in both cropping systems. Overall, it is possible to find a set of robust parameter values to simulate ET accurately in APEX in both cropping systems using any ET_p method. However, more computation time is required for dryland than irrigated cropping system due to a relatively larger number of sensitive input parameters. When all inputs are available, the Penman–Monteith method takes the shortest computation time to obtain one model run with robust parameter values in both cropping systems. However, in areas with limited datasets, one can still obtain reasonable ET simulations using either Priestley–Taylor or Hargreaves. Editor's note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Tutor choice and imitation accuracy during song learning in a wild population of the Puget Sound white-crowned sparrow

Songbirds learn to sing by modeling their songs on the songs of other males through a process of social learning. Models of social learning predict that animals should be selective in what and when they learn. In this study, we asked whether young males in a wild population of the Puget Sound white-crowned sparrow, Zonotrichia leucophrys pugetensis, were selective in their choice of tutor models and what factors influenced how accurately they imitated tutors’ songs. We first examined two strategies for tutor choice: whether pupils have a conformity bias and/or a preference for high-quality tutors. In keeping with a conformity bias, tutors that sang song types that were relatively common within a radius of about 500 m of their territory were more likely to be imitated than were tutors that sang rarer song types. Most potential tutors were not imitated by pupils. Aspects of tutor quality, such as age, pairing status, and survival to the next year had no effect on whether a tutor’s song was imitated. Secondly, we tested whether pupil repertoire size, pupil quality, and local abundance of tutor models affected the accuracy of song imitations. We found a trade-off between repertoire size and tutor imitation accuracy with males that sang two or more song types developing significantly poorer imitations than males that sang one type. We discuss possible functions of a conformity learning strategy and factors that could produce a trade-off between imitation accuracy and repertoire size.     

State-Space Modeling of the Relationship between Air Quality and Mortality

ABSTRACT

A portion of a population is assumed to be at risk, with the mortality hazard varying with atmospheric conditions including total suspended particulates (TSP). This at-risk population is not observed and the hazard function is unknown; we wish to estimate these from mortality count and atmospheric variables. Consideration of population dynamics leads to a state-space representation, allowing the Kalman Filter (KF) to be used for estimation. A harvesting effect is thus implied; high mortality is followed by lower mortality until the population is replenished by new arrivals.

The model is applied to daily data for Philadelphia, PA, 1973-1990. The estimated hazard function rises with the level of TSP and at extremes of temperature and also reflects a positive interaction between TSP and temperature. The estimated at-risk population averages about 480 and varies seasonally. We find that lags of TSP are statistically significant, but the presence of negative coefficients suggests their role may be partially statistical rather than biological. In the population dynamics framework, the natural metric for health damage from air pollution is its impact on life expectancy. The range of hazard rates over the sample period is 0.07 to 0.085, corresponding to life expectancies of 14.3 and 11.8 days, respectively.