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At least 181 records · Page 10Linked to original sources
USGS1955

Fifty-fifth Christmas Bird Count. 159. Ocean City, Md

The distribution of dynamic pressure behind a Harris' hawk's wing was sampled using a wake rake consisting of 15 pitot tubes and one static tube. The hawk was holding on to a perch, but at an air speed and gliding angle at which it was capable of gliding. The perch was instrumented, so that the lift developed by the wing was known and the lift coefficient could be calculated. The mean of 92 estimates of profile drag coefficient was 0.0207, with standard deviation 0.0079. Lift coefficients ranged from 0.51 to 1.08. Reynolds numbers were nearly all in the range 143000-194000. The estimates of profile drag coefficient were reconcilable with previous estimates of the wing profile drag of the same bird, obtained by the subtractive method, and also with values predicted by the ?Airfoil-ii? program for designing aerofoils, based on a digitized wing profile from the ulnar region of the wing. The thickness of the wake suggested that the boundary layer was mostly or fully turbulent in most observations and separated in some, possibly as an active means of creating drag for control purposes. It appears that the bird could momentarily either increase or decrease the profile drag of specific parts of the wing, by active changes of shape, and it appeared to use the carpo-metacarpal region especially for such control movements. Further investigation in a low turbulence wind tunnel would help to resolve doubts about the possible influence of airstream turbulence on the behaviour of the boundary layer.

Audubon Field Notes↗
USGS1956

Fifty-sixth Christmas Bird Count. 147. Southern Dorchester County, Md

Summary and Recommendations: We suggest that managers are approaching the limits of their ability to improve waterfowl harvest management, primarily because the information needed to make better decisions is being sacrificed by the current approach to setting regulations. We propose an actively adaptive management strategy in which regulatory decisions play a dominant role in reducing uncertainty about population dynamics. The proposed strategy recognizes 'value' in acquiring knowledge only to the extent that it contributes to the objective of optimizing harvests. To implement this strategy, managers will need: (1) a set of regulatory options, with possible constraints on their use; (2) quantifiable harvest management objectives; (3) a set of models that represent an array of meaningful hypotheses about the effects of regulations on populations; and (4) a measure of credibility (or likelihood) for each model, which can be updated regularly using information from waterfowl monitoring programs. Adaptive optimization is an iterative process in which the harvest-management policy converges over time to one that maximizes harvest under the most appropriate model. At each time step, an optimal regulatory decision is identified based on the state of the system and the model likelihoods. In the next time step, predicted population changes from the alternative models are compared with the actual changes provided by the monitoring program, The likelihoods are increased or decreased to the extent that predicted and actual population changes correspond. These updated likelihoods then are used in setting regulations in the next cycle and the process begins again. This iterative process produces the most informative regulations when uncertainty is prevalent and produces maximum sustainable yields as uncertainty is eliminated. We see no major obstacles to implementing this adaptive strategy, although there are a number of practical considerations. First and foremost, managers should assess the 'value' of learning. Only when there is a high degree of uncertainty about the effects of hunting regulations on population dynamics will the merit of our proposed strategy be evident. We suggest that this almost always will be true given our current understanding of the relationship between annual regulations, survival and population growth in waterfowl. Nonetheless, careful consideration should be given to formulating the set of alternative models. There is no value in distinguishing between models which differ in their mathematical formulation or biological realism, but which suggest similar harvest strategies. We suspect that 'mechanistic' models (i.e., those that attempt to capture the essence of biological processes) will make better candidates for model sets than so-called 'phenomenological' models. Assuming that all model sets include a good approximation of reality, learning rates will be dependent on the quality of monitoring programs. Fortunately, a variety of high-quality monitoring plans for many duck and goose populations of North America, when used with our adaptive approach, should provide new knowledge about population dynamics and response to hunting, and, thus, lead to improved management.

Audubon Field Notes↗