Northern bobwhite ( Colinus virginianus ) abundance in relation to yearly weather and long-term climate patterns

Northern bobwhite ( Colinus virginianus ) abundance in relation to yearly weather and long-term... We used a multilayered, backpropagation neural network to investigate the relative effects of yearly weather and long-term climate patterns on the abundance of northern bobwhites ( Colinus virginianus : hereafter, bobwhite) in Oklahoma, USA. Bobwhite populations have been declining for several decades across the United States, and predicted global climate change might accelerate the rate of decline. We were interested in whether bobwhite abundance was more responsive to yearly precipitation and temperature, or to annual deviations from long-term mean climate patterns. We used roadside count data collected over a 6 year period (1991–1997) by the Oklahoma Department of Wildlife Conservation as a measure of bobwhite abundance. We standardized quail counts among counties by calculating the standard normal deviate for each county. Weather data were obtained from weather stations closest to the roadside-count route. We had 280 training cases and 68 test-validation cases. Two data sets were constructed: one using yearly weather data (actual rainfall and temperature) and the second using annual deviations from long-term mean values. We conducted simulation analyses to determine the nature of the relationship between each dependent variable and the standardized bobwhite counts. A neural network with eight neurons was most efficient for the yearly weather data, accounting for 25% of the variation in the training data. The adjusted sum-of-squares for this model was 2.42. A four-neuron network was selected for the deviation-from-normal data set, accounting for 23% of the variation in the training data. The adjusted sum-of-squares for the deviation model was 1.44, indicating it performed better than the model for yearly weather patterns. Deviation from long-term mean July and August temperatures combined contributed 31.5% to the climate network's predictions, and deviations from mean winter, spring, and summer precipitation combined contributed 42.8% to the network's predictions. As July temperature increased over the long-term mean, the number of bobwhites counted increased over the route mean, but the relationship decelerated at high July temperatures. Predicted increases in bobwhites counted were highest when August temperatures were below the mean and decreased rapidly for all temperatures greater than the mean. Predicted bobwhite counts increased asymptotically as winter rain increased over the long-term mean, but were greatest at mean spring-rainfall amounts and at below average amounts of summer rainfall. We conclude that the absolute changes in yearly weather pattern predicted by some global change models will not have as great an impact on bobwhite abundance as will the magnitude of the deviations of these values from the climate bobwhites are adapted to in this portion of their range. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Northern bobwhite ( Colinus virginianus ) abundance in relation to yearly weather and long-term climate patterns

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Publisher
Elsevier
Copyright
Copyright © 2001 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
DOI
10.1016/S0304-3800(01)00292-7
Publisher site
See Article on Publisher Site

Abstract

We used a multilayered, backpropagation neural network to investigate the relative effects of yearly weather and long-term climate patterns on the abundance of northern bobwhites ( Colinus virginianus : hereafter, bobwhite) in Oklahoma, USA. Bobwhite populations have been declining for several decades across the United States, and predicted global climate change might accelerate the rate of decline. We were interested in whether bobwhite abundance was more responsive to yearly precipitation and temperature, or to annual deviations from long-term mean climate patterns. We used roadside count data collected over a 6 year period (1991–1997) by the Oklahoma Department of Wildlife Conservation as a measure of bobwhite abundance. We standardized quail counts among counties by calculating the standard normal deviate for each county. Weather data were obtained from weather stations closest to the roadside-count route. We had 280 training cases and 68 test-validation cases. Two data sets were constructed: one using yearly weather data (actual rainfall and temperature) and the second using annual deviations from long-term mean values. We conducted simulation analyses to determine the nature of the relationship between each dependent variable and the standardized bobwhite counts. A neural network with eight neurons was most efficient for the yearly weather data, accounting for 25% of the variation in the training data. The adjusted sum-of-squares for this model was 2.42. A four-neuron network was selected for the deviation-from-normal data set, accounting for 23% of the variation in the training data. The adjusted sum-of-squares for the deviation model was 1.44, indicating it performed better than the model for yearly weather patterns. Deviation from long-term mean July and August temperatures combined contributed 31.5% to the climate network's predictions, and deviations from mean winter, spring, and summer precipitation combined contributed 42.8% to the network's predictions. As July temperature increased over the long-term mean, the number of bobwhites counted increased over the route mean, but the relationship decelerated at high July temperatures. Predicted increases in bobwhites counted were highest when August temperatures were below the mean and decreased rapidly for all temperatures greater than the mean. Predicted bobwhite counts increased asymptotically as winter rain increased over the long-term mean, but were greatest at mean spring-rainfall amounts and at below average amounts of summer rainfall. We conclude that the absolute changes in yearly weather pattern predicted by some global change models will not have as great an impact on bobwhite abundance as will the magnitude of the deviations of these values from the climate bobwhites are adapted to in this portion of their range.

Journal

Ecological ModellingElsevier

Published: Dec 1, 2001

References

  • Maximum and minimum temperature trends for the globe
    Easterling, D.R; Horton, B; Jones, P.D; Peterson, T.C; Karl, T.R; Parker, D.E; Salinger, M.J; Razuvayev, V; Plummer, N; Jamason, P; Folland, C
  • Testing ecological models: the meaning of validation
    Rykiel, E.J
  • Neural Networks for Statistical Modeling
    Smith, M

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