Avian Use of Harvested Crop Fields in North Dakota During Spring Migration

Broad-spectrum herbicide applications and improved harvesting efficiency of crops have reduced the availability of weed seeds and waste grains for game and nongame wildlife. Over the last decade, corn and soybean plantings have steadily increased in the Prairie Pothole Region (PPR) of North Dakota, while sunflower plantings have declined. The PPR is an important corridor for migratory birds, and changes in food availabilities at stopover habitats may affect how food resources are used. In early spring 2003 and 2004, we compared bird use of harvested fields of sunflower, soybeans, small grains, and corn in the PPR of North Dakota. Across both years and all crop types, we observed 20,400 birds comprising 29 species. Flocks of Lapland Longspurs (Calcarius lapponicus) and Horned Larks (Eremophila alpestris) and flocks of Red-winged Blackbirds (Agelaius phoeniceus) made up 60% and 15%, respectively, of the bird counts. We found that species richness and bird densities were higher in harvested sunflower fields and cornfields than in harvested small-grain and soybean fields, with soybean fields harboring the fewest species and lowest bird density. Blackbird densities tended to be lower in fields tilled after fall harvest than in fields not tilled. These results suggest that some granivorous bird populations in the Northern Great Plains could be positively affected by planting of row crops with postharvest vertical structure (e.g., sunflower, corn) and use of no-till land management practices.

Generalizing the dynamic field theory of spatial cognition across real and developmental time scales

Within cognitive neuroscience, computational models are designed to provide insights into the organization of behavior while adhering to neural principles. These models should provide sufficient specificity to generate novel predictions while maintaining the generality needed to capture behavior across tasks and/or time scales. This paper presents one such model, the Dynamic Field Theory (DFT) of spatial cognition, showing new simulations that provide a demonstration proof that the theory generalizes across developmental changes in performance in four tasks—the Piagetian A-not-B task, a sandbox version of the A-not-B task, a canonical spatial recall task, and a position discrimination task. Model simulations demonstrate that the DFT can accomplish both specificity—generating novel, testable predictions—and generality—spanning multiple tasks across development with a relatively simple developmental hypothesis. Critically, the DFT achieves generality across tasks and time scales with no modification to its basic structure and with a strong commitment to neural principles. The only change necessary to capture development in the model was an increase in the precision of the tuning of receptive fields as well as an increase in the precision of local excitatory interactions among neurons in the model. These small quantitative changes were sufficient to move the model through a set of quantitative and qualitative behavioral changes that span the age range from 8 months to 6 years and into adulthood. We conclude by considering how the DFT is positioned in the literature, the challenges on the horizon for our framework, and how a dynamic field approach can yield new insights into development from a computational cognitive neuroscience perspective.