Net Energy Intake Rate as a Common Currency to Explain Swan Spatial Distribution in a Shallow Lake

• Published in: Wetlands (Wilmington, N.C.) Vol. 32; no. 1; pp. 119 - 127
• Main Authors: Gyimesi, Abel, Varghese, Sam, De Leeuw, Jan, Nolet, Bart A.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.02.2012; Springer Nature B.V
• Subjects: Accessibility; Animal migration; Aquatic plants; Biomass; Biomedical and Life Sciences; Birds; Coastal Sciences; Density; Ecology; Energy; Energy intake; Environmental Management; Food; Foraging behavior; Freshwater & Marine Ecology; Hydrogeology; Inlets; Lakes; Landscape Ecology; Life Sciences; Particle size; Sediments; Spatial distribution; Spatial variations; Taiga & tundra; Tundra; Waterfowl; Aggregative response; Carrying capacity; Remote sensing
• ISSN: 0277-5212; 1943-6246
• DOI: 10.1007/s13157-011-0256-6

Animal distribution is usually predicted from the spatial variation in food biomass, whereas foraging theory commonly uses net energy intake rate as the currency to be maximized. We tested whether net energy intake rate better predicted the distribution and abundance of tundra swans than food biomass. In a shallow lake, we mapped the density of sago pondweed tubers during 2 years, and calculated the foraging benefits and costs to tundra swans. Swan residence was expressed in bird-days, i.e. the sum of daily counts. We used four measures of increasing complexity to predict bird-days per inlet: total food biomass ( B ), total food biomass above giving-up density ( B +), total accessible food biomass above giving-up density ( aB +), and total achievable net energy intake rate above giving-up energy intake rate ( NEI +). Considering both years, observed bird-days of inlets correlated only with NEI +, and not with B , B +, or aB +. In both years, our predictions of bird-days based on the NEI +model better matched observed relationships than the predictions of the other three models. Our case study suggests that in heterogeneous wetlands, correcting for giving-up density, food accessibility and foraging costs may be necessary in order to predict bird distribution and abundance.