Posted by: oikosasa | July 29, 2013

Swans go with the flow

Many animal species show seasonal switches in their habitat use. For example, animals may move between aquatic and terrestrial habitats, flowing and still waters, coastal areas and open seas, or forest floors and canopies. How do animals decide which habitat to use? One way to understand animal habitat selection is to focus on the energetic gains and costs associated with foraging in each habitat. This has been the basis of much ‘optimal foraging’ research over the past few decades. Foraging models, which calculate the net energy intake per unit time (‘profitability’) available to the animal while foraging in different habitats, can yield a process-based understanding of why animals switch habitats.

In our paper Go with the flow: water velocity regulates herbivore foraging decisions in river catchments , now published Eary View, we used a combination of observational, experimental and modelling work to understand why flocks of non-breeding mute swans (Cygnus olor) show a seasonal switch in habitat use in shallow river catchments. From our previous work, we knew that swans switch from feeding on grasses in pasture grass fields, to feeding on aquatic plants in the river itself, between April and May each year. Due to their high food requirement (up to 4 kg of fresh vegetation per day), lack of predators and high tolerance to disturbance, non-breeding swans are ideal for studies of the influence of foraging profitability on habitat selection. Hence we suspected that the habitat shift would be linked to seasonal changes in one or more of three parameters: food quantity, food quality, and metabolic foraging cost.

 

A flock of mute swans feeding on submerged plants in a shallow rive

A flock of mute swans feeding on submerged plants in a shallow rive

 

We combined field and literature data with an optimal foraging model to investigate the observed seasonal habitat shift by mute swans. Our study system for this investigation was the River Frome in southern England, which has a population of approximately 300 swans. We measured the quantity and quality of the two food resources available to swans, aquatic plants and pasture grass. We took quantitative plant samples each month from 18 paired river and field sites within the catchment to measure how the biomass of each food resource changed over the study period. The energy content of plant and swan faeces samples from four of these sites were determined using bomb calorimetry, which showed that the food quality was relatively constant over the study period. We estimated the intake rates for aquatic plants by conducting feeding experiments on captive swans, and for pasture grass by allometric scaling of published data. We used published literature and calculated water velocities to estimate foraging costs. Whilst foraging costs of pasture grass feeding were stable over time, river feeding became more efficient as water velocities declined between spring and summer; slower water meant less energy had to be expended swimming.

 

The lead author with a tray of swan faeces for energy analysis, illustrating the less glamorous side of working with large, charismatic vertebrates

The lead author with a tray of swan faeces for energy analysis, illustrating the less glamorous side of working with large, charismatic vertebrates

 

The lead author delivers part of the ad libitum supply of aquatic plants to a captive swan at the start of the functional response experiments

The lead author delivers part of the ad libitum supply of aquatic plants to a captive swan at the start of the functional response experiments

 

Finally, we used an optimal foraging model to predict the average net rate of energy gain in each habitat, for each month between March and September. The model could have either fixed values (i.e. average values for the study period) or variable values (i.e. monthly values) for the key parameters, to allow us to assess the effects of seasonal changes on profitability and habitat use. We compared the predicted ‘best’ habitat for each month with the observed field data on habitat use. By sequentially testing alternative models with fixed or variable values for food quantity, food quality and foraging cost, we found that we needed to include seasonal variance in foraging costs in the model to accurately predict the observed habitat switch date (i.e. April to May). However, we did not need to include seasonal variance in food quantity and food quality, as accurate predictions could be obtained with fixed values for these two parameters. Therefore, our model indicated that the seasonal decrease in aquatic foraging costs was the key factor influencing the decision to switch from pasture to river feeding habitats. Many previous studies have ignored the role of seasonal changes in foraging costs in driving switches between habitats. Our study offers a mechanistic understanding, based on the gains and costs associated with different food resources, of the observed shifts of a generalist herbivore between alternative habitats.

Understanding the factors which determine habitat selection are necessary to explain the patterns of animal distributions that we observe in nature. Furthermore, we aim to use our understanding of swan habitat selection to inform ecosystem management. Where they feed in shallow rivers, flocks of mute swans may damage the plant community and threaten conservation objectives. Herbivore damage to valuable plant communities is a problem seen around the world, for example deer in temperate woodlands and geese in agricultural crops. Where we understand the factors which determine herbivore habitat use, we may be able to manipulate these factors to shift herbivores away from the threatened habitat. Whether or not we can successfully use our understanding of the rules of habitat selection to devise practical habitat management schemes to prevent overgrazing, it certainly provides an interesting applied focus for future research in this area of ecology.

Kevin A. Wood

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