Predicting grazing damage by white-fronted geese under different regimes of agricultural management and the physiological consequences for the geese.
Abstract
One of the most common human-wildlife conflicts is damage by wildlife to agricultural crops. In order to propose cost-effective measures to address wildlife damage and also to manage pest animals that are of conservation interest, both the effects of wildlife on agricultural crop yield and the effects of mitigation measures on wildlife must be evaluated. In this study, we applied a behaviour-based model to the conflict between agricultural production and white-fronted geese Anser albifrons causing damage to wheat crops around Lake Miyajimanuma in northern Japan. The model is spatially explicit, individual- and physiology based, and therefore tracks the day-to-day spatial distribution of geese and the physiological dynamics of fat deposition by each goose throughout the staging period. In our simulations, the establishment of alternative feeding areas (AFAs) for the geese was predicted to be cost-effective for alleviating wheat damage if the number of AFAs and the amount of alternative food supplied was balanced carefully. However, even with the most cost-effective combination, a reduction of up to 23% in heavily damaged areas was the best outcome that could be achieved. Alternatively, locating wheat fields further away from the roost site and also encouraging farmers to leave rice grain remains, the main food resource for the geese, could greatly reduce wheat damage without a detrimental impact on the geese (measured in terms of fat deposition) over a wide range of simulated population sizes. Synthesis and applications. The suggested combination of mitigation measures (relocation of wheat fields and inhibition of rice-reducing agricultural practices) should qualify to be tested in practice for alleviating agriculture-geese conflicts in the study area. This study demonstrates that behaviour-based models can be applied successfully to agriculture-wildlife conflicts, allowing us to evaluate the effects of mitigation measures by quantitative predictions. Behaviour-based models can be applied to most cases of agriculture-wildlife conflicts involving adaptive foraging behaviour of animals.