European populations of geese, swans and cranes have increased considerably since the 1970s raising conflicts between conservation and farming interests. Crop damage caused by geese, swans and cranes across the national scale needs a trans-boundary approach that captures the site-specific characteristics of crop damage at a more refined spatial scale, to deal with the high spatio-temporal variation inherent in the system and to avoid conflict displacement. In the present study we use long-term crop damage data (2000-2015) in Sweden to evaluate seasonal and annual patterns of crop damage. We show that crop damage increased over years but followed a fairly consistent seasonal pattern during the later parts of the study period. We show how these seasonal patterns differ across the country such that trans-boundary regions with similar patterns of crop damage, relating to different nuisance species and damaged crops, can be identified. These findings about spatio-temporal variation of damage can be used to find appropriate scales of management units (e.g. areas with similar conditions), and to adapt damage mitigation strategies to temporal and spatial-specific conditions, e.g. guidance of when and where certain crop may be suitable as sacrificial crops.

Time-series models applied in the study of animal population dynamics commonly assume linearity on the log-scale, leading to log-normally distributed rates of increase. While this is often computationally convenient, in particular when performing statistical inference in the presence of observation error, it may lead to unrealistic predictions for animals with a limited reproduction. We introduce a model that includes an explicit bound on the reproductive rate of an individual, and apply this to a population time series of ungulates in Kruger National Park, South Africa. Due to observational error, the year-to-year increases in animal counts occasionally exceeded the maximal reproductive rate of the animals. In such cases, the traditional unbounded model showed a tendency of overfitting data, leading to unrealistic predictions of the underlying population increase. An observed increase above the maximal reproductive rate also provides empirical confirmation that observation error exists. The model with an explicit bound was able to utilize this in order to separate observational error from population process noise, which the traditional unbounded model was unable to do. We conclude that enforcing a strict upper bound on the reproductive rate of an animal population model may lead to more realistic statistical inference than commonly applied log-linear models when an explicit bound on reproductive rate is known. We further conclude that introducing a bound on reproduction can greatly assist in separating observational error and population process noise for slow life histories, or more generally, when the rate of sampling is high compared to reproductive rates.