After protection from hunting on the wintering range in 1982/83, complete surveys of Greenland white-fronted geese at all known Irish and British wintering resorts have been carried out annually. These showed that this population increased by 5.0% per annum from 16,541 in spring 1983 to 30,459 in spring 1995, characterised by a 6.6% annual increase during 1982/83–1991/92, followed by a less rapid increase in subsequent years. In addition, regular counts of at least eight wintering flocks also exist prior to 1982/83. Five of these (including the two most important, Islay in Scotland and Wexford in Ireland) showed no trend before protection, but significant increases after legislation. Two other flocks at protected sites showed increasing numbers prior to changes in legislation, followed by stable numbers afterwards and the eighth flock increased in number before and after protection. On Islay, a significant increase in crude adult annual survival rate (based on census data) occurred after the hunting ban. Numbers on Islay continue to show linear increase. At Wexford, there was no significant difference between crude adult survival before and after the hunting ban where, after a short period of increase, numbers stabilised at 8,000–10,000 after 1990. There were no significant differences in the proportions of young birds before and after protection in these two flocks. Despite overall population increase, seven flocks have become extinct during 1982–1995 and a further five are close to extinction. Eighteen flocks have declined since protection, 35 showed no significant trends and 20 showed increases. Multivariate analysis suggests size, number and quality of feeding areas, levels of disturbance, flock size and latitude influence flock status - smallest most southerly flocks on fewest, poor quality limited feeding ranges showing most serious declines. The consequences of increasing concentration of the population at a few wintering areas need urgent attention and mechanisms should be sought to maintain current range, particularly on traditional semi-natural or low intensity agricultural land.

Hunting vulnerability of waterfowl species has often been associated with age and sex classes, or with body condition in relation to physiological constraints. In the Camargue, southern France, body weights and daily feeding duration of three dabbling duck species (teal Anas c. crecca, gadwall A. strepera and wigeon A. penelope) have recently been used to isolate three main periods in the winter season (August to March). These periods are characterised successively by high, low and high levels of energy demand, and they constitute the time schedule for a model of wintering strategy. For feeding, birds exploit the productive hunted marshes, mostly during the periods of high energy demand. In this study, we tested the hypothesis that hunting vulnerability can be predicted from these seasonal patterns, being highest when energy demand is highest (at the beginning and at the end of the winter), and being lowest in mid-winter when energy demand is lowest. We used numbers of birds killed (45,000 birds, including the species mentioned above, as well as mallard Anas platyrhynchos and coot Fulica atra) collected from hunting bags in three locations over 12 years, validated on 110,000 killed birds from another location, and adjusted to living (censused) birds. The results do not fit exactly to our predictions. They rather suggest that hunting vulnerability results from a combination of energy demand, habitat selection (both related to wintering strategy), chronology of migration and trophic status of duck species (granivorous vs herbivorous). At the beginning of the winter season, granivorous species, the first to arrive, are inexperienced to hunting, have a high energy demand and are highly vulnerable. At the middle of the winter season, when energy demands are low, the birds can escape from hunting to refuge areas. Meanwhile, herbivorous species, still arriving, must spend more time on feeding (vegetative food contains less energy than seeds) on productive hunted marshes; they suffer high hunting vulnerability from hunters who shift from granivorous to herbivorous species. At the end of the winter season, granivorous and herbivorous species rely on hunted areas for feeding and are very vulnerable. However, hunting vulnerability of a given species is lowered since hunting pressure during that last period of winter is shared among a maximum number of game species, some of which are migrating back from Africa.

The quality of common agricultural crops relative to natural plants was measured and the spatial feeding pattern of roe deer Capreolus capreolus on strawberry Fragaria ananassa fields in the Lier valley, southeastern Norway, were investigated during a winter (1992/93) with shallow snow depth. Strawberry plants were easily digestible, had a high mineral content and were readily harvested by roe deer. Distance to the nearest houses and forest edge affected the spatial pattern of feeding intensity differently when considered at between-field or within-field selection level. Distance to the forest edge or houses did not seem to affect choice of fields. Distance to the forest edge had no effect on feeding intensity within fields when fields were situated far from houses, but had a significant effect when fields were close to houses. When fields were far from the forest edge but close to houses, the distance to the houses but not to the forest edge affected within-field use by roe deer. Hence, roe deer seem to assess risk factors (distance to houses) and vary their response to the forest edge accordingly.

The flying squirrel Pteromys volans is an arboreal rodent and inhabitant of Palearctic boreal forests. In Finland, the flying squirrel has been classified as a declining species which needs to be monitored. I studied home ranges, habitat use and nocturnal activity of eight adult flying squirrels by radio tracking in fragmented coniferous forests in Finland during June - December, 1996. Average home-range size of the flying squirrel measured by the 100% MCP was 6.5 ha. In summer, the average size of the 95% cluster area was 2.3 ha and the 80% core area 0.5 ha. The core areas represented only 7.8% of the 100% MCP area and were composed of 2–6 separate patches in the home ranges of individual squirrels. Radio-tagged squirrels used several nests, both old woodpecker cavities and dreys for nesting and diurnal roosting. The combined density of all deciduous tree species was significantly greater in the 80% core areas than within the 100% MPC in the summer data set. In the polychotomous logistic regression model the great canopy cover, high densities of alders Alnus incana and A. glutinosa and aspen Populus tremula significantly explained the ranked utilisation classes (utilisation rank from highly used areas to least used areas: 80% core - 95% cluster - 100% MCP). The three most abundant deciduous trees species (birches Betula pendula and B. pubescens, aspen, alder) constituted 87% of trees used by squirrels in summer. Flying squirrels were found in aspens more often than expected according to their availability. The results show a clear preference for deciduous trees and a preference for the parts of home ranges with higher densities of alders and aspen. The flying squirrel seems to be capable of using several cover types, including young forest stands, as foraging and moving areas and are able to move across semi-open clear-cut areas.

Successful predictions of population fluctuations are valuable in game management, as population estimates are instrumental in increasing the time available for management decisions. However, finding a population model which produces predictions accurate enough to be used for management purposes is often precluded due to scarcity and noisiness of population data. Using two long-term population data sets, 1964–1984 data on Finnish grouse (Tetrao urogallus, T. tetrix and Bonasa bonasia) and 1914–1950 data on coloured fox Vulpes fulva from Canada, we demonstrate the use and power of an artificial neural network in predicting population fluctuations. The performance of an artificial neural network model is compared to two benchmark forecasts: time series mean and the previous data value. Unfortunate as it is, in practise management decisions often have to be made with limited data. Therefore, a notable advantage of neural network modelling is the forecast accuracy even in cases when the time series available are short and noisy, and the processes underlying population fluctuations are not fully understood.