Does the American mink displace the European polecat? A need for more research on interspecific competition between invasive and native species

Introduced alien species can negatively affect native competitors by reducing their populations or eliminating them from ecosystems. However, studies do not always find evidence for anticipated impacts, and changes in native populations can be difficult to estimate. Interactions between the invasive American mink Neovison vison and native European polecat Mustela putorius have been studied in several countries, but the mink’s impact on polecat populations at a large spatiotemporal scale remains unclear. In the years 1995–2018, we live-trapped mink and polecats at 60 study sites in Poland, and we analysed hunting bags of mink and polecats from the years 2009–2018. During 13,766 trap-nights, we captured 905 individuals. Mink comprised 91.2% and polecats 8.8% of trapped animals. The mean mink and polecat trappability was 6 and 0.6 individuals per 100 trap-nights, respectively. At rivers, polecat and mink trappability were negatively correlated, whereas at lakes, they were not correlated. The sex ratio of trapped polecats was more skewed toward males than that of mink. Mink comprised 63.6% and polecats 36.4% of 59,831 animals killed by hunters. Over 10 years, the numbers of mink shot annually increased slightly, whereas the numbers of polecat decreased slightly. There was a positive correlation between numbers of mink and polecats shot annually. We found weak evidence that at a large spatiotemporal scale, the invasion of mink has led to a decline in polecat numbers. Although the datasets we analysed were based on large samples, they were insufficient to show evidence of competitive interactions between these two mustelids.

Survival of wild and farmed-released mallards: the Swedish example

More than three million farmed mallards are released annually for hunting purposes in Europe. The ecological impact of these releases depends on how many birds survive to join the wild breeding population. We estimated annual survival in farmed-released and wild-caught Swedish mallards, using mark-recapture data. In 2011–2018, we ringed 13,533 farmed ducklings before release (26.5% recovered). Most recoveries were birds shot at the release site, while only about 4% were found >3 km away. In 2002–2018, 19,820 wild mallards were ringed in Sweden, yielding 1369 (6.9%) recoveries. Like in farmed-released birds, most recoveries were by hunting, but 91.1% of recovered wild mallards were >3 km away from the ringing site. Annual survival rate in farmed-released mallards (ringed as pulli) was 0.02. In wild mallards (ringed as fledged or fully grown), annual survival was lower in females (0.64) than in males (0.71). At two sites in 2018, farmed ducklings were released in two batches 3 weeks apart to study the effect of early versus late release date, while controlling for body condition (BCI). Ducklings released early had a higher BCI and were recovered earlier (lower longevity) than those released late. Individual BCI and longevity were not correlated in recovered ducklings. Based on our estimate of annual survival in farmed-released mallards, a substantial number, i.e., 5000 (95% CI, 3040–6960), join the wild population annually. Despite being fed, a large proportion of released ducklings does not survive until the hunting season. Early releases may maximize pre-hunting survival. Repeated releases may prolong hunting opportunities and increase hunting bags.

Strength of correlation between wildlife collision data and hunting bags varies among ungulate species and with management scale

Most European ungulate species are increasing in numbers and expanding their range. For the management and monitoring of these species, 64% of European countries rely on indirect proxies of abundance (e.g., hunting bag statistics). With increasing ungulate numbers, data on ungulate-vehicle collisions (UVC) may provide an important and inexpensive, complementary data source. Currently, it is unclear how bag statistics compare with UVC. A direct comparison of these two indices is important because both are used in ungulate management. We evaluated the relationship between UVC and ungulate hunting bags across bioclimatic, regional, and local scales, using five time lags (t−3 to t+1) for the five most common wild ungulate species in Sweden. For all species, hunting bags and UVC correlated positively, but correlation strength and time lags varied across scales and among species. The two indices correlated most strongly at the local management scale. Correlation between both indices was strong for the smaller deer species and wild boar, in particular, but much weaker for moose where we found the best fit using a 2-year time lag. For the other species, indices from the same year correlated best. We argue that the reason for moose data behaving differently is that, in Sweden, moose are formally managed using a 3-year time plan, while the other species are not. Accordingly, moose hunting bags are influenced more strongly by density-independent processes than bags of the other species. Consequently, the mismatch between the two indices may generate conflicting conclusions for management depending on the method applied.

Using the Darwin Core Standard for Estimated Records

The Darwin Core Standard is a widely shared data standard within the ecological community. Despite recent developments on sampling events and abiotic records (De Pooter 2017), the Darwin Core only allows recording raw data, while public policies and biodiversity evaluations often need synthetic, estimated data. To be reliable, those estimation records, such as population size, population density, community diversity, and primary production, need to display statistical characteristics, such as standard error. Within the Enetwild consortium (https://enetwild.com/), financed by the European Food and Safety Authority (EFSA), we face this issue while aggregating data across European countries on wildlife surveys: occurrences, abundances and hunting bags. We therefore propose a revision to the Darwin Core Standard, taking advantage of the recent development corresponding to the Event core of the European Biodiversity Observatory Network and the extended measurement or fact of the Ocean Biogeographic Information System (De Pooter 2017). The proposed nested extended measurement or fact extension allows the recording of estimated densities with their confidence intervals or other precision measurements. It allows a measurement (the confidence interval, the variance) to be nested within another measurement value (e.g., the point estimation). We also provide metadata to record the useful statistical procedure information to best evaluate and use these data. We believe this development will be useful for all institutions aggregating results, especially the international Essential Biodiversity Variables community, allowing them to aggregate results data along with raw data. The full technical report is published on EFSA website (Enetwild consortium et al. 2020).

European badger

The European badger (Meles meles) is the largest member of the mustelid family (Mustelidae) found in Poland. It lives almost all over Eurasia, except northern areas of the former Soviet Union and the Scandinavian countries. The European badger most often inhabits densely wooded areas. It feeds on both animal and plant foods, and is classified as a carnivore. Badger skins are not fully used in the fur industry They are usually used for production of skin rugs and accessories, such as hunting bags.

European badger

The European badger (Meles meles) is the largest member of the mustelid family (Mustelidae) found in Poland. It lives almost all over Eurasia, except northern areas of the former Soviet Union and the Scandinavian countries. The European badger most often inhabits densely wooded areas. It feeds on both animal and plant foods, and is classified as a carnivore. Badger skins are not fully used in the fur industry They are usually used for production of skin rugs and accessories, such as hunting bags.

Staging and wintering Taiga Bean Geese Anser fabalis fabalis in north-east Scania, south Sweden

In the municipalities of Bromölla and Kristianstad, south Sweden, monthly counts of Bean Geese have been carried out during October–March/April since November 1976. The seasonal peak count was up to 1987/1988 recorded in March, during the following six seasons in January, and from 1994/1995 onwards in November or December. April numbers decreased from more than 5,000 birds in 1977 to hardly any at all from 1997 onwards. Fewer Bean Geese were counted up to the 1986/1987 season than thereafter. In most of the last 25 seasons, the number of Taiga Bean Geese Anser fabalis fabalis in north-east Scania peaked at about 20% of the total Western Palearctic population, with a highest count of 24,000 birds in December 1997. Most or all Bean Geese left north-east Scania during severe winters. Checks of staging bean goose flocks and hunting bags showed that, except for Lake Hammarsjön from 2004/2005 onwards and a few flocks in the other areas, the Tundra Bean Goose Anser serrirostris rossicus was quite rare in the region.