GullsCMR

This Excel file contains raw capture-mark-recapture data of adult breeding gulls (Larus argentatus, L. cachinnans and hybrids) collected in the mixed colony in central Poland in the first sheet named "data". Further explanations in the ReadMe file and in the second sheet in excel file named "description"

Annual post-breeding body condition indices of migrant dunlins in relation to age, sex and origin.

<p>Grey symbols represent mean condition indices for a given group in subsequent years and whiskers indicate 95% confidence intervals. Pale red lines show productivity indices (solid) with 95% confidence intervals (dashed). The dashed grey horizontal line denotes zero, which approximates the mean condition index of all non-juveniles across all years.</p

Yearly numbers of non-juvenile dunlins (adults, immatures: 2nd calendar year) trapped at the Vistula Mouth during southward migration in relation to origin, sex and age.

<p>The total number of captured juveniles per year is also shown.</p

GAMMs used to explain variance in body condition indices of dunlins trapped at the Vistula mouth, Poland, during southward migration periods between 1990 and 2000.

<p>The number in the column entitled ‘smoothers’ indicates whether a model includes a single or two smoothing functions, ‘×’ indicates interaction between fixed factors. All the models included the random effect and the auto-correlation structure (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187370#sec002" target="_blank">Methods</a>). The best-supported model is given in italics. Models 1–12 were used in model-averaging.</p

Model-averaged coefficient estimates from the best-supported GAMMs explaining variation in condition indices of migrant dunlins.

<p>Models with Δ AIC ≤ 10 were considered to produce model-averaged coefficients.</p

Annual post-breeding body condition indices of adult dunlins plotted against productivity indices in 1990–2000 (excluding 1997).

<p>The outlying year 1992 is marked with red. Symbols represent mean productivity and mean condition index for one year, error bars– 95% confidence intervals for both productivity and condition indices. Regression lines (bold–mean, dashed– 95% confidence intervals) are drawn to illustrate the relationship for nine years, after excluding 1992. The relationship for immatures was nearly identical (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187370#pone.0187370.s002" target="_blank">S2 Fig</a>).</p

Population-level body condition correlates with productivity in an arctic wader, the dunlin <i>Calidris alpina</i>, during post-breeding migration

<div><p>Weather and predation constitute the two main factors affecting the breeding success of those Arctic waders whose productivity is highly variable over the years. We tested whether reproductive success is associated with the post-breeding condition of adults, in which in ‘good’ years (with warm weather, plentiful food and low predation pressure) the condition of breeders and their productivity is high. To verify this hypothesis, we used a 10-year dataset comprising 20,792 dunlins <i>Calidris alpina</i>, trapped during migration at a stopover site on the southern Baltic Sea shore. Males were consistently in a slightly worse condition than females, likely due to male-biased parental investment in brood rearing. Annual productivity indices were positively correlated with the respective condition indices of breeders from the Eurasian Arctic, indicating that in ‘good’ years, despite great effort spent on reproduction, breeders leave the breeding grounds in better condition. The pattern did not hold for 1992: productivity was low, but the average condition of adults during migration was the highest noted over the decade. We suggest that the delayed effect of the Mount Pinatubo eruption in the Philippines in 1991, could be responsible for the unexpected high condition of Arctic breeders in 1992. High population-level average condition, coupled with the low productivity could stem from severe weather caused by the volcano eruption a year before and strong predation pressure, which in turn lead to a reduced investment in reproduction. The importance of large-scale episodic phenomena, like this volcano eruption, may blur the statistical associations of wildlife with local environmental drivers.</p></div

Correlation between Zebra Mussel <i>Dreissena polymorpha</i> biomass and biomass of all other food taxa in the digestive tracts of 32 Greater Scaup <i>Aythya marila</i> from the Odra River Estuary during the non-breeding seasons in the years 2008–2013.

<p>Each point represents a single bird.</p

Spatial distribution and abundance of Greater Scaup <i>Aythya marila</i>–mean number of individuals per 1 km² based on censuses in the non-breeding period (October to April, 2002–2014), see also Table 2.

<p>(A). Spatial distribution and biomass of Zebra Mussel <i>Dreissena polymorpha</i> on the 2x2 km modeling grid (B).</p

Estimated smoothing functions modeling Greater Scaup <i>Aythya marila</i> abundance in relation to the area of occurrence of Zebra Mussel <i>Dreissena polymorpha</i> (km², left-hand panel– 5A) and Zebra Mussel density (tons per 1 km², right-hand panel– 5B) from the best-supported model.

<p>Shaded areas denote 95% confidence intervals around the smoothing functions.</p