Growth rates [mm day^-1] of perch in different clusters.

<p>Growth rates [mm day^-1] of perch in different clusters.</p

Cluster analysis based on diet of Eurasian perch as well as stomach content of perch.

<p>Left panel: non-metric multidimensional scaling (MDS) plot of hierarchical cluster analysis based on Bray–Curtis similarity index of stomach content analysis of Eurasian perch (<i>Perca fluviatilis</i>). The MDS plot was drawn for a similarity level of 30% and a stress of 0.01. Right panel: Stomach content [% of wet biomass] of perch in clusters 1 to 3 throughout the season. MI = Macroinvertebrates, ZP = Zooplankton.</p

Morphometric analysis of perch.

<p>Left panel: canonical variates scores of perch in different clusters (the calculation of clusters was based on stomach content analysis, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179339#pone.0179339.g001" target="_blank">Fig 1</a>) throughout the season, depicted along the first and the second canonical variates axis. Different shades indicate the food resource mainly used by perch in the cluster. Significant axes separating the morphs are shown as lines (one axis except for September when two significant axes were found). Right panel: Shape change correlated with the first CVA axis between perch in different clusters, obtained by regressing the shape on the CVA axis scores, depicted as growth vectors. The shape change depicted always starts from the lower trophic level (e.g., the change from planktivorous to piscivorous perch). Lettering inside the shapes of perch indicates which groups have been compared. MI = macroinvertebrates, ZP = zooplankton.</p

Weight distribution (g dry weight) of young-of-the-year perch simulated in the stage-structured model.

<p>Model output is shown for day 1, 15,25,35,45 and 55 of the growth season. Only the small size-cohort was modelled, where perch can be planktivorous, macroinvertivorous or piscivorous.</p

Mean length [mm] of perch in different clusters.

<p>The calculation of clusters was based on stomach content analysis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179339#pone.0179339.g001" target="_blank">Fig 1</a>). Different shades indicate the food resource mainly used by perch in the cluster, letters indicate significant differences of length between clusters (based on Student’s t-tests for June and July and one-way ANOVA and Bonferroni post hoc tests for pairwise comparison for the other sampling dates). Error bars give the standard deviation (SD). MI = macroinvertebrates, ZP = zooplankton.</p

Populations of infected livestock and mosquitoes.

<p>(a), (b) and (c) represent the populations of infectious livestock (solid line) and vectors (dashed line) in patches 1, 2 and 3, respectively. Same values of parameters are adopted in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003388#pntd-0003388-g003" target="_blank">Fig. 3</a>. Populations of both infected livestock and mosquitoes alter their patterns during the festival time. Due to the effect of increased movement rates during the festival, the peak of infected livestock population is not necessary to be the same as that of the infected mosquito population, i.e. patch 2.</p

Appendix A. Partial derivatives with respect to k for the positive and negative branches of Eq. 13.

Partial derivatives with respect to k for the positive and negative branches of Eq. 13

How the starting time of festival preparation impacts patterns of disease outbreaks: infectious classes.

<p>(a)-(c) and (d)-(f), simulations of the populations of infectious livestock and vectors in patches 1–3, respectively. The starting time of festival preparation varies from 2, 3, to 4 weeks ago (n =  days), represented by solid, dashed and dotted lines, respectively. Values of other parameters are identical with those used in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003388#pntd-0003388-g003" target="_blank">Fig. 3</a> and . Unit: daily. When the preparation starts early, we are expecting a larger scale of disease outbreaks due to the higher concentration of livestock, larger scale infectious population appear in patches 1 and 3. However, less number of infectious individuals exist due to the exposed period in patch 2.</p

Basic reproduction number.

<p>(a), (b) and (c) represent the populations of infectious livestock (solid line), the instantaneously local basic reproduction number (dashed line) and the instantaneously global basic reproduction number (dotted line) in patches 1, 2 and 3, respectively. Values of other parameters are identical with those in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003388#pntd-0003388-g003" target="_blank">Fig. 3</a>. The instantaneously global basic reproduction number is computed by considering the three patches as an entirety, while the instantaneously local basic reproduction number is measured only within the local patch based on the current disease dynamics.</p

Interaction between the daily increment in movement speeds and the daily imported number on the size of cumulative infected livestock population.

<p>(a)-(i), simulations of the cumulative numbers of infected livestock at year 4, 29, and 62 (by row) in patches 1, 2, and 3 (by column). Same values of parameters used in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003388#pntd-0003388-g003" target="_blank">Fig. 3</a>.</p