Percentage of respondents manipulating samples removed from live bats using personal protective equipment.

<p>The total number of scientists (n2 = 365) corresponds to the number of scientists manipulating live bats, and it is therefore a subset of the 587 respondents.</p

Table with individual- and population-level characteristics.

<p>This data set is from:</p> <p><strong>Pilosof et. al (2015) Potential parasite transmission in multi-host networks based on parasite sharing. PLoSOne.</strong></p> <p>Please cite the paper when using it.</p

Rank of importance of the different factors explaining the worldwide decline in bat populations according to respondents (n = 587).

<p>Rank of importance of the different factors explaining the worldwide decline in bat populations according to respondents (n = 587).</p

Matrix depicting infection of individual rodent hosts by parasite species

<p>This data set is from:</p> <p><strong>Pilosof et. al (2015) Potential parasite transmission in multi-host networks based on parasite sharing. PLoSOne.</strong></p> <p>Please cite the paper when using it.</p

Logistic regression of the representation of bats as a dangerous animal by lay-people according to scientists studying bats.

<p><i>(A higher number of asterisks * describes a higher level of statistical significance of the associated factor included in the analysis)</i>.</p

Representation of the percentage of respondents according to their perception of the portrayal of bats in the media (n = 587).

<p>Representation of the percentage of respondents according to their perception of the portrayal of bats in the media (n = 587).</p

Percentage of respondents manipulating live bats using personal protective equipment.

<p>The total number of scientists corresponds to the number of scientists (n1 = 395) manipulating live bats, and is therefore a subset of the 587 respondents.</p

This figure illustrates how aggregation varies with either host-parasite encounters or parasite success (in infecting hosts).

<p>In Panel A we have adopted the values Var(<i>S</i>)/<i>E</i>[<i>S</i>] = 1 and Var(ℰ)<i>E</i>[<i>S</i>] = 1. We show how parasite aggregation varies with the mean number of encounters. The level of aggregation decreases with the number of encounters, and asymptotically approaches a value that depends only on the variance-to-mean ratio of parasite success, i.e., Var(<i>S</i>)/<i>E</i>[<i>S</i>]. In Panel B we have adopted the values Var(<i>ℰ</i>)/<i>E</i>[<i>ℰ</i>] = 1 and Var(<i>S</i>) = 0.5. We show how parasite aggregation varies with the mean parasite success in infecting hosts. The level of aggregation initially decreases with the average success of parasites in infecting their hosts until a minimum is reached at a value of <i>E</i>[<i>S</i>] of </p><p></p><p><mi>M</mi><mo>=</mo></p><p></p><p>Var<mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><mi>E</mi><mo stretchy="false">[</mo><mo>ℰ</mo><mo stretchy="false">]</mo><mo>/</mo>Var<mo stretchy="false">(</mo><mo>ℰ</mo><mo stretchy="false">)</mo></p><p></p><p></p><p></p>, as indicated on the abscissa. The level of aggregation then starts to increase, with an asymptotically achieved slope that is directly proportional to the variance-to-mean-ratio of encounters, i.e., Var(ℰ)/<i>E</i>[ℰ].<p></p

Network structure of host-parasite networks from Central Europe

<p>Network metric for host-parasites communities, based on adjacency matrices, divided into all parasites (ALL), facultative parasites only (FAC). and obligatory parasites only (OPC).</p> <p><strong>network :</strong> unique network identifier (community number, parasite type, host type)<br><strong>connectance :</strong> number of infections / community richness<br><strong>size :</strong> total richness of the community (hosts + parasites)<br><strong>parasites :</strong> number of parasites<br><strong>hosts :</strong> number of hosts<br><strong>nestedness :</strong> NODF measure of nestedness<br><strong>average_host_range :</strong> mean host range, measured using the RR metric - values closer to 0 indicate generality<br><strong>number_modules :</strong> number of community modules found<br><strong>modularity :</strong> Qbip modularity, optimized using the LP-BRIM method<br><strong>null_nestedness :</strong> average NODF of 1000 null replicates<br><strong>nestedness_pvalue</strong> : significancy of the deviation between null and empirical nestedness values<br><strong>null_modularity :</strong> average Qbip of 1000 null replicates<br><strong>modularity_pvalue :</strong> significancy of the deviation between null and empirical nestedness values<br><strong>null_model :</strong> type of null model, either I or II<br><strong>parasite_type :</strong> type of parasites considered (all, facultative, or obligatory)</p

Centrality in single-versus multi-species networks.

<p>Data points depict Pearson correlation coefficients between the rescaled eigenvalue centrality of individuals of a particular species in the single-species network and in the multi-species network. Inset: an example for <i>Rattus tanezumi</i> in Sihanouk. Note that in the inset data points represent individuals, with some overlapping data points (i.e. individuals with identical centrality values).</p