Correlation plots between functional evenness R_U and the time since last fire.

<p>R²  =  0.306; p<0.05 (two-tailed test, 9999 randomizations).</p

SpeciesAbundance

Species abundance tables for the four investigated species. First column gives the site number, the following columns gives the number of individuals of the respective species (column heading) captured at each site

SpeciesTraits

Species traits collected from sources mentioned in the publication

ConnectivityVariables

Files containing connectivity measures for different binary landscape matrix (brown, non-vertical, urbangreen, vegebrown) at 4 different scales (100, 200, 300 and 400m radius). In each file 3 connectivity measures for each site are listed. Column headings: nr_final = number of site; CWED = edge density; MPI =mean proximity; MNN = mean nearest neighbour distance

LocalVariables

Description of local variables of study sites collected in the field. Column headings A-K: A: Study site number; B: ground or roof site; C: age of vegetation at site; D: height of site; E: area of study site; F: substrate depth in cm; G: type of substrate; H: proportion of bare ground; I: proportion of sedum plants; J: number of plant species; K: abundance of flowering plant

Seasonal Survival Probabilities Suggest Low Migration Mortality in Migrating Bats

<div><p>Migration is adaptive if survival benefits are larger than costs of residency. Many aspects of bat migration ecology such as migratory costs, stopover site use and fidelity are largely unknown. Since many migrating bats are endangered, such information is urgently needed to promote conservation. We selected the migrating Leisler's bat (<i>Nyctalus leisleri</i>) as model species and collected capture-recapture data in southern Switzerland year round during 6 years. We estimated seasonal survival and site fidelity with Cormack-Jolly-Seber models that accounted for the presence of transients fitted with Bayesian methods and assessed differences between sexes and seasons. Activity peaked in autumn and spring, whereas very few individuals were caught during summer. We hypothesize that the study site is a migratory stopover site used during fall and spring migration for most individuals, but there is also evidence for wintering. Additionally, we found strong clues for mating during fall. Summer survival that included two major migratory journeys was identical to winter survival in males and slightly higher in females, suggesting that the migratory journeys did not bear significant costs in terms of survival. Transience probability was in both seasons higher in males than in females. Our results suggest that, similarly to birds, Leisler's bat also use stopover sites during migration with high site fidelity. In contrast to most birds, the stopover site was also used for mating and migratory costs in terms of survival seemed to be low. Transients' analyses highlighted strong individual variation in site use which makes particularly challenging the study and modelling of their populations as well as their conservation.</p></div

Model selection results for survival (Survival model) and transience (Transience model) probabilities of Leisler's bat (<i>Nyctalus leisleri</i>) in southern Switzerland, during the period 2001–2006.

<p>Given the model names, the model deviance (Deviance), the model complexity (pD), the difference of the deviance information criterion between the current and the best model (ΔDIC) and the model weights (w_i). The recapture model was in always the same, i.e. <i>p</i>(sex * year * season). For each survival and transient model the linear equation on the logit scale is given. μ_s: mean survival for each sex; σ²_s: temporal variance of survival for each sex; η_s: mean transience for each sex; ς²_s: temporal variance of transience for each sex.</p

Model selection results for survival (Survival model) and transience (Transience model) probabilities of Leisler's bat (<i>Nyctalus leisleri</i>) sampled in southern Switzerland, during the period 2001–2006 in relation to seasonality.

<p>Only the best models are shown, a complete list of models is shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085628#pone.0085628.s003" target="_blank">Table S2</a> in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085628#pone.0085628.s004" target="_blank">File S1</a>). Given are the model names, the model deviance (deviance), the model complexity (pD), the difference of the deviance information criterion between the current and the best model (ΔDIC) and the model weights (w_i). The recapture model was always the same, i.e. <i>p</i>(sex * year * season). For each survival and transient model the linear equation on the logit scale is given. μ_s: mean survival for each sex; γ_s: fixed seasonal effect on survival for each sex; σ²_s: temporal variance of survival for each sex; η_s: mean transience for each sex; λ_s: fixed seasonal effect on transience for each sex; ς²_s: temporal variance of transience for each sex; m: parameter refers to males only; f: parameter refers to females only.</p

Model selection for recapture probabilities of Leisler's bat (<i>Nyctalus leisleri</i>) in southern Switzerland during the period 2001–2006.

<p>Given are the model names (Recapture model), the model deviance (Deviance), the model complexity (pD), the difference of the deviance information criterion between the current and the best model (ΔDIC) and the model weight (w_i). The models are ordered from the best (lowest ΔDIC) to the worst one.</p

Mean number of captures per control and proportion of males within boxes.

<p>Mean number of captured Leisler's bat (<i>Nyctalus leisleri</i>) individuals per complete control (A) (triangles males, dots females) and mean proportion of males in boxes (B) in four seasons from 2001–2006 in southern Switzerland. Winter refers to the period from November 16th to February 15th, spring to the period from February 16th to May 15th, summer to the period from May 16th to August 15th and autumn to the period from August 16th to November 15th. The vertical lines show the limits of the 95% credible intervals.</p