Model

Script of the Bayesian model

Data_preparation

Script for preparing the data, before running the model

Detection of the amphibian chytrid fungus Batrachochytrium dendrobatidis in museum specimens of andean aquatic birds: Implications for pathogen dispersal

The occurrence of the pathogenic chytrid fungus Batrachochytrium dendrobatidis (Bd) in the feet of live waterfowl has been documented, but the potential role of birds as dispersers has not been studied. We report the presence of Bd in the feet of preserved aquatic birds in the Bolivian high Andes during the time of drastic amphibian declines in the country. We sampled 48 aquatic birds from the Bolivian Andes that were preserved in museum collections. Birds were sampled for the presence of Bd DNA by swabbing, taking small pieces of tissue from toe webbing, or both. We detected Bd by DNA using quantitative PCR in 42% of the birds sampled via toe tissue pieces. This method was significantly better than swabbing at detecting Bd from bird feet. We confirmed Bd presence by sequencing Bd–positive samples and found 91–98% homology with Bd sequences from GenBank. Our study confirms that aquatic birds can carry Bd and thus may serve as potential vectors of this pathogen across large distances and complex landscapes. In addition, we recommend using DNA from preserved birds as a novel source of data to test hypotheses on the spread of chytridiomycosis in amphibians.Peer Reviewe

Use of airborne lidar data to improve plant species richness and diversity monitoring in lowland and mountain forests

<div><p>We explored the potential of airborne laser scanner (ALS) data to improve Bayesian models linking biodiversity indicators of the understory vegetation to environmental factors. Biodiversity was studied at plot level and models were built to investigate species abundance for the most abundant plants found on each study site, and for ecological group richness based on light preference. The usual abiotic explanatory factors related to climate, topography and soil properties were used in the models. ALS data, available for two contrasting study sites, were used to provide biotic factors related to forest structure, which was assumed to be a key driver of understory biodiversity. Several ALS variables were found to have significant effects on biodiversity indicators. However, the responses of biodiversity indicators to forest structure variables, as revealed by the Bayesian model outputs, were shown to be dependent on the abiotic environmental conditions characterizing the study areas. Lower responses were observed on the lowland site than on the mountainous site. In the latter, shade-tolerant and heliophilous species richness was impacted by vegetation structure indicators linked to light penetration through the canopy. However, to reveal the full effects of forest structure on biodiversity indicators, forest structure would need to be measured over much wider areas than the plot we assessed. It seems obvious that the forest structure surrounding the field plots can impact biodiversity indicators measured at plot level. Various scales were found to be relevant depending on: the biodiversity indicators that were modelled, and the ALS variable. Finally, our results underline the utility of lidar data in abundance and richness models to characterize forest structure with variables that are difficult to measure in the field, either due to their nature or to the size of the area they relate to.</p></div

Technical specifications for the ALS data that were acquired and summary of field variables for both study sites.

<p>Technical specifications for the ALS data that were acquired and summary of field variables for both study sites.</p

Description and summary of forest structure variables derived from ALS data.

<p>With <i>z</i>_<i>i</i> corresponding to the aboveground height of an ALS point <i>i</i>, <i>n</i> to the total number of ALS points, and <i>N</i> to the total number of 1 m² grid cells in the plot. Variables were extracted from circular plots with the same radius as the field plots (9 m at the Lowland site (L) and 15 m at the Mountain site (M) respectively). Vegetation points inferior to 2 m were considered to belong to the understory and were not taken into account as tree vegetation points when computing the following variables: <i>H</i>_<i>mean</i>, , <i>Gini</i>, <i>Cv</i>_<i>LAD</i>, <i>Gap</i>_<i>max</i>, <i>C</i>_<i>f</i>, <i>C</i>_<i>r</i>.</p

Process diagram describing the modelling framework developed to link species richness and abundance with both environmental and ALS variables.

<p>For the sake of clarity, the model presented in this diagram is a simplified shape of the real model, presented in Appendix. Analyses were carried out on the results from the second step.</p

Number of ALS variables which were negative non-negligible or positive non-negligible when used in floristic models.

<p>Abundance and richness models were considered in both the Lowland and Mountain sites. The ALS variables were extracted from circular plots within the same radius as the field plots (9 m at the Lowland site and 15 m at the Mountain site), and also with radii of 50 m, 100 m and 200 m.</p

<i>ΔDIC</i> for floristic models depending on abundance and richness indicators in the Lowland and Mountain sites.

<p>Dark horizontal lines represent the median; boxes represent the 25th and 75th percentiles; whiskers the 5th and 95th percentiles; outliers are represented by dots. The lower the <i>ΔDIC</i>, the more the model is improved by the ALS variable.</p

Effect of tDCS condition on recognition RTs.

<p>For faces encoded while under tDCS influence, the right anodal/left cathodal condition yielded faster recognition RTs than the right cathodal/left anodal (*<i>P</i><0.05) and sham conditions (**<i>P</i><0.01), ∼72 hours after encoding. There was no effect of tDCS on RTs of novel faces.</p