Muscarella_and_Uriarte_ProcB

Data to conduct analyses included in Muscarella & Uriarte, "Do community-weighted mean functional traits reflect optimal strategies?" Please see metadata file for more details

Presentation_1_Drone microrelief analysis to predict the presence of naturally regenerated seedlings.pdf

Three-dimensional (3D) mapping and unmanned aerial vehicles (UAVs) are essential components of the future development of forestry technology. Regeneration of forest stands must be ensured according to the law in the required quality and species composition. Forest management focuses on the optimization of economic costs and quality-assured seedlings. Predicting the suitability of the plots’ environment for natural forest regeneration can contribute to better strategic planning and save time and money by reducing manual work. Although the savings may be considered negligible on small forested plots, they are significant for large cleared areas, such as those harvested after large beetle infestations or strong windstorms, which are increasingly common in European forests. We present a methodology based on spatial analysis and 3D mapping to study the microrelief and surrounding of recently cleared areas. We collected data on four plots in the spring and autumn of a single year after the harvest of four Norway spruce [Picea abies (L.) Karst.] stands near Radlice, Czechia using a multirotor Phantom 4 Pro UAV with a red, green, blue (RGB) camera. We used RGB imagery to compute microrelief data at a very high spatial resolution and the surrounding forest stands after harvesting. We used the microrelief data to estimate the amount of water accumulation and incoming solar radiation across the sites. Based on presence data of newly-established seedlings, we used linear mixed effects models to create a suitability map for each site. Model variables included topographic wetness index, solar area radiation, fencing, type of soil preparation, and distance to the nearest mature forest edge. The topographic wetness index and fencing had strong positive influence on seedling establishment, while solar radiation had a negative influence. Our proposed methodology could be used to predict spontaneous regeneration on cleared harvest areas, or it can estimate how much area is suitable for regeneration, which can lead to important investment decisions.</p

The net relatedness index (NRI) (A, B) and nearest taxon index (NTI; C, D) based on species occurrence records from Little & Wadworth [63] and Little <i>et al</i>. [64] versus reserve area [90] for 15 state forests in Puerto Rico.

<p>Leftmost panels are based on a null model using the full island species pool; right panels (B, D) are based on species pools restricted to primary soil types. Forests are sorted from left to right in order of their mean annual precipitation. Positive values indicate phylogenetic clustering and negative values indicate phylogenetic evenness. Filled symbols indicate values that are significantly different from a null model. Refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112843#pone-0112843-t001" target="_blank">Table 1</a> for forest codes.</p

SEV_BRANCH_2013_1.0

Sevilleta Long-Term Ecological Research Area LTER (34°23′11″N,106°31′46″W; 1,911 m asl) in the Los Pinos Mountains of the Sevilleta National Wildlife Refuge, NEW MEXICO, US

Biodiversity as a multidimensional construct: Alternative SEM Models, Placement of Number of Taxa, and Correlations from Biodiversity as a multidimensional construct: a review, framework and case study of herbivory's impact on plant biodiversity

This file contains additional information in support of the study

Appendix A. Supplemental methods for Markov Chain Monte Carlo and additional results for ontogenetic changes in growth and survival.

Supplemental methods for Markov Chain Monte Carlo and additional results for ontogenetic changes in growth and survival

Supplement 1. JAGS and Stan code used in demographic models.

<h2>File List</h2><div> <p><a href="Stan_survival_model.txt">Stan_survival_model.txt</a> (MD5: 77ea91b7aa1d1294c63ab5a3e1a8b12c)</p> <p><a href="JAGS_survival_model.txt">JAGS_survival_model.txt</a> (MD5: 0a07b28a34f8deec8c02e208f79498d9)</p> <p><a href="JAGS_growth_model.txt">JAGS_growth_model.txt</a> (MD5: a924025e5b4fc49629d5a118367f6da7)</p> </div><h2>Description</h2><div> <p>Stan_survival_model.txt contains code for a Stan model of seedling survival.</p> <p>JAGS_survival_model.txt contains code for a JAGS model of survival for trees at least 1 cm DBH.</p> <p>JAGS_growth_model.txt contains code for a JAGS model of growth for trees at least 1 cm DBH</p> </div

Functional trait data

Functional trait dat

Species abundances

Contains the number of individuals in old-growth and secondary forest for each species at each site

Site locations

Latitude and longitude of sites used in this study