Issue 8.1 is now online!
All of the articles in this month’s issue of Methods in Ecology and Evolution are free for the whole year. You will not need a subscription to access or download any of them throughout 2017.
Our first issue of this year contains three Applications articles and two Open Access articles. These five papers will be freely available permanently.
– CDMetaPOP: Cost–Distance Meta-POPulation provides a novel tool for questions in landscape genetics by incorporating population viability analysis, while linking directly to conservation applications.
– Rphylopars: An R implementation of PhyloPars, a tool for phylogenetic imputation of missing data and estimation of trait covariance across species (phylogenetic covariance) and within species (phenotypic covariance). Rphylopars provides expanded capabilities over the original PhyloPars interface including a fast linear-time algorithm, thus allowing for extremely large data sets (which were previously computationally infeasible) to be analysed in seconds or minutes rather than hours.
– ggtree: An R package that provides programmable visualisation and annotation of phylogenetic trees. ggtree can read more tree file formats than other software and allows colouring and annotation of a tree by numerical/categorical node attributes, manipulating a tree by rotating, collapsing and zooming out clades, highlighting user selected clades or operational taxonomic units and exploration of a large tree by zooming into a selected portion.
Peter Mitchell et al. provide this month’s first open access article: ‘Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes‘. In this article the authors investigate whether locational uncertainty and sample size affect the performance and interpretation of fine-scale species distribution models. Their results suggest that seemingly high-performing, fine-scale models can be generated from data containing locational uncertainty, but interpreting predictions from them can be misleading if the predictions are interpreted at scales similar to the spatial errors.
‘Testing and recommending methods for fitting size spectra to data‘ by Andrew Edwards et al. is our second Open Access article in this issue. In this article, the authors demonstrate that estimated size-spectrum slopes are not always comparable between the seven regression-based methods because such methods are not estimating the same parameter. They recommend the use of maximum likelihood estimation when fitting size spectra and provide documented R code for fitting and plotting results.
This month’s cover shows the Trupchun Valley, located in the Swiss National Park (SNP). Studying the development of nature in the absence of human interference has been a key objective since the SNP was established in 1914. Assessing dynamic vegetation changes has played an important role in the SNP’s research tradition, with the establishment of ﬁrst long-term observation plots by Josias Braun-Blanquet already in 1917. Comparing vegetation maps produced for nearly 100 years motivated our research on “How to predict plant functional types using imaging spectroscopy: Linking vegetation community traits, plant functional types and spectral response”.
Despite the vegetation maps being elaborate, they either lack the spatial coverage or detail to allow us to understand how inter- and intraspeciﬁc plant trait variability and diversity patterns are inﬂuenced by topography, microclimate, herbivory and former land use. We were thus excited to ﬁnd strong relationships between plant life/growth forms, strategy types and indicators, and biochemical and structural vegetation traits which determine the spectral response in optical remote sensing instruments. Linking vegetation community’s functional signatures to spectral signatures allows us to accurately predict plant functional types using airborne imaging spectroscopy, substantially advancing our understanding of ecosystem processes in space and time.
Photo © Christian Schmid