Similar biodiversity of ectomycorrhizal fungi in set-aside plantations and ancient old-growth broadleaved forests

Setting aside overmature planted forests is currently seen as an option for preserving species associated with old-growth forests, such as those with dispersal limitation. Few data exist, however, on the utility of set-aside plantations for this purpose, or the value of this habitat type for biodiversity relative to old-growth semi-natural ecosystems. Here, we evaluate the contribution of forest type relative to habitat characteristics in determining species richness and composition in seven forest blocks, each containing an ancient old-growth stand (> 1000 yrs) paired with a set-aside even-aged planted stand (ca. 180 yrs). We investigated the functionally important yet relatively neglected ectomycorrhizal fungi (EMF), a group for which the importance of forest age has not been assessed in broadleaved forests. We found that forest type was not an important determinant of EMF species richness or composition, demonstrating that set-aside can be an effective option for conserving ancient EMF communities. Species richness of above-ground EMF fruiting bodies was principally related to the basal area of the stand (a correlate of canopy cover) and tree species diversity, whilst richness of below-ground ectomycorrhizae was driven only by tree diversity. Our results suggest that overmature planted forest stands, particularly those that are mixed-woods with high basal area, are an effective means to connect and expand ecological networks of ancient old-growth forests in historically deforested and fragmented landscapes for ectomycorrhizal fungi

K2 Variable Catalogue: Variable Stars and Eclipsing Binaries in K2 Campaigns 1 and 0

We have created a catalogue of variable stars found from a search of the publicly available K2 mission data from Campaigns 1 and 0. This catalogue provides the identifiers of 8395 variable stars, including 199 candidate eclipsing binaries with periods up to 60d and 3871 periodic or quasi-periodic objects, with periods up to 20d for Campaign 1 and 15d for Campaign 0. Lightcurves are extracted and detrended from the available data. These are searched using a combination of algorithmic and human classification, leading to a classifier for each object as an eclipsing binary, sinusoidal periodic, quasi periodic, or aperiodic variable. The source of the variability is not identified, but could arise in the non-eclipsing binary cases from pulsation or stellar activity. Each object is cross-matched against variable star related guest observer proposals to the K2 mission, which specifies the variable type in some cases. The detrended lightcurves are also compared to lightcurves currently publicly available. The resulting catalogue is made available online via the MAST archive at https://archive.stsci.edu/prepds/k2varcat/, and gives the ID, type, period, semi-amplitude and range of the variation seen. We also make available the detrended lightcurves for each object.Comment: Accepted by A&A. 6 pages, 6 figures. Catalogue and lightcurves are available online via MAST at https://archive.stsci.edu/prepds/k2varcat

K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4

We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximise the potential of such surveys, automated data analysis techniques are required. Here we implement a new methodology for variable star classification, through the combination of Kohonen Self Organising Maps (SOM, an unsupervised machine learning algorithm) and the more common Random Forest (RF) supervised machine learning technique. We apply this method to data from the K2 mission fields 0-4, finding 154 ab-type RR Lyraes (10 newly discovered), 377 Delta Scuti pulsators, 133 Gamma Doradus pulsators, 183 detached eclipsing binaries, 290 semi-detached or contact eclipsing binaries and 9399 other periodic (mostly spot-modulated) sources, once class significance cuts are taken into account. We present lightcurve features for all K2 stellar targets, including their three strongest detected frequencies, which can be used to study stellar rotation periods where the observed variability arises from spot modulation. The resulting catalogue of variable stars, classes, and associated data features are made available online. We publish our SOM code in Python as part of the open source PyMVPA package, which in combination with already available RF modules can be easily used to recreate the method.Comment: Accepted for publication in MNRAS, 16 pages, 13 figures. Updated with proof corrections. Full catalogue tables available at https://www2.warwick.ac.uk/fac/sci/physics/research/astro/people/armstrong/ or at the CD

One of the closest exoplanet pairs to the 3:2 Mean Motion Resonance: K2-19b \& c

The K2 mission has recently begun to discover new and diverse planetary systems. In December 2014 Campaign 1 data from the mission was released, providing high-precision photometry for ~22000 objects over an 80 day timespan. We searched these data with the aim of detecting further important new objects. Our search through two separate pipelines led to the independent discovery of K2-19b \& c, a two-planet system of Neptune sized objects (4.2 and 7.2 $R_\oplus$), orbiting a K dwarf extremely close to the 3:2 mean motion resonance. The two planets each show transits, sometimes simultaneously due to their proximity to resonance and alignment of conjunctions. We obtain further ground based photometry of the larger planet with the NITES telescope, demonstrating the presence of large transit timing variations (TTVs), and use the observed TTVs to place mass constraints on the transiting objects under the hypothesis that the objects are near but not in resonance. We then statistically validate the planets through the \texttt{PASTIS} tool, independently of the TTV analysis.Comment: 18 pages, 10 figures, accepted to A&A, updated to match published versio

Author correction : a global database for metacommunity ecology, integrating species, traits, environment and space

Correction to: Scientific Data https://doi.org/10.1038/s41597-019-0344-7, published online 08 January 202

Single transit candidates from K2 : detection and period estimation

Photometric surveys such as Kepler have the precision to identify exoplanet and eclipsing binary candidates from only a single transit. K2, with its 75 d campaign duration, is ideally suited to detect significant numbers of single-eclipsing objects. Here we develop a Bayesian transit-fitting tool (‘Namaste: An Mcmc Analysis of Single Transit Exoplanets’) to extract orbital information from single transit events. We achieve favourable results testing this technique on known Kepler planets, and apply the technique to seven candidates identified from a targeted search of K2 campaigns 1, 2 and 3. We find EPIC203311200 to host an excellent exoplanet candidate with a period, assuming zero eccentricity, of 540+410 −230 d and a radius of 0.51 ± 0.05RJup. We also find six further transit candidates for which more follow-up is required to determine a planetary origin. Such a technique could be used in the future with TESS, PLATO and ground-based photometric surveys such as NGTS, potentially allowing the detection of planets in reach of confirmation by Gaia

Author correction : a global database for metacommunity ecology, integrating species, traits, environment and space

Correction to: Scientific Data https://doi.org/10.1038/s41597-019-0344-7, published online 08 January 202

Understanding ‘it depends’ in ecology: A guide to hypothesising, visualising and interpreting statistical interactions

Ecologists routinely use statistical models to detect and explain interactions among ecological drivers, with a goal to evaluate whether an effect of interest changes in sign or magnitude in different contexts. Two fundamental properties of interactions are often overlooked during the process of hypothesising, visualising and interpreting interactions between drivers: the measurement scale – whether a response is analysed on an additive or multiplicative scale, such as a ratio or logarithmic scale; and the symmetry – whether dependencies are considered in both directions. Overlooking these properties can lead to one or more of three inferential errors: misinterpretation of (i) the detection and magnitude (Type-D error), and (ii) the sign of effect modification (Type-S error); and (iii) misidentification of the underlying processes (Type-A error). We illustrate each of these errors with a broad range of ecological questions applied to empirical and simulated data sets. We demonstrate how meta-analysis, a widely used approach that seeks explicitly to characterise context dependence, is especially prone to all three errors. Based on these insights, we propose guidelines to improve hypothesis generation, testing, visualisation and interpretation of interactions in ecology

Toward a theory of repeat purchase drivers for consumer services

The marketing discipline’s knowledge about the drivers of service customers’ repeat purchase behavior is highly fragmented. This research attempts to overcome that fragmented state of knowledge by making major advances toward a theory of repeat purchase drivers for consumer services. Drawing on means–end theory, the authors develop a hierarchical classification scheme that organizes repeat purchase drivers into an integrative and comprehensive framework. They then identify drivers on the basis of 188 face-to-face laddering interviews in two countries (USA and Germany) and assess the drivers’ importance and interrelations through a national probability sample survey of 618 service customers. In addition to presenting an exhaustive and coherent set of hierarchical repeat-purchase drivers, the authors provide theoretical explanations for how and why drivers relate to one another and to repeat purchase behavior. This research also tests the boundary conditions of the proposed framework by accounting for different service types. In addition to its theoretical contribution, the framework provides companies with specific information about how to manage long-term customer relationships successfully