Tancredi CarusoGiulio Virginio ClementeDiego GarlaschelliMatthias C. Rillig...
12页
查看更多>>摘要：Abstract Ecological networks such as plant–pollinator systems and food webs vary in space and time. This variability includes fluctuations in global properties such as the total number and intensity of interactions in the network but also in the number and intensity of local (i.e. node level) species interactions. Fluctuations of species' properties can significantly affect higher‐order network features, for example, robustness and nestedness, and should therefore be taken into account in null models for pattern detection and hypothesis testing. In ecological research, classical null models treat node‐level properties as ‘hard’ constraints that cannot fluctuate. Here, we review and synthesize a set of maximum‐entropy methods that allow for fluctuating (‘soft’) constraints, offering a new addition to the classical toolkit of the ecologist. We illustrate the methods with some practical examples, pointing to currently available open‐source computer codes. We clarify how this approach can be used by experimental ecologists to detect non‐random patterns with null models that not only rewire, but also redistribute interaction strengths by allowing fluctuations in the enforced constraints. Explicit modelling of interspecific heterogeneity through local (i.e. species level) fluctuations of topological and quantitative constraints offers a statistically robust and expanded (e.g. including weighted links) set of tools to understand the assembly and resilience of ecological networks.
Elizabeth N. RudzkiSara E. KuebbingDavid R. ClarkBurhan Gharaibeh...
13页
查看更多>>摘要：Abstract Field research can be an important component of the career trajectories for researchers in numerous academic fields; however, conducting research in field settings poses risks to health and safety, and researchers from marginalized groups often face greater risks than those experienced by other researchers in their fields; If these additional risks are not actively and thoughtfully mitigated, they are likely to hinder the participation of qualified investigators in field research and counteract efforts to improve and promote diversity, equity and inclusion in the field sciences. Here we provide, from our perspectives as co‐authors of a field safety manual for the Department of Biological Sciences at the University of Pittsburgh in Pennsylvania, United States, (A) background on risks and barriers that should be considered when planning and conducting field research and (B) suggestions on how to work as a collaborative team for developing an inclusive field safety manual. As an example of a manual this proposed process has yielded, we have included our own field safety manual written with diversity, equity and inclusion as a central focus. We hope this publication serves as a starting point for those interested in developing a similar document for use in their laboratory group, department or institution.
查看更多>>摘要：Abstract Time‐scaled phylogenetic trees are essential tools in modern biology and node‐based calibrations have been the main approach to time‐tree estimation. But methods for generating the required calibration information are scarce and difficult to parameterize. Here, I present CladeDate, an R package for the generation of empirical calibration information from the fossil record. CladeDate uses simple mathematical models to estimate the age of a clade and its uncertainty based on fossil times. Using a Monte Carlo approach, CladeDate generates empirical densities representing the uncertainty associated with the age of the clade and fits standard probability density functions that can be used in time‐tree inference software such as BEAST2, MrBayes and MCMCtree. I show with simulations that the calibration information generated with CladeDate produces accurate time‐trees and compares favourably with more complex methods. I demonstrate the use CladeDate for the generation of calibration information, including point estimates, upper bounds and calibration densities, for passerine birds. CladeDate is particularly suited for groups with limited fossil information or when assumptions of more complex methods are not met, and provides a general and practical solution to the problem of generating calibration information for divergence time estimation.
Rui BorgesBastien BoussauSebastian H?hnaRicardo J. Pereira...
8页
查看更多>>摘要：Abstract The availability of population genomic data through new sequencing technologies gives unprecedented opportunities for estimating important evolutionary forces such as genetic drift, selection and mutation biases across organisms. Yet, analytical methods that can handle polymorphisms jointly with sequence divergence across species are rare and not easily accessible to empiricists. We implemented polymorphism‐aware phylogenetic models (PoMos), an alternative approach for species tree estimation, in the Bayesian phylogenetic software RevBayes. PoMos naturally account for incomplete lineage sorting, which is known to cause difficulties for phylogenetic inference in species radiations, and scale well with genome‐wide data. Simultaneously, PoMos can estimate mutation and selection biases. We have applied our methods to resolve the complex phylogenetic relationships of a young radiation of Chorthippus grasshoppers, based on coding sequences. In addition to establishing a well‐supported species tree, we found a mutation bias favouring AT alleles and selection bias promoting the fixation of GC alleles, the latter consistent with GC‐biased gene conversion. The selection bias is two orders of magnitude lower than genetic drift, validating the critical role of nearly neutral evolutionary processes in species radiation. PoMos offer a wide range of models to reconstruct phylogenies and can be easily combined with existing models in RevBayes—for example, relaxed clock and divergence time estimation—offering new insights into the evolutionary processes underlying molecular evolution and, ultimately, species diversification.
查看更多>>摘要：Abstract Soundscapes contain rich acoustic information associated with animal behaviours, environmental characteristics and human activities, providing opportunities for predicting biodiversity changes and associated drivers. However, assessing the diversity of animal vocalizations remains challenging due to the interference of environmental and anthropogenic noise. A tool for separating sound sources and delineating changes in acoustic signals is crucial for an effective assessment of acoustic diversity. We present soundscape_IR, an open‐source Python toolbox dedicated to soundscape information retrieval in which nonnegative matrix factorization is applied. This toolbox provides algorithms for supervised and unsupervised source separation (SS). It also enables the use of a snapshot recording for model training and subsequently applying adaptive and semi‐supervised SS when target species produce sounds with varying features and when unseen sound sources are encountered. Our results demonstrated that SS could enhance the vocalizations of target species, characterize the complexity of vocal repertoires and investigate the spatio‐temporal divergence of soundscapes. In tropical forest soundscapes, the application of SS effectively detected the rutting vocalizations of sika deer and revealed a graded structure in their acoustic characteristics. In subtropical estuarine soundscapes, SS automated the process of identifying distinct biotic and abiotic sounds, and the result uncovered divergent sound compositions between inshore and offshore waters. Implementation of SS in soundscape analysis offers a promising method for streamlining the assessment of acoustic diversity in diverse environments. Future application of SS will open new directions to acoustically quantify ecological interactions across individual, species and ecosystem levels.
Bruno SilvaFrederico MestreSílvia BarreiroPedro J. Alves...
7页
查看更多>>摘要：Abstract Passive acoustic monitoring, a non‐invasive technique, is increasingly used to study animal populations and habitats at much larger spatial and temporal scales than standard methods. However, easy to apply tools for reliable detection and classification of signals of interest among hundreds or even thousands of hours of recording are still lacking. We introduce the r package soundClass, a tool to train convolutional neural networks, and employ them to classify sound events in recordings. soundClass provides a sound event classification pipeline, from annotating recordings to automating trained networks usage in real‐life situations. We illustrate the package functionality on bat echolocation calls, bird songs and whale echolocation clicks, showing that the package can be used to train networks for several types of sound events, taxonomic groups and environments; and exemplify its application. This tool facilitates the creation and usage of trained networks and was developed with a strong focus on graphical user interfaces to be used by non‐specialist scientists in statistics and programming.
Jürgen NiedballaJan AxtnerTimm Fabian D?bertAndrew Tilker...
9页
查看更多>>摘要：Abstract Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can, for example, be used to assess forest structural metrics. While CNN‐based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. Here, we present R package imageseg which implements a CNN‐based workflow for general purpose image segmentation using the U‐Net and U‐Net++ architectures in R. The workflow covers data (pre)processing, model training and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understorey vegetation density. We trained the models using large and diverse training datasets from a variety of forest types and biomes, consisting of 2877 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1285 understorey vegetation images. Overall segmentation accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understorey vegetation model (assessed with 821 and 367 images respectively). The image segmentation models performed significantly better than commonly used thresholding methods, and generalized well to data from study areas not included in training. This indicates robustness to variation in input images and good generalization strength across forest types and biomes. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pretrained models. Furthermore, the package facilitates custom image segmentation with single or multiple classes and based on colour or grayscale images, for example, for applications in cell biology or for medical images. Our package is free, open source and available from CRAN. It will enable easier and faster implementation of deep learning‐based image segmentation within R for ecological applications and beyond.
查看更多>>摘要：Abstract Buried scanners are often used to study fine root dynamics by continuously observing them from the images taken at a fixed point. Accordingly, software have been developed to support operators to quantitatively analyse fine roots from scanned images. However, image processing is still time‐consuming work. Deep learning has achieved impressive results as a method for recognising objects in pixel units. In this study, we attempted to automate the image analysis of fine roots using convolutional neural network. Using a root auto tracing and analysis (ARATA), we succeeded in extracting fine roots from scanned images and calculated projected area of fine roots for long‐term dynamics. Our software enables the automatic processing of scanned images acquired at various study sites and accelerates the study of fine root dynamics over extended time periods.
Wilson LaraMaria C. Londo?oIvan GonzalezVictor H. Gutierrez‐Velez...
10页
查看更多>>摘要：Abstract Spatial resources accessible for the derivation of biodiversity indicators of the class ecosystem structure are sparse and disparate, and their integration into computer algorithms for biodiversity monitoring remains problematic. We describe ecochange as an R‐package that integrates spatial analyses with a monitoring workflow for computing routines necessary for biodiversity monitoring. The ecochange comprises three modules for data integration, statistical analysis and graphics. The first module currently downloads and integrates diverse remote sensing products belonging to the essential biodiversity class of structure. The module for statistical analysis calculates RasterStack ecosystem‐change representations across areas of interest; this module also allows focusing on species habitats while deriving changes in a variety of indicators, including ecosystem areas, conditional entropy and fractal dimension indices. The graphics module produces level and bar plots that ease the development of indicator reports. Its functionality is described with an example workflow to calculate ecosystem‐class areas and conditional entropy across an area of interest contained in the package documentation. We conclude that ecochange features procedures necessary to derive ecosystem structure indicators integrating the retrieval of spatially explicit data with the use of workflows to calculate/visualize biodiversity indicators at the national/regional scales.
NSTL
Wiley