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  • Thinking like a naturalist: Enhancing computer vision of citizen science images by harnessing contextual data
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2020-01-13
    J. Christopher D. Terry; Helen E. Roy; Tom A. August

    The accurate identification of species in images submitted by citizen scientists is currently a bottleneck for many data uses. Machine learning tools offer the potential to provide rapid, objective and scalable species identification for the benefit of many aspects of ecological science. Currently, most approaches only make use of image pixel data for classification. However, an experienced naturalist would also use a wide variety of contextual information such as the location and date of recording. Here, we examine the automated identification of ladybird (Coccinellidae) records from the British Isles submitted to the UK Ladybird Survey, a volunteer‐led mass participation recording scheme. Each image is associated with metadata; a date, location and recorder ID, which can be cross‐referenced with other data sources to determine local weather at the time of recording, habitat types and the experience of the observer. We built multi‐input neural network models that synthesize metadata and images to identify records to species level. We show that machine learning models can effectively harness contextual information to improve the interpretation of images. Against an image‐only baseline of 48.2%, we observe a 9.1 percentage‐point improvement in top‐1 accuracy with a multi‐input model compared to only a 3.6% increase when using an ensemble of image and metadata models. This suggests that contextual data are being used to interpret an image, beyond just providing a prior expectation. We show that our neural network models appear to be utilizing similar pieces of evidence as human naturalists to make identifications. Metadata is a key tool for human naturalists. We show it can also be harnessed by computer vision systems. Contextualization offers considerable extra information, particularly for challenging species, even within small and relatively homogeneous areas such as the British Isles. Although complex relationships between disparate sources of information can be profitably interpreted by simple neural network architectures, there is likely considerable room for further progress. Contextualizing images has the potential to lead to a step change in the accuracy of automated identification tools, with considerable benefits for large‐scale verification of submitted records.

  • Koe: Web‐based software to classify acoustic units and analyse sequence structure in animal vocalizations
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2020-01-09
    Yukio Fukuzawa; Wesley H. Webb; Matthew D.M. Pawley; Michelle M. Roper; Stephen Marsland; Dianne H. Brunton; Andrew Gilman

    Classifying acoustic units is often a key step in studying repertoires and sequence structure in animal communication. Manual classification by eye and ear remains the primary method, but new tools and techniques are urgently needed to expedite the process for large, diverse datasets. Here we introduce Koe, an application for classifying and analysing animal vocalizations. Koe offers bulk‐labelling of units via interactive ordination plots and unit tables, as well as visualization and playback, segmentation, measurement, data filtering/exporting and new tools for analysing repertoire and sequence structure – in an integrated environment. We demonstrate Koe with a real‐world case study of New Zealand bellbird Anthornis melanura songs from an archipelago metapopulation. Having classified 21,500 units in Koe, we compare repertoires and sequence structure between sites and sexes. Koe is web‐based (koe.io.ac.nz.) and easy to use, making it ideal for collaboration, education and citizen science. By enabling large‐scale, high‐resolution classification and analysis of animal vocalizations, Koe expands the possibilities for bioacoustics research.

  • LeafByte: A mobile application that measures leaf area and herbivory quickly and accurately
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2020-01-07
    Zoe L. Getman‐Pickering; A. Campbell; Nicholas Aflitto; Ari Grele; Julie K. Davis; Todd A. Ugine

    In both basic and applied studies, quantification of herbivory on foliage is a key metric in characterizing plant–herbivore interactions, which underpin many ecological, evolutionary and agricultural processes. Current methods of quantifying herbivory are slow or inaccurate. We present LeafByte, a free iOS application for measuring leaf area and herbivory. LeafByte can save data automatically, read and record barcodes, handle both light and dark coloured plant tissue, and be used non‐destructively. We evaluate its accuracy and efficiency relative to existing herbivory assessment tools. LeafByte has the same accuracy as ImageJ, the field standard, but is 50% faster. Other tools, such as BioLeaf and grid quantification, are quick and accurate, but limited in the information they can provide. Visual estimation is quickest, but it only provides a coarse measure of leaf damage and tends to overestimate herbivory. LeafByte is a quick and accurate means of measuring leaf area and herbivory, making it a useful tool for research in fields such as ecology, entomology, agronomy and plant science.

  • Dermal denticle assemblages in coral reef sediments correlate with conventional shark surveys
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2020-01-04
    Erin M. Dillon; Kevin D. Lafferty; Douglas J. McCauley; Darcy Bradley; Richard D. Norris; Jennifer E. Caselle; Graziella V. DiRenzo; Jonathan P.A. Gardner; Aaron O'Dea

    1.It is challenging to assess long‐term trends in mobile, long‐lived, and relatively rare species such as sharks. Despite ongoing declines in many coastal shark populations, conventional surveys might be too fleeting and too recent to describe population trends over decades to millennia. Placing recent shark declines into historical context should improve management efforts as well as our understanding of past ecosystem dynamics.

  • polygene: Population genetics analyses for autopolyploids based on allelic phenotypes
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-29
    Kang Huang; Derek W. Dunn; Kermit Ritland; Baoguo Li

    Polyploidy has appeared in almost every ancestral plant lineage, and in extant species, occurs frequently. When present, polyploidy presents problems for genetic data analysis, which are caused by both genotypic ambiguities and double‐reduction. To address these problems, we developed a new software package, polygene, which enables the estimation of genotypic frequencies for a number of polysomic inheritance models. Specifically, polygene obtains posterior probabilities for genotypes hidden within allelic phenotypes. Comprehensive modes of genetic analyses are provided by polygene, which include genetic diversity analysis, tests for allelic phenotypic or genotypic distributions, linkage disequilibrium and genetic differentiation, genetic distance analysis, principal coordinates analysis, hierarchical clustering analysis, individual inbreeding coefficient estimation, individual heterozygosity index estimation, population assignment, pairwise relatedness estimation, parentage analysis, analysis of molecular variance and Bayesian clustering. polygene enables easy and convenient allelic phenotype‐ or genotype‐based analysis for both autopolyploids and diploids. polygene will thus facilitate molecular ecology research involving autopolyploids.

  • Camera transects as a method to monitor high temporal and spatial ephemerality of flying nocturnal insects
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-26
    Ireneusz Ruczyński; Zuzanna Hałat; Marcin Zegarek; Tomasz Borowik; Dina K. N. Dechmann

    The current global decline of insects will have profound cascading effects as insects serve numerous roles in ecosystems. Effective but simple methods are needed to describe spatial and temporal distribution of flying insects in detail. This applies especially to important but short‐lived phenomena such as insect swarms. We developed, tested and implemented a non‐invasive unbiased method with camera transects to measure spatio‐temporal fluctuations in the abundance of nocturnal flying insects in different habitats. To test the sensitivity of the method, we then tested for the influence of environmental factors on this abundance. Our results show that the method is useful for the temporal and spatial comparison of insect abundances. Astonishingly, over 90% of the 63,180 photos lacked insects. We found profound differences in insect abundance and dynamic changes between the studied habitats. Photos with a large number of insects were rare, but occurred predominantly during the warmest period (June/July) and shortly after sunset. Our findings emphasize the importance of quantifying the dynamics of flying insects at a high spatio‐temporal resolution. This method can be expanded to monitor long‐ and short‐term changes in nocturnal insect abundance even at continental scales. With proper development, the camera transects we describe could be used for insect monitoring similar to the way camera traps are used to monitor terrestrial vertebrate populations, and could become an important tool for addressing the current mass disappearances of insects.

  • Joint species distribution modelling with the R‐package Hmsc
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-25
    Gleb Tikhonov; Øystein H. Opedal; Nerea Abrego; Aleksi Lehikoinen; Melinda M.J. de Jonge; Jari Oksanen; Otso Ovaskainen

    Joint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analyzing data in community ecology. Hierarchical Modelling of Species Communities (HMSC) is a general and flexible framework for fitting JSDMs. HMSC allows the integration of community ecology data with data on environmental covariates, species traits, phylogenetic relationships, and the spatio‐temporal context of the study, providing predictive insights into community assembly processes from non‐manipulative observational data of species communities. The full range of functionality of HMSC has remained restricted to Matlab users only. To make HMSC accessible to the wider community of ecologists, we introduce Hmsc 3.0, a user‐friendly R implementation of the framework described in Ovaskainen et al. (Ecology Letters, 20 (5), 561–576, 2017) and further extended in several later publications. We illustrate the use of the package by applying Hmsc 3.0 to a range of case studies on real and simulated data. The real data consist of bird counts in a spatio‐temporally structured dataset, environmental covariates, species traits and phylogenetic relationships. Vignettes on simulated data involve single‐species models, models of small communities, models of large species communities, and models for large spatial data. We demonstrate the estimation of species responses to environmental covariates and how these depend on species traits, as well as the estimation of residual species associations. We demonstrate how to construct and fit many kinds of models, how to examine MCMC convergence, how to examine the explanatory and predictive powers of the models, how to assess parameter estimates, and how to make predictions. We further demonstrate how Hmsc 3.0 can be applied to normally distributed data, count data, and presence‐absence data. The package, along with the extended vignettes, makes JSDM fitting and post‐processing easily accessible to ecologists familiar with R.

  • Ten years of Methods in Ecology and Evolution
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-20
    Robert P Freckleton

    The start of 2020 marks the end of the first decade of Methods in Ecology and Evolution. When we launched the journal in 2010, we did so because of feedback from the community that there was a need for a journal that promoted the publication of new methods. In Issue 1, the launch issue, we published an editorial to summarise the aims and ambitions for the journal; the need for the new journal was outlined, as well as some of our objectives and strategies for developing it.

  • MOTMOT: Models of trait macroevolution on trees (an update)
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-20
    M.N Puttick; T Ingram; M Clarke; G.H Thomas

    1. The disparity in species’ traits arises through variation in the tempo and mode of evolution over time and between lineages. Understanding these patterns is a core goal in evolutionary biology.

  • CamoGAN: Evolving optimum camouflage with Generative Adversarial Networks
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-17
    Laszlo Talas; John G. Fennell; Karin Kjernsmo; Innes C. Cuthill; Nicholas E. Scott‐Samuel; Roland J. Baddeley

    One of the most challenging issues in modelling the evolution of protective colouration is the immense number of potential combinations of colours and textures. We describe CamoGAN, a novel method to exploit Generative Adversarial Networks to simulate an evolutionary arms race between the camouflage of a synthetic prey and its predator. Patterns evolved using our methods are shown to provide progressively more effective concealment and outperform two recognized camouflage techniques, as validated by using humans as visual predators. We believe CamoGAN will be highly useful, particularly for biologists, for rapidly developing and testing optimal camouflage or signalling patterns in multiple environments.

  • The Chord‐Normalized Expected Species Shared (CNESS)‐distance represents a superior measure of species turnover patterns
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-13
    Yi Zou; Jan Christoph Axmacher

    Measures of β‐diversity characterizing the difference in species composition between samples are commonly used in ecological studies. Nonetheless, commonly used dissimilarity measures require high sample completeness, or at least similar sample sizes between samples. In contrast, the Chord‐Normalized Expected Species Shared (CNESS) dissimilarity measure calculates the probability of collecting the same set of species in random samples of a standardized size, and hence is not sensitive to completeness or size of compared samples. To date, this index has enjoyed limited use due to difficulties in its calculation and scarcity of studies systematically comparing it with other measures. Here, we developed a novel R function that enables users to calculate ESS (Expected Species Shared)‐associated measures. We evaluated the performance of the CNESS index based on simulated datasets of known species distribution structure, and compared CNESS with more widespread dissimilarity measures (Bray–Curtis index, Chao–Sørensen index, and proportionality‐based Euclidean distances) for varying sample completeness and sample sizes. Simulation results indicated that for small sample size (m) values, CNESS chiefly reflects similarities in dominant species, while selecting large m values emphasizes differences in the overall species assemblages. Permutation tests revealed that CNESS has a consistently low CV (coefficient of variation) even where sample completeness varies, while the Chao–Sørensen index has a high CV particularly for low sampling completeness. CNESS distances are also more robust than other indices with regards to undersampling, particularly when chiefly rare species are shared between two assemblages. Our results emphasize the superiority of CNESS for comparisons of samples diverging in sample completeness and size, which is particularly important in studies of highly mobile and species‐rich taxa where sample completeness is often low. Via changes in the sample size parameter m, CNESS furthermore cannot only provide insights into the similarity of the overall distribution structure of shared species, but also into the differences in dominant and rare species, hence allowing additional, valuable insights beyond the capability of more widespread measures.

  • LeWoS: A Universal Leaf‐wood Classification Method to Facilitate the 3D Modelling of Large Tropical Trees Using Terrestrial LiDAR
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-09
    Wang Di, Stéphane Momo Takoudjou, Eric Casella

    Leaf‐wood separation in terrestrial LiDAR data is a prerequisite for nondestructively estimating biophysical forest properties such as standing wood volumes and leaf area distributions. Current methods have not been extensively applied and tested on tropical trees. Moreover, their impacts on the accuracy of subsequent wood volume retrieval were rarely explored. We present LeWoS, a new fully automatic tool to automate the separation of leaf and wood components, based only on geometric information at both the plot and individual tree scales. This data‐driven method utilizes recursive point cloud segmentation and regularization procedures. Only one parameter is required, which makes our method easily and universally applicable to data from any LiDAR technology and forest type. We conducted a two‐fold evaluation of the LeWoS method on an extensive data set of 61 tropical trees. We first assessed the point‐wise classification accuracy, yielding a score of 0.91 ± 0.03 in average. Secondly, we evaluated the impact of the proposed method on 3D tree models by cross‐comparing estimates in wood volume and branch length with those based on manually separated wood points. This comparison showed similar results, with relative biases of less than 9% and 21% on volume and length, respectively. LeWoS allows an automated processing chain for non‐destructive tree volume and biomass estimation when coupled with 3D modelling methods. The average processing time on a laptop was 90s for 1 million points. We provide LeWoS as an open source tool with an end‐user interface, together with a large data set of labelled 3D point clouds from contrasting forest structures. This study closes the gap for stand volume modelling in tropical forests where leaf and wood separation remain a crucial challenge.

  • The handbook for standardized field and laboratory measurements in terrestrial climate change experiments and observational studies (ClimEx)
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-09
    Aud H. Halbritter, Hans J. De Boeck, Amy E. Eycott, Sabine Reinsch, David A. Robinson, Sara Vicca, Bernd Berauer, Casper T. Christiansen, Marc Estiarte, José M. Grünzweig, Ragnhild Gya, Karin Hansen, Anke Jentsch, Hanna Lee, Sune Linder, John Marshall, Josep Peñuelas, Inger Kappel Schmidt, Ellen Stuart‐Haëntjens, Peter Wilfahrt, , Vigdis Vandvik

    Climate change is a world‐wide threat to biodiversity and ecosystem structure, functioning and services. To understand the underlying drivers and mechanisms, and to predict the consequences for nature and people, we urgently need better understanding of the direction and magnitude of climate change impacts across the soil–plant–atmosphere continuum. An increasing number of climate change studies are creating new opportunities for meaningful and high‐quality generalizations and improved process understanding. However, significant challenges exist related to data availability and/or compatibility across studies, compromising opportunities for data re‐use, synthesis and upscaling. Many of these challenges relate to a lack of an established ‘best practice’ for measuring key impacts and responses. This restrains our current understanding of complex processes and mechanisms in terrestrial ecosystems related to climate change. To overcome these challenges, we collected best‐practice methods emerging from major ecological research networks and experiments, as synthesized by 115 experts from across a wide range of scientific disciplines. Our handbook contains guidance on the selection of response variables for different purposes, protocols for standardized measurements of 66 such response variables and advice on data management. Specifically, we recommend a minimum subset of variables that should be collected in all climate change studies to allow data re‐use and synthesis, and give guidance on additional variables critical for different types of synthesis and upscaling. The goal of this community effort is to facilitate awareness of the importance and broader application of standardized methods to promote data re‐use, availability, compatibility and transparency. We envision improved research practices that will increase returns on investments in individual research projects, facilitate second‐order research outputs and create opportunities for collaboration across scientific communities. Ultimately, this should significantly improve the quality and impact of the science, which is required to fulfil society's needs in a changing world.

  • The FrogPhone: A novel device for real‐time frog call monitoring
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-04
    Adrian Garrido Sanchis, Lorenzo Bertolelli, Anke Maria Hoefer, Marta Yebra Alvarez, Kumudu Munasinghe

    Auditory surveys are instrumental for a range of absent–present studies of vocal species. However, they are generally time and resource‐intensive. To keep the cost of time and travel associated with auditory surveys at a minimum, we have developed a device, the FrogPhone, for remote monitoring. The system simulates the main features of a phone device working within the 3G/4G Cellular network frequencies or a satellite network. By dialling up the invention, the user can conduct an auditory survey in real time and obtain environmental data (e.g. water and air temperature) through a text message sent to his mobile phone. The FrogPhone allows extending auditory survey projects while decreasing overall project costs and enable surveys that previously were too costly.

  • ProtASR2: Ancestral Reconstruction of Protein Sequences accounting for Folding Stability
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-05
    Miguel Arenas, Ugo Bastolla

    1. The ancestral sequence reconstruction (ASR) is a molecular evolution technique that provides applications to a variety of fields such as biotechnology and biomedicine. In order to infer ancestral sequences with realistic biological properties, the accuracy of ASR methods is crucial. We previously developed an ASR framework for proteins, called ProtASR, which is based on our site‐specific stability constrained substitution (SCS) model with selection on protein folding stability against both unfolding and misfolding. This model improved the empirical substitution models traditionally applied in ASR without increasing the computational complexity. However, it adopted a global exchangeability matrix, an approximation that we overcome here by considering site‐specific exchangeability matrices based on the Halpern‐Bruno approach.

  • Optimizing dissection, sample collection and cell isolation protocols for frugivorous bats
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-02
    Aaron T. Irving, Justin H. J. Ng, Victoria Boyd, Charles‐Antoine Dutertre, Florent Ginhoux, Milou H. Dekkers, Joanne Meers, Hume E. Field, Gary Crameri, Lin‐Fa Wang

    Bats are becoming increasingly important as an experimental model due to their unique biological features. These include the ability of powered flight, minimal consequences from a heightened metabolic state, extended longevity in most species and minimal inflammation in response to most otherwise pathogenic viruses. To date there has been limited work done on the optimal procedures for necropsy, extraction of tissues or preparation of cell suspensions for downstream experimental work. Here we use Pteropus alecto black flying fox as an example model of the fruit bat to develop optimal procedures for anaesthetizing, necropsy methods, safety, sequence and protocols for cell/tissue extraction and isolation protocols. These methods were successfully used to yield high‐quality RNA, DNA and protein samples from tissues along with viable cells for various molecular and immunological studies. Procedures utilized are suitable for comparative biology studies with most protocols being directly modified from those used in mice and humans. While mainly optimized for the larger fruit bats (flying foxes) in this study, the majority of protocols can readily be adapted to all species of bats. This study provides the framework for greater consistency with in vivo bat experiments, application for comparative biology studies and greater engagement of the bat community for suitable protocols to be harmoniously adopted.

  • Quantitative Colour Pattern Analysis (QCPA): A comprehensive framework for the analysis of colour patterns in nature
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-02
    Cedric P. van den Berg, Jolyon Troscianko, John A. Endler, N. Justin Marshall, Karen L. Cheney

    To understand the function of colour signals in nature, we require robust quantitative analytical frameworks to enable us to estimate how animal and plant colour patterns appear against their natural background as viewed by ecologically relevant species. Due to the quantitative limitations of existing methods, colour and pattern are rarely analysed in conjunction with one another, despite a large body of literature and decades of research on the importance of spatio‐chromatic colour pattern analyses. Furthermore, key physiological limitations of animal visual systems such as spatial acuity, spectral sensitivities, photoreceptor abundances and receptor noise levels are rarely considered together in colour pattern analyses. Here, we present a novel analytical framework, called the Quantitative Colour Pattern Analysis (QCPA). We have overcome many quantitative and qualitative limitations of existing colour pattern analyses by combining calibrated digital photography and visual modelling. We have integrated and updated existing spatio‐chromatic colour pattern analyses, including adjacency, visual contrast and boundary strength analysis, to be implemented using calibrated digital photography through the Multispectral Image Analysis and Calibration (MICA) Toolbox. This combination of calibrated photography and spatio‐chromatic colour pattern analyses is enabled by the inclusion of psychophysical colour and luminance discrimination thresholds for image segmentation, which we call ‘Receptor Noise Limited Clustering’, used here for the first time. Furthermore, QCPA provides a novel psycho‐physiological approach to the modelling of spatial acuity using convolution in the spatial or frequency domains, followed by ‘Receptor Noise Limited Ranked Filtering’ to eliminate intermediate edge artefacts and recover sharp boundaries following smoothing. We also present a new type of colour pattern analysis, the ‘local edge intensity analysis’ as well as a range of novel psycho‐physiological approaches to the visualization of spatio‐chromatic data. QCPA combines novel and existing pattern analysis frameworks into what we hope is a unified, free and open source toolbox and introduces a range of novel analytical and data‐visualization approaches. These analyses and tools have been seamlessly integrated into the MICA toolbox providing a dynamic and user‐friendly workflow.

  • Using stable isotope analysis to answer fundamental questions in invasion ecology: Progress and prospects
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-02
    Marshall D. McCue, Marion Javal, Susana Clusella‐Trullas, Johannes J. Le Roux, Michelle C. Jackson, Allan G. Ellis, David M. Richardson, Alex J. Valentine, John S. Terblanche

    What makes some species successful invaders while others fail, and why some invaders have major impacts in invaded ecosystems are pivotal questions that are attracting major research effort. The increasing availability of high resolution, georeferenced stable isotope landscapes (‘isoscapes’), coupled with the commercialization of stable isotope‐enriched tracer molecules and the development of new analytical approaches, is facilitating novel applications of stable isotope techniques in ecology. We can now address ecological questions that were previously intractable. We review and discuss how stable isotope analysis (SIA) can complement fundamental research themes in the study of biological invasions, especially in answering questions relating to the physiological and ecological mechanisms underlying invasion processes and invader impacts. SIA was first used for simply describing the diet of invaders but, more recently, SIA‐informed metrics of population and community trophic structure have been advanced. These approaches now permit the comparison of diets across space and time and provide quantitative tools to compare food webs across different stages of invasion. SIA has also been pivotal in quantifying competition for resources between native and non‐native species (e.g. competition for food, water, or nutrient use). Specific questions related to modes of dispersal (e.g. origin and distance/direction travelled) and mechanisms of establishment can also be addressed using SIA in diverse taxa. An overarching goal is to highlight examples of recent studies that have used SIA in key areas of invasion ecology and use these to synthesize testable predictions where SIA could be applied to future studies. We conclude by highlighting several paths forward and describing how unresolved challenges in quantifying the rates, impacts, and mechanisms underlying invasions could potentially benefit from the use of SIA.

  • A method for computing hourly, historical, terrain‐corrected microclimate anywhere on earth
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-12-02
    Michael R. Kearney, Phillipa K. Gillingham, Isobel Bramer, James P. Duffy, Ilya M.D. Maclean

    Microclimates are the thermal and hydric environments organisms actually experience, and estimates of them are increasingly needed in environmental research. The availability of global weather and terrain datasets, together with increasingly sophisticated microclimate modelling tools, makes the prospect of a global, web‐based microclimate estimation procedure feasible. We have developed such an approach for the r programming environment which integrates existing r packages for obtaining terrain and sub‐daily atmospheric forcing data (elevatr and rncep), and two complementary microclimate modelling packages (NicheMapR and microclima). The procedure can be used to generate NicheMapR’s hourly time‐series outputs of above‐ and below‐ground conditions, including convective and radiative environments, soil temperature, soil moisture and snow cover, for a single point, using microclima to account for local topographic and vegetation effects. Alternatively, it can use microclima to produce high‐resolution grids of near‐surface temperatures, using NicheMapR to derive calibration coefficients normally obtained from experimental data. We validate this integrated approach against a series of microclimate observations used previously in the tests of the respective models and show equivalent performance. It is thus now feasible to produce realistic estimates of microclimate at fine (<30 m) spatial and temporal scales anywhere on earth, from 1957 to present.

  • DBTree ‐ Very large phylogenies in portable databases
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-27
    Rutger A. Vos

    1. Growing numbers of large phylogenetic syntheses are being published. Sometimes as part of a hypothesis testing framework, sometimes to present novel methods of phylogenetic inference, and sometimes as a snapshot of the diversity within a database. Commonly used methods to reuse these trees in scripting environments have their limitations.

  • A practical method to account for variation in detection range in acoustic telemetry arrays to accurately quantify the spatial ecology of aquatic animals
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-22
    Jacob W. Brownscombe, Lucas P. Griffin, Jacqueline M. Chapman, Danielle Morley, Alejandro Acosta, Glenn T. Crossin, Sara J. Iverson, Aaron J. Adams, Steven J. Cooke, Andy J. Danylchuk

    Acoustic telemetry is a popular tool for long‐term tracking of aquatic animals to describe and quantify patterns of movement, space use, and diverse ecological interactions. Acoustic receivers are imperfect sampling instruments, and their detection range (DR; the area surrounding the receiver in which tag transmissions can be detected) often varies dramatically over space and time due to dynamic environmental conditions. Therefore, it is prudent to quantify and account for variation in DR to prevent telemetry system performance from confounding the understanding of real patterns in animal space use. However, acoustic receiver DR consists of a complex, dynamic, three‐dimensional area that is challenging to quantify. Although quantifying the absolute DR of all receivers is infeasible in the context of most acoustic telemetry studies, we outline a practical approach to quantify relative variation among receiver DR over space and time. This approach involves selecting a set of sentinel receivers to monitor drivers of variation in detection range. Each sentinel receiver is subject to a range testing procedure to estimate detection efficiency (DE; the proportion of total transmissions detected by the receiver), at a range of distances from the receiver, to derive the maximum range (MR; distance from the receiver where DE is 5%) and Midpoint (distance from the receiver where DE is 50%). A reference transmitter is then placed at the Midpoint, providing a standardized measure of long‐term variation in DE, with each station having similar freedom of variance. Variation in reference tag DE is then combined with MR to calculate a DR correction factor (DRc). A modelling approach is then used to estimate DRc for all receivers in the array at spatial and temporal scales of ecological interest, which can be used to correct animal detection data in various ways. We demonstrate this method with a hypothetical dataset, as well as empirical data from an acoustic telemetry array to delineate spatio‐temporal patterns of fish habitat use. This is a flexible and practical approach to account for variation in acoustic receiver performance, allowing more accurate spatial and temporal patterns in aquatic animal spatial ecology to be revealed.

  • popler: An r package for extraction and synthesis of population time series from the long‐term ecological research (LTER) network
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-20
    Aldo Compagnoni, Andrew J. Bibian, Brad M. Ochocki, Sam Levin, Kai Zhu, Tom E. X. Miller

    Population dynamics play a central role in the historical and current development of fundamental and applied ecological science. The nascent culture of open data promises to increase the value of population dynamics studies to the field of ecology. However, synthesis of population data is constrained by the difficulty in identifying relevant datasets, by the heterogeneity of available data and by access to raw (as opposed to aggregated or derived) observations. To obviate these issues, we built a relational database, popler, and its R client, the library "popler". popler accommodates the vast majority of population data under a common structure, and without the need for aggregating raw observations. The "popler" R library is designed for users unfamiliar with the structure of the database and with the SQL language. This R library allows users to identify, download, explore and cite datasets salient to their needs. We implemented popler as a PostgreSQL instance, where we stored population data originated by the United States Long Term Ecological Research (LTER) Network. Our focus on the US LTER data aims to leverage the potential of this vast open data resource. The database currently contains 305 datasets from 25 LTER sites. popler is designed to accommodate automatic updates of existing datasets, and to accommodate additional datasets from LTER as well as non‐LTER studies. The combination of the online database and the R library "popler" is a resource for data synthesis efforts in population ecology. The common structure of popler simplifies comparative analyses, and the availability of raw data confers flexibility in data analysis. The "popler" R library maximizes these opportunities by providing a user‐friendly interface to the online database.

  • The multiple population genetic and demographic routes to islands of genomic divergence
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-19
    Claudio S. Quilodrán, Kristen Ruegg, Ashley T. Sendell‐Price, Eric C. Anderson, Tim Coulson, Sonya M. Clegg

    The way that organisms diverge into reproductively isolated species is a major question in biology. The recent accumulation of genomic data provides promising opportunities to understand the genomic landscape of divergence, which describes the distribution of differences across genomes. Genomic areas of unusually high differentiation have been called genomic islands of divergence. Their formation has been attributed to a variety of mechanisms, but a prominent hypothesis is that they result from divergent selection over a small portion of the genome, with surrounding areas homogenized by gene flow. Such islands have often been interpreted as being associated with divergence with gene flow. However, other mechanisms related to genomic structure and population history can also contribute to the formation of genomic islands of divergence. We currently lack a quantitative framework to examine the dynamics of genomic landscapes under the complex and nuanced conditions that are found in natural systems. Here, we develop an individual‐based simulation to explore the dynamics of diverging genomes under various scenarios of gene flow, selection and genotype–phenotype maps. Our modelling results are consistent with empirical observations demonstrating the formation of genomic islands under genetic isolation. Importantly, we have quantified the range of conditions that produce genomic islands. We demonstrate that the initial level of genetic diversity, drift, time since divergence, linkage disequilibrium, strength of selection and gene flow are all important factors that can influence the formation of genomic islands. Because the accumulation of genomic differentiation over time tends to erode the signal of genomic islands, genomic islands are more likely to be observed in recently divergent taxa, although not all recently diverged taxa will necessarily exhibit islands of genomic divergence. Gene flow primarily slows the swamping of islands of divergence with time. By using this framework, further studies may explore the relative influence of particular suites of events that contribute to the emergence of genomic islands under sympatric, parapatric and allopatric conditions. This approach represents a novel tool to explore quantitative expectations of the speciation process, and should prove useful in elucidating past and projecting future genomic evolution of any taxa.

  • Spatially balanced designs for transect‐based surveys
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-19
    Scott D. Foster, Geoffrey R. Hosack, Jacquomo Monk, Emma Lawrence, Neville S. Barrett, Alan Williams, Rachel Przeslawski

    Many sampling techniques rely on taking measurements along a transect; an example is underwater imagery from towed platforms used for marine ecological studies. Despite transect‐based sampling being commonly used, methods to generate randomized survey designs have not hitherto been developed. We develop methods to generate random transect designs, which respect the user‐defined probability of sampling each grid cell (the cell inclusion probabilities). We show how to: (a) define transect inclusion probabilities from user‐specified cell inclusion probabilities, which allows particular environments to be sampled more often; (b) alter the cell and transect inclusion probabilities so that when transects are sampled the frequencies of sampling cells approximate the cell inclusion probabilities, and; (c) draw a spatially balanced probability sample of transects. The spatially balanced transect designs approximately maintain the cell inclusion probabilities. The greatest of the small departures occur near the extreme corners of our convex study region, which are difficult to place transects into. The proposed designs also exhibit superior spatial balance compared to the non‐balanced counterparts. We illustrate the successful application of the method to a towed‐camera survey of deep‐sea (500–2,000 m depths) seamounts off Tasmania, Australia. This was a challenging application due to the complex topology of the setting, and uneven inclusion probabilities for the property of interest – the presence of a stony coral. Our approach develops the randomization approach to transect‐based surveys, thereby ensuring that transect‐based surveys can enjoy the same benefits as random point‐based surveys. The method approximates the cell inclusion probabilities, and does so while spatially balancing the transects throughout the study area. Practitioners can access the methods through the R‐package MBHdesign, which is available from CRAN. We anticipate that it will act as a cornerstone for transect‐based ecological monitoring programmes.

  • Genetic assignment of individuals to source populations using network estimation tools
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-19
    Markku Kuismin, Dilan Saatoglu, Alina K. Niskanen, Henrik Jensen, Mikko J. Sillanpää

    Dispersal, the movement of individuals between populations, is crucial in many ecological and genetic processes. However, direct identification of dispersing individuals is difficult or impossible in natural populations. By using genetic assignment methods, individuals with unknown genetic origin can be assigned to source populations. This knowledge is necessary in studying many key questions in ecology, evolution and conservation. We introduce a network‐based tool BONE (Baseline Oriented Network Estimation) for genetic population assignment, which borrows concepts from undirected graph inference. In particular, we use sparse multinomial Least Absolute Shrinkage and Selection Operator (LASSO) regression to estimate probability of the origin of all mixture individuals and their mixture proportions without tedious selection of the LASSO tuning parameter. We compare BONE with three genetic assignment methods implemented in R packages radmixture, assignPOP and RUBIAS. Probability of the origin and mixture proportion estimates of both simulated and real data (an insular house sparrow metapopulation and Chinook salmon populations) given by BONE are competitive or superior compared to other assignment methods. Our examples illustrate how the network estimation method adapts to population assignment, combining the efficiency and attractive properties of sparse network representation and model selection properties of the L1 regularization. As far as we know, this is the first approach showing how one can use network tools for genetic identification of individuals' source populations. BONE is aimed at any researcher performing genetic assignment and trying to infer the genetic population structure. Compared to other methods, our approach also identifies outlying mixture individuals that could originate outside of the baseline populations. BONE is a freely available R package under the GPL licence and can be downloaded at GitHub. In addition to the R package, a tutorial for BONE is available at https://github.com/markkukuismin/BONE/.

  • Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-19
    Maximilian Pichler, Virginie Boreux, Alexandra‐Maria Klein, Matthias Schleuning, Florian Hartig

    Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait‐matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait‐matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naïve Bayes, and k‐Nearest‐Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We found that the best ML models can successfully predict species interactions in plant–pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait‐matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant–pollinator database and inferred ecologically plausible trait‐matching rules for a plant–hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition.

  • Inferring competitive outcomes, ranks and intransitivity from empirical data: A comparison of different methods
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-19
    Yanhao Feng, Santiago Soliveres, Eric Allan, Benjamin Rosenbaum, Cameron Wagg, Andrea Tabi, Enrica De Luca, Nico Eisenhauer, Bernhard Schmid, Alexandra Weigelt, Wolfgang W. Weisser, Christiane Roscher, Markus Fischer

    The inference of pairwise competitive outcomes (PCO) and multispecies competitive ranks and intransitivity from empirical data is essential to evaluate how competition shapes plant communities. Three categories of methods, differing in theoretical background and data requirements, have been used: (a) theoretically sound coexistence theory‐based methods, (b) index‐based methods, and (c) ‘process‐from‐pattern’ methods. However, how they are related is largely unknown. In this study, we explored the relations between the three categories by explicitly comparing three representatives of them: (a) relative fitness difference (RFD), (b) relative yield (RY), and (c) a reverse‐engineering approach (RE). Specifically, we first conducted theoretical analyses with Lotka–Volterra competition models to explore their theoretical linkages. Second, we used data from a long‐term field experiment and a short‐term greenhouse experiment with eight herbaceous perennials to validate the theoretical findings. The theoretical analyses showed that RY or RE applied with equilibrium data indicated equivalent, or very similar, PCO respectively to RFD, but these relations became weaker or absent with data further from equilibrium. In line with this, both RY and RE converged with RFD in indicating PCO over time in the field experiment as the communities became closer to equilibrium. Moreover, the greenhouse PCO (far from equilibrium) were only similar to the field PCO of earlier rather than later years. Intransitivity was more challenging to infer because it could be reshuffled by even a small competitive shift among similar competitors. For example, the field intransitivity inferred by three methods differed greatly: no intransitivity was detected with RFD; intransitivity detected with RY and RE was poorly correlated, changed substantially over time (even after equilibrium) and failed to explain coexistence. Our findings greatly help the comparison and generalization of studies using different methods. For future studies, if equilibrium data are available, one can infer PCO and multispecies competitive ranks with RY or RE. If not, one should apply RFD with density gradient or time‐series data. Equilibria could be evaluated with T tests or standard deviations. To reliably infer intransitivity, one needs high quality data for a given method to first accurately infer PCO, especially among similar competitors.

  • Topographic path analysis for modelling dispersal and functional connectivity: Calculating topographic distances using the topoDistance r package
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-11
    Ian J. Wang

    Estimating biologically meaningful geographic distances is essential for research in disciplines ranging from landscape genetics to community ecology. Topographically correcting distances to account for the total overland distance between locations imposed by topographic relief provides one method for calculating geographic distances that account for landscape structure. Here, I present topoDistance, an r package for calculating shortest topographic distances, weighted topographic paths and topographic least cost paths (LCPs). Topographic distances are calculated by weighting the edges of a graph by the hypotenuse of the horizontal and vertical distances between raster cells and then finding the shortest total path between cells of interest. The package also includes tools for mapping topographic paths and plotting elevation profiles. Examples from a species with moderate dispersal abilities, the western fence lizard, inhabiting a topographically complex landscape, Yosemite National Park (USA), demonstrate that topographic distances can vary significantly from straight‐line distances, and topographic LCPs can trace very different routes from LCPs and shortest topographic paths. Topographic paths and distances are broadly useful for modelling geographic isolation resulting from dispersal limitation for organisms that interact with the topographic structure of a landscape during movement and dispersal.

  • Using machine vision to estimate fish length from images using regional convolutional neural networks
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-06
    Graham G. Monkman, Kieran Hyder, Michel J. Kaiser, Franck P. Vidal

    An image can encode date, time, location and camera information as metadata and implicitly encodes species information and data on human activity, for example the size distribution of fish removals. Accurate length estimates can be made from images using a fiducial marker; however, their manual extraction is time‐consuming and estimates are inaccurate without control over the imaging system. This article presents a methodology which uses machine vision to estimate the total length (TL) of a fusiform fish (European sea bass). Three regional convolutional neural networks (R‐CNN) were trained from public images. Images of European sea bass were captured with a fiducial marker with three non‐specialist cameras. Images were undistorted using the intrinsic lens properties calculated for the camera in OpenCV; then TL was estimated using machine vision (MV) to detect both marker and subject. MV performance was evaluated for the three R‐CNNs under downsampling and rotation of the captured images. Each R‐CNN accurately predicted the location of fish in test images (mean intersection over union, 93%) and estimates of TL were accurate, with percent mean bias error (%MBE [95% CIs]) = 2.2% [2.0, 2.4]). Detections were robust to horizontal flipping and downsampling. TL estimates at absolute image rotations >20° became increasingly inaccurate but %MBE [95% CIs] was reduced to −0.1% [−0.2, 0.1] using machine learning to remove outliers and model bias. Machine vision can classify and derive measurements of species from images without specialist equipment. It is anticipated that ecological researchers and managers will make increasing use of MV where image data are collected (e.g. in remote electronic monitoring, virtual observations, wildlife surveys and morphometrics) and MV will be of particular utility where large volumes of image data are gathered.

  • MGDrivE: A modular simulation framework for the spread of gene drives through spatially explicit mosquito populations
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-05
    Héctor M. Sánchez C., Sean L. Wu, Jared B. Bennett, John M. Marshall

    Malaria, dengue, Zika and other mosquito‐borne diseases continue to pose a major global health burden through much of the world, despite the widespread distribution of insecticide‐based tools and antimalarial drugs. The advent of CRISPR/Cas9‐based gene editing and its demonstrated ability to streamline the development of gene drive systems has reignited interest in the application of this technology to the control of mosquitoes and the diseases they transmit. The versatility of this technology has enabled a wide range of gene drive architectures to be realized, creating a need for their population‐level and spatial dynamics to be explored. We present MGDrivE (Mosquito Gene Drive Explorer): a simulation framework designed to investigate the population dynamics of a variety of gene drive architectures and their spread through spatially explicit mosquito populations. A key strength of the MGDrivE framework is its modularity: (a) a genetic inheritance module accommodates the dynamics of gene drive systems displaying user‐defined inheritance patterns, (b) a population dynamic module accommodates the life history of a variety of mosquito disease vectors and insect agricultural pests, and (c) a landscape module generates the metapopulation model by which insect populations are connected via migration over space. Example MGDrivE simulations are presented to demonstrate the application of the framework to CRISPR/Cas9‐based homing gene drive for: (a) driving a disease‐refractory gene into a population (i.e. population replacement), and (b) disrupting a gene required for female fertility (i.e. population suppression), incorporating homing‐resistant alleles in both cases. Further documentation and use examples are provided at the project's Github repository. MGDrivE is an open‐source r package freely available on CRAN. We intend the package to provide a flexible tool capable of modelling novel inheritance‐modifying constructs as they are proposed and become available. The field of gene drive is moving very quickly, and we welcome suggestions for future development.

  • AquaFlux: Rapid, transparent and replicable analyses of plant transpiration
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-04
    Heather Speckman, Brent E. Ewers, Daniel P. Beverly

    Plant transpiration is the largest evaporative flux from most vegetated ecosystems, playing a dominant role in energy balance, water and element cycling, ecosystem services and water security. Quantification of plant‐level transpiration, for example sap flux, is essential to land managers and scientists. Thermal dissipation probes (TDP) are reliable and affordable tools for measuring sap flux, but difficulties in replicable data processing often serve as a barrier to their use and interpretation of data. AquaFlux is an r package designed to efficiently process and analyse TDP data. This program maximizes data collection by continually importing raw TDP values and alerting the user of any malfunctioning sensors. Data processing is expedited through a user‐friendly graphical interface, predictive algorithms and data recovery options. AquaFlux's post‐processing options address gapfilling, radial trends in sap flux across sapwood and rescaling from points to whole stems. To ensure reproducibility and transparency, all data processing steps are automatically documented, highlighting the impact of user decisions. AquaFlux confirms to emerging best practices in data science and TDP data processing and analyses. Understanding spatiotemporal patterns of sap flux and how they relate to plant traits is essential for enhancing agricultural productivity, optimizing land management planning, ecological studies and improving climate modelling. AquaFlux provides a robust tool to facilitate predictive understanding of plant transpiration.

  • Toytree: A minimalist tree visualization and manipulation library for Python
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-04
    Deren A. R. Eaton

    Toytree is a lightweight Python library for programmatically visualizing and manipulating tree‐based data structures. It implements a minimalist design aesthetic and modern plotting architecture suited for interactive coding in IPython/Jupyter. Tree drawings are generated in HTML using the toyplot library backend, and display natively in Jupyter notebooks with interactivity features. Tree drawings can be combined with other plotting functions from the toyplot library (e.g. scatterplots, histograms) to create composite figures on a shared coordinate grid, and can be exported to additional formats including PNG, PDF and SVG. To parse and store tree data, toytree uses a modified fork of the ete3 TreeNode object, which includes functions for manipulating, annotating and comparing trees. Toytree integrates these functions with a plotting layout to allow node values to be extracted from trees in the correct order to style nodes for plotting. In addition, toytree provides functions for parsing additional tree formats, generating random trees, inferring consensus trees and drawing grids or clouds from multiple trees to visualize discordance. The goal of toytree is to provide a simple Python equivalent to commonly used tree manipulation and plotting libraries in R, and in doing so, to promote further development of phylogenetic and other tree‐based methods in Python. Toytree is released under the GPLv3 license. Source code is available on GitHub and documentation is available at https://toytree.readthedocs.io.

  • Modelling misclassification in multi‐species acoustic data when estimating occupancy and relative activity
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-01
    Wilson J. Wright, Kathryn M. Irvine, Emily S. Almberg, Andrea R. Litt

    Surveying wildlife communities provides data for informing conservation and management decisions that affect multiple species. Autonomous recording units (ARUs) can efficiently gather community data for a variety of taxa, but generally require software algorithms to classify each recorded call to a species. Species classification errors are possible during this process and result in both false‐negative and false‐positive detections. Available approaches for analysing ARU data do not model the species classification probabilities, meaning erroneous detections are attributed to an omnibus source instead of the presence of another species. Additionally, counts of call recordings for each species are often summarized to binary detection data for analyses. Expanding statistical models to capture these nuances of ARU data would allow for improved inferences about occupancy and relative activity. Motivated by bat acoustic surveys, we developed a model to analyse counts of call recordings from multiple species simultaneously while accounting for species classification errors. Our model expands on previously developed false‐positive occupancy models to better describe acoustic data. We used simulations to compare our model to other false‐positive occupancy models for an example scenario with ARU data from two species. We also analyse acoustic data for eight bat species in Montana using our model. In simulations, single‐species models resulted in biased estimates of occupancy and relative activity because they failed to associate false positives with the presence of the second species. Models analysing binary observations ignored available information on relative activity and led to less precise estimates. Applying our model to bat acoustic data from Montana allowed for species‐specific estimates of occupancy and relative activity. This analysis illustrates the flexibility in our model framework while also highlighting the assumptions and data requirements for implementation. Specifically, additional information on the species classification probabilities is needed and we discuss considerations for reliably estimating these parameters. Directly modelling the species classification probabilities allows for improved ecological inferences for both occupancy and relative activity using community ARU data. Our statistical framework helps address the challenges posed by acoustic data, allowing ecologists to better utilize this technology to monitor wildlife communities.

  • ReClustOR: a re‐clustering tool using an open‐reference method that improves operational taxonomic unit definition
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-01
    Sébastien Terrat, Christophe Djemiel, Corentin Journay, Battle Karimi, Samuel Dequiedt, Walid Horrigue, Pierre‐Alain Maron, Nicolas Chemidlin Prévost‐Bouré, Lionel Ranjard

    Environmental microbial communities are now widely studied using metabarcoding approaches, thanks to the democratization of high‐throughput DNA sequencing technologies. The massive number of reads produced with these technologies requires bioinformatic solutions to be treated. A key step in the analysis is to cluster reads into Operational Taxonomic Units (or OTUs) and thus reduce the amount of data for downstream analyses. Due to the important impact of the clustering method on the quantity and quality of OTUs, finding an equilibrium between the reliability and time‐consuming nature of the chosen strategy is a real challenge. The present article proposes a new post‐clustering tool called ReClustOR aimed at improving the stability and reliability of OTUs whatever the initial clustering method. We compared several clustering methods: a homemade de novo method, VSEARCH, Swarm and ReClustOR associated with these three clustering methods, and the ESV definition, using two datasets (a simulated one and an environmental one). All methods were analysed for their ability to efficiently describe microbial diversity in terms of alpha‐diversity, beta‐diversity and phylogeny. Dataset analysis showed that post‐clustering with ReClustOR improved OTU detection not only in terms of diversity, but also in terms of reliability and stability as compared to the initial clustering methods. More precisely, the post‐clustering step improved the congruence of the results (alpha‐diversity, beta‐diversity, composition) whatever the initial clustering method. Moreover, ReClustOR, by defining a database of centroids, precludes the need to re‐cluster all the reads each time when new reads are generated. ReClustOR is a new post‐clustering method that overcomes problems (OTU stability and reliability) associated with classical clustering methods and thereby increases the quality and the congruence of the reconstructed OTUs. Moreover, the OTU database defined with ReClustOR can be used as a reference gradually enriched by merging new studies and samples. In this way, huge datasets (e.g. the Earth Microbiome Project or the Tara Oceans project) can be used as references for other projects within their range of application, and increase the quality of comparisons among studies and datasets.

  • Clustering and correlations: Inferring resilience from spatial patterns in ecosystems
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-11-01
    Sumithra Sankaran, Sabiha Majumder, Ashwin Viswanathan, Vishwesha Guttal

    In diverse ecosystems, organisms cluster together in such a manner that the frequency distribution of cluster sizes is a power law function. Spatially explicit computational models of ecosystems suggest that a loss of such power law clustering may indicate a loss of ecosystem resilience; the empirical evidence in support for this hypothesis has been mixed. On the other hand, a well‐known dynamical feature of systems with reduced resilience is the slower recovery from perturbations, a phenomenon known as critical slowing down (CSD). Here, we examine the relationship between spatial clustering and CSD to better understand the use of cluster size distributions as indicators of ecosystem resilience. Local positive feedback is an important driver of spatial clustering, while also affecting the dynamics of the ecosystem: Studies have demonstrated that positive feedback promotes abrupt regime shifts. Here, we analyse a spatial model of ecosystem transitions that enables us to disentangle the roles of local positive feedback and environmental stress on spatial patterns and ecosystem resilience. We demonstrate that, depending on the strength of positive feedback, power law clustering can occur at any distance from the critical threshold of ecosystem collapse. In fact, we find that for systems with strong positive feedback, which are more likely to exhibit abrupt transitions, there may be no loss of power law clustering prior to critical thresholds. Our analyses show that cluster size distributions are unrelated to the phenomenon of CSD and that loss of power law clustering is not a generic indicator of ecosystem resilience. Further, due to CSD, a power law feature does occur near critical thresholds but in a different quantity; specifically, a power law decay of spatial covariance of ecosystem state. Our work highlights the importance of links between local positive feedback, emergent spatial properties and how they may be used to interpret ecosystem resilience.

  • How to evaluate community predictions without thresholding?
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2019-10-31
    Daniel Scherrer, Heidi K. Mod, Antoine Guisan

    Stacked species distribution models (S‐SDM) provide a tool to make spatial predictions about communities by first modelling individual species and then stacking the modelled predictions to form assemblages. The evaluation of the predictive performance is usually based on a comparison of the observed and predicted community properties (e.g. species richness, composition). However, the most available and widely used evaluation metrics require the thresholding of single species' predicted probabilities of occurrence to obtain binary outcomes (i.e. presence/absence). This binarization can introduce unnecessary bias and error. Herein, we present and demonstrate the use of several groups of new or rarely used evaluation approaches and metrics for both species richness and community composition that do not require thresholding but instead directly compare the predicted probabilities of occurrences of species to the presence/absence observations in the assemblages. Community AUC, which is based on traditional AUC, measures the ability of a model to differentiate between species presences or absences at a given site according to their predicted probabilities of occurrence. Summing the probabilities gives the expected species richness and allows the estimation of the probability that the observed species richness is not different from the expected species richness based on the species' probabilities of occurrence. The traditional Sørensen and Jaccard similarity indices (which are based on presences/absences) were adapted to maxSørensen and maxJaccard and to probSørensen and probJaccard (which use probabilities directly). A further approach (improvement over null models) compares the predictions based on S‐SDMs with the expectations from the null models to estimate the improvement in both species richness and composition predictions. Additionally, all metrics can be described against the environmental conditions of sites (e.g. elevation) to highlight the abilities of models to detect the variation in the strength of the community assembly processes in different environments. These metrics offer an unbiased view of the performance of community predictions compared to metrics that requiring thresholding. As such, they allow more straightforward comparisons of model performance among studies (i.e. they are not influenced by any subjective thresholding decisions).

  • An efficient extension of N-mixture models for multi-species abundance estimation.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2018-06-13
    Juan Pablo Gomez,Scott K Robinson,Jason K Blackburn,José Miguel Ponciano

    In this study we propose an extension of the N-mixture family of models that targets an improvement of the statistical properties of rare species abundance estimators when sample sizes are low, yet typical for tropical studies. The proposed method harnesses information from other species in an ecological community to correct each species' estimator. We provide guidance to determine the sample size required to estimate accurately the abundance of rare tropical species when attempting to estimate the abundance of single species.We evaluate the proposed methods using an assumption of 50 m radius plots and perform simulations comprising a broad range of sample sizes, true abundances and detectability values and a complex data generating process. The extension of the N-mixture model is achieved by assuming that the detection probabilities are drawn at random from a beta distribution in a multi-species fashion. This hierarchical model avoids having to specify a single detection probability parameter per species in the targeted community. Parameter estimation is done via Maximum Likelihood.We compared our multi-species approach with previously proposed multi-species N-mixture models, which we show are biased when the true densities of species in the community are less than seven individuals per 100 hectares. The beta N-mixture model proposed here outperforms the traditional Multi-species N-mixture model by allowing the estimation of organisms at lower densities and controlling the bias in the estimation.We illustrate how our methodology can be used to suggest sample sizes required to estimate the abundance of organisms, when these are either rare, common or abundant. When the interest is full communities, we show how the multi-species approaches, and in particular our beta model and estimation methodology, can be used as a practical solution to estimate organism densities from rapid inventory datasets. The statistical inferences done with our model via Maximum Likelihood can also be used to group species in a community according to their detectabilities.

  • patternize: An R package for quantifying colour pattern variation.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2018-05-15
    Steven M Van Belleghem,Riccardo Papa,Humberto Ortiz-Zuazaga,Frederik Hendrickx,Chris D Jiggins,W Owen McMillan,Brian A Counterman

    The use of image data to quantify, study and compare variation in the colors and patterns of organisms requires the alignment of images to establish homology, followed by color-based segmentation of images. Here we describe an R package for image alignment and segmentation that has applications to quantify color patterns in a wide range of organisms. patternize is an R package that quantifies variation in color patterns obtained from image data. patternize first defines homology between pattern positions across specimens either through manually placed homologous landmarks or automated image registration. Pattern identification is performed by categorizing the distribution of colors using an RGB threshold, k-means clustering or watershed transformation.We demonstrate that patternize can be used for quantification of the color patterns in a variety of organisms by analyzing image data for butterflies, guppies, spiders and salamanders. Image data can be compared between sets of specimens, visualized as heatmaps and analyzed using principal component analysis (PCA). patternize has potential applications for fine scale quantification of color pattern phenotypes in population comparisons, genetic association studies and investigating the basis of color pattern variation across a wide range of organisms.

  • Analysis of nectar from low-volume flowers: A comparison of collection methods for free amino acids.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2018-06-26
    Eileen F Power,Daniel Stabler,Anne M Borland,Jeremy Barnes,Geraldine A Wright

    Floral nectar is a reward offered by flowering plants to visiting pollinators. Nectar chemistry is important for understanding plant nutrient allocation and plant-pollinator interactions. However, many plant species are difficult to sample as their flowers are small and produce low amounts of nectar.We compared the effects of different methods of nectar collection on the amino acid composition of flowers with low volumes of nectar. We used five methods to collect nectar from 60 (5 × 12) Calluna vulgaris flowers: microcapillary tubes, a low-volume flower rinse (the micro-rinse method, using 2 μl water), filter paper, a high-volume flower rinse (2 ml water) and a flower wash (2 ml water). We analysed the samples for free amino acids using quantitative UHPLC methods .We found that the micro-rinse method (rinsing the nectary with enough water to only cover the nectary) recovered amino acid proportions similar to raw nectar extracted using microcapillary tubes. The filter paper, 2 ml rinse and 2 ml wash methods measured significantly higher values of free amino acids and also altered the profile of amino acids. We discuss our concerns about the increased contamination risk of the filter paper and high-volume rinse and wash samples from dried nectar across the floral tissue (nectar unavailable to floral visitors), pollen, vascular fluid and cellular fluid.Our study will enable researchers to make informed decisions about nectar collection methods depending on their intended chemical analysis. These methods of sampling will enable researchers to examine a larger array of plant species' flowers to include those with low volumes of nectar.

  • Identifying consistent allele frequency differences in studies of stratified populations.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2017-12-22
    R Axel W Wiberg,Oscar E Gaggiotti,Michael B Morrissey,Michael G Ritchie

    With increasing application of pooled-sequencing approaches to population genomics robust methods are needed to accurately quantify allele frequency differences between populations. Identifying consistent differences across stratified populations can allow us to detect genomic regions under selection and that differ between populations with different histories or attributes. Current popular statistical tests are easily implemented in widely available software tools which make them simple for researchers to apply. However, there are potential problems with the way such tests are used, which means that underlying assumptions about the data are frequently violated.These problems are highlighted by simulation of simple but realistic population genetic models of neutral evolution and the performance of different tests are assessed. We present alternative tests (including Generalised Linear Models [GLMs] with quasibinomial error structure) with attractive properties for the analysis of allele frequency differences and re-analyse a published dataset.The simulations show that common statistical tests for consistent allele frequency differences perform poorly, with high false positive rates. Applying tests that do not confound heterogeneity and main effects significantly improves inference. Variation in sequencing coverage likely produces many false positives and re-scaling allele frequencies to counts out of a common value or an effective sample size reduces this effect.Many researchers are interested in identifying allele frequencies that vary consistently across replicates to identify loci underlying phenotypic responses to selection or natural variation in phenotypes. Popular methods that have been suggested for this task perform poorly in simulations. Overall, quasibinomial GLMs perform better and also have the attractive feature of allowing correction for multiple testing by standard procedures and are easily extended to other designs.

  • The Automated Root Exudate System (ARES): a method to apply solutes at regular intervals to soils in the field.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2017-10-11
    Luis Lopez-Sangil,Charles George,Eduardo Medina-Barcenas,Ali J Birkett,Catherine Baxendale,Laëtitia M Bréchet,Eduard Estradera-Gumbau,Emma J Sayer

    Root exudation is a key component of nutrient and carbon dynamics in terrestrial ecosystems. Exudation rates vary widely by plant species and environmental conditions, but our understanding of how root exudates affect soil functioning is incomplete, in part because there are few viable methods to manipulate root exudates in situ. To address this, we devised the Automated Root Exudate System (ARES), which simulates increased root exudation by applying small amounts of labile solutes at regular intervals in the field.The ARES is a gravity-fed drip irrigation system comprising a reservoir bottle connected via a timer to a micro-hose irrigation grid covering c. 1 m2; 24 drip-tips are inserted into the soil to 4-cm depth to apply solutions into the rooting zone. We installed two ARES subplots within existing litter removal and control plots in a temperate deciduous woodland. We applied either an artificial root exudate solution (RE) or a procedural control solution (CP) to each subplot for 1 min day-1 during two growing seasons. To investigate the influence of root exudation on soil carbon dynamics, we measured soil respiration monthly and soil microbial biomass at the end of each growing season.The ARES applied the solutions at a rate of c. 2 L m-2 week-1 without significantly increasing soil water content. The application of RE solution had a clear effect on soil carbon dynamics, but the response varied by litter treatment. Across two growing seasons, soil respiration was 25% higher in RE compared to CP subplots in the litter removal treatment, but not in the control plots. By contrast, we observed a significant increase in microbial biomass carbon (33%) and nitrogen (26%) in RE subplots in the control litter treatment.The ARES is an effective, low-cost method to apply experimental solutions directly into the rooting zone in the field. The installation of the systems entails minimal disturbance to the soil and little maintenance is required. Although we used ARES to apply root exudate solution, the method can be used to apply many other treatments involving solute inputs at regular intervals in a wide range of ecosystems.

  • Measuring site fidelity and spatial segregation within animal societies.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2017-09-26
    Thomas O Richardson,Luca Giuggioli,Nigel R Franks,Ana B Sendova-Franks

    Animals often display a marked tendency to return to previously visited locations that contain important resources, such as water, food, or developing brood that must be provisioned. A considerable body of work has demonstrated that this tendency is strongly expressed in ants, which exhibit fidelity to particular sites both inside and outside the nest. However, thus far many studies of this phenomena have taken the approach of reducing an animal's trajectory to a summary statistic, such as the area it covers.Using both simulations of biased random walks, and empirical trajectories from individual rock ants, Temnothorax albipennis, we demonstrate that this reductive approach suffers from an unacceptably high rate of false negatives.To overcome this, we describe a site-centric approach which, in combination with a spatially-explicit null model, allows the identification of the important sites towards which individuals exhibit statistically significant biases.Using the ant trajectories, we illustrate how the site-centric approach can be combined with social network analysis tools to detect groups of individuals whose members display similar space-use patterns.We also address the mechanistic origin of individual site fidelity; by examining the sequence of visits to each site, we detect a statistical signature associated with a self-attracting walk - a non-Markovian movement model that has been suggested as a possible mechanism for generating individual site fidelity.

  • Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2016-12-23
    Michele Dalponte,David A Coomes

    Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high-fidelity mapping of carbon stocks at regional scales.We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region-growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown-width estimate. From that point on, we use well-established approaches developed for field-based inventories: above-ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density.We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field- and ALS-based estimates of carbon stocks (r2 = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect.An advantage of the tree-centric approach over existing area-based methods is that it can produce maps at any scale and is fundamentally based on field-based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.

  • High-throughput monitoring of wild bee diversity and abundance via mitogenomics.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2015-09-01
    Min Tang,Chloe J Hardman,Yinqiu Ji,Guanliang Meng,Shanlin Liu,Meihua Tan,Shenzhou Yang,Ellen D Moss,Jiaxin Wang,Chenxue Yang,Catharine Bruce,Tim Nevard,Simon G Potts,Xin Zhou,Douglas W Yu

    Bee populations and other pollinators face multiple, synergistically acting threats, which have led to population declines, loss of local species richness and pollination services, and extinctions. However, our understanding of the degree, distribution and causes of declines is patchy, in part due to inadequate monitoring systems, with the challenge of taxonomic identification posing a major logistical barrier. Pollinator conservation would benefit from a high-throughput identification pipeline.We show that the metagenomic mining and resequencing of mitochondrial genomes (mitogenomics) can be applied successfully to bulk samples of wild bees. We assembled the mitogenomes of 48 UK bee species and then shotgun-sequenced total DNA extracted from 204 whole bees that had been collected in 10 pan-trap samples from farms in England and been identified morphologically to 33 species. Each sample data set was mapped against the 48 reference mitogenomes.The morphological and mitogenomic data sets were highly congruent. Out of 63 total species detections in the morphological data set, the mitogenomic data set made 59 correct detections (93·7% detection rate) and detected six more species (putative false positives). Direct inspection and an analysis with species-specific primers suggested that these putative false positives were most likely due to incorrect morphological IDs. Read frequency significantly predicted species biomass frequency (R2 = 24·9%). Species lists, biomass frequencies, extrapolated species richness and community structure were recovered with less error than in a metabarcoding pipeline.Mitogenomics automates the onerous task of taxonomic identification, even for cryptic species, allowing the tracking of changes in species richness and distributions. A mitogenomic pipeline should thus be able to contain costs, maintain consistently high-quality data over long time series, incorporate retrospective taxonomic revisions and provide an auditable evidence trail. Mitogenomic data sets also provide estimates of species counts within samples and thus have potential for tracking population trajectories.

  • Calibrating animal-borne proximity loggers.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2015-06-01
    Christian Rutz,Michael B Morrissey,Zackory T Burns,John Burt,Brian Otis,James J H St Clair,Richard James

    Growing interest in the structure and dynamics of animal social networks has stimulated efforts to develop automated tracking technologies that can reliably record encounters in free-ranging subjects. A particularly promising approach is the use of animal-attached 'proximity loggers', which collect data on the incidence, duration and proximity of spatial associations through inter-logger radio communication. While proximity logging is based on a straightforward physical principle - the attenuation of propagating radio waves with distance - calibrating systems for field deployment is challenging, since most study species roam across complex, heterogeneous environments.In this study, we calibrated a recently developed digital proximity-logging system ('Encounternet') for deployment on a wild population of New Caledonian crows Corvus moneduloides. Our principal objective was to establish a quantitative model that enables robust post hoc estimation of logger-to-logger (and, hence, crow-to-crow) distances from logger-recorded signal-strength values. To achieve an accurate description of the radio communication between crow-borne loggers, we conducted a calibration exercise that combines theoretical analyses, field experiments, statistical modelling, behavioural observations, and computer simulations.We show that, using signal-strength information only, it is possible to assign crow encounters reliably to predefined distance classes, enabling powerful analyses of social dynamics. For example, raw data sets from field-deployed loggers can be filtered at the analysis stage to include predominantly encounters where crows would have come to within a few metres of each other, and could therefore have socially learned new behaviours through direct observation. One of the main challenges for improving data classification further is the fact that crows - like most other study species - associate across a wide variety of habitats and behavioural contexts, with different signal-attenuation properties.Our study demonstrates that well-calibrated proximity-logging systems can be used to chart social associations of free-ranging animals over a range of biologically meaningful distances. At the same time, however, it highlights that considerable efforts are required to conduct study-specific system calibrations that adequately account for the biological and technological complexities of field deployments. Although we report results from a particular case study, the basic rationale of our multi-step calibration exercise applies to many other tracking systems and study species.

  • Shedding light on the 'dark side' of phylogenetic comparative methods.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2016-08-09
    Natalie Cooper,Gavin H Thomas,Richard G FitzJohn

    Phylogenetic comparative methods are becoming increasingly popular for investigating evolutionary patterns and processes. However, these methods are not infallible - they suffer from biases and make assumptions like all other statistical methods.Unfortunately, although these limitations are generally well known in the phylogenetic comparative methods community, they are often inadequately assessed in empirical studies leading to misinterpreted results and poor model fits. Here, we explore reasons for the communication gap dividing those developing new methods and those using them.We suggest that some important pieces of information are missing from the literature and that others are difficult to extract from long, technical papers. We also highlight problems with users jumping straight into software implementations of methods (e.g. in r) that may lack documentation on biases and assumptions that are mentioned in the original papers.To help solve these problems, we make a number of suggestions including providing blog posts or videos to explain new methods in less technical terms, encouraging reproducibility and code sharing, making wiki-style pages summarising the literature on popular methods, more careful consideration and testing of whether a method is appropriate for a given question/data set, increased collaboration, and a shift from publishing purely novel methods to publishing improvements to existing methods and ways of detecting biases or testing model fit. Many of these points are applicable across methods in ecology and evolution, not just phylogenetic comparative methods.

  • HEXT, a software supporting tree-based screens for hybrid taxa in multilocus data sets, and an evaluation of the homoplasy excess test.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2016-04-12
    Kevin Schneider,Stephan Koblmüller,Kristina M Sefc

    The homoplasy excess test (HET) is a tree-based screen for hybrid taxa in multilocus nuclear phylogenies. Homoplasy between a hybrid taxon and the clades containing the parental taxa reduces bootstrap support in the tree. The HET is based on the expectation that excluding the hybrid taxon from the data set increases the bootstrap support for the parental clades, whereas excluding non-hybrid taxa has little effect on statistical node support. To carry out a HET, bootstrap trees are calculated with taxon-jackknife data sets, that is excluding one taxon (species, population) at a time. Excess increase in bootstrap support for certain nodes upon exclusion of a particular taxon indicates the hybrid (the excluded taxon) and its parents (the clades with increased support).We introduce a new software program, hext, which generates the taxon-jackknife data sets, runs the bootstrap tree calculations, and identifies excess bootstrap increases as outlier values in boxplot graphs. hext is written in r language and accepts binary data (0/1; e.g. AFLP) as well as co-dominant SNP and genotype data.We demonstrate the usefulness of hext in large SNP data sets containing putative hybrids and their parents. For instance, using published data of the genus Vitis (~6,000 SNP loci), hext output supports V. × champinii as a hybrid between V. rupestris and V. mustangensis.With simulated SNP and AFLP data sets, excess increases in bootstrap support were not always connected with the hybrid taxon (false positives), whereas the expected bootstrap signal failed to appear on several occasions (false negatives). Potential causes for both types of spurious results are discussed.With both empirical and simulated data sets, the taxon-jackknife output generated by hext provided additional signatures of hybrid taxa, including changes in tree topology across trees, consistent effects of exclusions of the hybrid and the parent taxa, and moderate (rather than excessive) increases in bootstrap support. hext significantly facilitates the taxon-jackknife approach to hybrid taxon detection, even though the simple test for excess bootstrap increase may not reliably identify hybrid taxa in all applications.

  • How many more? Sample size determination in studies of morphological integration and evolvability.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2017-05-16
    Mark Grabowski,Arthur Porto

    1. The variational properties of living organisms are an important component of current evolutionary theory. As a consequence, researchers working on the field of multivariate evolution have increasingly used integration and evolvability statistics as a way of capturing the potentially complex patterns of trait association and their effects over evolutionary trajectories. Little attention has been paid, however, to the cascading effects that inaccurate estimates of trait covariance have on these widely used evolutionary statistics. 2. Here, we analyze the relationship between sampling effort and inaccuracy in evolvability and integration statistics calculated from 10-trait matrices with varying patterns of covariation and magnitudes of integration. We then extrapolate our initial approach to different numbers of traits and different magnitudes of integration and estimate general equations relating the inaccuracy of the statistics of interest to sampling effort. We validate our equations using a dataset of cranial traits, and use them to make sample size recommendations. 3. Our results suggest that highly inaccurate estimates of evolvability and integration statistics resulting from small sample sizes are likely common in the literature, given the sampling effort necessary to properly estimate them. We also show that patterns of covariation have no effect on the sampling properties of these statistics, but overall magnitudes of integration interact with sample size and lead to varying degrees of bias, imprecision, and inaccuracy. 4. Finally, we provide R functions that can be used to calculate recommended sample sizes or to simply estimate the level of inaccuracy that should be expected in these statistics, given a sampling design.

  • Modelling heterogeneity among fitness functions using random regression.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2016-03-08
    Richard J Reynolds,Gustavo de Los Campos,Scott P Egan,James R Ott

  • Towards the identification of the loci of adaptive evolution.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2015-05-06
    Carolina Pardo-Diaz,Camilo Salazar,Chris D Jiggins

    1. Establishing the genetic and molecular basis underlying adaptive traits is one of the major goals of evolutionary geneticists in order to understand the connection between genotype and phenotype and elucidate the mechanisms of evolutionary change. Despite considerable effort to address this question, there remain relatively few systems in which the genes shaping adaptations have been identified. 2. Here, we review the experimental tools that have been applied to document the molecular basis underlying evolution in several natural systems, in order to highlight their benefits, limitations and suitability. In most cases, a combination of DNA, RNA and functional methodologies with field experiments will be needed to uncover the genes and mechanisms shaping adaptation in nature.

  • Split diversity in constrained conservation prioritization using integer linear programming.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2015-04-22
    Olga Chernomor,Bui Quang Minh,Félix Forest,Steffen Klaere,Travis Ingram,Monika Henzinger,Arndt von Haeseler

    Phylogenetic diversity (PD) is a measure of biodiversity based on the evolutionary history of species. Here, we discuss several optimization problems related to the use of PD, and the more general measure split diversity (SD), in conservation prioritization.Depending on the conservation goal and the information available about species, one can construct optimization routines that incorporate various conservation constraints. We demonstrate how this information can be used to select sets of species for conservation action. Specifically, we discuss the use of species' geographic distributions, the choice of candidates under economic pressure, and the use of predator-prey interactions between the species in a community to define viability constraints.Despite such optimization problems falling into the area of NP hard problems, it is possible to solve them in a reasonable amount of time using integer programming. We apply integer linear programming to a variety of models for conservation prioritization that incorporate the SD measure.We exemplarily show the results for two data sets: the Cape region of South Africa and a Caribbean coral reef community. Finally, we provide user-friendly software at http://www.cibiv.at/software/pda.

  • Measuring telomere length and telomere dynamics in evolutionary biology and ecology.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2015-04-03
    Daniel H Nussey,Duncan Baird,Emma Barrett,Winnie Boner,Jennifer Fairlie,Neil Gemmell,Nils Hartmann,Thorsten Horn,Mark Haussmann,Mats Olsson,Chris Turbill,Simon Verhulst,Sandrine Zahn,Pat Monaghan

    Telomeres play a fundamental role in the protection of chromosomal DNA and in the regulation of cellular senescence. Recent work in human epidemiology and evolutionary ecology suggests adult telomere length (TL) may reflect past physiological stress and predict subsequent morbidity and mortality, independent of chronological age.Several different methods have been developed to measure TL, each offering its own technical challenges. The aim of this review is to provide an overview of the advantages and drawbacks of each method for researchers, with a particular focus on issues that are likely to face ecologists and evolutionary biologists collecting samples in the field or in organisms that may never have been studied in this context before.We discuss the key issues to consider and wherever possible try to provide current consensus view regarding best practice with regard to sample collection and storage, DNA extraction and storage, and the five main methods currently available to measure TL.Decisions regarding which tissues to sample, how to store them, how to extract DNA, and which TL measurement method to use cannot be prescribed, and are dependent on the biological question addressed and the constraints imposed by the study system. What is essential for future studies of telomere dynamics in evolution and ecology is that researchers publish full details of their methods and the quality control thresholds they employ.

  • Predicting local and non-local effects of resources on animal space use using a mechanistic step selection model.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2015-04-03
    Jonathan R Potts,Guillaume Bastille-Rousseau,Dennis L Murray,James A Schaefer,Mark A Lewis

    Predicting space use patterns of animals from their interactions with the environment is fundamental for understanding the effect of habitat changes on ecosystem functioning. Recent attempts to address this problem have sought to unify resource selection analysis, where animal space use is derived from available habitat quality, and mechanistic movement models, where detailed movement processes of an animal are used to predict its emergent utilization distribution. Such models bias the animal's movement towards patches that are easily available and resource-rich, and the result is a predicted probability density at a given position being a function of the habitat quality at that position. However, in reality, the probability that an animal will use a patch of the terrain tends to be a function of the resource quality in both that patch and the surrounding habitat.We propose a mechanistic model where this non-local effect of resources naturally emerges from the local movement processes, by taking into account the relative utility of both the habitat where the animal currently resides and that of where it is moving. We give statistical techniques to parametrize the model from location data and demonstrate application of these techniques to GPS location data of caribou (Rangifer tarandus) in Newfoundland.Steady-state animal probability distributions arising from the model have complex patterns that cannot be expressed simply as a function of the local quality of the habitat. In particular, large areas of good habitat are used more intensively than smaller patches of equal quality habitat, whereas isolated patches are used less frequently. Both of these are real aspects of animal space use missing from previous mechanistic resource selection models.Whilst we focus on habitats in this study, our modelling framework can be readily used with any environmental covariates and therefore represents a unification of mechanistic modelling and step selection approaches to understanding animal space use.

  • Calculating second derivatives of population growth rates for ecology and evolution.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2015-03-21
    Esther Shyu,Hal Caswell

    1. Second derivatives of the population growth rate measure the curvature of its response to demographic, physiological or environmental parameters. The second derivatives quantify the response of sensitivity results to perturbations, provide a classification of types of selection and provide one way to calculate sensitivities of the stochastic growth rate. 2. Using matrix calculus, we derive the second derivatives of three population growth rate measures: the discrete-time growth rate λ, the continuous-time growth rate r = log λ and the net reproductive rate R0, which measures per-generation growth. 3. We present a suite of formulae for the second derivatives of each growth rate and show how to compute these derivatives with respect to projection matrix entries and to lower-level parameters affecting those matrix entries. 4. We also illustrate several ecological and evolutionary applications for these second derivative calculations with a case study for the tropical herb Calathea ovandensis.

  • Simultaneously estimating evolutionary history and repeated traits phylogenetic signal: applications to viral and host phenotypic evolution.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2015-03-18
    Bram Vrancken,Philippe Lemey,Andrew Rambaut,Trevor Bedford,Ben Longdon,Huldrych F Günthard,Marc A Suchard

    Phylogenetic signal quantifies the degree to which resemblance in continuously-valued traits reflects phylogenetic relatedness. Measures of phylogenetic signal are widely used in ecological and evolutionary research, and are recently gaining traction in viral evolutionary studies. Standard estimators of phylogenetic signal frequently condition on data summary statistics of the repeated trait observations and fixed phylogenetics trees, resulting in information loss and potential bias. To incorporate the observation process and phylogenetic uncertainty in a model-based approach, we develop a novel Bayesian inference method to simultaneously estimate the evolutionary history and phylogenetic signal from molecular sequence data and repeated multivariate traits. Our approach builds upon a phylogenetic diffusion framework that model continuous trait evolution as a Brownian motion process and incorporates Pagel's λ transformation parameter to estimate dependence among traits. We provide a computationally efficient inference implementation in the BEAST software package. We evaluate the synthetic performance of the Bayesian estimator of phylogenetic signal against standard estimators, and demonstrate the use of our coherent framework to address several virus-host evolutionary questions, including virulence heritability for HIV, antigenic evolution in influenza and HIV, and Drosophila sensitivity to sigma virus infection. Finally, we discuss model extensions that will make useful contributions to our flexible framework for simultaneously studying sequence and trait evolution.

  • The mean and variance of phylogenetic diversity under rarefaction.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2013-07-09
    David A Nipperess,Frederick A Matsen

    Phylogenetic diversity (PD) depends on sampling depth, which complicates the comparison of PD between samples of different depth. One approach to dealing with differing sample depth for a given diversity statistic is to rarefy, which means to take a random subset of a given size of the original sample. Exact analytical formulae for the mean and variance of species richness under rarefaction have existed for some time but no such solution exists for PD.We have derived exact formulae for the mean and variance of PD under rarefaction. We confirm that these formulae are correct by comparing exact solution mean and variance to that calculated by repeated random (Monte Carlo) subsampling of a dataset of stem counts of woody shrubs of Toohey Forest, Queensland, Australia. We also demonstrate the application of the method using two examples: identifying hotspots of mammalian diversity in Australasian ecoregions, and characterising the human vaginal microbiome.There is a very high degree of correspondence between the analytical and random subsampling methods for calculating mean and variance of PD under rarefaction, although the Monte Carlo method requires a large number of random draws to converge on the exact solution for the variance.Rarefaction of mammalian PD of ecoregions in Australasia to a common standard of 25 species reveals very different rank orderings of ecoregions, indicating quite different hotspots of diversity than those obtained for unrarefied PD. The application of these methods to the vaginal microbiome shows that a classical score used to quantify bacterial vaginosis is correlated with the shape of the rarefaction curve.The analytical formulae for the mean and variance of PD under rarefaction are both exact and more efficient than repeated subsampling. Rarefaction of PD allows for many applications where comparisons of samples of different depth is required.

  • The Primate Life History Database: A unique shared ecological data resource.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2011-06-24
    Karen B Strier,Jeanne Altmann,Diane K Brockman,Anne M Bronikowski,Marina Cords,Linda M Fedigan,Hilmar Lapp,Xianhua Liu,William F Morris,Anne E Pusey,Tara S Stoinski,Susan C Alberts

    The importance of data archiving, data sharing, and public access to data has received considerable attention. Awareness is growing among scientists that collaborative databases can facilitate these activities.We provide a detailed description of the collaborative life history database developed by our Working Group at the National Evolutionary Synthesis Center (NESCent) to address questions about life history patterns and the evolution of mortality and demographic variability in wild primates.Examples from each of the seven primate species included in our database illustrate the range of data incorporated and the challenges, decision-making processes, and criteria applied to standardize data across diverse field studies. In addition to the descriptive and structural metadata associated with our database, we also describe the process metadata (how the database was designed and delivered) and the technical specifications of the database.Our database provides a useful model for other researchers interested in developing similar types of databases for other organisms, while our process metadata may be helpful to other groups of researchers interested in developing databases for other types of collaborative analyses.

  • Oligotyping: Differentiating between closely related microbial taxa using 16S rRNA gene data.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2013-12-21
    A Murat Eren,Loïs Maignien,Woo Jun Sul,Leslie G Murphy,Sharon L Grim,Hilary G Morrison,Mitchell L Sogin

    Bacteria comprise the most diverse domain of life on Earth, where they occupy nearly every possible ecological niche and play key roles in biological and chemical processes. Studying the composition and ecology of bacterial ecosystems and understanding their function is of prime importance. High-throughput sequencing technologies enable nearly comprehensive descriptions of bacterial diversity through 16S ribosomal RNA gene amplicons. Analyses of these communities generally rely upon taxonomic assignments through reference databases, or clustering approaches using de facto sequence similarity thresholds to identify operational taxonomic units. However, these methods often fail to resolve ecologically meaningful differences between closely related organisms in complex microbial datasets.In this paper we describe oligotyping, a novel supervised computational method that allows researchers to investigate the diversity of closely related but distinct bacterial organisms in final operational taxonomic units identified in environmental datasets through 16S ribosomal RNA gene data by the canonical approaches.Our analysis of two datasets from two distinct environments demonstrates the capacity of oligotyping at discriminating distinct microbial populations of ecological importance.Oligotyping can resolve the distribution of closely related organisms across environments and unveil previously overlooked ecological patterns for microbial communities. The URL http://oligotyping.org offers an open-source software pipeline for oligotyping.

  • A Hierarchical Distance Sampling Approach to Estimating Mortality Rates from Opportunistic Carcass Surveillance Data.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2013-11-14
    Steve E Bellan,Olivier Gimenez,Rémi Choquet,Wayne M Getz

    Distance sampling is widely used to estimate the abundance or density of wildlife populations. Methods to estimate wildlife mortality rates have developed largely independently from distance sampling, despite the conceptual similarities between estimation of cumulative mortality and the population density of living animals. Conventional distance sampling analyses rely on the assumption that animals are distributed uniformly with respect to transects and thus require randomized placement of transects during survey design. Because mortality events are rare, however, it is often not possible to obtain precise estimates in this way without infeasible levels of effort. A great deal of wildlife data, including mortality data, is available via road-based surveys. Interpreting these data in a distance sampling framework requires accounting for the non-uniformity sampling. Additionally, analyses of opportunistic mortality data must account for the decline in carcass detectability through time. We develop several extensions to distance sampling theory to address these problems.We build mortality estimators in a hierarchical framework that integrates animal movement data, surveillance effort data, and motion-sensor camera trap data, respectively, to relax the uniformity assumption, account for spatiotemporal variation in surveillance effort, and explicitly model carcass detection and disappearance as competing ongoing processes.Analysis of simulated data showed that our estimators were unbiased and that their confidence intervals had good coverage.We also illustrate our approach on opportunistic carcass surveillance data acquired in 2010 during an anthrax outbreak in the plains zebra of Etosha National Park, Namibia.The methods developed here will allow researchers and managers to infer mortality rates from opportunistic surveillance data.

  • Advances in multiplex PCR: balancing primer efficiencies and improving detection success.
    Methods Ecol. Evol. (IF 7.099) Pub Date : 2013-04-04
    Daniela Sint,Lorna Raso,Michael Traugott

    1. Multiplex PCR is a valuable tool in many biological studies but it is a multifaceted procedure that has to be planned and optimised thoroughly to achieve robust and meaningful results. In particular, primer concentrations have to be adjusted to assure an even amplification of all targeted DNA fragments. Until now, total DNA extracts were used for balancing primer efficiencies; however, the applicability for comparisons between taxa or different multiple-copy genes was limited owing to the unknown number of template molecules present per total DNA. 2. Based on a multiplex system developed to track trophic interactions in high Alpine arthropods, we demonstrate a fast and easy way of generating standardised DNA templates. These were then used to balance the amplification success for the different targets and to subsequently determine the sensitivity of each primer pair in the multiplex PCR. 3. In the current multiplex assay, this approach led to an even amplification success for all seven targeted DNA fragments. Using this balanced multiplex PCR, methodological bias owing to variation in primer efficiency will be avoided when analysing field-derived samples. 4. The approach outlined here allows comparing multiplex PCR sensitivity, independent of the investigated species, genome size or the targeted genes. The application of standardised DNA templates not only makes it possible to optimise primer efficiency within a given multiplex PCR, but it also offers to adjust and/or to compare the sensitivity between different assays. Along with other factors that influence the success of multiplex reactions, and which we discuss here in relation to the presented detection system, the adoption of this approach will allow for direct comparison of multiplex PCR data between systems and studies, enhancing the utility of this assay type.

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