样式: 排序: IF: - GO 导出 标记为已读
-
Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-25 Benoît Pichon, Rémy Le Goff, Hélène Morlon, Benoît Perez‐Lamarque
Characterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology and conservation. A highly debated question is whether and how the structure of a bipartite ecological network differs between antagonistic (e.g. herbivory) and mutualistic (e.g. pollination) interaction
-
Filtering heart rates using data densities: The boxfilter R package Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-25 Thomas Ruf, Claudio Signer, Walter Arnold, Sebastian G. Vetter, Claudia Bieber
Over the past decades, there has been a growing interest in long‐term heart rate records, especially from free‐living animals. Largely, this increase is because most of the metabolic activity of tissues is based on oxygen delivery by the heart. Therefore, heart rate has served as a proxy for energy expenditure in animals. However, heart rates or other physiological variables recorded in humans and
-
A FAIR and modular image‐based workflow for knowledge discovery in the emerging field of imageomics Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-23 Meghan A. Balk, John Bradley, M. Maruf, Bahadir Altintaş, Yasin Bakiş, Henry L. Bart, David Breen, Christopher R. Florian, Jane Greenberg, Anuj Karpatne, Kevin Karnani, Paula Mabee, Joel Pepper, Dom Jebbia, Thibault Tabarin, Xiaojun Wang, Hilmar Lapp
Image‐based machine learning tools are an ascendant ‘big data’ research avenue. Citizen science platforms, like iNaturalist, and museum‐led initiatives provide researchers with an abundance of data and knowledge to extract. These include extraction of metadata, species identification, and phenomic data. Ecological and evolutionary biologists are increasingly using complex, multi‐step processes on data
-
A method for sampling the living wood microbiome Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-23 Wyatt Arnold, Jonathan Gewirtzman, Peter A. Raymond, Mark A. Bradford, Claire Butler, Jordan Peccia
Efforts to characterize microbial life across diverse environments have progressed tremendously, yet the microbiome of Earth's largest biomass reservoir—the wood of living trees—has been largely unexplored. Current understanding of the tree microbiome is largely confined to roots and leaves, with little attention given to the endophytic microbiome of wood, even though emergent studies have indicated
-
Ghostbusting—Reducing bias due to identification errors in spatial capture‐recapture histories Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-22 Abinand Reddy Kodi, Jasmin Howard, David Louis Borchers, Hannah Worthington, Justine Shanti Alexander, Purevjav Lkhagvajav, Gantulga Bayandonoi, Munkhtogtokh Ochirjav, Sergelen Erdenebaatar, Choidogjamts Byambasuren, Nyamzav Battulga, Örjan Johansson, Koustubh Sharma
Identifying individuals is key to estimating population sizes by spatial capture–recapture, but identification errors are sometimes made. The most common identification error is the failure to recognise a previously detected individual, thus creating a ‘ghost’ Johansson. This results in positively biased abundance estimates. Ghosts typically manifest as single detection individuals (‘singletons’) in
-
Performance of five statistical methods to infer interactions among moving individuals in a predator–prey system Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-16 Thibault Fronville, Niels Blaum, Stephanie Kramer‐Schadt, Ulrike Schlägel, Viktoriia Radchuk
Rapid development of tracking technologies allow the collection of high‐quality data on multiple simultaneously moving individuals. This, in turn, initiated the development of several methods to infer interactions among moving animals. However, the performance of these methods has not been studied systematically, especially with regard to the factors that are highly relevant for field ecologists, such
-
Modelling individual variability in habitat selection and movement using integrated step‐selection analysis Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-15 Nilanjan Chatterjee, David Wolfson, Dongmin Kim, Juliana Velez, Smith Freeman, Nathan M. Bacheler, Kyle Shertzer, J. Christopher Taylor, John Fieberg
Integrated step‐selection analysis (ISSA) is frequently used to study habitat selection using animal movement data. Methods for incorporating random effects in ISSA have been developed, making it possible to quantify variability among animals in their space‐use patterns. Although it is possible to model variability in both habitat selection and movement parameters, applications to date have focused
-
Turnover importance: Operationalizing beta diversity to quantify the generalism continuum Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-13 Lydia Morley, Benjamin J. Crain, Gary Krupnick, Daniel Spalink
Generalization is difficult to quantify, and many classifications exist. A beta diversity framework can be used to establish a numeric measure of generalist tendencies that jointly describes many important features of species interactions, namely spatiotemporal heterogeneity. This framework is promising for studying generalized symbiotic relationships of any form. We formulated a novel index, turnover
-
Using camera traps and N‐mixture models to estimate population abundance: Model selection really matters Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-12 Lisa Jeanne Koetke, Dexter P. Hodder, Chris J. Johnson
Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the sampling and statistical methods, sampling and ecological variation, and sample size. We used images of moose (Alces americanus) from camera traps to parameterize N‐mixture models and tested the effect of ecological
-
Monitoring fast‐moving animals—Building a customized camera system and evaluation toolset Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-10 Katharina Wittmann, Mohamed Gamal Ibrahim, Andrew David Straw, Alexandra‐Maria Klein, Michael Staab
Automated cameras (including camera traps) are an established observation tool, allowing, for example the identification of behaviours and monitoring without harming organisms. However, limitations including imperfect detection, insufficient data storage and power supply restrict the use of camera traps, making inexpensive and customizable solutions desirable. We describe a camera system and evaluation
-
The accuracy of length measurements made using imaging SONAR is inversely proportional to the beamwidth Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-08 Iain M. Parnum, Benjamin J. Saunders, Melanie Stott, Travis S. Elsdon, Michael J. Marnane, Euan S. Harvey
Multibeam imaging SONARs have been used for a range of measurement applications, such as measurements of fish lengths. This study aimed to quantify the accuracy of imaging SONAR systems, that varied in frequency and beam geometry, to measure the length of synthetic targets positioned perpendicularly. Blueprint Oculus imaging SONAR systems, with four different (centre) frequencies (750 kHz, 1.2 MHz
-
Phenological patterns in ecology: Problems using circular statistics and solutions based on simulations Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-06 Michael R. Willig, Julissa Rojas‐Sandoval, Steven J. Presley
Quantification of phenological patterns (e.g. migration, hibernation or reproduction) should involve statistical assessments of non‐uniform temporal patterns. Circular statistics (e.g. Rayleigh test or Hermans‐Rasson test) provide useful approaches for doing so based on the number of individuals that exhibit particular activities during a number of time intervals. This study used monthly reproductive
-
-
Classical tests, linear models and their extensions for the analysis of 2 × 2 contingency tables Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-04-02 Rebecca Nagel, Graeme D. Ruxton, Michael B. Morrissey
Ecologists and evolutionary biologists are regularly tasked with the comparison of binary data across groups. There is, however, some discussion in the biostatistics literature about the best methodology for the analysis of data comprising binary explanatory and response variables forming a 2 × 2 contingency table. We assess several methodologies for the analysis of 2 × 2 contingency tables using a
-
eDITH: An R‐package to spatially project eDNA‐based biodiversity across river networks with minimal prior information Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-28 Luca Carraro, Florian Altermatt
Ecological and ecosystem monitoring is rapidly shifting towards using environmental DNA (eDNA) data, particularly in aquatic systems. This approach enables a combined coverage of biodiversity across all major organismal groups and the assessment of ecological indices. Yet, most current approaches are not exploiting the full potential of eDNA data, largely interpreting results in a localized perspective
-
3D photogrammetry and deep‐learning deliver accurate estimates of epibenthic biomass Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-26 Joseph Marlow, John Edward Halpin, Thomas Andrew Wilding
Accurate biomass estimates are key to understanding a wide variety of ecological functions. In marine systems, epibenthic biomass estimates have traditionally relied on either destructive/extractive methods that are limited to horizontal soft‐sediment environments, or simplistic geometry‐based biomass conversions that are unsuitable for more complex morphologies. Consequently, there is a requirement
-
Dynamic carbon allocation trade‐off: A robust approach to model tree biomass allometry Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-25 Mingxia Yang, Xiaolu Zhou, Zelin Liu, Peng Li, Caixia Liu, Huabing Huang, Jiayi Tang, Cicheng Zhang, Ziying Zou, Binggeng Xie, Changhui Peng
Forest above‐ground biomass (AGB) is often estimated by converting the observed tree size using allometric scaling between the dry weight and size of an organism. However, the variations in biomass allocation and scaling between tree crowns and stems due to survival competition during a tree's lifecycle remain unclear. This knowledge gap can improve the understanding of modelling tree biomass allometry
-
OCCUR Shiny application: A user‐friendly guide for curating species occurrence records Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-21 Cristina Ronquillo, Juliana Stropp, Joaquin Hortal
Ecology increasingly relies on a massive volume of biodiversity occurrence records to draw insights into large‐scale biogeographical, ecological and evolutionary phenomena. This often involves defining a set of criteria that guides the collection, filtering and standardising of available records. These curation processes are often neither described in detail nor well documented. This hampers the comparability
-
Managing ecosystems with resist–accept–direct (RAD) Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-20 Byron K. Williams, Eleanor D. Brown
In recent years considerable interest has been generated in a new approach known as resist–accept–direct, or RAD, for managing ecosystems in the face of climate change. Under RAD, strategic responses to climate change are described in terms of three broad categories: resisting climate transformation, accepting the transformation and continuing to manage as best one can, and directing the transformed
-
Bayesian hierarchical modelling of size spectra Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-20 Jeff S. Wesner, Justin P. F. Pomeranz, James R. Junker, Vojsava Gjoni
A fundamental pattern in ecology is that smaller organisms are more abundant than larger organisms. This pattern is known as the individual size distribution (ISD), which is the frequency distribution of all individual body sizes in an ecosystem. The ISD is described by a power law and a major goal of size spectra analyses is to estimate the exponent of the power law, λ. However, while numerous methods
-
Analysing biodiversity observation data collected in continuous time: Should we use discrete‐ or continuous‐time occupancy models? Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-20 Léa Pautrel, Sylvain Moulherat, Olivier Gimenez, Marie‐Pierre Etienne
Biodiversity monitoring is undergoing a revolution, with fauna observation data being increasingly gathered continuously over extended periods, through sensors like camera traps and acoustic recorders, or via opportunistic observations. These data are often analysed with discrete‐time ecological models, requiring the transformation of continuously collected data into arbitrarily chosen, non‐independent
-
Scalable semantic 3D mapping of coral reefs with deep learning Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-14 Jonathan Sauder, Guilhem Banc‐Prandi, Anders Meibom, Devis Tuia
Coral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existentially threatened by global climate change and local anthropogenic pressures. To better understand the dynamics underlying deterioration of reefs
-
Automated detection of an insect‐induced keystone vegetation phenotype using airborne LiDAR Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-13 Zhengyang Wang, Robert Huben, Peter B. Boucher, Chase Van Amburg, Jimmy Zeng, Nina Chung, Jocelyn Wang, Jeffrey King, Richard J. Knecht, Ivy Ng'iru, Augustine Baraza, Christopher C. M. Baker, Dino J. Martins, Naomi E. Pierce, Andrew B. Davies
Ecologists, foresters and conservation practitioners need ‘biodiversity scanners’ to effectively inventory biodiversity, audit conservation progress and track changes in ecosystem function. Quantifying biological diversity using remote sensing methods remains challenging, especially for small invertebrates. However, insect aggregations can drastically alter landscapes and vegetation, and these ‘extended
-
treats: A modular R package for simulating trees and traits Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-08 Thomas Guillerme
1 INTRODUCTION Comparing biological patterns is one of the key ways to understand mechanisms in evolutionary biology. This leads to the development of phylogenetic comparative methods as key methodologically driven topic in ecology, evolution and palaeontology (Felsenstein, 1985; Pennell & Harmon, 2013). These methods rely on comparing patterns in a phylogenetic context to understand biological mechanisms
-
-
Statistical inference methods for n-dimensional hypervolumes: Applications to niches and functional diversity Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-01 Daniel Chen, Alex Laini, Benjamin Wong Blonder
1 INTRODUCTION Hutchinson (1957) introduced the hypervolume concept, which describes the requirements of species along multiple axes, that is their niche, or the functional diversity of an assemblage. The concept, reviewed in Blonder (2018), has seen wide use. An n-dimensional hypervolume is a shape defined within multiple continuously valued dimensions, for which a distance metric exists. Hypervolumes
-
dentist: Quantifying uncertainty by sampling points around maximum likelihood estimates Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-28 James D. Boyko, Brian C. O'Meara
1 INTRODUCTION It is standard statistical practice to provide measures of uncertainty around parameter estimates. Unfortunately, this very basic and necessary enterprise is often absent in macroevolutionary studies. Problems with parameter estimation can be readily apparent empirically if one can accurately assess the confidence of these estimates and the uncertainty around the best estimate. Instead
-
Widespread analytical pitfalls in empirical coexistence studies and a checklist for improving their statistical robustness Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-28 J. Christopher D. Terry, David W. Armitage
1 INTRODUCTION The quest to understand how similar species can avoid competitive exclusion is a central goal of community ecology (Chase & Leibold, 2003; Hutchinson, 1959) and has generated an extensive body of theory describing the formal conditions required for pairwise coexistence (Amarasekare, 2019; Barabás et al., 2018; Chesson, 2000). A key strength of this approach, frequently referred to as
-
Correction to: Standardized nuclear markers improve and homogenize species delimitation in Metazoa Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-28
Dietz, L., Eberle, J., Mayer, C., Kukowka, S., Bohacz, C., Baur, H., Espeland, M., Huber, B.A., Hutter, C., Mengual, X., Peters, R.S., Vences, M., Wesener, T., Willmott, K., Misof, B., Niehuis, O., & Ahrens, D. (2023). Standardized nuclear markers improve and homogenize species delimitation in Metazoa. Methods in Ecology and Evolution, 14, 543–555. https://doi.org/10.1111/2041-210X.14041. In the article
-
Deep learning- and image processing-based methods for automatic estimation of leaf herbivore damage Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-27 Zihui Wang, Yuan Jiang, Abdoulaye Baniré Diallo, Steven W. Kembel
1 INTRODUCTION Herbivory of plants by insects is a key biotic interaction that has been intensively investigated in both basic and applied science. Numerous theories and applications have been established on the importance of insect herbivores in plant ecology, evolution, crop production and ecosystem functioning, such as the green world hypothesis in explaining plant biomass (Hairston et al., 1960)
-
HSC3D: A Python package to quantify three-dimensional habitat structural complexity Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-27 Yi-Fei Gu, Jiamian Hu, Kai Han, Jackson W. T. Lau, Gray A. Williams
1 INTRODUCTION Habitat structural complexity (hereafter, HSC) has been extensively studied in relation to spatial organisation, ecological function and resilience across multiple ecosystems (Menge & Sutherland, 1976; Richardson et al., 2017; Thrush et al., 2008). In general, higher HSC tends to provide a broader range of potential niches at multiple spatial scales (Gámez & Harris, 2022; Hutchinson
-
MoonShine: A software-hardware system for simulating moonlight ground illuminance and re-creating artificial moonlight cycles in a laboratory environment Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-23 Lok Poon, Ian T. Jenks, W. G. R. Crampton
1 INTRODUCTION Some 30% of vertebrate and over 60% of invertebrate species express primarily nocturnal activity (Hölker et al., 2010). Many nocturnal animals are profoundly influenced by moonlight, the most significant natural source of night-time light (Hänel et al., 2018). Moonlight ground illuminance (or luminous flux incident on the ground, Johnsen, 2012) varies over the lunar cycle in a complex
-
Empirical dynamic programming for model-free ecosystem-based management Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-23 Stephan B. Munch, Antoine Brias
1 INTRODUCTION In recent decades, there has been a push to engage in ecosystem-based management (EBM; Christensen et al., 1996; McLeod et al., 2005; Pikitch et al., 2004), which requires us to recognize the interactions between system components and make explicit the trade-offs that arise among competing uses (Meffe, 2002). Quantitative approaches to EBM typically begin by constructing ecosystem models
-
A framework to study and predict functional trait syndromes using phylogenetic and environmental data Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-23 Pablo Sanchez-Martinez, David D. Ackerly, Jordi Martínez-Vilalta, Maurizio Mencuccini, Kyle G. Dexter, Todd E. Dawson
1 INTRODUCTION Functional traits are defined as morpho-physio-phenological attributes that impact fitness via their effects on individual performance (Violle et al., 2007). As such, they are likely to undergo adaptive evolution in response to environmental drivers (Ackerly et al., 2000). The functional significance of any one trait depends on coordination with other traits, creating functional strategies
-
A kernel integral method to remove biases in estimating trait turnover Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-22 Guillaume Latombe, Paul Boittiaux, Cang Hui, Melodie A. McGeoch
1 INTRODUCTION Biodiversity is a complex concept and can most easily be quantified by distinguishing three complementary and interrelated facets: taxonomic diversity based on a site-by-species matrix that captures the compositional properties of a community; phylogenetic diversity that captures the evolutionary relatedness among community members, using phylogenetic distance between species alongside
-
Connectivity conservation planning through deep reinforcement learning Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-22 Julián Equihua, Michael Beckmann, Ralf Seppelt
1 INTRODUCTION Currently, 40% of all land has been transformed into agriculture (UN, 2022), accounting for 88% of global deforestation (FAO, 2022), devastating or starkly modifying the habitats where many species live. Habitat areas become separated and surrounded, making them too small to sustain viable animal populations or too far apart to move between them to feed and reproduce, eventually capping
-
Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-21 Ryoma Otsuka, Naoya Yoshimura, Kei Tanigaki, Shiho Koyama, Yuichi Mizutani, Ken Yoda, Takuya Maekawa
1 INTRODUCTION 1.1 Behaviour classification of wild animals using time-series sensor data Knowing when, where and what an animal is doing is fundamental to understanding animal behaviour. Bio-logging is a modern research technique that employs animal-borne data loggers to record a variety of time-series sensor data such as acceleration, temperature, water depth and location data (Fehlmann & King, 2016;
-
Strengthening resilience potential assessments for coral reef management Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-21 Mishal Gudka, David Obura, Eric A. Treml, Emily Nicholson
1 INTRODUCTION Coral reefs are one of the most biodiverse and valuable ecosystem types in the world (Adey, 2000; Anthony et al., 2017) but are also among the most threatened (Bellwood et al., 2019; Burke et al., 2011; Halpern et al., 2015; Hoegh-Guldberg et al., 2017) due to climate-driven and local pressures increasing in magnitude and frequency (Anthony et al., 2017; Beyer et al., 2018; Costa et al
-
Scalable phylogenetic Gaussian process models improve the detectability of environmental signals on local extinctions for many Red List species Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-15 Misako Matsuba, Keita Fukasawa, Satoshi Aoki, Munemitsu Akasaka, Fumiko Ishihama
1 INTRODUCTION Climate change and land cover change are major drivers of species extinction (Di Marco et al., 2018, 2019; Powers & Jetz, 2019). Current species extinction risks are already about 100–1000 times higher than that in nature (Pimm et al., 2014), and the risk of biodiversity decline continues to increase (Butchart et al., 2010). Conservation biologists are now faced with the challenging
-
Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-13 James T. Thorson, Alexander G. Andrews, Timothy E. Essington, Scott I. Large
1 INTRODUCTION Ecological systems typically involve many interacting variables. Scientists typically seek to understand how these variables will change given a hypothetical policy, experimental manipulation or global change scenario. These predictions require understanding how a change in one variable will cause a subsequent change in another (termed ‘causal analysis’). Causal analysis has motivated
-
A unified framework for time-to-detection occupancy and abundance models Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-11 Dinusha Priyadarshani, Huu-Dinh Huynh, Res Altwegg, Wen-Han Hwang
1 INTRODUCTION Abundance of wild organisms is notoriously difficult to estimate because some individuals usually go undetected during surveys. In situations where it is sufficient to know whether a species occupies a site or not, that is, abundance is summarized as 0 versus >0 individuals, occupancy models (MacKenzie et al., 2002) can estimate the true occupancy probability while accounting for imperfect
-
-
Sizing mudsnails: Applying superpixels to scale growth detection under ocean warming Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-07 Liam MacNeil, Léa J. Joly, Maysa Ito, Anna Steinmann, Knut Mehler, Marco Scotti
1 INTRODUCTION Imaging data have proliferated throughout studies of ecology and evolution (Høye et al., 2021; Schürholz & Chennu, 2023; Weinstein, 2018). Digital images are data-rich, conventionally represented as matrices of pixel intensities across three colour channels (red, green and blue; RGB) with millions of colour variations possible for each pixel in a 24-bit image. Instance segmentation (object
-
A new method for voxel-based modelling of three-dimensional forest scenes with integration of terrestrial and airborne LiDAR data Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-05 Wenkai Li, Xiaomei Hu, Yanjun Su, Shengli Tao, Qin Ma, Qinghua Guo
1 INTRODUCTION Forest is one of the most important ecosystems on the planet, playing important roles in biophysical processes and biodiversity (Davies & Asner, 2014; Fouqueray et al., 2022; Schlund et al., 2022). Forest structure is a major driver of ecosystem functions (Béland & Kobayashi, 2021). The distribution of solar radiation in forest ecosystems is affected by canopy structure (Braghiere et al
-
LC-ICP-MS analysis of inositol phosphate isomers in soil offers improved sensitivity and fine-scale mapping of inositol phosphate distribution Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-04 Joseph J. Carroll, Colleen Sprigg, Graham Chilvers, Ignacio Delso, Megan Barker, Filipa Cox, David Johnson, Charles A. Brearley
1 INTRODUCTION Phosphorus (P) is a major nutrient that limits plant growth in diverse ecosystems, including tropical forests (Cunha et al., 2022), boreal forest (Giesler et al., 2012) and species-rich calcareous grassland (Johnson et al., 1999). Sustained input of nitrogen (N) from atmospheric deposition or fertilization increases P limitation of ecosystems globally (Chen et al., 2020) and often results
-
A conceptual framework for host-associated microbiomes of hybrid organisms Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-30 Benjamin T. Camper, Zachary Laughlin, Daniel Malagon, Robert Denton, Sharon Bewick
1 INTRODUCTION Hybridization is increasingly recognized as an important component of ecological and evolutionary processes. Consequences of hybridization span the fitness spectrum ranging from infertility and death (Brucker & Bordenstein, 2013; Zhang et al., 2014) to innovation and adaptation (Abbott et al., 2013; Dowling & Secor, 1997; Patton et al., 2020; Seehausen, 2004). Ultimately, these fitness
-
Population assignment from genotype likelihoods for low-coverage whole-genome sequencing data Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-29 Matthew G. DeSaix, Marina D. Rodriguez, Kristen C. Ruegg, Eric C. Anderson
1 INTRODUCTION In just a few years, next-generation sequencing (NGS) technologies have revolutionized the study of evolution and ecology in both model and non-model organisms, and have become established as standard tools in molecular ecology. In particular, whole-genome sequencing (WGS) can provide sequence data from a large proportion of the genome and is increasing in use. While large-scale WGS
-
On the diversity-based measures of equalness and evenness Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-29 Hans-Rolf Gregorius, Elizabeth M. Gillet
1 INTRODUCTION The still most commonly applied approaches to the assessment of evenness in community ecology and population genetics are based on relations between measures of diversity and the associated number of types (richness). Measures normalized to the unit interval conventionally appear as the difference of the observed diversity from the minimum diversity (defined for monomorphism), divided
-
FAMeLeS: A multispecies and fully automated method to measure morphological leaf traits Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-23 Nicolas Montès, Lorène Tosini, Isabelle Laffont-Schwob, Yoann Le Bagousse-Pinguet, Hélène Folzer
1 INTRODUCTION Due to their crucial role in plant development and productivity and their high plasticity, plant leaf traits are key features to understand both plant responses to environmental conditions (i.e. response traits) and impacts on ecosystem functioning (i.e. effect traits) (Violle et al., 2007). Indeed, morphological leaf traits such as leaf length, perimeter, shape and area (see, e.g. Wright
-
Facilitating comparable research in seedling functional ecology Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-19 Daniel E. Winkler, Magda Garbowski, Kevin Kožić, Emma Ladouceur, Julie Larson, Sarah Martin, Christoph Rosche, Christiane Roscher, Mandy L. Slate, Lotte Korell
1 INTRODUCTION The seedling stage represents one of the most vulnerable and elusive periods of the plant life cycle (Leck et al., 2008). Seedling recruitment can be one of the greatest bottlenecks to population growth (e.g. Eriksson & Ehrlén, 2008) and determinants of conservation and restoration success (e.g. Shackelford et al., 2021). However, despite their outsized importance, small sizes and short
-
occupancyTuts: Occupancy modelling tutorials with RPresence Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-19 Therese Donovan, James Hines, Darryl MacKenzie
1 INTRODUCTION Understanding how species are distributed in both space and time are central questions in ecology. Abundance, distribution and species richness patterns are ‘state’ variables that describe an ecological system of interest (Figure 1, adapted from Kéry and Royle (2020)). These state variables are often unknown but are, for any number of reasons, of interest to ecologists. FIGURE 1 Open
-
A new index to estimate ecological generalisation in consumer-resource interactions Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-15 Sebastián Montoya-Bustamante, Carsten F. Dormann, Boris R. Krasnov, Marco A. R. Mello
1 INTRODUCTION Generalisation and specialisation are key ecological processes (Darwin, 1859, 1862). The former results in an organism interacting with (i.e. using) a broad range of potential resources, while the latter involves an organism becoming highly adapted to, and increasing the use of, a restricted subset of resources (Poisot et al., 2012). Despite food being the most common example of resources
-
Four principles for improved statistical ecology Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-15 Gordana Popovic, Tanya Jane Mason, Szymon Marian Drobniak, Tiago André Marques, Joanne Potts, Rocío Joo, Res Altwegg, Carolyn Claire Isabelle Burns, Michael Andrew McCarthy, Alison Johnston, Shinichi Nakagawa, Louise McMillan, Kadambari Devarajan, Patrick Leo Taggart, Alison Wunderlich, Magdalena Mair, Juan Andrés Martínez-Lanfranco, Malgorzata Lagisz, Patrice Pottier
1 INTRODUCTION When reporting research findings, ecologists, like other scientists, want their results to reflect what truly happens in the system being studied and to communicate both ecological relevance and the level of support for their conclusions. For their results to hold up, researchers need to follow good research practices. Failure to follow good practices has led to low reproducibility of
-
-
Tracking the frequency of phytoplankton clonal lineages using multispectral image flow cytometry and neural networks Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-08 Ruben J. Hermann, Lutz Becks
1 INTRODUCTION Adaption to environmental change is often described through changes in mean traits of populations. Examples range from prey evolving to become larger in response to gape-limited predators (Miehls et al., 2014), resistance evolution of hosts when exposed to parasites (Frickel et al., 2016) or the colonisation of a novel habitat when ecological opportunity is high (Rainey & Travisano, 1998)
-
ECKOchain: A FAIR blockchain-based database for long-term ecological data Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-08 Kjell-Erik Marstein, John-Arvid Grytnes, Robert John Lewis
1 INTRODUCTION In an era of unprecedented global environmental change, open data are vital. Ecologists are increasingly tasked to address pressing societal questions requiring data spanning larger spatial scales and over longer time periods. This is a challenge that cannot be met individually. It requires collaborative research and, importantly, data prosperity (Hampton et al., 2013). In the field
-
Overcoming data gaps using integrated models to estimate migratory species' dynamics during cryptic periods of the annual cycle Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-08 Matthew T. Farr, Erin R. Zylstra, Leslie Ries, Elise F. Zipkin
1 INTRODUCTION Migratory species offer many ecosystem services that are highly valued by humans including nutrient cycling, pest control, seed dispersal, recreational opportunities, and food (Green & Elmberg, 2014; Mattsson et al., 2018; Thogmartin et al., 2022). Across taxonomic groups, migratory species have declined and face ongoing threats from myriad factors including climate change and habitat
-
ontophylo: Reconstructing the evolutionary dynamics of phenomes using new ontology-informed phylogenetic methods Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-08 Diego S. Porto, Josef Uyeda, István Mikó, Sergei Tarasov
1 INTRODUCTION Reconstruction of ancestral states for discrete characters is commonly used to understand trait evolution in organisms. However, most methods for ancestral reconstruction were developed for individual characters, which represent some elementary phenotypic observation with a limited number of states. When we focus on individual traits, phenotypic evolution becomes oversimplified, as organisms
-
An open-source general purpose machine learning framework for individual animal re-identification using few-shot learning Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-01-04 Oscar Wahltinez, Sarah J. Wahltinez
1 INTRODUCTION Re-identifying individuals is important for animals in managed care as well as wildlife research; however, it represents a surprisingly challenging feat in many species and scenarios. In zoos, individual animal identification is needed to track where the animal came from, reproductive history, medical records and evaluate lifespan (Reuther, 1968); while in agricultural settings, the
-
bistro: An R package for vector bloodmeal identification by short tandem repeat overlap Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-30 Zena Lapp, Lucy Abel, Judith Mangeni, Andrew A. Obala, Wendy P. O'Meara, Steve M. Taylor, Christine F. Markwalter
1 INTRODUCTION Vector-borne diseases cause over 700,000 deaths each year (WHO, 2020). Understanding vector biting behaviour in a natural setting can inform precise targeting of interventions to efficiently interrupt transmission. One approach is to identify human factors associated with increased vector biting by matching the human DNA in vector bloodmeals to the individuals who were bitten. Analogous