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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
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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
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treats: A modular R package for simulating trees and traits Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-08 Thomas Guillerme
Simulating biological realistic data is an important step to understand and investigate biodiversity. Simulated data can be used to generate null, base line or neutral models. These can be used either in comparison to observed data to estimate the mechanisms that generated the data. Or they can be used to explore, understand and develop theoretical advances by proposing toy models. In evolutionary
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Statistical inference methods for n‐dimensional hypervolumes: Applications to niches and functional diversity Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-03-02 Daniel Chen, Alex Laini, Benjamin Wong Blonder
The size and shape of niche spaces or trait spaces are often analysed using hypervolumes estimated from data. The hypervolume R package has previously supported such analyses via descriptive but not inferential statistics. This gap has limited the use of hypothesis testing and confidence intervals when comparing or analysing hypervolumes. We introduce a new version of this R package that provides nonparametric
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dentist: Quantifying uncertainty by sampling points around maximum likelihood estimates Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-29 James D. Boyko, Brian C. O'Meara
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 using maximum likelihood estimates (MLEs). dentist is an R package that allows an approximation of confidence intervals (CI) around parameter estimates without an analytic solution to likelihood equations
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Widespread analytical pitfalls in empirical coexistence studies and a checklist for improving their statistical robustness Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-29 J. Christopher D. Terry, David W. Armitage
Modern coexistence theory (MCT) offers a conceptually straightforward approach for connecting empirical observations with an elegant theoretical framework, gaining popularity rapidly over the past decade. However, beneath this surface‐level simplicity lie various assumptions and subjective choices made during data analysis. These can lead researchers to draw qualitatively different conclusions from
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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
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Deep learning‐ and image processing‐based methods for automatic estimation of leaf herbivore damage Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-28 Zihui Wang, Yuan Jiang, Abdoulaye Baniré Diallo, Steven W. Kembel
Quantifying the intensity of leaf herbivory pressure is crucial for understanding the interaction between plants and herbivores in both applied and basic science. Visual estimates and digital analysis have been commonly used to estimate leaf herbivore damage but are time‐consuming which limits the amount of data that can be collected and prevent answering big picture questions that require large‐scale
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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
Habitat structural complexity (HSC) is a key variable to help interpret ecological patterns and processes among different ecosystems. Existing metrics used to quantify HSC often, however, result in insufficient or biased representation of structural complexity. As such, our understanding of how HSC affects biodiversity and related ecological patterns is often limited by these measures. Recent advances
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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 : 2024-02-24 Lok Poon, Ian T. Jenks, W. G. R. Crampton
Moonlight exerts profound ecological, behavioural and physiological effects on animals. However, lunar cycles are characterised by complex changes in the illuminance and timing of illumination, making it challenging to re‐create and manipulate moonlight cycles in the laboratory using artificial lights. As a result, ecological experiments on the effects of moonlight cycles are uncommon, and existing
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Empirical dynamic programming for model‐free ecosystem‐based management Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-24 Stephan B. Munch, Antoine Brias
Quantitative ecosystem‐based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best‐studied systems. Here, we develop a management scheme that is based on predictive models driven by the observed dynamics. We show that near‐optimal management policies can be constructed from time‐series data by merging empirical dynamic modelling and stochastic
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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
Traits do not evolve in isolation but often as part of integrated trait syndromes, yet the relative contributions of environmental effects and evolutionary history on traits and their correlations are not easily resolved. In the present study, we develop a methodological framework to elucidate eco‐evolutionary patterns in functional trait syndromes. We do so by separating the amount of variance and
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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
Trait diversity, including trait turnover, that differentiates the roles of species and communities according to their functions, is a fundamental component of biodiversity. Accurately capturing trait diversity is crucial to better understand and predict community assembly, as well as the consequences of global change on community resilience. Existing methods to compute trait turnover have limitations
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Connectivity conservation planning through deep reinforcement learning Methods Ecol. Evol. (IF 6.6) Pub Date : 2024-02-22 Julián Equihua, Michael Beckmann, Ralf Seppelt
The United Nations has declared 2021–2030 the decade on ecosystem restoration with the aim of preventing, stopping and reversing the degradation of the ecosystems of the world, often caused by the fragmentation of natural landscapes. Human activities separate and surround habitats, making them too small to sustain viable animal populations or too far apart to enable foraging and gene flow. Despite
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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
Machine learning‐based behaviour classification using acceleration data is a powerful tool in bio‐logging research. Deep learning architectures such as convolutional neural networks (CNN), long short‐term memory (LSTM) and self‐attention mechanism as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies
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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
The persistence of diverse yet threatened ecosystems like coral reefs will require urgent action underpinned by effective assessments of resilience. Resilience potential assessments are commonly used to identify coral reefs likely to be more resilient to disturbances, based on indicators of state and function. Assessments are intended to support decision‐making, therefore, using principles from decision‐science
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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)
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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
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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
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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
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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
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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
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Uncertainty propagation in matrix population models: Gaps, importance and guidelines Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-30 Emily G. Simmonds, Owen R. Jones
1 INTRODUCTION The complexity of natural systems and the limitations of our investigative tools make uncertainty an inevitable part of science (Kampourakis & McCain, 2019). This uncertainty can profoundly affect inferences drawn from ecological models, and therefore, the quantification and reporting of uncertainty is a fundamental and increasingly recognised part of robust science, particularly for
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Fieldwork in conservation organisations–A review of methodological challenges, opportunities and ethics Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-29 Omar Saif, Sam Staddon, Aidan Keane
1 INTRODUCTION Conservation organisations, particularly those working nationally and internationally, are a major driving force of modern conservation practice. Their accomplishments are significant: saving multiple species from extinction, preventing forest degradation, mobilising significant funds to protect nature, and learning to work with Indigenous People and Local Communities (IPLCs). Yet ecological
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Nighthawk: Acoustic monitoring of nocturnal bird migration in the Americas Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-26 Benjamin M. Van Doren, Andrew Farnsworth, Kate Stone, Dylan M. Osterhaus, Jacob Drucker, Grant Van Horn
1 INTRODUCTION Seasonal migration is fundamental to the life histories of countless organisms. Migrating animals have captured human imagination for millennia, and movement is a key mechanism by which organisms can adjust to rapid environmental change (Van Doren et al., 2021). Movement is a fundamental mediator of organisms' interactions with their environment and with each other. As anthropogenic
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“Fractional replication” in single-visit multi-season occupancy models: Impacts of spatiotemporal autocorrelation on identifiability Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-26 Jeffrey W. Doser, Sara Stoudt
1 INTRODUCTION Species are influenced by a combination of abiotic and biotic processes that interact across local to macroscales to determine species distributions (MacArthur, 1972). Global change is contributing to population declines and range shifts across species and taxa at alarming rates (Rosenberg et al., 2019), with factors such as climate change (e.g. Spooner et al., 2018), habitat loss (e
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Quantitative processing of broadband data as implemented in a scientific split-beam echosounder Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-21 Lars Nonboe Andersen, Dezhang Chu, Nils Olav Handegard, Harald Heimvoll, Rolf Korneliussen, Gavin J. Macaulay, Egil Ona, Ruben Patel, Geir Pedersen
1 INTRODUCTION Echosounders are used for remote sensing marine ecosystems. As early as 1935, Sund (1935) observed the distribution of spawning cod in the Lofoten area using a single beam echosounder. The method was further developed to map the abundance of fish, driven by the need for fisheries management (Simmonds & MacLennan, 2005). More recently, fisheries acoustics sensors have been deployed on
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A multi-property assessment of intensity of use provides a functional understanding of animal movement Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-22 G. Bastille-Rousseau, S. A. Crews, E. B. Donovan, M. E. Egan, N. T. Gorman, J. B. Pitman, A. M. Weber, E. M. Audia, M. R. Larreur, H. Manninen, S. Blake, M. W. Eichholz, E. Bergman, N. D. Rayl
1 INTRODUCTION Animal movement is a fundamental behaviour by which individuals respond to their environment. As such, movement is often at the core of how an animal will access resources, avoid risks, interact with conspecifics and encounter disease (Lima & Zollner, 1996; Wittemyer et al., 2019). Given its role in a myriad of ecological processes, understanding animal movement has become a predominant
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Microbial lag calculator: A shiny-based application and an R package for calculating the duration of microbial lag phase Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-20 Bogna J. Smug, Monika Opalek, Maks Necki, Dominika Wloch-Salamon
1 INTRODUCTION Microbial planktonic populations grown in a batch culture usually follow a predictable pattern in terms of how the population size changes in time. Such growth kinetics can be represented by a growth curve which is typically divided into the following phases: (1) lag phase, when cells adjust to a new environment before they start dividing; (2) exponentially growing phase (or logarithmic
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Extension of Pradel capture–recapture survival-recruitment model accounting for transients Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-19 Tomáš Telenský, David Storch, Petr Klvaňa, Jiří Reif
1 INTRODUCTION Population dynamics is one of the most important and interesting phenomena in ecology and conservation. Decomposition of population growth into demographic parameters (survival, recruitment, fecundity, dispersal) can bring a deeper understanding of the drivers of population dynamics. This can be achieved in several ways. A population can be described using different types of data, such
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Evo-Scope: Fully automated assessment of correlated evolution on phylogenetic trees Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-20 Maxime Godfroid, Charles Coluzzi, Amaury Lambert, Philippe Glaser, Eduardo P. C. Rocha, Guillaume Achaz
1 INTRODUCTION The study of correlated evolution between biological traits illustrates the tight interdependence between processes in evolutionary biology. The constant development of new tools (Table S1) to understand these dependencies allows to predict residues in contact in 3D structures of proteins (Morcos et al., 2011) or to identify protein–protein interactions (Barker & Pagel, 2005). Nevertheless
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A computational neuroscience framework for quantifying warning signals Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-11 O. Penacchio, C. G. Halpin, I. C. Cuthill, P. G. Lovell, M. Wheelwright, J. Skelhorn, C. Rowe, J. M. Harris
1 INTRODUCTION Aposematic prey have striking colour patterns that warn potential predators that they are unpleasant or unprofitable to eat (Cott, 1940; Mappes et al., 2005; Poulton, 1890; Rowe & Halpin, 2013; Wallace, 1867). Although diverse in nature (Briolat et al., 2019), some common visual characteristics, such as being yellow or red, or having ‘high contrast internal boundaries’ or ‘repetitive
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Generative spatial generalized dissimilarity mixed modelling (spGDMM): An enhanced approach to modelling beta diversity Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-09 Philip A. White, Henry A. Frye, Jasper A. Slingsby, John A. Silander, Alan E. Gelfand
1 INTRODUCTION Change in the composition of biotic assemblages in space and time is important for revealing the ecological processes that structure and maintain biodiversity spatially across landscapes, detecting biodiversity loss or change in the composition of assemblages, or tracking global biodiversity change (Ferrier et al., 2020; Hoskins et al., 2020). Such turnover is referred to as beta diversity
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Maximising the informativeness of new records in spatial sampling design Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-08 Ian Flint, Chung-Huey Wu, Roozbeh Valavi, Wan-Jyun Chen, Te-En Lin
1 INTRODUCTION 1.1 Cost-effective additional sampling Spatial modelling of ecological events, including species occurrence, habitat destruction and wildlife-vehicle collision, relies on carefully collected data (Thibaud et al., 2014) and well-constructed models (Guisan et al., 2013). Thus, model performance and overall usefulness depend on well-devised surveys yielding as much relevant information
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Simulating animal space use from fitted integrated Step-Selection Functions (iSSF) Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-08 J. Signer, J. Fieberg, B. Reineking, U. Schlägel, B. Smith, N. Balkenhol, T. Avgar
1 INTRODUCTION Integrated step selection analysis (iSSA; Avgar et al., 2016; Fieberg et al., 2021) has emerged as a powerful and unifying methodological framework for quantifying different aspects of animal space use, including habitat selection patterns, movement behaviour, and transient and steady-state utilisation distributions (UDs). An iSSA results in a fully parametrised individual-based movement
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Assessing repeatability of spatial trajectories Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-08 Josep L. Carrasco
1 INTRODUCTION In the last decades, the availability of georeferenced data on animals' spatial locations obtained through animal-attached autonomous recording tags (or bio-logging devices; Ropert-Coudert & Wilson, 2005) has allowed researchers to obtain high-density information about animal movement (Nathan et al., 2022). These data, commonly termed as tracking data, involve locations of an animal
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Understanding step selection analysis through numerical integration Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-08 Théo Michelot, Natasha J. Klappstein, Jonathan R. Potts, John Fieberg
1 INTRODUCTION The increased availability of animal tracking data has led to the widespread use of statistical methods to estimate habitat selection at the scale of the observed movement step. Perhaps the most common model is the step selection function (SSF; Rhodes et al., 2005; Fortin et al., 2005), whereby the likelihood of moving to the spatial location x t + 1 given previous locations x 1 : t
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Quantitative photography for rapid, reliable measurement of marine macro-plastic pollution Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-08 Joseph Razzell Hollis, Gabrielle Henderson, Jennifer L. Lavers, Edward Rea, Valeriya Komyakova, Alexander L. Bond
1 INTRODUCTION Since the development of cheap, durable plastics in the 1950s, plastic debris has become a major source of anthropogenic pollution found in polar regions (Obbard et al., 2014), on remote oceanic islands (Lavers & Bond, 2017), mountains (Napper & Thompson, 2020), abyssal regions (Chiba et al., 2018), and the atmosphere (Brahney et al., 2020). Plastics are present throughout the entire
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Plus ça change, plus c'est la même chose: On our quattuordecennial, a good Methods paper still is not about my friend the dolphin Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-06 A. M. Ellison
Methods in Ecology & Evolution (MEE) has seen four substantive changes in the 2 years since I took on the executive editorship of the journal. First, the BES policy of term-limits for associate editors and senior editors of the journals ensures their continuing evolution, and so MEE has a new team of senior editors and an editorial board with a healthy mix of rookies and veterans. Second, since January
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Correction to “An integrated animal tracking technology combining a GPS tracking system with a UAV” Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-06
Jin, T., Si, X., Liu, J., & Ding, P. (2023). An integrated animal tracking technology combining a GPS tracking system with a UAV. Methods in Ecology and Evolution, 14, 505–511. In the Supporting Information, the wrong version of Figure S5 was used. This has now been updated in the original paper. The analyses and results in this paper are unchanged. We apologize for this error.
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Integrated trophic position as a proxy for food-web complexity Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-05 Naoto F. Ishikawa, Ayaka Takashima, Hirokazu Maruoka, Michio Kondoh
1 INTRODUCTION Species interactions, such as prey–predator relationships, are a critical factor in determining ecosystem structure and function (Estes et al., 2011; Halpern et al., 2008; Pauly et al., 1998; Rooney & McCann, 2012). Trophic networks, that is a collection of all prey–predator relationships in a given ecosystem, have been extensively studied within this context (e.g. Christianen et al
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A new centrality index designed for multilayer networks Methods Ecol. Evol. (IF 6.6) Pub Date : 2023-12-02 Nastaran Lotfi, Henrique S. Requejo, Francisco A. Rodrigues, Marco A. R. Mello
1 INTRODUCTION The keystone species concept is one of the most successful theoretical frameworks in ecology (Mello, 2019). Since its inception in the mid-20th century (Paine, 1966, 1969), it has opened many avenues for research. It allowed us to better understand what makes some species more important than others for holding together the ecological systems to which they belong (Lafferty & Suchanek