Martin Wikelski, Michael Quetting, John Bates, Tanya Berger‐Wolf, Gil Bohrer, Luca Börger, Taylor Chapple, Margaret C. Crofoot, Sarah C. Davidson, Dina K. N. Dechmann, Diego Ellis‐Soto, Elizabeth R. Ellwood, Wolfgang Fiedler, Andrea Flack, Barbara Fruth, Novella Franconi, Rasmus Worsøe Havmøller, Julian Hirt, Nigel E. Hussey, Fabiola Iannarilli, Matthias Landwehr, Maximilian E. Müller, Thomas Mueller, Uschi Mueller, Ruth Y. Oliver, Jesko Partecke, Ivan Pokrovsky, Liya Pokrovskaya, Dustin R. Rubenstein, Christian Rutz, Kamran Safi, Andrea Santangeli, O. Louis van Schalkwyk, Ana M. M. Sequeira, Sherub Sherub, Tharmalingam Ramesh, Pauli Viljoen, Kaja A. Wasik, Timm A. Wild, Scott Yanco, Roland Kays
Over the past five decades, a large number of wild animals have been individually identified by various observation systems and/or temporary tracking methods, providing unparalleled insights into their lives over both time and space. However, so far there is no comprehensive record of uniquely individually identified animals nor where their data and metadata are stored, for example photos, physiological and genetic samples, disease screens, information on social relationships.Databases currently do not offer unique identifiers for living, individual wild animals, similar to the permanent ID labelling for deceased museum specimens.To address this problem, we introduce two new concepts: (1) a globally unique animal ID (UAID) available to define uniquely and individually identified animals archived in any database, including metadata archived at the time of publication; and (2) the digital ‘home’ for UAIDs, the Movebank Life History Museum (MoMu), storing and linking metadata, media, communications and other files associated with animals individually identified in the wild. MoMu will ensure that metadata are available for future generations, allowing permanent linkages to information in other databases.MoMu allows researchers to collect and store photos, behavioural records, genome data and/or resightings of UAIDed animals, encompassing information not easily included in structured datasets supported by existing databases. Metadata is uploaded through the Animal Tracker app, the MoMu website, by email from registered users or through an Application Programming Interface (API) from any database. Initially, records can be stored in a temporary folder similar to a field drawer, as naturalists routinely do. Later, researchers and specialists can curate these materials for individual animals, manage the secure sharing of sensitive information and, where appropriate, publish individual life histories with DOIs. The storage of such synthesized lifetime stories of wild animals under a UAID (unique identifier or ‘animal passport’) will support basic science, conservation efforts and public participation.
{"title":"Introducing a unique animal ID and digital life history museum for wildlife metadata","authors":"Martin Wikelski, Michael Quetting, John Bates, Tanya Berger‐Wolf, Gil Bohrer, Luca Börger, Taylor Chapple, Margaret C. Crofoot, Sarah C. Davidson, Dina K. N. Dechmann, Diego Ellis‐Soto, Elizabeth R. Ellwood, Wolfgang Fiedler, Andrea Flack, Barbara Fruth, Novella Franconi, Rasmus Worsøe Havmøller, Julian Hirt, Nigel E. Hussey, Fabiola Iannarilli, Matthias Landwehr, Maximilian E. Müller, Thomas Mueller, Uschi Mueller, Ruth Y. Oliver, Jesko Partecke, Ivan Pokrovsky, Liya Pokrovskaya, Dustin R. Rubenstein, Christian Rutz, Kamran Safi, Andrea Santangeli, O. Louis van Schalkwyk, Ana M. M. Sequeira, Sherub Sherub, Tharmalingam Ramesh, Pauli Viljoen, Kaja A. Wasik, Timm A. Wild, Scott Yanco, Roland Kays","doi":"10.1111/2041-210x.14407","DOIUrl":"https://doi.org/10.1111/2041-210x.14407","url":null,"abstract":"<jats:list> <jats:list-item>Over the past five decades, a large number of wild animals have been individually identified by various observation systems and/or temporary tracking methods, providing unparalleled insights into their lives over both time and space. However, so far there is no comprehensive record of uniquely individually identified animals nor where their data and metadata are stored, for example photos, physiological and genetic samples, disease screens, information on social relationships.</jats:list-item> <jats:list-item>Databases currently do not offer unique identifiers for living, individual wild animals, similar to the permanent ID labelling for deceased museum specimens.</jats:list-item> <jats:list-item>To address this problem, we introduce two new concepts: (1) a globally unique animal ID (UAID) available to define uniquely and individually identified animals archived in any database, including metadata archived at the time of publication; and (2) the digital ‘home’ for UAIDs, the Movebank Life History Museum (MoMu), storing and linking metadata, media, communications and other files associated with animals individually identified in the wild. MoMu will ensure that metadata are available for future generations, allowing permanent linkages to information in other databases.</jats:list-item> <jats:list-item>MoMu allows researchers to collect and store photos, behavioural records, genome data and/or resightings of UAIDed animals, encompassing information not easily included in structured datasets supported by existing databases. Metadata is uploaded through the Animal Tracker app, the MoMu website, by email from registered users or through an Application Programming Interface (API) from any database. Initially, records can be stored in a temporary folder similar to a field drawer, as naturalists routinely do. Later, researchers and specialists can curate these materials for individual animals, manage the secure sharing of sensitive information and, where appropriate, publish individual life histories with DOIs. The storage of such synthesized lifetime stories of wild animals under a UAID (unique identifier or ‘animal passport’) will support basic science, conservation efforts and public participation.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rubén G. Mateo, Jennifer Morales‐Barbero, Alejandra Zarzo‐Arias, Herlander Lima, Virgilio Gómez‐Rubio, Teresa Goicolea
Species distribution models have evolved to combine species‐environment interactions across multiple scales. Spatially nested hierarchical models (NSDMs) offer a promising avenue by integrating datasets and predictive models from broad to fine scales. But a user‐friendly tool to execute these models remains an important ongoing challenge.To address this gap, we introduce the sabinaNSDM R package that provides a straightforward approach to develop NSDMs. This package merges global scale models, capturing extensive ecological niches, with regional scale models featuring high‐resolution covariates, to form a unified hierarchical modelling framework. This toolkit is designed to facilitate the implementation of NSDMs for ecologists, conservationists and researchers aiming to produce more reliable species distribution predictions.sabinaNSDM streamlines the data preparation, calibration, integration and projection of models across two scales. It automates (if necessary) the generation of background points, spatial thinning of species occurrence and absence (if available) data, covariate selection and the generation of NSDMs.This paper outlines the workflow and functions integrated into the sabinaNSDM package, complemented by an applied case study involving a pool of 76 tree species. Consistent with previous publications, the generated NSDMs facilitated precise predictions (mean AUC value through independent evaluation higher than 0.88) of species distributions under current and future environmental scenarios.
{"title":"sabinaNSDM: An R package for spatially nested hierarchical species distribution modelling","authors":"Rubén G. Mateo, Jennifer Morales‐Barbero, Alejandra Zarzo‐Arias, Herlander Lima, Virgilio Gómez‐Rubio, Teresa Goicolea","doi":"10.1111/2041-210x.14417","DOIUrl":"https://doi.org/10.1111/2041-210x.14417","url":null,"abstract":"<jats:list> <jats:list-item>Species distribution models have evolved to combine species‐environment interactions across multiple scales. Spatially nested hierarchical models (NSDMs) offer a promising avenue by integrating datasets and predictive models from broad to fine scales. But a user‐friendly tool to execute these models remains an important ongoing challenge.</jats:list-item> <jats:list-item>To address this gap, we introduce the <jats:italic>sabinaNSDM</jats:italic> R package that provides a straightforward approach to develop NSDMs. This package merges global scale models, capturing extensive ecological niches, with regional scale models featuring high‐resolution covariates, to form a unified hierarchical modelling framework. This toolkit is designed to facilitate the implementation of NSDMs for ecologists, conservationists and researchers aiming to produce more reliable species distribution predictions.</jats:list-item> <jats:list-item><jats:italic>sabinaNSDM</jats:italic> streamlines the data preparation, calibration, integration and projection of models across two scales. It automates (if necessary) the generation of background points, spatial thinning of species occurrence and absence (if available) data, covariate selection and the generation of NSDMs.</jats:list-item> <jats:list-item>This paper outlines the workflow and functions integrated into the <jats:italic>sabinaNSDM</jats:italic> package, complemented by an applied case study involving a pool of 76 tree species. Consistent with previous publications, the generated NSDMs facilitated precise predictions (mean AUC value through independent evaluation higher than 0.88) of species distributions under current and future environmental scenarios.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michela Leonardi, Margherita Colucci, Andrea Vittorio Pozzi, Eleanor M. L. Scerri, Andrea Manica
In species distribution modelling (SDM), it is common practice to explore multiple machine learning (ML) algorithms and combine their results into ensembles. In R, many implementations of different ML algorithms are available but, as they were mostly developed independently, they often use inconsistent syntax and data structures. For this reason, repeating an analysis with multiple algorithms and combining their results can be challenging.Specialised SDM packages solve this problem by providing a simpler, unified interface by wrapping the original functions to tackle each specific requirement. However, creating and maintaining such interfaces is time‐consuming, and with this approach, the user cannot easily integrate other methods that may become available.Here, we present tidysdm, an R package that solves this problem by taking advantage of the tidymodels universe. tidymodels provide standardised grammar, data structures and modelling interfaces, and a well‐documented infrastructure to integrate new algorithms and metrics. The wide adoption of tidymodels means that most ML algorithms and metrics are already integrated, and the user can add additional ones. Moreover, because of the broad adoption of tidymodels, new statistical approaches tend to be implemented quickly, making them easily integrated into existing pipelines and analyses.tidysdm takes advantage of the tidymodels universe to provide a flexible and fully customisable pipeline to fit SDM. It includes SDM‐specific algorithms and metrics, and methods to facilitate the use of spatial data within tidymodels.Additionally, tidysdm is the first software that natively allows SDM to be performed using data from different periods, expanding the availability of SDM for scholars working in palaeontology, archaeology, palaeobiology, palaeoecology and other disciplines focussing on the past.
在物种分布建模（SDM）中，通常的做法是探索多种机器学习（ML）算法，并将其结果组合成集合。在 R 语言中，有许多不同 ML 算法的实现方法，但由于它们大多是独立开发的，因此经常使用不一致的语法和数据结构。因此，使用多种算法重复分析并将其结果组合起来是一项挑战。专门的 SDM 软件包可以解决这个问题，它通过封装原始函数来提供更简单、统一的界面，以满足各种特定要求。然而，创建和维护这样的界面非常耗时，而且采用这种方法，用户无法轻松集成可能出现的其他方法。tidymodels 提供了标准化的语法、数据结构和建模接口，以及文档齐全的基础设施，可用于集成新算法和度量标准。tidymodels 的广泛采用意味着大多数 ML 算法和度量标准已经集成，用户可以添加其他算法和度量标准。此外，由于 tidymodels 被广泛采用，新的统计方法往往能很快实施，从而很容易集成到现有的管道和分析中。tidysdm 利用 tidymodels 的优势，提供了一个灵活、完全可定制的管道，以适应 SDM。它包括 SDM 专用算法和指标，以及便于在 tidymodels 中使用空间数据的方法。此外，tidysdm 还是第一款允许使用不同时期数据进行 SDM 的软件，为古生物学、考古学、古生物学、古生态学和其他关注过去的学科的学者提供了 SDM 的更多可能性。
{"title":"tidysdm: Leveraging the flexibility of tidymodels for species distribution modelling in R","authors":"Michela Leonardi, Margherita Colucci, Andrea Vittorio Pozzi, Eleanor M. L. Scerri, Andrea Manica","doi":"10.1111/2041-210x.14406","DOIUrl":"https://doi.org/10.1111/2041-210x.14406","url":null,"abstract":"<jats:list> <jats:list-item>In species distribution modelling (SDM), it is common practice to explore multiple machine learning (ML) algorithms and combine their results into ensembles. In R, many implementations of different ML algorithms are available but, as they were mostly developed independently, they often use inconsistent syntax and data structures. For this reason, repeating an analysis with multiple algorithms and combining their results can be challenging.</jats:list-item> <jats:list-item>Specialised SDM packages solve this problem by providing a simpler, unified interface by wrapping the original functions to tackle each specific requirement. However, creating and maintaining such interfaces is time‐consuming, and with this approach, the user cannot easily integrate other methods that may become available.</jats:list-item> <jats:list-item>Here, we present <jats:italic>tidysdm</jats:italic>, an R package that solves this problem by taking advantage of the <jats:italic>tidymodels</jats:italic> universe. <jats:italic>tidymodels</jats:italic> provide standardised grammar, data structures and modelling interfaces, and a well‐documented infrastructure to integrate new algorithms and metrics. The wide adoption of <jats:italic>tidymodels</jats:italic> means that most ML algorithms and metrics are already integrated, and the user can add additional ones. Moreover, because of the broad adoption of <jats:italic>tidymodels</jats:italic>, new statistical approaches tend to be implemented quickly, making them easily integrated into existing pipelines and analyses.</jats:list-item> <jats:list-item><jats:italic>tidysdm</jats:italic> takes advantage of the <jats:italic>tidymodels</jats:italic> universe to provide a flexible and fully customisable pipeline to fit SDM. It includes SDM‐specific algorithms and metrics, and methods to facilitate the use of spatial data within <jats:italic>tidymodels</jats:italic>.</jats:list-item> <jats:list-item>Additionally, <jats:italic>tidysdm</jats:italic> is the first software that natively allows SDM to be performed using data from different periods, expanding the availability of SDM for scholars working in palaeontology, archaeology, palaeobiology, palaeoecology and other disciplines focussing on the past.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joint species distribution models (JSDMs) are a popular method for analysing multivariate abundance data, with important applications such as uncovering how species communities are driven by environmental processes, model‐based ordination to visualise community composition patterns across sites and variance partitioning to quantify the relative contributions of different processes in shaping a species community.One issue that has received relatively little attention in the study of joint species distributions is that of spatial confounding: when one or more of the environmental predictors exhibit spatial correlation, and spatially structured random effects such as spatial factors are also included in the model, then these two components may be collinear with each other.Through a combination of simulations and case studies, we show that if not managed properly, spatial confounding can result in misleading inference on covariate effects in a spatially structured JSDM, along with difficulties in interpreting ordination results and incorrect attribution of variation to environmental processes in a species community.We present one approach to treat spatial confounding called restricted spatial factor analysis, which is designed to ensure that the covariate effects retain their full explanatory power, and ordinations constructed using the spatial factors explain species covariation beyond that accounted for by the measured predictors. We encourage ecologists to consider the inferences they seek to make from spatially structured JSDMs and to ensure that the covariate effects and ordinations they estimate and interpret are aligned with their scientific questions of interest.
{"title":"Spatial confounding in joint species distribution models","authors":"Francis K. C. Hui, Quan Vu, Mevin B. Hooten","doi":"10.1111/2041-210x.14420","DOIUrl":"https://doi.org/10.1111/2041-210x.14420","url":null,"abstract":"<jats:list> <jats:list-item>Joint species distribution models (JSDMs) are a popular method for analysing multivariate abundance data, with important applications such as uncovering how species communities are driven by environmental processes, model‐based ordination to visualise community composition patterns across sites and variance partitioning to quantify the relative contributions of different processes in shaping a species community.</jats:list-item> <jats:list-item>One issue that has received relatively little attention in the study of joint species distributions is that of spatial confounding: when one or more of the environmental predictors exhibit spatial correlation, and spatially structured random effects such as spatial factors are also included in the model, then these two components may be collinear with each other.</jats:list-item> <jats:list-item>Through a combination of simulations and case studies, we show that if not managed properly, spatial confounding can result in misleading inference on covariate effects in a spatially structured JSDM, along with difficulties in interpreting ordination results and incorrect attribution of variation to environmental processes in a species community.</jats:list-item> <jats:list-item>We present one approach to treat spatial confounding called restricted spatial factor analysis, which is designed to ensure that the covariate effects retain their full explanatory power, and ordinations constructed using the spatial factors explain species covariation beyond that accounted for by the measured predictors. We encourage ecologists to consider the inferences they seek to make from spatially structured JSDMs and to ensure that the covariate effects and ordinations they estimate and interpret are aligned with their scientific questions of interest.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Forests display tremendous structural diversity, shaping carbon cycling, microclimates and terrestrial habitats. An important tool for forest structure assessments are canopy height models (CHMs): high resolution maps of canopy height obtained using airborne laser scanning (ALS). CHMs are widely used for monitoring canopy dynamics, mapping forest biomass and calibrating satellite products, but surprisingly little is known about how differences between CHM algorithms impact ecological analyses.Here, we used high‐quality ALS data from nine sites in Australia, ranging from semi‐arid shrublands to 90‐m tall Mountain Ash canopies, to comprehensively assess CHM algorithms. This included testing their sensitivity to point cloud degradation and quantifying the propagation of errors to derived metrics of canopy structure.We found that CHM algorithms varied widely both in their height predictions (differences up to 10 m, or 60% of canopy height) and in their sensitivity to point cloud characteristics (biases of up to 5 m, or 40% of canopy height). Impacts of point cloud properties on CHM‐derived metrics varied, from robust inference for height percentiles, to considerable errors in above‐ground biomass estimates (~50 Mg ha−1, or 10% of total) and high volatility in metrics that quantify spatial associations in canopies (e.g. gaps). However, we also found that two CHM algorithms—a variation on a ‘spikefree’ algorithm that adapts to local pulse densities and a simple Delaunay triangulation of first returns—allowed for robust canopy characterisation and should thus create a secure foundation for ecological comparisons in space and time.We show that CHM choice has a strong impact on forest structural characterisation that has previously been largely overlooked. To address this, we provide a sample workflow to create robust CHMs and best‐practice guidelines to minimise biases and uncertainty in downstream analyses. In doing so, our study paves the way for more rigorous large‐scale assessments of forest structure and dynamics from airborne laser scanning.
{"title":"Robust characterisation of forest structure from airborne laser scanning—A systematic assessment and sample workflow for ecologists","authors":"Fabian Jörg Fischer, Toby Jackson, Grégoire Vincent, Tommaso Jucker","doi":"10.1111/2041-210x.14416","DOIUrl":"https://doi.org/10.1111/2041-210x.14416","url":null,"abstract":"<jats:list> <jats:list-item>Forests display tremendous structural diversity, shaping carbon cycling, microclimates and terrestrial habitats. An important tool for forest structure assessments are canopy height models (CHMs): high resolution maps of canopy height obtained using airborne laser scanning (ALS). CHMs are widely used for monitoring canopy dynamics, mapping forest biomass and calibrating satellite products, but surprisingly little is known about how differences between CHM algorithms impact ecological analyses.</jats:list-item> <jats:list-item>Here, we used high‐quality ALS data from nine sites in Australia, ranging from semi‐arid shrublands to 90‐m tall Mountain Ash canopies, to comprehensively assess CHM algorithms. This included testing their sensitivity to point cloud degradation and quantifying the propagation of errors to derived metrics of canopy structure.</jats:list-item> <jats:list-item>We found that CHM algorithms varied widely both in their height predictions (differences up to 10 m, or 60% of canopy height) and in their sensitivity to point cloud characteristics (biases of up to 5 m, or 40% of canopy height). Impacts of point cloud properties on CHM‐derived metrics varied, from robust inference for height percentiles, to considerable errors in above‐ground biomass estimates (~50 Mg ha<jats:sup>−1</jats:sup>, or 10% of total) and high volatility in metrics that quantify spatial associations in canopies (e.g. gaps). However, we also found that two CHM algorithms—a variation on a ‘spikefree’ algorithm that adapts to local pulse densities and a simple Delaunay triangulation of first returns—allowed for robust canopy characterisation and should thus create a secure foundation for ecological comparisons in space and time.</jats:list-item> <jats:list-item>We show that CHM choice has a strong impact on forest structural characterisation that has previously been largely overlooked. To address this, we provide a sample workflow to create robust CHMs and best‐practice guidelines to minimise biases and uncertainty in downstream analyses. In doing so, our study paves the way for more rigorous large‐scale assessments of forest structure and dynamics from airborne laser scanning.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. M. Sumby, J. R. Stephen, J. J. Austin, R. K. Schilling, T. R. Cavagnaro
Archival DNA samples collected and analysed for a range of research and applied questions have accumulated in the laboratories of universities, government agencies and commercial service providers for decades. These DNA archives represent a valuable, yet largely un‐tapped repository of genomic information. With lowering costs of, and increasing access to, high‐throughput sequencing, we predict an increase in retrospective research to explore the wealth of information that resides in these archival samples. However, for this to occur, we need confidence in the integrity of the DNA samples, often stored under suboptimal conditions and their fitness of purpose for downstream genomic analysis.Here, we borrow from a well‐established concept in ancient DNA to evaluate sample integrity, defined as loss of information content in recovered amplicons, of frozen DNA samples and based on the ratio of ⍺‐diversity of short‐ and long‐read 16S rRNA gene sequences.The 16S rRNA variable region of 87 stored DNA samples, extracted from soil, collected from western and southern agricultural regions of Australia between 2001 and 2021 were sequenced using both PacBio full length reads (V1–V9, 1.5 kbp) and Illumina short‐reads (V3–V4, 200–450 bp). When ⍺‐diversity ratios were calculated between the long and short reads to assess DNA degradation, the ratio of ⍺‐diversity did not decrease in older samples versus younger samples.We suggest this as a novel method to confirm integrity of DNA before embarking on large‐scale diversity profiling projects using archival DNA.
几十年来，在大学、政府机构和商业服务提供商的实验室中积累了为一系列研究和应用问题收集和分析的 DNA 档案样本。这些 DNA 档案是宝贵的基因组信息库，但在很大程度上尚未得到开发利用。随着高通量测序成本的降低和获取途径的增加，我们预测回溯性研究将会增加，以探索这些档案样本中蕴藏的丰富信息。然而，要做到这一点，我们需要对 DNA 样本的完整性有信心，因为这些样本通常是在不理想的条件下保存的，而且它们也不适合用于下游基因组分析。在此，我们借鉴古 DNA 中一个成熟的概念来评估样本的完整性，即冷冻 DNA 样本中恢复的扩增子信息含量的损失，并基于短线程和长线程 16S rRNA 基因序列的⍺-多样性比率。使用 PacBio 全长读数（V1-V9，1.5 kbp）和 Illumina 短读数（V3-V4，200-450 bp）对 2001 年至 2021 年期间从澳大利亚西部和南部农业区采集的 87 个从土壤中提取的储存 DNA 样本的 16S rRNA 可变区进行测序。当计算长读码和短读码的多样性比值以评估DNA降解情况时，年龄较大的样本与年龄较小的样本相比，多样性比值并没有降低。我们建议将此作为一种新方法，在利用档案 DNA 开展大规模多样性分析项目之前确认 DNA 的完整性。
{"title":"A novel method to assess the integrity of frozen archival DNA samples: Alpha‐diversity ratios of short‐ and long‐read 16S rRNA gene sequences","authors":"K. M. Sumby, J. R. Stephen, J. J. Austin, R. K. Schilling, T. R. Cavagnaro","doi":"10.1111/2041-210x.14411","DOIUrl":"https://doi.org/10.1111/2041-210x.14411","url":null,"abstract":"<jats:list> <jats:list-item>Archival DNA samples collected and analysed for a range of research and applied questions have accumulated in the laboratories of universities, government agencies and commercial service providers for decades. These DNA archives represent a valuable, yet largely un‐tapped repository of genomic information. With lowering costs of, and increasing access to, high‐throughput sequencing, we predict an increase in retrospective research to explore the wealth of information that resides in these archival samples. However, for this to occur, we need confidence in the integrity of the DNA samples, often stored under suboptimal conditions and their fitness of purpose for downstream genomic analysis.</jats:list-item> <jats:list-item>Here, we borrow from a well‐established concept in ancient DNA to evaluate sample integrity, defined as loss of information content in recovered amplicons, of frozen DNA samples and based on the ratio of ⍺‐diversity of short‐ and long‐read 16S rRNA gene sequences.</jats:list-item> <jats:list-item>The 16S rRNA variable region of 87 stored DNA samples, extracted from soil, collected from western and southern agricultural regions of Australia between 2001 and 2021 were sequenced using both PacBio full length reads (V1–V9, 1.5 kbp) and Illumina short‐reads (V3–V4, 200–450 bp). When ⍺‐diversity ratios were calculated between the long and short reads to assess DNA degradation, the ratio of ⍺‐diversity did not decrease in older samples versus younger samples.</jats:list-item> <jats:list-item>We suggest this as a novel method to confirm integrity of DNA before embarking on large‐scale diversity profiling projects using archival DNA.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Most models of ecological and eco‐evolutionary processes involve creating trajectories of something, be it population densities, average trait values, or environmental states, over time. This requires decision‐making regarding how to represent the flow of time in models. Most ecologists are exposed to continuous‐time models (typically in the form of ordinary differential equations) as part of their training, especially since the famous Lotka‐Volterra predator–prey dynamics are formulated this way. However, few appear sufficiently well trained to produce their own work with continuous‐time models and may lack exposure to the true versatility of available methods. Specifically, knowledge that discrete individuals can be modelled in continuous time using the Gillespie algorithm is not as widespread as it should be.I will illustrate the flexibility of continous‐time modelling methods such that researchers can make informed choices, and not resort to discretizing time as a ‘default’ without a clear biological motivation to do so. I provide three example‐based tutorials: (1) a comparison of deterministic and stochastic dynamics of the Lotka‐Volterra predator–prey model, (2) an evaluation of matelessness in a hypothetical insect population (and of selection to mate more often by either searching more efficiently or by shortening the ‘time out’ after each mating) and (3) within‐season density dependence followed by a birth pulse leading to Beverton‐Holt or Ricker dynamics depending on whether the deaths of conspecifics help reduce the mortality of others or not (compensatory mortality).I highlight properties of the exponential distribution that, while counter‐intuitive, are good to know when deriving expected lifetime reproductive success or other similar quantities. I also give guidance on how to proceed if the so‐called memorylessness assumption does not hold in a given situation, and show how continuous and discrete times can be freely mixed if the biological situation dictates this to be the preferred option.Continuous‐time models can also be empirically fitted to data, and I review briefly the insight this gives into the so‐called ‘do hares eat lynx?’ paradox that has been plaguing the interpretation of the Hudson Bay hare and lynx dataset.
{"title":"Who is afraid of modelling time as a continuous variable?","authors":"Hanna Kokko","doi":"10.1111/2041-210x.14394","DOIUrl":"https://doi.org/10.1111/2041-210x.14394","url":null,"abstract":"<jats:list> <jats:list-item>Most models of ecological and eco‐evolutionary processes involve creating trajectories of something, be it population densities, average trait values, or environmental states, over time. This requires decision‐making regarding how to represent the flow of time in models. Most ecologists are exposed to continuous‐time models (typically in the form of ordinary differential equations) as part of their training, especially since the famous Lotka‐Volterra predator–prey dynamics are formulated this way. However, few appear sufficiently well trained to produce their own work with continuous‐time models and may lack exposure to the true versatility of available methods. Specifically, knowledge that discrete individuals can be modelled in continuous time using the Gillespie algorithm is not as widespread as it should be.</jats:list-item> <jats:list-item>I will illustrate the flexibility of continous‐time modelling methods such that researchers can make informed choices, and not resort to discretizing time as a ‘default’ without a clear biological motivation to do so. I provide three example‐based tutorials: (1) a comparison of deterministic and stochastic dynamics of the Lotka‐Volterra predator–prey model, (2) an evaluation of matelessness in a hypothetical insect population (and of selection to mate more often by either searching more efficiently or by shortening the ‘time out’ after each mating) and (3) within‐season density dependence followed by a birth pulse leading to Beverton‐Holt or Ricker dynamics depending on whether the deaths of conspecifics help reduce the mortality of others or not (compensatory mortality).</jats:list-item> <jats:list-item>I highlight properties of the exponential distribution that, while counter‐intuitive, are good to know when deriving expected lifetime reproductive success or other similar quantities. I also give guidance on how to proceed if the so‐called memorylessness assumption does not hold in a given situation, and show how continuous and discrete times can be freely mixed if the biological situation dictates this to be the preferred option.</jats:list-item> <jats:list-item>Continuous‐time models can also be empirically fitted to data, and I review briefly the insight this gives into the so‐called ‘do hares eat lynx?’ paradox that has been plaguing the interpretation of the Hudson Bay hare and lynx dataset.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giant trees are pivotal in forest ecosystems, yet our current understanding of their significance is constrained primarily by the limited knowledge of their precise locations and structural characteristics. Amidst escalating human‐induced disturbances globally, there is an urgent need to devise a practical approach to discover and measure giant trees accurately and efficiently.Here, we propose a novel light detection and ranging (lidar)‐based framework designed for the discovery and measurement of giant trees. Our framework integrates cutting‐edge lidar platforms, including spaceborne, Unmanned Aerial Vehicle (UAV), and backpack lidar, to create an end‐to‐end workflow. The algorithm involved in the proposed framework was compiled into a code package and made available as open source.The method successfully identified the tallest trees in China, including the tallest tree in Asia, a Cupressus austrotibetica with a height of 102.3 m, discovered in Yarlung Zangbo Grand Canyon in May 2023. This finding has not only established a new record but also demonstrated the efficacy of our proposed framework. Utilising lidar data, we performed meticulous measurements at both individual and stand levels, revealing the unique characteristics of this giant tree.The new framework for the discovery and measurement of giant trees, encompassing detailed procedures and codes, is expected to facilitate the discovery and measurement of giant trees with high efficiency, thus fostering advancements in giant tree ecology.
{"title":"Discovering and measuring giant trees through the integration of multi‐platform lidar data","authors":"Yu Ren, Hongcan Guan, Haitao Yang, Yanjun Su, Shengli Tao, Kai Cheng, Wenkai Li, Zekun Yang, Guoran Huang, Cheng Li, Guangcai Xu, Zhi Lu, Qinghua Guo","doi":"10.1111/2041-210x.14401","DOIUrl":"https://doi.org/10.1111/2041-210x.14401","url":null,"abstract":"<jats:list> <jats:list-item>Giant trees are pivotal in forest ecosystems, yet our current understanding of their significance is constrained primarily by the limited knowledge of their precise locations and structural characteristics. Amidst escalating human‐induced disturbances globally, there is an urgent need to devise a practical approach to discover and measure giant trees accurately and efficiently.</jats:list-item> <jats:list-item>Here, we propose a novel light detection and ranging (lidar)‐based framework designed for the discovery and measurement of giant trees. Our framework integrates cutting‐edge lidar platforms, including spaceborne, Unmanned Aerial Vehicle (UAV), and backpack lidar, to create an end‐to‐end workflow. The algorithm involved in the proposed framework was compiled into a code package and made available as open source.</jats:list-item> <jats:list-item>The method successfully identified the tallest trees in China, including the tallest tree in Asia, a <jats:italic>Cupressus austrotibetica</jats:italic> with a height of 102.3 m, discovered in Yarlung Zangbo Grand Canyon in May 2023. This finding has not only established a new record but also demonstrated the efficacy of our proposed framework. Utilising lidar data, we performed meticulous measurements at both individual and stand levels, revealing the unique characteristics of this giant tree.</jats:list-item> <jats:list-item>The new framework for the discovery and measurement of giant trees, encompassing detailed procedures and codes, is expected to facilitate the discovery and measurement of giant trees with high efficiency, thus fostering advancements in giant tree ecology.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Much of our understanding of the history of life hinges upon time calibration, the process of assigning absolute times to cladogenetic events. Bayesian approaches to time‐scaling phylogenetic trees have dramatically grown in complexity, and depend today upon numerous methodological choices. Arriving at objective justifications for all of these is difficult and time‐consuming. Thus, divergence times are routinely inferred under only one or a handful of parametric conditions, often times chosen arbitrarily. Progress towards building robust biological timescales necessitates the development of better methods to visualize and quantify the sensitivity of results to these decisions.Here, we present an R package that assists in this endeavour through the use of chronospaces, that is, graphical representations summarizing variation in the node ages contained in time‐calibrated trees. We further test this approach by estimating divergence times for three empirical datasets—spanning widely differing evolutionary timeframes—using the software PhyloBayes.Our results reveal large differences in the impact of many common methodological decisions, with the choice of clock (uncorrelated vs autocorrelated) and loci having strong effects on inferred ages. Other decisions have comparatively minor consequences, including the use of the computationally intensive site‐heterogeneous model CAT‐GTR, whose effect might only be discernible for exceedingly old divergences (e.g. the deepest eukaryote nodes).The package chronospace implements a range of graphical and analytical tools that assist in the exploration of sensitivity and the prioritization of computational resources in the inference of divergence times.
我们对生命历史的理解在很大程度上取决于时间校准，即为系统发育事件分配绝对时间的过程。贝叶斯方法对系统发育树进行时间标定的复杂性急剧增加，如今依赖于众多的方法选择。为所有这些选择找到客观的理由既困难又耗时。因此，通常只能在一个或少数几个参数条件下推断分异时间，而这些条件往往是任意选择的。要在构建稳健的生物时间尺度方面取得进展，就必须开发更好的方法来直观地量化结果对这些决定的敏感性。在这里，我们提出了一个 R 软件包，通过使用时间空间（即总结时间校准树所含节点年龄变化的图形表示法）来协助这项工作。我们使用 PhyloBayes 软件估算了三个经验数据集的分歧时间，从而进一步检验了这种方法。我们的结果表明，许多常见的方法学决定对推断年龄的影响存在很大差异，时钟（不相关与自相关）和位点的选择对推断年龄有很大影响。其他决定的影响相对较小，包括使用计算密集的位点异构模型 CAT-GTR，其影响可能只在极古老的分化（如最深的真核生物节点）中才能发现。chronospace 软件包实现了一系列图形和分析工具，有助于在推断发散时间时探索敏感性和计算资源的优先级。
{"title":"Chronospaces: An R package for the statistical exploration of divergence times promotes the assessment of methodological sensitivity","authors":"Nicolás Mongiardino Koch, Pablo Milla Carmona","doi":"10.1111/2041-210x.14404","DOIUrl":"https://doi.org/10.1111/2041-210x.14404","url":null,"abstract":"<jats:list> <jats:list-item>Much of our understanding of the history of life hinges upon time calibration, the process of assigning absolute times to cladogenetic events. Bayesian approaches to time‐scaling phylogenetic trees have dramatically grown in complexity, and depend today upon numerous methodological choices. Arriving at objective justifications for all of these is difficult and time‐consuming. Thus, divergence times are routinely inferred under only one or a handful of parametric conditions, often times chosen arbitrarily. Progress towards building robust biological timescales necessitates the development of better methods to visualize and quantify the sensitivity of results to these decisions.</jats:list-item> <jats:list-item>Here, we present an R package that assists in this endeavour through the use of chronospaces, that is, graphical representations summarizing variation in the node ages contained in time‐calibrated trees. We further test this approach by estimating divergence times for three empirical datasets—spanning widely differing evolutionary timeframes—using the software PhyloBayes.</jats:list-item> <jats:list-item>Our results reveal large differences in the impact of many common methodological decisions, with the choice of clock (uncorrelated vs autocorrelated) and loci having strong effects on inferred ages. Other decisions have comparatively minor consequences, including the use of the computationally intensive site‐heterogeneous model CAT‐GTR, whose effect might only be discernible for exceedingly old divergences (e.g. the deepest eukaryote nodes).</jats:list-item> <jats:list-item>The package <jats:italic>chronospace</jats:italic> implements a range of graphical and analytical tools that assist in the exploration of sensitivity and the prioritization of computational resources in the inference of divergence times.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aarne Hovi, Růžena Janoutová, Zbyněk Malenovský, Daniel Schraik, Jean‐Philippe Gastellu‐Etchegorry, Nicolas Lauret, Jan Novotný, Miina Rautiainen
Physically based models simulating the spectral transmittance of solar radiation through forest canopies are useful tools for examining the connections between the shortwave radiation environment and the productivity and biodiversity of the forest floor. We report a comprehensive evaluation of two approaches simulating forest canopy spectral transmittance.The approaches were (i) three‐dimensional radiative transfer modelling in canopies composed of individual trees filled with turbid media and (ii) photon recollision probability theory (p‐theory), and were implemented using DART‐FT and PARAS models, respectively. The simulations were evaluated against mean and standard deviation of canopy transmittance spectra measured under clear‐sky conditions in forest plots across central and Northern Europe.In general, both models agreed well with the in situ measurements. They performed equally in conifer forests, while PARAS had a slightly lower accuracy than DART‐FT in broadleaved forests.We conclude that both approaches produce realistic simulations of canopy spectral transmittance at the spatial scale tested in this study, and that p‐theory constitutes a computationally efficient and easy‐to‐parameterize alternative to three‐dimensional radiative transfer.
模拟太阳辐射穿过林冠的光谱透射率的物理模型是研究短波辐射环境与林地生产力和生物多样性之间联系的有用工具。我们报告了对两种模拟森林冠层光谱透射率方法的综合评估。这两种方法分别是：(i) 由充满浑浊介质的单个树木组成的树冠中的三维辐射传递建模；(ii) 光子再碰撞概率理论（p 理论），并分别使用 DART-FT 和 PARAS 模型实现。模拟结果与中欧和北欧各地森林地块晴空条件下测得的树冠透射光谱的平均值和标准偏差进行了对比评估。总体而言，两个模型都与现场测量结果吻合。它们在针叶林中的表现相当，而在阔叶林中，PARAS 的精确度略低于 DART-FT。我们的结论是，在本研究测试的空间尺度上，这两种方法都能对树冠光谱透射率进行逼真的模拟，而且 p 理论是三维辐射传递的一种计算效率高、易于参数化的替代方法。
{"title":"Physically based modelling of spectral transmittance through forest canopies","authors":"Aarne Hovi, Růžena Janoutová, Zbyněk Malenovský, Daniel Schraik, Jean‐Philippe Gastellu‐Etchegorry, Nicolas Lauret, Jan Novotný, Miina Rautiainen","doi":"10.1111/2041-210x.14402","DOIUrl":"https://doi.org/10.1111/2041-210x.14402","url":null,"abstract":"<jats:list> <jats:list-item>Physically based models simulating the spectral transmittance of solar radiation through forest canopies are useful tools for examining the connections between the shortwave radiation environment and the productivity and biodiversity of the forest floor. We report a comprehensive evaluation of two approaches simulating forest canopy spectral transmittance.</jats:list-item> <jats:list-item>The approaches were (i) three‐dimensional radiative transfer modelling in canopies composed of individual trees filled with turbid media and (ii) photon recollision probability theory (<jats:italic>p</jats:italic>‐theory), and were implemented using DART‐FT and PARAS models, respectively. The simulations were evaluated against mean and standard deviation of canopy transmittance spectra measured under clear‐sky conditions in forest plots across central and Northern Europe.</jats:list-item> <jats:list-item>In general, both models agreed well with the in situ measurements. They performed equally in conifer forests, while PARAS had a slightly lower accuracy than DART‐FT in broadleaved forests.</jats:list-item> <jats:list-item>We conclude that both approaches produce realistic simulations of canopy spectral transmittance at the spatial scale tested in this study, and that <jats:italic>p</jats:italic>‐theory constitutes a computationally efficient and easy‐to‐parameterize alternative to three‐dimensional radiative transfer.</jats:list-item> </jats:list>","PeriodicalId":208,"journal":{"name":"Methods in Ecology and Evolution","volume":null,"pages":null},"PeriodicalIF":6.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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