A standard protocol for reporting species distribution models

Ecography, Journal Year: 2020, Volume and Issue: 43(9), P. 1261 - 1277

Published: June 1, 2020

Species distribution models (SDMs) constitute the most common class of across ecology, evolution and conservation. The advent ready‐to‐use software packages increasing availability digital geoinformation have considerably assisted application SDMs in past decade, greatly enabling their broader use for informing conservation management, quantifying impacts from global change. However, must be fit purpose, with all important aspects development applications properly considered. Despite widespread SDMs, standardisation documentation modelling protocols remain limited, which makes it hard to assess whether steps are appropriate end use. To address these issues, we propose a standard protocol reporting an emphasis on describing how study's objective is achieved through series modeling decisions. We call this ODMAP (Overview, Data, Model, Assessment Prediction) protocol, as its components reflect main involved building other empirically‐based biodiversity models. serves two purposes. First, provides checklist authors, detailing key model analyses, thus represents quick guide generic workflow modern SDMs. Second, introduces structured format documenting communicating models, ensuring transparency reproducibility, facilitating peer review expert evaluation quality, well meta‐analyses. detail elements ODMAP, explain can used different objectives applications, complements efforts store associated metadata define standards. illustrate utility by revisiting nine previously published case studies, provide interactive web‐based facilitate plan advance encouraging further refinement adoption scientific community.

Language: Английский

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Amazonia is the primary source of Neotropical biodiversity

Proceedings of the National Academy of Sciences, Journal Year: 2018, Volume and Issue: 115(23), P. 6034 - 6039

Published: May 14, 2018

Significance Amazonia is not only the world’s most diverse rainforest but also region in tropical America that has contributed to its total biodiversity. We show this by estimating and comparing evolutionary history of a large number animal plant species. find there been extensive interchange lineages among different regions biomes, over course tens millions years. stands out as primary source diversity, which can be mainly explained amount time Amazonian have occupied region. The exceedingly rich heterogeneous diversity American tropics could achieved high rates dispersal events across continent.

Language: Английский

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Sampling biases shape our view of the natural world

Ecography, Journal Year: 2021, Volume and Issue: 44(9), P. 1259 - 1269

Published: June 21, 2021

Spatial patterns of biodiversity are inextricably linked to their collection methods, yet no synthesis bias or consequences exists. As such, views organismal distribution and the ecosystems they make up may be incorrect, undermining countless ecological evolutionary studies. Using 742 million records 374 900 species, we explore global impacts biases related taxonomy, accessibility, ecotype data type across terrestrial marine systems. Pervasive sampling observation exist animals, with only 6.74% globe sampled, disproportionately poor tropical sampling. High elevations deep seas particularly unknown. Over 50% in most groups account for under 2% species citizen‐science exacerbates biases. Additional will needed overcome many these biases, but must increasingly value publication bridge this gap better represent species' distributions from more distant inaccessible areas, provide necessary basis conservation management.

Language: Английский

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A checklist for maximizing reproducibility of ecological niche models

Nature Ecology & Evolution, Journal Year: 2019, Volume and Issue: 3(10), P. 1382 - 1395

Published: Sept. 23, 2019

Abstract Reporting specific modelling methods and metadata is essential to the reproducibility of ecological studies, yet guidelines rarely exist regarding what information should be noted. Here, we address this issue for niche or species distribution modelling, a rapidly developing toolset in ecology used across many aspects biodiversity science. Our quantitative review recent literature reveals general lack sufficient fully reproduce work. Over two-thirds examined studies neglected report version access date underlying data, only half reported model parameters. To problem, propose adopting checklist guide reporting at least minimum necessary reproducibility, offering straightforward way balance efficiency accuracy. We encourage community, as well journal reviewers editors, utilize further develop framework facilitate improve future The proposed generalizable other areas ecology, especially those utilizing environmental data statistical could also adopted by broader array disciplines.

Language: Английский

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The commonness of rarity: Global and future distribution of rarity across land plants

Science Advances, Journal Year: 2019, Volume and Issue: 5(11)

Published: Nov. 1, 2019

A key feature of life's diversity is that some species are common but many more rare. Nonetheless, at global scales, we do not know what fraction biodiversity consists rare species. Here, present the largest compilation plant to quantify Earth's large fraction, ~36.5% ~435,000 species, exceedingly Sampling biases and prominent models, such as neutral theory k-niche model, cannot account for observed prevalence rarity. Our results indicate (i) climatically stable regions have harbored hence a via reduced extinction risk (ii) climate change human land use now disproportionately impacting Estimates abundance distributions important implications assessments conservation planning in this era rapid change.

Language: Английский

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Biological collections for understanding biodiversity in the Anthropocene

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2018, Volume and Issue: 374(1763), P. 20170386 - 20170386

Published: Nov. 19, 2018

Global change has become a central focus of modern biology. Yet, our knowledge how anthropogenic drivers affect biodiversity and natural resources is limited by lack biological data spanning the Anthropocene. We propose that hundreds millions plant, fungal animal specimens deposited in history museums have potential to transform field global suggest museum are underused, particularly ecological studies, given their capacity reveal patterns not observable from other sources. Increasingly, becoming mobilized online, providing unparalleled access physiological, evolutionary decades sometimes centuries. Here, we describe diversity collections archived provide an overview diverse uses applications these as discussed accompanying collection papers within this theme issue. As under threat owing budget cuts institutional pressures, aim shed light on unique discoveries possible and, thus, singular value period rapid change. This article part issue ‘Biological for understanding Anthropocene’.

Language: Английский

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Digitization and the Future of Natural History Collections

BioScience, Journal Year: 2020, Volume and Issue: 70(3), P. 243 - 251

Published: Jan. 17, 2020

Abstract Natural history collections (NHCs) are the foundation of historical baselines for assessing anthropogenic impacts on biodiversity. Along these lines, online mobilization specimens via digitization—the conversion specimen data into accessible digital content—has greatly expanded use NHC across a diversity disciplines. We broaden current vision digitization (Digitization 1.0)—whereby digitized within NHCs—to include new approaches that rely products rather than physical 2.0). Digitization 2.0 builds data, workflows, and infrastructure produced by 1.0 to create digital-only workflows facilitate digitization, curation, links, thus returning value creating layers annotation, empowering global community, developing automated advance biodiversity discovery conservation. These efforts will transform large-scale assessments address fundamental questions including those pertaining critical issues change.

Language: Английский

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The history and impact of digitization and digital data mobilization on biodiversity research

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2018, Volume and Issue: 374(1763), P. 20170391 - 20170391

Published: Nov. 19, 2018

The first two decades of the twenty-first century have seen a rapid rise in mobilization digital biodiversity data. This has thrust natural history museums into forefront research, underscoring their central role modern scientific enterprise. advent initiatives such as United States National Science Foundation's Advancing Digitization Biodiversity Collections (ADBC), Australia's Atlas Living Australia (ALA), Mexico's Commission for Knowledge and Use (CONABIO), Brazil's Centro de Referência em Informação (CRIA) China's Specimen Information Infrastructure (NSII) led to data aggregators an exponential increase research arguably provide best evidence where species live. international Global Facility (GBIF) now serves about 131 million museum specimen records, Integrated Digitized Biocollections (iDigBio) USA amassed more than 115 million. These resources expose collections wider audience researchers, era outside nature itself ensure primacy specimen-based research. Here, we brief worldwide mobilization, impact on challenges ensuring quality, contribution publications rising profiles collections.This article is part theme issue 'Biological understanding Anthropocene'.

Language: Английский

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Using herbaria to study global environmental change

New Phytologist, Journal Year: 2018, Volume and Issue: 221(1), P. 110 - 122

Published: Aug. 30, 2018

During the last centuries, humans have transformed global ecosystems. With their temporal dimension, herbaria provide otherwise scarce long-term data crucial for tracking ecological and evolutionary changes over this period of intense change. The sheer size herbaria, together with increasing digitization possibility sequencing DNA from preserved plant material, makes them invaluable resources understanding species' responses to environmental Following chronology change, we highlight how can inform about effects on plants at least four main drivers change: pollution, habitat climate change invasive species. We summarize herbarium specimens so far been used in research, discuss future opportunities challenges posed by nature these data, advocate an intensified use 'windows into past' research beyond.

Language: Английский

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Deep learning as a tool for ecology and evolution

Methods in Ecology and Evolution, Journal Year: 2022, Volume and Issue: 13(8), P. 1640 - 1660

Published: May 30, 2022

Abstract Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing autonomous driving. It also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, ecological modelling, behavioural studies, DNA sequencing population genetics phylogenetics, among other applications. relies on artificial neural networks predictive modelling excels at recognizing complex patterns. In this review we synthesize 818 studies using deep the context of ecology evolution to give a discipline‐wide perspective necessary promote rethinking inference approaches field. We provide an introduction machine contrast with mechanistic inference, followed by gentle primer learning. applications discuss its limitations efforts overcome them. practical biologists interested their toolkit identify possible future find that being rapidly adopted evolution, 589 (64%) published since beginning 2019. Most use convolutional (496 studies) supervised identification but tasks molecular data, sounds, data or video as input. More sophisticated uses biology are appear. Operating within paradigm, can be viewed alternative modelling. desirable properties good performance scaling increasing complexity, while posing unique challenges such sensitivity bias input data. expect rapid adoption will continue, especially automation biodiversity monitoring discovery from genetic Increased unsupervised visualization clusters gaps, simplification multi‐step analysis pipelines, integration into graduate postgraduate training all likely near future.

Language: Английский

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