Para, piensa, y ten cuidado con las configuraciones por defecto

Post escrito por Paula Pappalardo (con aportes de Elizabeth Hamman, Jim Bence, Bruce Hungate & Craig Osenberg)

This post is also available in English.

Pasaste meses laboriosamente colectando datos de artículos científicos acerca de tu pregunta favorita, tienes decenas de artículos perfectamente organizados en una base de datos, ya encontraste el programa o código para analizar los datos, y entonces imaginas como tu publicación va a ser la más citada en tu campo de investigación mientras haces unos gráficos lindísimos. Si esto te suena familiar, seguramente has hecho un meta-análisis. Un meta-análisis usa modelos estadísticos para combinar datos de distintas publicaciones para responder a una pregunta específica.

Lo que quizás no te diste cuenta mientras navegabas los pasos del meta-análisis, es que pequeñas decisiones (a veces pareciendo de muy poca importancia) pueden tener grandes efectos en los resultados. Si quieres saber más acerca de una de estas decisiones en particular… ¡sigue leyendo!

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Uma breve história sobre o pacote R ‘metan’

Post ESCRITO POR Tiago Olivoto

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Em nosso recente artigo na Methods in Ecology and Evolution, Alessandro D. Lúcio e eu descrevemos um novo pacote R para análise de ensaios multi-ambientes chamado metan. Ensaios multi-ambientes são um tipo de ensaio em programas de melhoramento de plantas, onde vários genótipos são avaliados em um conjunto de ambientes. A análise desses dados requer a combinação de várias abordagens, incluindo manipulação, visualização e modelagem de dados. A versão estável mais recente do metan (v1.5.1) está disponível agora no repositório CRAN. Então, pensei em compartilhar a história da minha primeira incursão no uso do R criando um pacote e submetendo um artigo para uma revista que nunca havia submetido antes.

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O Problema com os ‘Fósseis Vivos’: Uma Perspectiva Filogenética Molecular

Blog escrito por: gustavo burin

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Fóssil de caranguejo-ferradura (Museu de História Natural de Berlin)

Há alguns dias, me deparei com um interessante vídeo sobre os chamados “fósseis vivos”. O vídeo focou mais nos problemas de usá-los como argumentos contra a teoria da evolução, e aproveitei a oportunidade para falar mais sobre essas linhagens longevas.

Fóssil vivo‘ é um termo usado para descrever linhagens que acredita-se terem se originado há muito tempo e que mantêm características que se assemelham a seus parentes fósseis. Alguns exemplos bem conhecidos dessas linhagens são os Tuatara da Nova Zelândia (Sphenodon punctatus) e as árvores Gingkos (Gingko biloba).

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The Problem with ‘Living Fossils’: A Molecular Phylogenetic Perspective

post provided by: gustavo burin

Este post também pode ser lido em Português

Fossil of a Horseshoe crab (Museum of Natural History Berlin)

A couple of days ago I came across a nice video (in Portuguese only, sorry) about so-called “living fossils”. The video focused on the problems of using them as arguments against evolution. But I’d like to take the opportunity to talk more about these long-lived lineages.

Living fossil’ is a term used to describe lineages that are thought to have been around for a very long time and retain characteristics that resemble of their fossil relatives. A couple of well-known examples of these lineages are the Tuatara of New Zealand (Sphenodon punctatus) and the Gingko tree (Gingko biloba).

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Standardising Methods in Climate Change Experiments: A Community Effort

Post provided by AUD HALBRITTER

CHINESE TRANSLATION PROVIDED BY HUI TANG

這篇博客文章也有中文版

Climate change is threatening biodiversity and ecosystems around the world. We urgently need to better understand how species and ecosystems respond to these changes. There are already thousands of climate change experiments and observational studies out there that could be used to synthesise findings across systems and regions. But it turns out that making meaningful syntheses isn‘t always so straightforward!

The Need for Standardised Methods and Reporting

There are two major challenges (and some minor ones too) for synthesising data across different experiments. First, the data are not always available. This problem arises because key study information – such as metadata, covariates or methodological details – are often not adequately or consistently reported across studies.

The second problem is that scientists use different protocols. This leads to a diversity of ways of measuring and quantifying the same variables. Different protocols may measure or report the same variables in slightly different ways, so the data are not compatible. Consistency in measurements and protocols is one reason why working in large networks – such as ITEX, Herbivory, or NutNet – to name only a few, is so powerful. In these networks, experiments and observations are repeated across large regions or worldwide using strict protocols for experimental design and measurements. Continue reading

通过研究者们的共同努力实现气候变化实验方法的标准化

Post provided by AUD HALBRITTER

提供的中文翻译唐辉

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气候变化正在严重威胁全球生态系统服务功能和生物多样性。我们迫切需要更好的理解不同物种和生态系统对气候变化的响应。气候变化相关的生态实验和观测研究已有上千个。 这为跨系统和区域的综合分析提供了可能。但是实践表面有意义的综合分析并没有想象中那么简单容易。

标准化方法和报告的必要性

跨实验的生态数据综合主要受到两方面的挑战 (以及一些小的挑战)。首先是数据的可获得性。 这个问题的产生是因为关键的研究信息,例如元数据,协同变量或者方法的细节,在不同研究中报告的详细程度不足且差别很大。

其次,科学家们通常使用各自不同的实验流程。这导致在观测和量化同一变量时会用到多种不同方法。 不同的实验流程会使得在测量和报告同一变量时产生细微差别,从而导致数据不具有可比性。测量和流程的一致性是一些大型合作性实验项目(例如ITEX, HerbivoryNutNet等)能够产生重大影响力的原因之一。在这些大型合作实验项目中,实验的设计和测量都遵循严格的流程,并在大范围的区域或者全球推广应用。但是如果我们的实验不在这些大型合作项目中,我们应该如何做呢?理论上,答案很简单:如果在整个研究领域,我们都使用标准化的方法和流程,那么我们的研究数据将能够被重复利用并和其他研究进行比较。但是实际情况与之有很大差距,主要的问题在于:我们究竟如何才能将标准化方法和流程推广到整个研究领域中? Continue reading

Gwneud Tagiau’n Fwy Cyfleus:Optimeiddio Dyfeisiau Biogofnodi gyda Dynameg Hylifau Gyfrifiadurol

Post wedi’i ddarparu gan William Kay

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Dyfeisiau llusgo a biogofnodi

A harbour seal tagged with a biologging device. ©Dr Abbo van Neer

Morlo harbwr gyda dyfais fiogofnodi wedi’i hatodi iddo. ©Dr Abbo van Neer

Michael Phelps yw un o’r athletwyr Olympaidd mwyaf clodfawr erioed, ynghyd â’r nofiwr cyflymaf yn y byd. Ac eto, gallai nofio’n gyflymach. Gan wisgo siwt arbennig LZR Racer Speedo, gallai Michael Phelps leihau’i lusgiad hydrodynamig, neu’i wrthiant dŵr, 40% neu fwy. O ganlyniad gallai ei gyflymdra nofio gynyddu dros 4%! Mewn cystadleuaeth, dyna’r gwahaniaeth rhwng gwobrau arian ac aur. Ond, petai Phelps yn anghofio tynnu’i “hosanau llusgo” –  sef hosanau rhwystrus a ddyluniwyd i gynyddu gwrthiant dŵr er mwyn cynyddu cryfder y nofiwr – caiff ei gyflymder ei leihau’n sylweddol. Byddai’n ffodus i ennill gwobr efydd!

Mae nofwyr proffesiynol yn gyfarwydd â defnyddio technolegau i wella eu perfformiad drwy leihau eu llusgiad ond ni all hynny gymharu â’r addasiadau a wnaed gan anifeiliaid gwyllt. Mae creaduriaid yn y môr wedi esblygu addasiadau anghredadwy i leihau llusgiad, megis lliflinio eithafol mewn mamaliaid ac adar y môr. Mae hyn yn eu galluogi i symud dan y dŵr mor gyflym ac effeithlon â phosib. Mae morloi, er enghraifft, yn eithaf afrosgo ar y tir ond maent yn osgeiddig ac yn gyflym o dan y dŵr. Mae siâp eu cyrff wedi’i ddylunio er mwyn iddynt symud yn gyflymaf pan fyddant yn nofio.

Pan fyddwn yn astudio mamaliaid y môr, rydym yn aml yn defnyddio dyfeisiau olrhain y gellir eu hatodi gan ddefnyddio harneisiau, glud neu sugnolion. Mae’r “dyfeisiau biogofnodi” hyn, a elwir hefyd yn dagiau, yn debyg i Fitbits. Mae atodi’r rhain i anifeiliaid yn ein galluogi i gofnodi symudiadau ac ymddygiad yr anifail, ynghyd â phethau eraill. Mae’r wybodaeth hon yn hanfodol o ran deall eu hecoleg a gwella’r ffordd y rheolir eu cadwraeth. Continue reading

Reconnecting the Web of Life: Rewiring and Network Robustness

Post provided by VINICIUS A. G. BASTAZINI, JEF VIZENTIN-BUGONI and JINELLE H. SPERRY

Esta publicação no blogue também está disponível em português

Species Loss and Cascading Effects

Scale-throated Hermit (Phaethornis eurynome). ©Pedro Lorenzo.

Scale-throated Hermit (Phaethornis eurynome). ©Pedro Lorenzo.

Minimising the effects the ongoing Anthropocene mass extinction has become one of the main challenges of our era. The data suggest that the current rate of species loss is 100–1,000 greater than the background rates seen in the geological record. “But does it really matter if species are lost?” This question has permeated social and political debates. It’s usually used to demean conservation efforts. But it has also intrigued conservation scientists.

We know that species don’t occur alone in their environment. They’re entangled by their interactions, forming complex networks. In these networks the loss of one species may result in the loss of other species that depend on it. This process is known as co-extinction. Estimates of the magnitude of past and future extinction rates have often failed to account for the interdependence among species and the consequences of primary species loss on other species though. Continue reading

Religando a rede da vida: Reconexões de interações e a robustez de redes ecológicas

Postagem fornecida por VINICIUS A. G. BASTAZINI, JEF VIZENTIN-BUGONI and JINELLE H. SPERRY

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Perda de espécies e efeitos em cascata

Scale-throated Hermit (Phaethornis eurynome). ©Pedro Lorenzo.

Rabo-branco-de-garganta-rajada (Phaethornis eurynome). ©Pedro Lorenzo.

Minimizar os efeitos do atual processo de extinção em massa do Antropoceno se tornou um dos principais desafios da nossa era. Os dados sugerem que a taxa atual de perda de espécies é 100-1.000 vezes maior do que as taxas de fundo observadas no registro geológico. “Mas realmente importa se uma espécie é perdida?” Essa questão que permeia os debates sociais e políticos, geralmente para desqualificar os esforços de conservação, também tem intrigado os cientistas da conservação.

Sabemos que as espécies não ocorrem sozinhas em seu ambiente. Elas estão  interligadas por suas interações ecológicas, formando redes complexas. Nessas redes, a perda de uma espécie pode resultar em um efeito dominó, culminando na perda secundária de outras espécies. Esse processo é conhecido como co-extinção. As estimativas da magnitude das taxas de extinção passadas e futuras muitas vezes falharam em explicar a interdependência entre as espécies e as conseqüências da perda primaria de espécies. Continue reading

Stuck between Zero and One: Modelling Non-Count Proportions with Beta and Dirichlet Regression

Post provided by JAMES WEEDON & BOB DOUMA

Chinese translation provided by Zishen Wang

這篇博客文章也有中文版

Proportion of leaf damage is a type of measurement that can lead to proportional data.

Imagine the scene: you’re presenting your exciting research results at an important international conference. Being conscientious and aware of statistical best-practice and so you’ve included test statistics and confidence intervals on all your result figures. Not just P values! Some of the data you are presenting involves the proportion of leaf surface damaged by an insect herbivore under different treatments. You finish your presentation (on time!) and there’s time for questions. From the audience a polite but insistent colleague asks: “Your confidence interval for that estimate goes from -0.3 to 0.5… how should we interpret a negative proportion of a leaf?”.

Someone chuckles. As you nervously flick back to the slide in question, you mutter something about the difference between confidence intervals and point estimates. You start to feel dizzy. A murmur of confused voices slowly builds amongst the audience members. In the distance, a dog barks.

How can you avoid this?

Proportional Data in Ecology and Evolution

Many kinds of quantities that ecologists and evolutionary biologists routinely measure are most conveniently expressed as proportions. In many cases these proportions are derived from counts. The data are based on discrete entities that can be assigned to two or more classes: success or failure, male or female, invasive or non-invasive. In other cases the proportions are derived from continuous measurements: the proportion of time an animal spends on different activities;  percent cover of a plant functional type in a vegetation survey quadrat; allocation of total plant biomass to different organs and tissues. What these data types have in common is that they can only take values between zero and one. Negative values, or values greater than one, don’t make any sense. Continue reading