Issue 11.3: Tracking, Slicing, Classifying, Modelling and More

The March issue of Methods is now online!

The latest issue of Methods in Ecology and Evolution is now online! This month’s issue is a little shorter than our last few. But, as they say, good things come in small packages!

Executive Editor Aaron Ellison has selected six Featured Articles this month. You can find out about all of them below. We’ve also got five Applications articles in the March issue that we’re going to cover.

On top of all that, the March issue includes articles on 3D modelling, estimating plant density and more.

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2019 Robert May Early Career Researcher Prize Shortlist

Each year Methods in Ecology and Evolution awards the Robert May Prize to the best paper in the journal by an author at the start of their career. Today we present the shortlisted papers for 2019’s award, based on articles published in volume 10 of the journal.

The winner will be chosen by the journal’s Senior Editors in a few weeks. Keep an eye on the blog for the announcement.

This year’s shortlisted candidates are:

Extracting individual trees from lidar point clouds using treeseg – Andrew Burt

A quantitative framework for investigating the reliability of empirical network construction – Alyssa R. Cirtwill

A novel biomechanical approach for animal behaviour recognition using accelerometers – Pritish Chakravarty

Anacapa Toolkit: An environmental DNA toolkit for processing multilocus metabarcode datasets – Emily E. Curd

MistNet: Measuring historical bird migration in the US using archived weather radar data and convolutional neural networks – Tsung‐Yu Lin

Using quantum dots as pollen labels to track the fates of individual pollen grains – Corneile Minnaar

Untangling direct species associations from indirect mediator species effects with graphical models – Gordana C. Popovic

Matrix methods for stochastic dynamic programming in ecology and evolutionary biology – Jody R. Reimer

Current and emerging statistical techniques for aquatic telemetry data: A guide to analysing spatially discrete animal detections – Kim Whoriskey

Over the next month or so, we’ll be finding out more about these articles. You’ll be able to keep up to date with all of the Robert May Prize news here.

Teaching Computers to Think like Ecologists

Post provided by CHRIS TERRY

Artificial intelligence (or AI) is an enormously hot topic, regularly hitting the news with the latest milestone where computers matching or exceeding the capacity of humans at a particular task. For ecologists, one of the most exciting and promising uses of artificial intelligence is the automatic identification of species. If this could be reliably cracked, the streams of real-time species distribution data that could be unlocked worldwide would be phenomenal.

ladybird-stock_thumbDespite the hype and rapid improvements, we’re not quite there yet. Although AI naturalists have had some successes, they can also often make basic mistakes. But we shouldn’t be too harsh on the computers, since identifying the correct species just from a picture can be really hard. Ask an experienced naturalist and they’ll often need to know where and when the photo was taken. This information can be crucial for ruling out alternatives. There’s a reason why field guides include range maps!

Currently, most AI identification tools only use an image. So, we set out to see if a computer can be taught to think more like a human, and make use of this extra information. Continue reading

The Evolution of Love

Post provided by Chloe Robinson

The sending of letters under the pen name ‘St. Valentine’ began back in the middle ages as a way of communicating affection during the practice of courting. Fast forward to 2020 and Valentine’s Day is a day for celebrating romance, but now it typically features the exchange of gifts and cards between lovers.

Credit: Pixabay

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Issue 11.2: Stable Isotopes, in situ Monitoring, Image Analysis and more

The February issue of Methods is now online!

The latest issue of Methods in Ecology and Evolution is now online!

Executive Editor Rob Freckleton has selected six Featured Articles this month. You can find out about all of them below. We’ve also got six Applications articles and five Open Access articles in the February issue – we’ll talk about all of those here too.

On top of all that, the February issue includes articles on population genetics, ecological assemblages, and reconstruction of protein sequences.

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A Surgical Approach to Dissection of an Exotic Animal

Post provided by Aaron T Irving, Justin HJ Ng and Lin-fa Wang

An Australian black flying fox – missing an ear, but fit for release.

Bats. They’re amazing creatures. Long-lived (with relevance to their body size), echolocating (for microbats and some megabats), metabolically-resilient (apparently resilient to most virus infections) flying mammals (with heart beats up to 1200 bpm for hours during flight). There are 1,411 species of this incredible creature. But very little is known about their physiology and unique biological traits. And detailed evolutionary analysis has only just begun.

The problem is, they’re an ‘exotic’ animal (wildlife that most people do not come into contact with). Being a long-lived animal producing minimal offspring (most only have one baby per year), they’re not suited to the kind of experimental studies we do with other animals like mice. Unavoidably, some aspects of biology require the use of tissues and cells. These samples can be used for sequencing, genomics, molecular evolution studies, detailed transcriptomic analysis, functional experiments with specific cell types and much more. Some methodology is beginning to be published – such as capture techniques and wing punch/genomic isolation – but there’s been an absence of protocols for the processing of bats. This is essential for the field to maximise the potential application of each individual and for minimising non-essential specimen collection.

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Issue 11.1: Climate Change, Genomic Divergence, Bayesian Modelling and More

The January issue of Methods is now online!

It’s a new year and the new issue of Methods in Ecology and Evolution is now online!

We’re starting 2020 with a great issue – and ALL of the articles are completely free. And they’ll remain free for the whole year. No subscription required.

You can find out more about our Featured Articles (selected by the Senior Editor) below. We also discuss this month’s Open Access, Practical Tools and Applications articles. There are also articles on species distributions, biotic interactions, taxonomic units and much more.

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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

This post is also available in English

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

This blog post is also available in English

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

Understanding Deep Learning

Post provided by Sylvain Christin

We have now entered the era of artificial intelligence. In just a few years, the number of applications using AI has grown tremendously, from self-driving cars to recommendations from your favourite streaming provider. Almost every major research field is now using AI. Behind all this, there is one constant: the reliance, in one way or another, on deep learning. Thanks to its power and flexibility, this new subset of AI approach is now everywhere, even in ecology we show in ‘Applications for deep learning in ecology’.

But what is deep learning exactly? What makes it so special?

Deep Learning: The Basics

Deep learning is a set of methods based on representation learning: a way for machines to automatically detect how to classify data from raw examples. This means they can detect features in data by themselves, without any prior knowledge of the system. While some models can learn without any supervision (i.e. they can learn to detect and classify objects without knowing anything about them) so far these models are outperformed by supervised models. Supervised models require labelled data to train. So, if we want the model to detect cars in pictures, it will need examples with cars in them to learn to recognise them.

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