Category Archives: Statistical methods

Reconnecting the Web of Life: Rewiring and Network Robustness

Posted on by
1

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

Posted on by
1

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

This post is also available in English

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 →

Responding to New Weeds Needs Speed: Spatial Modelling with riskmapr Can Help

Posted on by
Reply

Post provided by JENS FROESE

Disclaimer: this post is NOT about the drug or the TV series, but about invasive alien plants. Yes, even biologists often refer to them as ‘weeds’.

Responding to New Weed Incursions

Responding to new weed incursions early and rapidly is very important. ©Panda8pie2

Responding to new weed incursions early and rapidly is very important. ©Panda8pie2

Weeds are a major threat to biodiversity and agricultural industries globally. New alien plant species are constantly introduced across borders, regions or landscapes. We know that some (such as those listed in the IUCN Global Invasive Species Database) are likely become problematic invasive weeds from experiences elsewhere.

When a weed is first introduced, population growth and spread is typically slow. This ‘invasion lag’ may be due to straightforward mathematics (population dynamics) as well as geography, environmental change or genetics. In any case, the lag period often presents the only window of opportunity where weed eradication or effective containment can be achieved. So, responding to new weed incursions early and rapidly is very important. Anyone who has ever battled with a bad weed infestation in their backyard knows it’s best to get in early and decisively! But decisions about where to target surveillance and control activities are often made under considerable time, knowledge and capacity constraints. Continue reading →

Using Artificial Intelligence to Track Birds’ Dark-of-Night Migrations

Posted on by
Reply

Below is a press release about the Methods in Ecology and Evolution article ‘MistNet: Measuring historical bird migration in the US using archived weather radar data and convolutional neural networks‘ taken from the University of Massachusetts Amherst.

Wood thrush. ©CheepShot

On many evenings during spring and fall migration, tens of millions of birds take flight at sunset and pass over our heads, unseen in the night sky. Though these flights have been recorded for decades by the National Weather Services’ network of constantly-scanning weather radars, until recently these data have been mostly out of reach for bird researchers.

“That’s because the sheer magnitude of information and lack of tools to analyse it made only limited studies possible,” says artificial intelligence (AI) researcher Dan Sheldon at the University of Massachusetts Amherst. Continue reading →

Solving the Midpoint Melee: Introducing New Methods for Plant Cover Classes

Posted on by
1

Post provided by KATHI IRVINE and TOM RODHOUSE

Collecting ordinal data. ©NPS

Or better yet, this post could be named ‘Our Cathartic Journey to Convince Ecologists to STOP Using the Midpoint Values for Analysing Plant Cover Classes’. Our work picks up where another recent Methods.blog post (Stuck between Zero and One) and Methods in Ecology and Evolution article (‘Analysing continuous proportions in ecology and evolution’) by Douma and Weedon left off. They introduced the benefits of using beta and Dirichlet regression. We’re going to tackle the sticky wicket of ordinal data. So, what should you do if you assign a range (like 0.2 to 0.3) instead of record a value (like 0.22) for a continuous proportion?

What is Ordinal Data?

It’s probably a good idea to start by defining the type of data we’re talking about. The best example is from plant surveys. Biologists visually assess the percentage of a pre-defined area covered by a certain plant species. They then record a ‘cover class value’ as an estimate of abundance. Each cover class value corresponds to the percentage of the area that is taken up by the plant in question (e.g., record a 0 for 0%, record a 1 for >0-5%, record a 2 for >5-25%, …, record a 6 for >95%). Continue reading →

Researchers Develop Tools to Help Manage Seagrass Survival

Posted on by
Reply

Below is a press release about the Methods in Ecology and Evolution article ‘Analysing the dynamics and relative influence of variables affecting ecosystem responses using functional PCA and boosted trees: a seagrass case study‘ taken from Queensland University of Technology.

©Paul Asman and Jill Lenoble

A new QUT-led study has developed a statistical toolbox to help avoid seagrass loss which provides shelter, food and oxygen to fish and at-risk species like dugongs and green turtles. Seagrasses are a critical habitat that have been declining rapidly globally.

The research has been published in Methods in Ecology and Evolution describing key monitoring and management designs to maximise seagrass resilience to human activities. They will help to better inform seagrass dredging operations and development of coastal areas.

Led by statistical data researcher and lecturer Dr Paul Wu, from QUT’s School of Mathematical Sciences, the study identified and analysed factors that drove variations in a global seagrass dredging case study. Continue reading →

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

Posted on by
2

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 →

0与1的游戏：使用Beta和Dirichlet回归方法模拟非计数比例

Posted on by
1

海报作者：JAMES WEEDON & BOB DOUMA

中文翻译：Zishen Wang （王子申）

This post is also available in English

请设想一下这个场景：你正在一个重要的国际会议上汇报一个激动人心的成果。秉承一向对统计学理论和方法的严谨态度，你对所有的数据都做了统计学检验并给出了置信区间。这些统计分析结果并不只包含P值！你提供的一些数据涉及在不同处理下食草昆虫破坏的叶面积比例。当你准时完成报告时，一位同行问道：“你对破坏比例估计的置信区间是-0.3到0.5，该怎么解释叶面积出现的负值呢？”

观众席里有人笑了。你满脸通红地翻到被提问到的这张幻灯片，嘟囔着给大家解释置信区间和点估计之间的区别。观众们开始小声嘀咕，你好像听到不远处有一只狗在叫。

你该怎么避免这种尴尬又让大家疑惑的情况呢？

生态学和进化学中的比例数据

生态学家和进化生物学家会经常测定许多定量数据，为了方便展示，他们通常会把这些数据表示为比例。许多情况下，这些比例是由计数得来的。在一种情况下，这些比例数据是基于可划分为两个或者更多类别的离散实体的：成功或失败，男性或女性，侵入性或非侵入性。比例数据也可以针对连续型变量：动物进行不同活动的比例；植被调查样本中一种植物功能类型的百分比覆盖率；植物生物量在各个器官和组织上的分配比例。这些比例数据的共同点是只能在0到1之间取值。小于0或大于1的值没有意义。

两种可以得到比例数据的测量：叶片损坏的比例和植被覆盖百分比。

两种可以得到比例数据的测量：叶片损坏的比例和植被覆盖百分比。

如果您使用常规统计工具来分析此类数据，可能会导致一些问题。线性回归，方差分析等方法假设因变量可以用正态分布建模。正态分布包含从负无穷大到正无穷大的值，因此不太适合模拟比例数据。用正态分布得出的预测值和置信区间很可能包含比例数据定义区间外的值。此外，残差与预测值有很强的相关性。这些现象都表明，选择错误的模型，会导致不准确的统计推断。 Continue reading →

Conservation or Construction? Deciding Waterbird Hotspots

Posted on by
Reply

Below is a press release about the Methods in Ecology and Evolution article ‘A comparative analysis of common methods to identify waterbird hotspots‘ taken from Michigan State University.

A mixed flock of waterbirds on the shore of Lake St. Clair. ©Michigan DNR

Imagine your favourite beach filled with thousands of ducks and gulls. Now envision coming back a week later and finding condos being constructed on that spot. This many ducks in one place surely should indicate this spot is exceptionally good for birds and must be protected from development, right?

It depends, say Michigan State University researchers.

In a new paper published in Methods in Ecology and Evolution, scientists show that conservation and construction decisions should rely on multiple approaches to determine waterbird “hotspots,” not just on one analysis method as is often done. Continue reading →

Mosquitoes, Climate Change and Disease Transmission: How the Suitability Index P Can Help Improve Public Health and Contribute to Education

Posted on by
3

Post Provided by JOSÉ LOURENÇO

Esta publicação no blogue também está disponível em português

©BARILLET-PORTAL David

©BARILLET-PORTAL David

Vector-borne viruses (like those transmitted by mosquitoes) are (re)emerging and they’re hurting local economies and public health. Some typical examples are the West Nile, Zika, dengue, chikungunya and yellow fever viruses. The eco-evolutionary and epidemiological histories of these viruses differ massively. But they share one important factor: their transmission potential is highly dependent on the underlying mosquito population dynamics.

An ultimate challenge in infectious disease control is to prevent the start of an outbreak or alter the course of an ongoing outbreak. To achieve this, understanding the ecological, demographic and epidemiological factors driving a pathogen’s transmission success is essential. Without this information, public health planning is immensely difficult. To get this information, dynamic mathematical models of pathogen transmission have been successfully applied since the mid-20th century (e.g. malaria and dengue). Continue reading →