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

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

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

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 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

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

Post provided by JAMES WEEDON & BOB DOUMA

Chinese translation provided by Zishen Wang


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



中文翻译:Zishen Wang (王子申)

This post is also available in English








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

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

Post Provided by JOSÉ LOURENÇO

Esta publicação no blogue também está disponível em português



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

Mosquitos, o clima e a transmissão de patógenos: como o índice P pode contribuir para saúde pública e educação


This blog post is also available in English



Vírus transmitidos por vetores (ex. mosquitos, carraças) estão a (re)emergir e a ter consequências negativas para a saúde pública e para as economias locais. Exemplos típicos recentes de vírus transmitidos por mosquitos incluem o vírus West Nile na América do Norte, Israel e Europa, e os vírus Zika, dengue, chikungunya, Mayaro e febre amarela na América do Sul e África. A epidemiologia, ecologia, e evolução destes vírus são altamente diversas,  mas todos eles partilham um fator crítico: o seus potenciais de transmissão são altamente dependentes da dinâmica de população das espécies de mosquitos envolvidas.

Um dos objetivos principais do controlo de doenças infeciosas é prevenir o inicio (ou alterar o curso) de  epidemias. Para esse fim, modelos dinâmicos de transmissão têm sido usados com sucesso desde meados do século XX (ex. no contexto de malaria). Esses modelos são aproximações computacionais dos sistemas biológicos reais, permitindo simular uma multitude de cenários nos nossos computadores pessoais, e com tal testar, reconstruir e projetar o potencial e comportamento epidemiológico de patógenos. Quando tais simulações são comparadas com observações reais (ex. número de casos reportados por um sistema de vigilância), os modelos oferecem respostas sobre a mecânica de transmissão e os fatores epidemiológicos ou demográficos que terão contribuído para determinados padrões observados nos dados. Enquanto que modelos dinâmicos são uma das peças fundamentais da epidemiologia contemporânea, dados imperfeitos ou a falta deles pode tornar difícil (se não impossível) a conceção, implementação e utilidade esses modelos. As razões pelas quais dados podem ser imperfeitos são várias, desde sistemas de vigilância fracos, erros humanos, falta de investimento, etc. Continue reading