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 →
It’s estimated that a person sheds between 30,000 to 40,000 skin cells per day. These cells and their associated DNA leave genetic traces of ourselves in showers, dust — pretty much everywhere we go.
Other organisms shed cells, too, leaving traces throughout their habitats. This leftover genetic material is known as environmental DNA, or eDNA. Research using eDNA began about a decade ago, but was largely limited to a small cadre of biologists who were also experts in computers and big data. However, a new tool from UCLA could be about to make the field accessible and useful to many more scientists.
A team of UCLA researchers recently launched the Anacapa Toolkit — open-source software that makes eDNA research easier, allowing researchers to detect a broad range of species quickly and producing sortable results that are simple to understand. Continue reading →
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?
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 →
Dr. Dao (crouching on right) and team with Dr. Tovi Lehmann (with sandals), Dr. Yaro (with white cap), and Moussa Diallo (front).
The fact that mosquitoes are insects of massive importance is of little dispute. With malaria still killing almost half a million people annually and after recent outbreaks of Zika, dengue and West-Nile viruses the threat of mosquito-borne diseases is becoming common knowledge. The meme of ‘Mosquitoes are the No.1 killer of all time,’ is also growing more popular (I even heard it from my 8-year-old kid one day after he returned from school!). Yet, with all we think we know about the little bug(ger)s, it’s probably only the tip of the iceberg.
Much work was done over the past century to try to answer basic questions about mosquitoes like:
How big are their populations?
How long do they live?
Where do they go when we don’t see or feel them?
Different methods have been developed to provide insights and notions on the mosquitoes’ movements, survival, and populations estimates; but the limitations and conditions of these methods mean that our knowledge is still incomplete.
One of the gold-standard tools for answering questions like those above is Mark-Release-Recapture (MRR). It was developed almost a century ago and has been modified and remodified through the years, as different marking technologies became available. Continue reading →
Understanding Population Responses to Environmental Change
Rapid climatic change has increased interest about how populations respond to environmental change. This has broad applications, for example in the management of endangered and economically important species, the control of harmful species, and the spread of disease. At the population level changes in abundance are driven by changes in vital rates, such as survival and fecundity. So studies that track individual survival and reproduction over time can provide useful insights into the drivers of such changes. They allow us to make future population level predictions on things like abundance, extinction risk and evolutionary strategies.
Predicting the future isn’t a simple task though. Anyone whose washing has got soaked through after the weather forecast suggested the day would be dry and sunny will know that (though the accuracy of short term weather forecasts has increased dramatically in recent years). Ideally, if we want to predict what will happen to populations as their environment changes, we would identify the drivers of variation in their survival and reproduction. We do this by asking questions like ‘are years of low survival associated with high rainfall?’ But, this is not a simple task; identifying drivers and the time periods over which they act and accurately estimating their effects requires long-term demographic data. Continue reading →
Knowing how many individuals there are in a population is a fundamental objective in ecology and conservation biology. But estimating abundance is often extremely difficult. It’s particularly difficult in the management of exploited marine, anadromous and freshwater populations. In marine fisheries, abundance estimation traditionally relies on demographic models, costly and time consuming mark recapture (MR) approaches if they are feasible at all, and the relationship between fishery catches and effort (catch per unit effort or CPUE). CPUEs can be subject to bias and uncertainty. This is why they tend to be considered relatively unreliable and contentious.
Close-Kin Mark-Recapture: Reducing Bias and Uncertainty
There is an alternative method though. It’s known as “Close-Kin Mark-Recapture” (CKMR), and is grounded in genomics and was first proposed by Skaug in 2001. The method is based on the principle that an individual’s genotype can be considered a “recapture” of the genotypes of each of its parents. Assuming the sampling of offspring and parents is independent of each other, the number of Parent-Offspring pairs (POP) genetically identified in a large collection of both groups can be used to estimate abundance. Continue reading →
Researchers at Washington State University and Smith-Root recently invented an environmental DNA (eDNA) filter housing that automatically preserves captured eDNA by desiccation. This eliminates the need for filter handling in the field and/or liquid DNA preservatives. The new material is also biodegradable, helping to reduce long-lasting plastic waste associated with eDNA sampling.
This video explains their new innovation in the field of eDNA sampling technology:
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 →
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 →