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

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©BARILLET-PORTAL David
©BARILLET-PORTAL David

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

Advances in Modelling Demographic Processes: A New Cross-Journal Special Feature

Analysis of datasets collected on marked individuals has spurred the development of statistical methodology to account for imperfect detection. This has relevance beyond the dynamics of marked populations. A couple of great examples of this are determining site occupancy or disease infection state.

EURING Meetings

The regular series of EURING-sponsored meetings (which began in 1986) have been key to this development. They’ve brought together biological practitioners, applied modellers and theoretical statisticians to encourage an exchange of ideas, data and methods.

This new cross-journal Special Feature between Methods in Ecology and Evolution and Ecology and Evolution, edited by Rob Robinson and Beth Gardner, brings together a collection of papers from the most recent EURING meeting. That meeting was held in Barcelona, Spain, 2017, and was hosted by the Museu de Ciènces Naturals de Barcelona. Although birds have provided a convenient focus, the methods are applicable to a wide range of taxa, from plants to large mammals. Continue reading “Advances in Modelling Demographic Processes: A New Cross-Journal Special Feature”

Spatial Cross-Validation of Species Distribution Models in R: Introducing the blockCV Package

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Modelling species distributions involves relating a set of species occurrences to relevant environmental variables. An important step in this process is assessing how good your model is at figuring out where your target species is. We generally do this by evaluating the predictions made for a set of locations that aren’t included in the model fitting process (the ‘testing points’).

Random splitting of the species occurrence data into training and testing points
Random splitting of the species occurrence data into training and testing points

The normal, practical advice people give about this suggests that, for reliable validation, the testing points should be independent of the points used to train the model. But, truly independent data are often not available. Instead, modellers usually split their data into a training set (for model fitting) and a testing set (for model validation), and this can be done to produce multiple splits (e.g. for cross-validation). The splitting is typically done randomly. So testing points sometimes end up located close to training points. You can see this in the figure to the right: the testing points are in red and training points are in blue. But, could this cause any problem? Continue reading “Spatial Cross-Validation of Species Distribution Models in R: Introducing the blockCV Package”

اعتبارسنجی متقاطع مکانی در مدلسازی توزیع گونه‌‌ها

نویسنده: روزبه وَلَوی

This post is available in English

مدلسازی توزیع گونه‌ها به تخمین و برآورد ارتباط بین مجموعه‌ای از نقاط حضور گونه با متغیرهای زیست‌محیطی مرتبط می پردازد. یکی از مراحل اساسی این فرایند، ارزیابی قدرت مدل برای پیش­بینی مکان‌هایی است که احتمال حضورگونه در آنجا وجود دارد. این کار اغلب با ارزیابی پیش­بینی انجام شده در مجموعه‌ای ازنقاط که در فرآیند مدلسازی مورد استفاده قرار نگرفته اند (نقاط آزمایشی) صورت می‌گیرد.

تقسیم تصادفی داده‌های حضور گونه به نقاط آزمایشی و آموزشی
تقسیم تصادفی داده‌های حضور گونه به نقاط آزمایشی و آموزشی

مطالعات پیشین بر این نکته تاکید دارند که به منظور ارزیابی معتبر، نقاط آزمایشی باید مستقل از نقاط آموزشی باشند، این درحالیست که داده مستقل واقعی به ندرت در دسترس می باشد. به همین دلیل، در فرایند مدلسازی معمولا داده‌های موجود را به دو قسمت داده‌های آموزشی (برای کالیبره کردن مدل) و داده های آزمایشی (برای ارزیابی دقت مدل) تقسیم می‌کنند، این استراتژی می‌تواند چند قسمتی هم باشد (برای مثال اعتبارسنجی متقاطع یا cross-validation). از آنجاییکه این تقسیم بندی معمولا بصورت تصادفی انجام می‌شود، بنابراین گاهی اوقات نقاط آزمایشی در فواصل نزدیک به نقاط آموزشی قرار می‌گیرند. شکل زیر این مساله را به خوبی نشان می دهد که در آن نقاط آزمایشی به رنگ قرمز و نقاط آموزشی آبی هستند. اما آیا این مساله می‌تواند مشکلی ایجاد کند؟ Continue reading “اعتبارسنجی متقاطع مکانی در مدلسازی توزیع گونه‌‌ها”

How do You Know that the Top Dog is Really the Top Dog? Using Elo-Ratings and Bayesian Inference to Determine Rankings in Animal Groups

Post provided by Julia Fischer

A female chacma baboon (rear) signals her submission to another female by raising her tail. ©Julia Fischer.
A female chacma baboon (rear) signals her submission to another female by raising her tail. ©Julia Fischer.

Anyone who studies social animals in the wild (or human groups, for that matter), will soon find that some individuals threaten or attack others frequently, while others try to get out of the way or signal their submission in response to aggression. Observers tally the outcome of such aggressive interactions between any given two individuals (or ‘dyads’) and try to deduce the rank hierarchy from such winner-loser matrices. One drawback of this approach is that all temporal information is lost.

Imagine Royal, a baboon, dominating over Power, another baboon, 20 times, and Power dominating over Royal 20 times as well. If we just look at these data, we might think that they have the same fighting ability and similar ranks. But, if we know that Royal beat Power the first 20 of the interactions, then Power beat Royal in all further interactions, we’d come to a totally different conclusion. We’d infer that Power had toppled Royal and a rank change had taken place.

How do Rank Hierarchies Change Over Time?

One prominent method that takes the temporal dynamics of winner-loser interactions into account was originally developed to calculate the relative skill level of chess players. This method was introduced by Arpad Elo and is hence known as Elo-Rating. Elo-Rating has also been applied to rate the relative skills in a variety of competitive fields, including Major League Baseball, video games, and Scrabble. Continue reading “How do You Know that the Top Dog is Really the Top Dog? Using Elo-Ratings and Bayesian Inference to Determine Rankings in Animal Groups”

The Manager’s Dilemma: Which Species to Monitor?

Post provided by Payal Bal and Jonathan Rhodes

The greater bilby (M.Lagotis). ©Save the Bilby Fund
The greater bilby (M.Lagotis). ©Save the Bilby Fund

Imagine you’re the manager of a national park. One that’s rich in endemic biodiversity found nowhere else on the planet. It’s under the influence of multiple human pressures causing irreversible declines in the biodiversity, possibly even leading to the extinction of some of the species. You’re working with a complex system of multiple species and threats, limited knowledge of which threats are causing the biggest declines and limited resources. How do you decide what course of action to take to conserve the biodiversity of the park? This is the dilemma faced by biodiversity managers across the globe.

In our recent paper, ‘Quantifying the value of monitoring species in multi‐species, multi‐threat systems’, we address this problem and propose a method using value of information (VOI) analysis. VOI estimates the benefit of monitoring for management decision-making. Specifically, it’s a valuation tool that can be used to disentangle the trade-offs in competing monitoring actions. It helps managers decide how to invest (or whether to invest) their money in monitoring actions when faced with imminent biodiversity declines and the urgency of efficient conservation action. Continue reading “The Manager’s Dilemma: Which Species to Monitor?”

Statistical Ecology Virtual Issue

To celebrate the International Statistical Ecology Conference and British Ecological Society Quantitative Ecology Annual Meeting, Laura Graham and Susan Jarvis have compiled a virtual issue celebrating all things statistical and quantitative in ecology.

Statistical and quantitative methods within ecology have increased substantially in recent years. This rise can be attributed both to the growing need to address global environmental change issues, as well as the increase in data sources to address these challenges. Continue reading “Statistical Ecology Virtual Issue”

Fourier Methods Gain Wide Appeal for Tropical Phenology Analysis

Post provided by Emma Bush

Lopé National Park. ©Jeremy Cusack
Lopé National Park. ©Jeremy Cusack

Like all living things, plant species must reproduce to persist. Key stages in successful plant reproduction must be carefully timed to make sure resources are available and conditions are optimal. There will be little success if flowers mature in bad weather conditions for their insect pollinators or if fruits ripen but the seed dispersers have migrated elsewhere.

Because plants rely on the abiotic environment for sunlight, nutrients and water, and in some cases for the dispersal of pollen and seeds, it is not surprising that their life stages are closely linked to environmental cycles. Continue reading “Fourier Methods Gain Wide Appeal for Tropical Phenology Analysis”