Reliably Predicting Pollinator Abundance with Process-Based Ecological Models

Post provided by Emma Gardner and Tom Breeze

Bumblebee. Picture credit: Tom Breeze.

Pollination underpins >£600 million of British crop production and wild insects provide a substantial contribution to the productivity of many crops. There is mounting evidence that our wild pollinators are struggling and that pollinator populations may be declining. Reliably modelling pollinator populations is important to target conservation efforts and to identify areas at risk of pollination service deficits. In our study, ‘Reliably predicting pollinator abundance: Challenges of calibrating process-based ecological models’, we aimed to develop the first fully validated pollinator model, capable of reliably predicting pollinator abundance across Great Britain.

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10th Anniversary Volume 4: Capture-Recapture Models Editor’s Choice

To celebrate our 10th Anniversary, we are highlighting a key article from each of our volumes. For Volume 4, we selected Estimating age‐specific survival when age is unknown: open population capture–recapture models with age structure and heterogeneity by Matechou et al. (2013).

In this post, Matt Schofield, our Associate Editor with expertise in capture-recapture models shares his favourite MEE modelling papers.

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10th Anniversary Volume 4: Open population capture–recapture models with age structure and heterogeneity

Post provided by Eleni Matechou

To celebrate the 10th Anniversary of the launch of Methods in Ecology and Evolution, we are highlighting an article from each volume to feature on the Methods.blog. For Volume 4, we have selected ‘Estimating age‐specific survival when age is unknown: open population capture–recapture models with age structure and heterogeneity’ by Matechou et al. (2013). In this post, the authors discuss the background and key concepts of the article, and changes in the field that have happened since the paper was published seven years ago.

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10th Anniversary Volume 5: Citizen Science Editor’s Choice

To celebrate our 10th Anniversary, we are highlighting a key article from each of our volumes. For Volume 5, we selected Statistics for citizen science: extracting signals of change from noisy ecological data by Isaac et al. (2014) and the authors looked back on their article and how the field of citizen science has changed since.

In this Editor’s Choice, Res Altwegg, our Associate Editor with expertise in citizen science, shares his favourite MEE papers in the field of citizen science and beyond.

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10th Anniversary Volume 3: paleotree: A Retrospective

Post provided by David bapst

To celebrate the 10th Anniversary of the launch of Methods in Ecology and Evolution, we are highlighting an article from each volume to feature on the Methods.blog. For Volume 3, we have selected ‘paleotree: an R package for paleontological and phylogenetic analyses of evolution‘ by David W. Bapst (2012). In this post, David discusses the background to the Application he wrote as a graduate student, and how the field has changed since.

I was a fourth year graduate student when I first had the idea to make an R package. Quite a few people thought it was a bit silly, or a bit of a time-waste, but I thought it was the right thing to do at the time, and I think it has proven to be the right decision in hindsight.

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Stop, think, and beware of default options

Post provided by Paula Pappalardo (with contributions from Elizabeth Hamman, Jim Bence, Bruce Hungate & Craig Osenberg)

Esta publicación también está disponible en español.

You spent months carefully collecting data from articles addressing your favorite scientific question, you have dozens of articles neatly arranged on a spreadsheet, you found software or code to analyze the data, and then daydream about how your publication will be the most cited in your field while making cool plots. If that sounds familiar, you have probably done a meta-analysis. Meta-analysis uses statistical models to combine data from different publications to answer a specific question.

What you may not have realized when going down the meta-analysis rabbit hole, is that small, seemingly inconsequential, choices can greatly affect your results. If you want to know about one of them lurking behind the scenes… read on!

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Para, piensa, y ten cuidado con las configuraciones por defecto

Post escrito por Paula Pappalardo (con aportes de Elizabeth Hamman, Jim Bence, Bruce Hungate & Craig Osenberg)

This post is also available in English.

Pasaste meses laboriosamente colectando datos de artículos científicos acerca de tu pregunta favorita, tienes decenas de artículos perfectamente organizados en una base de datos, ya encontraste el programa o código para analizar los datos, y entonces imaginas como tu publicación va a ser la más citada en tu campo de investigación mientras haces unos gráficos lindísimos. Si esto te suena familiar, seguramente has hecho un meta-análisis. Un meta-análisis usa modelos estadísticos para combinar datos de distintas publicaciones para responder a una pregunta específica.

Lo que quizás no te diste cuenta mientras navegabas los pasos del meta-análisis, es que pequeñas decisiones (a veces pareciendo de muy poca importancia) pueden tener grandes efectos en los resultados. Si quieres saber más acerca de una de estas decisiones en particular… ¡sigue leyendo!

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MInOSSE: all you need to reconstruct past species geographic range is in the fossil record!

Post provided by Francesco Carotenuto

A very important ecological feature of a species is its geographic range, which can be described by its size, position and shape. Studying the geographic range can be useful to understand the ecological needs of a species and, thereby, to plan conservation strategies. In ecological studies, mathematical models are the new standard to reconstruct the distribution of living species on Earth because of their accuracy in predicting a species presence or absence at unsampled locations. These methods are able to reconstruct the climatic niche of a species and to project it onto a geographic domain in order to predict the species’ spatial distribution. To do this, besides the occurrences of a species, the models necessarily require the spatial maps of environmental variables, like temperature and precipitation, for all the study area.

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Biomechanically-Aware Behaviour Recognition using Accelerometers

Post provided by Pritish Chakravarty

 

Accelerometers, Ground Truthing, and Supervised Learning

Accelerometers are sensitive to movement and the lack of it. They are not sentient and must recognise animal behaviour based on a human observer’s cognition. Therefore, remote recognition of behaviour using accelerometers requires ground truth data which is based on human observation or knowledge. The need for validated behavioural information and for automating the analysis of the vast amounts of data collected today, have resulted in many studies opting for supervised machine learning approaches.

Ground-truthing. The acceleration data stream (recorded using the animal-borne data logger, bottom-left) is synchronised with simultaneously recorded video (near top right). Picture credit: Kamiar Aminian

In such approaches, the process of ground truthing involves time-synchronising acceleration signals with simultaneously recorded video, having an animal behaviour expert create an ethogram, and then annotate the video according to this ethogram. This links the recorded acceleration signal to the stream of observed animal behaviours that produced it. After this, acceleration signals are chopped up into finite sections of pre-set size (e.g. two seconds), called windows. From acceleration data within windows, quantities called ‘features’ are engineered with the aim of summarising characteristics of the acceleration signal. Typically, ~15-20 features are computed. Good features will have similar values for the same behaviour, and different values for different behaviours.

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An interview with the editors of “Population Ecology in Practice”: Part II

Post provided by Daniel Caetano

Today we bring the second part of an interview with Dennis Murray and Brett Sandercock about their brand new book in population ecology methods: “Population Ecology in Practice.” This time we talked about their experience as editors, including some useful advice for new editors.

If you missed the first part of the interview, check it out here.

Population Ecology in Practice introduces a synthesis of analytical and modelling approaches currently used in demographic, genetic, and spatial analyses. Chapters provide examples based on real datasets together with a companion website with study cases and exercises implemented in the R statistical programming language.

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