Searching for snow leopards

Post provided by Ian Durbach and Koustubh Sharma

Snow leopard captured via camera trap in Mongolia. Picture credit: Snow Leopard Conservation Foundation/Snow Leopard Trust/Panthera (OR SLCF/SLT/PF).

Snow leopards are notoriously elusive creatures and monitoring their population status within the remote, inhospitable habitats they call home, can be challenging.  In this post, co-authors Ian Durbach and Koustubh Sharma discuss the applications of their Methods in Ecology and Evolution article, ‘Fast, flexible alternatives to regular grid designs for spatial capture–recapture’, for monitoring snow leopard populations.

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10th Anniversary Volume 11: Updates on the ClimEx Handbook

Post provided by Aud H. Halbritter

To celebrate the 10th Anniversary of the launch of Methods in Ecology and Evolution, we are highlighting an article from each volume to feature in the Methods.blog. For Volume 11, we have selected ‘The handbook for standardized field and laboratory measurements in terrestrial climate change experiments and observational studies (ClimEx)’ by Halbritter et al. (2019).

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Anniversary Volume 9: Estimating Effective Detection Area of Static Passive Acoustic Data

Post provided by Hanna K Nuuttila

To celebrate the 10th Anniversary of the launch of Methods in Ecology and Evolution, we are highlighting an article from each volume to feature in the Methods.blog. For Volume 9, we have selected ‘Estimating effective detection area of static passive acoustic data loggers from playback experiments with cetacean vocalisations’ by Nuuttila et al. (2018).  In this post, the authors discuss the background and key concepts of the article, and the application of the article for assessing abundance of cetaceans.

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Individual History and the Matrix Projection Model

Post provided by Rich Shefferson

A single time-step projection of a historical matrix projection model (hMPM), for a 7 life stage life history model of Cypripedium parviflorum, the small yellow lady’s slipper. In this case, the vector of biologically plausible stage pairs in time 2 is equal to the full projection matrix multiplied by the vector of biologically plausible stage pairs in time 1.

Matrix projection modeling is a mainstay of population ecology. Ecologists working in natural area management and conservation, as well as in theoretical and academic realms such as the study of life history evolution, develop and use these models routinely. Matrix projection models (MPMs) have advanced dramatically in complexity over the years, originating from age-based and stage-based matrix models parameterized directly from the data, to complex matrices developed from statistical models of vital rates such as integral projection models (IPMs) and age-by-stage models. We consider IPMs to be a class of function-based MPM, while age-by-stage MPMs may be raw or function-based, but are typically the latter due to a better ability to handle smaller dataset. The rapid development of these methods can leave many feeling bewildered if they need to use these methods but lack sufficient understanding of scientific programming and of the background theory to analyze them properly.

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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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