Journal Overview

Methods in Ecology and Evolution (MEE) is a British Ecological Society journal that promotes the development of new methods in ecology & evolution, and facilitates their dissemination and uptake by the research community.

Features in Methods in Ecology and Evolution

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Applications for deep learning in ecology

Christin et al. review existing implementations of deep learning and show that it has been used successfully to identify species, classify animal behaviour and estimate biodiversity in large datasets like camera‐trap images, audio recordings and videos. They demonstrate that deep learning can be beneficial to most ecological disciplines, including applied contexts, such as management and conservation.

Optimizing sampling of flying insects using a modified window trap

A typical window trap only has a collection unit below the windows, but Knuff et al. have added an additional one on top of the windows. These modified traps are suitable for collecting a broader range of flying insects compared to conventional window traps. The additional top unit is fast and easy to build and the traps require little maintenance while operating in the field.

An automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks

Systematic reviews take an experimental design approach to reviewing the literature. They’re rigorous, but require time and resources. To combat these problems, Grames et al. developed a quick, objective, reproducible method for generating search strategies. Find out more about it in this video: https://methodsblog.com/2019/08/19/litsearchr/ 

Optimality in prioritizing conservation projects

Hanson et al. investigate the performance of exact algorithms (a class of algorithms that guarantee optimality) compared with conventional algorithms for project prioritization. Their results suggest that conservation agencies could benefit enormously from exact algorithms. To help make exact algorithms more accessible, they developed the oppr R package (https://CRAN.R-project.org/package=oppr) which can use open‐source and commercial exact algorithm solvers to identify optimal solutions for a range of objectives and constraints.

Semi‐quantitative characterisation of mixed pollen samples using MinION sequencing and Reverse Metagenomics (RevMet)

Peel et al. have developed a pipeline, RevMet (Reverse Metagenomics) that allows reliable and semi‐quantitative characterization of the species composition of mixed‐species eukaryote samples, such as bee‐collected pollen, without requiring reference genomes.

The simulation extrapolation technique meets ecology and evolution: A general and intuitive method to account for measurement error

Measurement error and other forms of uncertainty are commonplace in ecology and evolution, and may bias estimates of parameters of interest. Ponzi et al. propose to generalize the simulation extrapolation (SIMEX) technique, a heuristic approach to correct for measurement error, to situations where it is difficult to explicitly formulate an error model or latent model for a variable of interest.

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