We’re a little lat on this post, but there’s another great issue of Methods in Ecology and Evolution online now.
There’s more information below on the Featured Articles selected by the Senior Editor and all of our freely available papers (Practical Tools and Applications articles are always free to access for everyone upon publication, whether you have a subscription or not).
Deep Learning in Ecology: Deep learning has been used successfully to identify species, classify animal behaviour and estimate biodiversity in large datasets like camera‐trap images, audio recordings and videos. Christin et al. show that it can also be beneficial to most ecological disciplines, including applied contexts, such as management and conservation.
Sampling Flying Insects: 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.
Systematic Reviews: 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 litsearchr: a quick, objective, reproducible method for generating search strategies. Find out more about litsearchr in this video.
Prioritising Conservation Projects: Hanson et al. investigate the performance of exact algorithms (a class of algorithms that guarantee optimality) compared with conventional algorithms for project prioritisation. 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 which can use open‐source and commercial exact algorithm solvers to identify optimal solutions for a range of objectives and constraints.
Characterising Mixed Pollen Samples: Peel et al. have developed a pipeline, RevMet (Reverse Metagenomics) that allows reliable and semi‐quantitative characterisation of the species composition of mixed‐species eukaryote samples, such as bee‐collected pollen, without requiring reference genomes. RevMet could be adapted to generate semi‐quantitative datasets for a wide range of mixed eukaryote samples.
Simulation Extrapolation Technique: Measurement error and other forms of uncertainty are commonplace in ecology and evolution. But they may bias estimates of parameters of interest. Ponzi et al. suggest generalising the simulation extrapolation (SIMEX) technique – a heuristic approach to correct for measurement error – to situations where it’s difficult to explicitly formulate an error model or latent model for a variable of interest.
Applications and Practical Tools
With five Applications papers and one Practical Tools article, we’ve got a lot to cover in this section. So, let’s get straight to it!
BA3‐SNPs: Mussmann et al. have modified a popular legacy program for migrant detection (i.e. BayesAss3) to accept SNP (single nucleotide polymorphism) data. They validated the programme using empirical data to demonstrate its suitability for both high‐performance and desktop computing environments. This article shows that the BA3 algorithm remains a viable option for analysing modern SNP datasets.
Atlantis: Marine ecosystem management is increasingly expected to take into account a wide range of ecological and socio‐economic factors. Atlantis is an end‐to‐end marine ecosystem model that takes into account dynamically interacting physics, biology, fisheries, management, assessment and economics submodels. As human demands on the ocean increase and the effects of climate change become more apparent, integrated ecosystem modelling tools like Atlantis will be increasingly important.
Open Access Articles
There are two freely available papers that we have not yet mentioned, both of which are Open Access.
Classification of Fungi: The internal transcribed spacer (ITS) is often used in DNA metabarcoding of fungi. Its high variability means that it can fail to classify operational taxonomic units (OTUs) when no similar reference sequence exists though. Heeger et al. test whether the 5.8S region (often sequenced with ITS2 but discarded before analysis) could provide OTU classifications when ITS2 fails. Using 5.8S to complement ITS classification will likely provide better estimates of diversity in lineages for which database coverage is poor.
Microplastics and Top Predators: Nelms et al. present a new technique which gives insights into the exposure of marine predators to microplastics. Their novel and effective methodology pipeline combines scat‐based molecular techniques with a microplastic isolation method. This non‐invasive, data‐rich approach maximises time and resource-efficiency, while minimising costs and sample volumes required for analysis.
The Drone on the Cover
Our October cover image shows the Unmanned Aerial Vehicle Radio Telemetry (UAV‐RT) system being tested southeast of Flagstaff, Arizona, USA. This open source system integrates a software defined radio and an unmanned aerial system to help localise very high frequency (VHF) wildlife transmitter tags. The improved vantage and mobility of the system allows for increased received signal power and a significant improvement in land area that can be searched. With flight times of approximately 20 minutes and speeds of approximately 10 m/s, wide spacing between bearing estimates is possible.
The project website contains system assembly plans and code. In the article “UAV wildlife radiotelemetry: System and methods of localization”, the development, data processing methods, and baseline characterization test results are presented. The authors show how, using a directional antenna, the processing software can develop bearing estimates and then localisation estimates from those bearings. This capacity can be leveraged by experienced seasoned human trackers to help reduce the time and cost of wildlife localisation.
Photo credit: ©José G. Martínez‐Fonseca