The May Issue of Methods in Ecology and Evolution is now online! This issue includes five Featured Articles selected by our Senior Editor Aaron Ellison, highlighting methods for identifying flow modules in ecological networks, detecting rare terrestrial mammals, assessing functional diversity in plants and more!
We also have 12 articles, including one Practical Tools and two Applications, which are completely free to read. Find out more below.
Infomap (Application) Although many ecological networks describe flows, such as biomass flows in food webs or disease transmission, most modularity analyses have ignored network flows. This can hinder our understanding of the interplay between structure and dynamics. Here, Farage et al. present Infomap, a method based on network flows to the field of ecological networks. Infomap is a flexible tool that can identify modules in virtually any type of ecological network and is particularly useful for directed, weighted and multilayer networks.
Quick detection of a rare species (open access) eDNA surveys have proven benefits in efficiency and accuracy for identifying some taxa, but generally rely on the extraction and amplification of DNA from water, soil or sediment, which are not yet dependable samples for rare terrestrial mammals. As such, the threatened hazel dormouse is typically confirmed by looking for its nest in survey tubes. Here, Priestley et al. demonstrate that eDNA swabbed from a clean substrate placed in survey apparatus can significantly hasten the detection of the hazel dormouse. This method has the potential to broaden the application of eDNA to other terrestrial vertebrates, including surveys at large spatiotemporal scales.
Telemetry validated nitrogen stable isotope clocks (open access) Throughout their life history, many animals transition among heterogeneous environments to facilitate behaviours such as reproduction, foraging and predator avoidance. The dynamic environmental and biological conditions experienced by mobile species are integrated in the chemical composition of their tissues, providing retrospective insight into movement. Here, Shipley et al. present a unique application of nitrogen stable isotope clocks which integrate tissue turnover rates, consumer stable isotope ratios and habitat‐specific isotope baselines to predict time‐since‐immigration and the timing of habitat shifts.
Jointly estimating spatial sampling effort and habitat suitability (open access) Building reliable species distribution models (SDMs) from presence‐only information requires a good understanding of the spatial variation in the sampling effort. However, in most cases, the sampling effort is unknown, leading to biases in SDMs. Here, Botella et al. propose a method to jointly estimate the parameters of sampling effort and species densities to avoid such biases. The method is particularly suited to the analysis of massive but highly heterogeneous presence‐only data.
Avoiding distortion of functional diversity (open access) The functional diversity of a community is assessed by sampling traits at one or more scales (individuals, populations and species) and calculating a summary index of the variation in trait values. However, it remains unclear how the scales at which traits are sampled and the indices used may alter the patterns observed and inferences about ecological processes. Here, Wong & Carmona assess functional diversity for 40 plant and 61 ant communities using six methods, testing whether including trait variability at different scales (from individuals to species) alters functional diversity values calculated using volume‐based and dissimilarity‐based indices.
GPSeqClus (free access) Evaluating the concentration of animal locations in space and time provides insight regarding animal activity and behaviour. Most commonly collected via GPS technology, these concentrations are identified using rule sets resulting in location ‘clusters’. Cluster algorithms provide a framework for modelling and predicting behavioural states when paired with field data to evaluate habitat characteristics and validate results. Here, Clapp et al. present GPSeqClus, a sequential clustering algorithm package to process location datasets based on user‐defined parameters.
Measuring soil carbonates (free access) Geochemical models used to estimate carbonate precipitation–dissolution rates require important inputs including carbonate content and calcite reactive surface area in soil, as well as dissolved inorganic carbon (DIC) content in soil solution. However, most methods currently available to accurately measure these parameters can be time‐consuming and/or require expensive laboratory equipment. To tackle this problem, Lopez‐Canfin et al. developed a sensitive, accurate device to measure these variables at low cost and with little time investment.
The Pollen on the Cover
This month’s cover image shows a random sample of pollen grains from a large library of fuchsine stained pollen, produced by Olsson et al. for their article ‘Efficient, automated and robust pollen analysis using deep learning’. Each image represents a pollen grain of a different species, scanned at 0.25 μm, and varying in size from <10 to >100 μm. With training, humans can learn to identify pollen based on variation in shape, features, and texture. However, some species are similar and there is also variation within species, so it is not possible to identify all species with certainty. Convoluted neural networks (CNN) can learn to identify pollen, but Olsson et al. show that they are approximately on par with humans, struggling to separate the same groups as us. As usual, though, the computers outperform us in speed, and once trained, a CNN can locate, classify and count a sample with up to 10,000 pollen in less than a minute. Thus, the method described by Olsson et al. can increase efficiency of pollen analysis dramatically, allowing large‐scale pollen analysis in situations where it was previously too expensive. Image credit: ©Ola Olsson.