Our May issue is now online! This issue contains 19 fantastic articles about the latest methods in ecology and evolution, including methods for investigating animal movements, predicting species coexistence patterns, exploring primary production in macroalgae tents, and more!

Read on to find out about this month’s featured articles and the article behind our beautiful bee and sunflower cover.

Separate articles

PhycoCanopy * open access * Macroalgal coverings are considered important for coastal food networks and may play a role in carbon sequestration. Until recently, tent photosynthesis measurements have been relatively rare, and simulations have sometimes deviated from key aspects. Here, Mark P Johnson introduces the PhycoCanopy R illumination tool, which provides a way to explore how different algae parameters and environmental settings can affect the net photosynthesis of the canopy.

Unmanned aerial vehicles as a useful tool for investigating animal movements * open access * Determining the abundance of animals is essential to assessing the effectiveness of management measures against pest animals, and investigating animal movements has become important for conducting estimates of the abundance of unmarked animals. Here, Iwamoto et al. propose a method for estimating animal movements using unmanned aerial vehicles, conducting a case study aimed at investigating the movement of feral pigs.

Predicting aquatic animal movements and behavioral states Small-scale tracking with passive acoustic telemetry can provide excellent knowledge of the ecology of aquatic animal movement. To predict the small-scale positions of animals labeled in continuous space from discrete spatial intelligence data, state space modeling through the R YAPS package offers a promising alternative to the frequently used positioning algorithms. However, YAPS currently cannot classify multiple types of movements that can be used as representative of the individual behaviors of the animals under study (behavioral states), an endeavor that is of growing interest to movement ecologists. Here, Whoriskey et al. advance YAPS including functionality to predict behavioral states using an iterative maximization framework.

Predicting species coexistence patterns Predicting coexistence patterns is a current challenge to understand the maintenance of diversity, especially in affluent communities where the complexity of these patterns is magnified through indirect interactions that hinder their approximation to classical experimental approaches. Here, Hirn et al. explore the most advanced Machinery Learning techniques called Artificial Generative Intelligence (GenAI) to predict species coexistence patterns in parts of vegetation, training opposing generator networks, and automatic variation coders to later discover some of the mechanisms after community gathering.

New methods for spatial prioritization Spatial prioritization integrates data on biodiversity distribution, costs, and threats. It produces priority spatial maps that can support ecologically well-informed land use planning in general, including applications in avoiding environmental impact outside protected areas. In this article, Moilanen et al. describe new methods that significantly increase the usefulness of spatial prioritization in large analyzes and with interactive planning.

Bees on the cover

Cover of the May issue of MEE, featuring a bumpy bee on a sunflower.

A golden northern bee, Bombus fervidusforages in Helianthus annuus. Numerous species of bees, important pollinators of agricultural and natural ecosystems, have experienced population decline recently. Improved methods, such as terrestrial DNA techniques, are needed to monitor the abundance and distribution of such species. In this issue, Richardson presents statistical procedures for controlling false detections related to critical errors within metagenetic data. This work is particularly important to use cases where the presence of species is inferred from a minimal number of metagenetic sequences, as in the case of pollination community surveillance using eDNA methods. In this context, most sequences represent non-target organisms, while species of interest make up a small portion of sequence coverage and can be difficult to distinguish from ranking artifacts, such as pollutants and critical error events. Statistical methods for controlling false positives improve the sustainability of metagenetic techniques and extend their applicability to new research contexts. Photo: © R. Richardson

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