This issue contains the latest methods in ecology and evolution, including papers from the special feature Innovation in Practice. Read to find out about this month’s featured articles and the article behind our cover!

Featured

ECKOchain: A FAIR blockchain‐based database for long‐term ecological data

Open data practices in ecology are increasingly accepted, yet primary long-term ecological data remain hard to find. To incentivise open primary ecological data and ensure long-term preservation, authors propose a decentralised data management approach using blockchain technology. They introduce the ECKOchain, a ‘proof of concept’ ecological blockchain-based database. While metadata and access policies are distributed to all network members, primary data remains with data owners and are served on-demand to approved parties according to specified usage licences. Details of data requests are preserved indefinitely on the blockchain and serve as auditable data usage agreements. With the distributed blockchain-based database, authors advocate for open science and transparency in long-term management of ecological data.

Enabling data‐driven collaborative and reproducible environmental synthesis science

Brun et al. share the lessons learned from providing scientific computing support to over 600 researchers and discipline experts, helping them develop reproducible and scalable analytical workflows to process large amounts of heterogeneous data. Authors share their experiences in enabling researchers to do science more collaboratively and more reproducibly beyond any specific project, with long-lasting effects on the way researchers conduct science.

phylospatial: An R package for spatial phylogenetic analysis with quantitative community data

Authors present phylospatial, a new R package that fully supports probability, abundance, and binary community data across a range of spatial phylogenetic diversity (PD) analyses. The package processes all three data types in a common framework, while handling them in distinct ways at key points in the analysis pipeline. It also integrates with raster and vector data formats, providing efficient workflows for geospatial data. Authors illustrate the package’s functionality using a dataset of phylogeny and modelled occurrence probabilities for 5200 species of California plants. The phylospatial library represents an important addition to existing tools for spatial PD analysis.

CEPHALOPOD, a package to standardize marine habitat‐modelling practices and enhance inter‐comparability across biological observations

Authors introduce CEPHALOPOD (Comprehensive Ensemble Pipeline for Habitat modelling Across Large-scale Ocean Pelagic Observation Datasets), a standardized, highly automated and flexible framework designed to integrate and analyse heterogeneous marine data for multi-species habitat modelling following best practices in the field. In this study, authors document their statistical ensemble modelling approach and then assess its strengths and limitations with a virtual ecologist approach. They show how their framework performs in reproducing a range of distributions from biased field samples. The framework serves as a foundation for the consistent generation of Essential Biodiversity and Ocean Variables and carries the potential to significantly advance our comprehension of biodiversity and marine ecosystem functioning. 

Agent‐based versus correlative models of species distributions: Evaluation of predictive performance with real and simulated data

Species distribution models (SDMs) have been widely used in ecology to understand how species relate to environmental variation. In this study, Sirén et al., compare correlative and mechanistic species distribution models in prediction tasks under different scenarios. They define a mechanistic agent-based models of resource-consumer dynamics to generate data with known processes and parameter values. They fit correlative and mechanistic models to these data to study under which conditions mechanistic models might give more accurate predictions and how robust they are to possible model misspecification. Authors find mechanistic species distribution models may provide a significant advantage in prediction compared to more commonly used correlative models when predicting new environmental conditions.

Cover Image

The cover image features a male Marenestha inconspicua, a tiny bush-cricket endemic to central Chile. Although the most common morph in this species is green, this yellow specimen seized its coloration as camouflage while feeding on Cestrum flowers, showcasing how insects like this can play important roles in pollination.

This species was one of the inspirations behind Rthoptera, a Shiny-powered R package for insect bioacoustics. Although the field of bioacoustics began with studies on cricket song nearly a century ago, today researchers still lack a comprehensive tool for standardized analysis. Thus, many rely on multi-software approaches that complicate analyses and figures.

To solve this gap, Rthoptera allows researchers with any level of experience in R to easily obtain accurate measurements and standard plots from focalized insect signals, especially for those produced by species in the order Orthoptera. We hope that this tool will encourage more researchers to include bioacoustic analysis when describing new species or documenting taxonomic group revisions.

Read the article here.