This issue contains the latest methods in ecology and evolution. Read to find out about this month’s featured articles and the article behind our cover!

Featured

MicroEcoTools: An R package for comprehensive theoretical microbial ecology analysis

Authors introduce MicroEcoTools, an R package designed to test ecological framework predictions using microbial community data. It assesses microbial diversity and evaluates the relative impacts of stochastic and deterministic assembly mechanisms through a taxa-based null model approach for replicated designs. Furthermore, the package allows application of Grime’s trait-based life-history categories—competitor, stress-tolerant and ruderal (CSR)—to taxa, functional traits and ecosystem functions within microbial communities. MicroEcoTools also includes relevant statistical tests, numeric simulations and publicly available datasets for demonstration. In conclusion, MicroEcoTools facilitates the application of ecological frameworks, including community assembly mechanisms, diversity analysis, and life-history strategies, to microbial ecosystems under disturbance.

pnetr: An R package for the PnET family of forest ecosystem models

Many ecosystem models are difficult to manage and apply by scientists because of complex model structures, lack of consistent documentation, and low-level programming implementation. In this article, authors present the ‘pnetr’ R package, which is designed to provide an easy-to-manage ecosystem modelling framework and detailed documentation in both model structure and programming. The framework implements a family of widely used PnET ecosystem models, which are relatively parsimonious but capture essential biogeochemical cycles of water, carbon and nitrogen. They hope ‘pnetr’ can facilitate further development of ecological theory and increase the accessibility of ecosystem modelling and ecological forecasting.

Leaf-wood classification of terrestrial laser scanning data with co-registered near-infrared photography

To assess leaf area index (LAI) from Terrestrial laser scanning (TLS) data collected during leaf-on conditions, a fundamental requirement is the classification of points as either leaves or wood. A novel leaf-wood classification approach is presented that avoids current issues by exploiting the spectral transmittance properties of leaves and wood, which, at near-infrared wavelengths, demonstrate much larger differences than for reflectance. The approach relies on classification of near-infrared images collected by a co-registered camera integrated with the TLS instrument and can be directly applied to the whole point cloud without segmentation. The results provide evidence of the efficacy of the approach, and its use has the potential to reduce uncertainty in essential climate variables critical to climate change monitoring, modelling and adaptation.

Bridging the gap between remotely sensed phenology and the underlying ecophysiological processes: The SWELL model

Remote sensing has become a key tool for monitoring phenological events on large spatial and temporal scales, primarily using vegetation indices like the Normalized Difference Vegetation Index (NDVI). This study presents SWELL (Simulated Waves of Energy, Light, and Life), a process-based phenology model that simulates the temporal NDVI profile, from leaf unfolding to dormancy release, based on species-specific photothermal response functions. SWELL allows bridging the gap between remotely sensed phenology and the underlying ecophysiological processes. By overcoming current limitations in process-based phenology modelling, SWELL may represent a novel tool for understanding and predicting vegetation phenology in the context of climate change.

The ecological forecast limit revisited: Potential, absolute and relative system predictability

Ecological forecasts are model-based statements about currently unknown ecosystem states in time or space. In this article, authors synthesise existing methods that assess ‘forecast limits’ and apply them to quantify model predictability and system behaviour. This work explores the relationship between ecological model verification and predictability analysis. It formalises the distinctions between the potential and relative forecast limits that we assume to represent the upper and lower predictability limits, and a use case of the absolute forecast limit. Authors demonstrate this framework using three case studies that address three different ecological scales, exemplifying the diverse applications of forecast limits in evaluating models across different systems and complexities.

Cover Image

As atmospheric CO₂ concentrations continue to rise, ecology and evolution researchers will explore new questions about how ecosystems respond to the interacting stressors of climate change. This month’s cover image features TinyCO₂, a high-performance, low-cost system for elevated CO₂ experiments on field-grown plants. TinyCO₂ can be adapted to meet the needs of various research sites and timelines. Most importantly, TinyCO₂ can be built at a fraction of the cost of similar CO₂ enrichment systems, making this method available to research teams from diverse institutions. In our article, we describe the design and performance of TinyCO₂ in its first iteration at Quail Ridge Reserve, an oak woodland in California’s Inner Coast Range. We hope that this method can help broaden the reach of CO₂ enrichment experiments on field-grown plants, allowing researchers to gather empirical data about climate change effects in under-studied ecosystems and biomes.

Read the article here.