Our April Issue is now online! This issue contains 14 brilliant articles about the latest methods in ecology and evolution, including methods for creating habitat structures for cavity‐dependent animals, quantifying small animal activity, identifying trends in multivariate time series and much more!
Read on to find out about this month’s featured articles.
The einet package *Open Access* Understanding noise in networks and finding the right scale to represent a system are important problems in network biology. Most research focuses on the raw, micro-scale network from data/simulations and seldom explores the scale dependence of properties. In this Application article, Klein et al. introduce the einet package, which looks at the most informative scale in a biological network using recent concepts from information theory and network science.
Portable locomotion activity monitor Characterising the frequency and timing of biological processes such as locomotion, eclosion or foraging is often needed to get a complete picture of a species’ ecology. Automated trackers are an invaluable tool for high-throughput collection of activity data and have become more accurate and efficient with advances in computer vision and deep learning. However, tracking activity of small and fast flying animals remains a hurdle, especially in a field setting with variable light conditions. In this Practical Tools article, Sondhi et al. present a portable locomotion activity monitor (pLAM), a mobile activity detector to quantify small animal activity.
Identifying trends in multivariate time series Ecological processes are rarely directly observable, and inference often relies on estimating hidden or latent processes. State-space models have become widely used for this task because of their ability to simultaneously estimate the multiple sources of variation. Here, Ward et al. introduce a new class of models, where latent processes are modelled as smooth functions (basis splines, penalized splines or Gaussian process models). They implement these models in the bayesdfa R package, which uses the rstan package for fitting.
Integrated community occupancy models Modelling the dynamics of multiple species simultaneously can require large amounts of diverse data, and few modeling approaches exist to simultaneously provide species and community level inferences. Here, Doser et al. present an “integrated community occupancy model” (ICOM) that unites principles of data integration and hierarchical community modeling in a single framework to provide inferences on species-specific and community occurrence dynamics using multiple data sources. The ICOM combines replicated and nonreplicated detection-nondetection data sources using a hierarchical framework that explicitly accounts for different detection and sampling processes across data sources.
More Application Articles
Rcompadre and Rage The COMPADRE Plant Matrix Database and COMADRE Animal Matrix Database are the most extensive resources for matrix population model (MPM) data. Although these databases represent an unparalleled resource for researchers, land managers and educators, the current computational tools available to answer questions with MPMs impose significant barriers to potential COM(P)ADRE database users by requiring advanced knowledge to handle diverse data structures and program custom analysis functions. To close this knowledge gap, Jones et al. present two interrelated R packages designed to facilitate the use of these databases by providing functions to acquire, quality control and manage both the MPM data contained in COMPADRE and COMADRE, and a user’s own MPM data, and present a range of functions to calculate life-history traits from MPMs in support of ecological and evolutionary analyses.
rdacca.hp Canonical analysis, a generalisation of multiple regression to multiple-response variables, is widely used in ecology. Because these models often involve many parameters (one slope per response per predictor), they pose challenges to model interpretation. Among these challenges, we lack quantitative frameworks for estimating the overall importance of single predictors in multi-response regression models. In this Application, Lai et al. demonstrate the mathematical links between commonality analysis, variation and hierarchical partitioning, generalise these frameworks to allow the analysis of any number of predictor variables or groups of predictor variables as in the case of variation partitioning and introduce and demonstrate the implementation of these generalised frameworks in the R package rdacca.hp.
pliman Manual measurements of quantitative traits are time consuming and error prone. Therefore, high-throughput phenotyping methods that allow a rapid and accurate assessment are vital to a growing range of researchers such as agronomists, breeders, phytopathologists, geneticists, ecologists and biologists. Here, Tiago Olivoto describes the pliman R package, a collection of functions designed (but not limited) to conduct plant image analysis.
The Artificial Hollow on the Cover
This issue’s cover shows a small model of a 3D-printed artificial hollow. There is an urgent need to provide artificial hollows in response to the decline in numbers of large old trees around the world. The hollows that develop in these trees provide critical shelter and nesting sites for many birds, mammals, and reptiles, but can take centuries to form. Existing artificial hollows, such as nest boxes, offer an important conservation tool. However, they often have shortcomings. For some species, such as the powerful owl (Ninox strenua), they have not been successful. In response, Parker et al. explored how computer-aided design and manufacturing can improve the design of artificial hollows. An interdisciplinary team of architectural designers and urban ecologists, they developed a reusable process that utilises new technologies, materials, and approaches. The pictured model is one of several prototypes for testing design ideas with stakeholders. The design considers the needs of the target species, the humans installing the hollows, and other members of local ecosystems, including animals and plants. This research shows how collaborative teams and innovative design technologies can create new opportunities for restoring animal habitats in urban areas. Photo credit: ©Dan Parker.