The forestecology R package for fitting and assessing neighborhood models of the effect of interspecific competition on the growth of trees

1. Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking.
2. We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross‐validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model.
3. We demonstrate the package''s functionality using data from the Smithsonian Conservation Biology Institute''s large forest dynamics plot, part of the ForestGEO global network of research sites. Given ForestGEO’s data collection protocols and data formatting standards, the package was designed with cross‐site compatibility in mind. We highlight the importance of spatial cross‐validation when interpreting model results.
4. The package features (a) tidyverse‐like structure whereby verb‐named functions can be modularly “piped” in sequence, (b) functions with standardized inputs/outputs of simple features sf package class, and (c) an S3 object‐oriented implementation of the Bayesian linear regression model. These three facts allow for clear articulation of all the steps in the sequence of analysis and easy wrangling and visualization of the geospatial data. Furthermore, while the package only has Bayesian linear regression implemented, the package was designed with extensibility to other methods in mind.

     

fullfact: an R package for the analysis of genetic and maternal variance components from full factorial mating designs

Full factorial breeding designs are useful for quantifying the amount of additive genetic, nonadditive genetic, and maternal variance that explain phenotypic traits. Such variance estimates are important for examining evolutionary potential. Traditionally, full factorial mating designs have been analyzed using a two‐way analysis of variance, which may produce negative variance values and is not suited for unbalanced designs. Mixed‐effects models do not produce negative variance values and are suited for unbalanced designs. However, extracting the variance components, calculating significance values, and estimating confidence intervals and/or power values for the components are not straightforward using traditional analytic methods. We introduce fullfact – an R package that addresses these issues and facilitates the analysis of full factorial mating designs with mixed‐effects models. Here, we summarize the functions of the fullfact package. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap‐t confidence intervals, which can then be plotted using a simple function. We explore the fullfact package through a worked example. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses.     

Modeling herbivore functional responses causing boom‐bust dynamics following predator removal

Native biodiversity is threatened by invasive species in many terrestrial and marine systems, and conservation managers have demonstrated successes by responding with eradication or control programs. Although invasive species are often the direct cause of threat to native species, ecosystems can react in unexpected ways to their removal or reduction. Here, we use theoretical models to predict boom‐bust dynamics, where the removal of predatory or competitive pressure from a native herbivore results in oscillatory population dynamics (boom‐bust), which can endanger the native species’ population in the short term. We simulate control activities, applied to multiple theoretical three‐species Lotka‐Volterra ecosystem models consisting of vegetation, a native herbivore, and an invasive predator. Based on these communities, we then develop a predictive tool that—based on relative parameter values—predicts whether control efforts directed at the invasive predator will lead to herbivore release followed by a crash. Further, by investigating the different functional responses, we show that model structure, as well as model parameters, are important determinants of conservation outcomes. Finally, control strategies that can mitigate these negative consequences are identified. Managers working in similar data‐poor ecosystems can use the predictive tool to assess the probability that their system will exhibit boom‐bust dynamics, without knowing exact community parameter values.     

Bridging implementation gaps to connect large ecological datasets and complex models

Merging robust statistical methods with complex simulation models is a frontier for improving ecological inference and forecasting. However, bringing these tools together is not always straightforward. Matching data with model output, determining starting conditions, and addressing high dimensionality are some of the complexities that arise when attempting to incorporate ecological field data with mechanistic models directly using sophisticated statistical methods. To illustrate these complexities and pragmatic paths forward, we present an analysis using tree‐ring basal area reconstructions in Denali National Park (DNPP) to constrain successional trajectories of two spruce species (Picea mariana and Picea glauca) simulated by a forest gap model, University of Virginia Forest Model Enhanced—UVAFME. Through this process, we provide preliminary ecological inference about the long‐term competitive dynamics between slow‐growing P. mariana and relatively faster‐growing P. glauca. Incorporating tree‐ring data into UVAFME allowed us to estimate a bias correction for stand age with improved parameter estimates. We found that higher parameter values for P. mariana minimum growth under stress and P. glauca maximum growth rate were key to improving simulations of coexistence, agreeing with recent research that faster‐growing P. glauca may outcompete P. mariana under climate change scenarios. The implementation challenges we highlight are a crucial part of the conversation for how to bring models together with data to improve ecological inference and forecasting.     

MINT: software to identify motifs and short-range interactions in trajectories of nucleic acids

Structural biology experiments and structure prediction tools have provided many high-resolution three-dimensional structures of nucleic acids. Also, molecular dynamics force field parameters have been adapted to simulating charged and flexible nucleic acid structures on microsecond time scales. Therefore, we can generate the dynamics of DNA or RNA molecules, but we still lack adequate tools for the analysis of the resulting huge amounts of data. We present MINT (Motif Identifier for Nucleic acids Trajectory) — an automatic tool for analyzing three-dimensional structures of RNA and DNA, and their full-atom molecular dynamics trajectories or other conformation sets (e.g. X-ray or nuclear magnetic resonance-derived structures). For each RNA or DNA conformation MINT determines the hydrogen bonding network resolving the base pairing patterns, identifies secondary structure motifs (helices, junctions, loops, etc.) and pseudoknots. MINT also estimates the energy of stacking and phosphate anion-base interactions. For many conformations, as in a molecular dynamics trajectory, MINT provides averages of the above structural and energetic features and their evolution. We show MINT functionality based on all-atom explicit solvent molecular dynamics trajectory of the 30S ribosomal subunit.     

“Jack‐of‐all‐trades” is parthenogenetic

Sex is evolutionarily more costly than parthenogenesis, evolutionary ecologists therefore wonder why sex is much more frequent than parthenogenesis in the majority of animal lineages. Intriguingly, parthenogenetic individuals and species are as common as or even more common than sexuals in some major and putative ancient animal lineages such as oribatid mites and rotifers. Here, we analyzed oribatid mites (Acari: Oribatida) as a model group because these mites are ancient (early Paleozoic), widely distributed around the globe, and include a high number of parthenogenetic species, which often co‐exist with sexual oribatid mite species. There is evidence that the reproductive mode is phylogenetically conserved in oribatid mites, which makes them an ideal model to test hypotheses on the relationship between reproductive mode and species'' ecological strategies. We used oribatid mites to test the frozen niche variation hypothesis; we hypothesized that parthenogenetic oribatid mites occupy narrow specialized ecological niches. We used the geographic range of species as a proxy for specialization as specialized species typically do have narrower geographic ranges than generalistic species. After correcting for phylogenetic signal in reproductive mode and demonstrating that geographic range size has no phylogenetic signal, we found that parthenogenetic lineages have a higher probability to have broader geographic ranges than sexual species arguing against the frozen niche variation hypothesis. Rather, the results suggest that parthenogenetic oribatid mite species are more generalistic than sexual species supporting the general‐purpose genotype hypothesis. The reason why parthenogenetic oribatid mite species are generalists with wide geographic range sizes might be that they are of ancient origin reflecting that they adapted to varying environmental conditions during evolutionary history. Overall, our findings indicate that parthenogenetic oribatid mite species possess a widely adapted general‐purpose genotype and therefore might be viewed as “Jack‐of‐all‐trades.”     

Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial

Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.     

Plasticity is a locally adapted trait with consequences for ecological dynamics in novel environments

Phenotypic plasticity is predicted to evolve in more variable environments, conferring an advantage on individual lifetime fitness. It is less clear what the potential consequences of that plasticity will have on ecological population dynamics. Here, we use an invertebrate model system to examine the effects of environmental variation (resource availability) on the evolution of phenotypic plasticity in two life history traits—age and size at maturation—in long‐running, experimental density‐dependent environments. Specifically, we then explore the feedback from evolution of life history plasticity to subsequent ecological dynamics in novel conditions. Plasticity in both traits initially declined in all microcosm environments, but then evolved increased plasticity for age‐at‐maturation, significantly so in more environmentally variable environments. We also demonstrate how plasticity affects ecological dynamics by creating founder populations of different plastic phenotypes into new microcosms that had either familiar or novel environments. Populations originating from periodically variable environments that had evolved greatest plasticity had lowest variability in population size when introduced to novel environments than those from constant or random environments. This suggests that while plasticity may be costly it can confer benefits by reducing the likelihood that offspring will experience low survival through competitive bottlenecks in variable environments. In this study, we demonstrate how plasticity evolves in response to environmental variation and can alter population dynamics—demonstrating an eco‐evolutionary feedback loop in a complex animal moderated by plasticity in growth.     

“Third‐wave” cognitive and behavioral therapies and the emergence of a process‐based approach to intervention in psychiatry

For decades, cognitive and behavioral therapies (CBTs) have been tested in randomized controlled trials for specific psychiatric syndromes that were assumed to represent expressions of latent diseases. Although these protocols were more effective as compared to psychological control conditions, placebo treatments, and even active pharmacotherapies, further advancement in efficacy and dissemination has been inhibited by a failure to focus on processes of change. This picture appears now to be evolving, due both to a collapse of the idea that mental disorders can be classified into distinct, discrete categories, and to the more central attention given to processes of change in newer, so‐called “third‐wave” CBTs. Here we review the context for this historic progress and evaluate the impact of these newer methods and models, not as protocols for treating syndromes, but as ways of targeting an expanded range of processes of change. Five key features of “third‐wave” therapies are underlined: a focus on context and function; the view that new models and methods should build on other strands of CBT; a focus on broad and flexible repertoires vs. an approach to signs and symptoms; applying processes to the clinician, not just the client; and expanding into more complex issues historically more characteristic of humanistic, existential, analytic, or system‐oriented approaches. We argue that these newer methods can be considered in the context of an idiographic approach to process‐based functional analysis. Psychological processes of change can be organized into six dimensions: cognition, affect, attention, self, motivation and overt behavior. Several important processes of change combine two or more of these dimensions. Tailoring intervention strategies to target the appropriate processes in a given individual would be a major advance in psychiatry and an important step toward precision mental health care.     

“Micropersonality” traits and their implications for behavioral and movement ecology research

1. Many animal personality traits have implicit movement‐based definitions and can directly or indirectly influence ecological and evolutionary processes. It has therefore been proposed that animal movement studies could benefit from acknowledging and studying consistent interindividual differences (personality), and, conversely, animal personality studies could adopt a more quantitative representation of movement patterns.
2. Using high‐resolution tracking data of three‐spined stickleback fish (Gasterosteus aculeatus), we examined the repeatability of four movement parameters commonly used in the analysis of discrete time series movement data (time stationary, step length, turning angle, burst frequency) and four behavioral parameters commonly used in animal personality studies (distance travelled, space use, time in free water, and time near objects).
3. Fish showed repeatable interindividual differences in both movement and behavioral parameters when observed in a simple environment with two, three, or five shelters present. Moreover, individuals that spent less time stationary, took more direct paths, and less commonly burst travelled (movement parameters), were found to travel farther, explored more of the tank, and spent more time in open water (behavioral parameters).
4. Our case study indicates that the two approaches—quantifying movement and behavioral parameters—are broadly equivalent, and we suggest that movement parameters can be viewed as “micropersonality” traits that give rise to broad‐scale consistent interindividual differences in behavior. This finding has implications for both personality and movement ecology research areas. For example, the study of movement parameters may provide a robust way to analyze individual personalities in species that are difficult or impossible to study using standardized behavioral assays.

     

pyResearchInsights—An open‐source Python package for scientific text analysis

1. With an increasing number of scientific articles published each year, there is a need to synthesize and obtain insights across ever‐growing volumes of literature. Here, we present pyResearchInsights, a novel open‐source automated content analysis package that can be used to analyze scientific abstracts within a natural language processing framework.
2. The package collects abstracts from scientific repositories, identifies topics of research discussed in these abstracts, and presents interactive concept maps to visualize these research topics. To showcase the utilities of this package, we present two examples, specific to the field of ecology and conservation biology.
3. First, we demonstrate the end‐to‐end functionality of the package by presenting topics of research discussed in 1,131 abstracts pertaining to birds of the Tropical Andes. Our results suggest that a large proportion of avian research in this biodiversity hotspot pertains to species distributions, climate change, and plant ecology.
4. Second, we retrieved and analyzed 22,561 abstracts across eight journals in the field of conservation biology to identify twelve global topics of conservation research. Our analysis shows that conservation policy and landscape ecology are focal topics of research. We further examined how these conservation‐associated research topics varied across five biodiversity hotspots.
5. Lastly, we compared the utilities of this package with existing tools that carry out automated content analysis, and we show that our open‐source package has wider functionality and provides end‐to‐end utilities that seldom exist across other tools.

     

Bighorn sheep show similar in‐host responses to the same pathogen strain in two contrasting environments

Ecological context—the biotic and abiotic environment, along with its influence on population mixing dynamics and individual susceptibility—is thought to have major bearing on epidemic outcomes. However, direct comparisons of wildlife disease events in contrasting ecological contexts are often confounded by concurrent differences in host genetics, exposure histories, or pathogen strains. Here, we compare disease dynamics of a Mycoplasma ovipneumoniae spillover event that affected bighorn sheep populations in two contrasting ecological contexts. One event occurred on the herd''s home range near the Rio Grande Gorge in New Mexico, while the other occurred in a captive facility at Hardware Ranch in Utah. While data collection regimens varied, general patterns of antibody signal strength and symptom emergence were conserved between the two sites. Symptoms appeared in the captive setting an average of 12.9 days postexposure, average time to seroconversion was 24.9 days, and clinical signs peaked at approximately 36 days postinfection. These patterns were consistent with serological testing and subsequent declines in symptom intensity in the free‐ranging herd. At the captive site, older animals exhibited more severe declines in body condition and loin thickness, higher symptom burdens, and slower antibody response to the pathogen than younger animals. Younger animals were more likely than older animals to clear infection by the time of sampling at both sites. The patterns presented here suggest that environment may not be a major determinant of epidemiological outcomes in the bighorn sheep—M. ovipneumoniae system, elevating the possibility that host‐ or pathogen‐factors may be responsible for observed variation.     

The data‐index: An author‐level metric that values impactful data and incentivizes data sharing

Author‐level metrics are a widely used measure of scientific success. The h‐index and its variants measure publication output (number of publications) and research impact (number of citations). They are often used to influence decisions, such as allocating funding or jobs. Here, we argue that the emphasis on publication output and impact hinders scientific progress in the fields of ecology and evolution because it disincentivizes two fundamental practices: generating impactful (and therefore often long‐term) datasets and sharing data. We describe a new author‐level metric, the data‐index, which values both dataset output (number of datasets) and impact (number of data‐index citations), so promotes generating and sharing data as a result. We discuss how it could be implemented and provide user guidelines. The data‐index is designed to complement other metrics of scientific success, as scientific contributions are diverse and our value system should reflect that both for the benefit of scientific progress and to create a value system that is more equitable, diverse, and inclusive. Future work should focus on promoting other scientific contributions, such as communicating science, informing policy, mentoring other scientists, and providing open‐access code and tools.     

An open spatial capture–recapture model for estimating density,movement, and population dynamics from line‐transect surveys

The purpose of many wildlife population studies is to estimate density, movement, or demographic parameters. Linking these parameters to covariates, such as habitat features, provides additional ecological insight and can be used to make predictions for management purposes. Line‐transect surveys, combined with distance sampling methods, are often used to estimate density at discrete points in time, whereas capture–recapture methods are used to estimate movement and other demographic parameters. Recently, open population spatial capture–recapture models have been developed, which simultaneously estimate density and demographic parameters, but have been made available only for data collected from a fixed array of detectors and have not incorporated the effects of habitat covariates. We developed a spatial capture–recapture model that can be applied to line‐transect survey data by modeling detection probability in a manner analogous to distance sampling. We extend this model to a) estimate demographic parameters using an open population framework and b) model variation in density and space use as a function of habitat covariates. The model is illustrated using simulated data and aerial line‐transect survey data for North Atlantic right whales in the southeastern United States, which also demonstrates the ability to integrate data from multiple survey platforms and accommodate differences between strata or demographic groups. When individuals detected from line‐transect surveys can be uniquely identified, our model can be used to simultaneously make inference on factors that influence spatial and temporal variation in density, movement, and population dynamics.     

“Cancer Reviews 2021” series: Dynamic EMT: a multi‐tool for tumor progression

The process of epithelial–mesenchymal transition (EMT) is fundamental for embryonic morphogenesis. Cells undergoing it lose epithelial characteristics and integrity, acquire mesenchymal features, and become motile. In cancer, this program is hijacked to confer essential changes in morphology and motility that fuel invasion. In addition, EMT is increasingly understood to orchestrate a large variety of complementary cancer features, such as tumor cell stemness, tumorigenicity, resistance to therapy and adaptation to changes in the microenvironment. In this review, we summarize recent findings related to these various classical and non‐classical functions, and introduce EMT as a true tumorigenic multi‐tool, involved in many aspects of cancer. We suggest that therapeutic targeting of the EMT process will—if acknowledging these complexities—be a possibility to concurrently interfere with tumor progression on many levels.