Regularized Regression: A New Tool for Investigating and Predicting Tree Growth

Neighborhood models have allowed us to test many hypotheses regarding the drivers of variation in tree growth, but require considerable computation due to the many empirically supported non-linear relationships they include. Regularized regression represents a far more efficient neighborhood modeling method, but it is unclear whether such an ecologically unrealistic model can provide accurate insights on tree growth. Rapid computation is becoming increasingly important as ecological datasets grow in size, and may be essential when using neighborhood models to predict tree growth beyond sample plots or into the future. We built a novel regularized regression model of tree growth and investigated whether it reached the same conclusions as a commonly used neighborhood model, regarding hypotheses of how tree growth is influenced by the species identity of neighboring trees. We also evaluated the ability of both models to interpolate the growth of trees not included in the model fitting dataset. Our regularized regression model replicated most of the classical model’s inferences in a fraction of the time without using high-performance computing resources. We found that both methods could interpolate out-of-sample tree growth, but the method making the most accurate predictions varied among focal species. Regularized regression is particularly efficient for comparing hypotheses because it automates the process of model selection and can handle correlated explanatory variables. This feature means that regularized regression could also be used to select among potential explanatory variables (e.g., climate variables) and thereby streamline the development of a classical neighborhood model. Both regularized regression and classical methods can interpolate out-of-sample tree growth, but future research must determine whether predictions can be extrapolated to trees experiencing novel conditions. Overall, we conclude that regularized regression methods can complement classical methods in the investigation of tree growth drivers and represent a valuable tool for advancing this field toward prediction.

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 i) specify neighborhood competition models, ii) evaluate the effect of competitor species identity using permutation tests, and iii) measure model performance using spatial cross-validation. Following Allen (2020), 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 i) tidyverse-like structure whereby verb-named functions can be modularly “piped” in sequence, ii) functions with standardized inputs/outputs of simple features ‘sf‘ package class, and iii) 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.

Overstory Longleaf Pines and Hardwoods Create Diverse Patterns of Energy Release and Fire Effects During Prescribed Fire

Litter from pine trees in open woodlands is an important fuel for surface fires, but litter from hardwood species may quell fire behavior. Lower intensity fires favor hardwood over longleaf pine regeneration, and while overstory hardwoods are important sources of food and shelter for many wildlife species, too many could result in canopy closure and a loss of ground layer diversity. Although some researchers have found synergies in fire effects when leaves of different species are combined, field tests of effects of tree guild diversity on fire behavior are lacking from the literature. We used neighborhood modeling to understand how diverse overstory trees in longleaf pine forests affect fire radiative energy density (FRED), and to determine the effect on top-kill of shrub-form hardwood trees. We measured the effects of three guilds of overstory trees (longleaf pine, upland oaks, and mesic oaks) on FRED, and related FRED to post-fire damage in four guilds of understory hardwoods (sandhill oaks, upland oaks, mesic oaks, and fleshy-fruited hardwoods). We found that FRED increased 33–56% near overstory longleaf pine but decreased 23–37% near overstory mesic oaks. Additive models of FRED performed well and no synergies or antagonisms were present. Seventy percent of stems of understory hardwoods survived fire with energy release typical of dormant-season fires in canopy gaps and near overstory mesic oaks. We also found that among understory trees >2 m tall, upland and sandhill oaks were more likely than mesic oaks or fleshy-fruited hardwoods to avoid top-kill. We conclude that neighborhood models provide a method to predict longleaf pine forest structure and composition that allows for the ecological benefits of overstory hardwoods while maintaining ground-layer diversity. To maintain hardwood control, fire practitioners may need to select fire weather conditions to increase fire behavior especially during dormant-season burns.

Classical term-modal logics

We introduce classical term-modal logics and argue that they are useful for modelling agent-relative notions of obligation, evidence and abilities, and their interaction with properties of and relations between the agents in question. We spell out the semantics of these logics in terms of neighborhood models, provide sound and strongly complete axiomatizations and establish the decidability of specific (agent-finite) variants.

Mapping lesbian and queer lines of desire: Constellations of queer urban space

The path to lesbian, gay, bisexual, transgender, and queer (LGBTQ) liberation has been narrated through a claim to long-term, propertied territory in the form of urban neighborhoods and bars. However, lesbians and queers fail to retain these spaces over generations, often due to their lesser political and economic power. What then is the lesbian–queer production of urban space in their own words? Drawing on interviews with and archival research about lesbians and queers who lived in New York City from 1983 to 2008, my participants queered the fixed, property-driven neighborhood models of LGBTQ space in producing what I call constellations. Like stars in the sky, contemporary urban lesbians and queers often create and rely on fragmented and fleeting experiences in lesbian–queer places, evoking patterns based on generational, racialized, and classed identities. They are connected by overlapping, embodied paths and stories that bind them over generations and across many identities, like drawing lines between the stars in the sky. This queer feminist contribution to critical urban theory adds to the models of queering and producing urban space–time.

Recommender System Based on Temporal Models: A Systematic Review

Over the years, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users’ needs through mapping the available products to users based on their observed interests towards items. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended, a problem known as concept drift or sometimes called temporal dynamics in RS. Although the traditional techniques of RS have attained significant success in providing recommendations, they are insufficient in providing accurate recommendations due to concept drift problems. These issues have triggered a lot of researches on the development of dynamic recommender systems (DRSs) which is focused on the design of temporal models that will account for concept drifts and ensure more accurate recommendations. However, in spite of the several research efforts on the DRSs, only a few secondary studies were carried out in this field. Therefore, this study aims to provide a systematic literature review (SLR) of the DRSs models that can guide researchers and practitioners to better understand the issues and challenges in the field. To achieve the aim of this study, 87 papers were selected for the review out of 875 total papers retrieved between 2010 and 2019, after carefully applying the inclusion/exclusion and the quality assessment criteria. The results of the study show that concept drift is mostly applied in the multimedia domain, then followed by the e-commerce domain. Also, the results showed that time-dependent neighborhood models are the popularly used temporal models for DRS followed by the Time-dependent Matrix Factorization (TMF) and time-aware factors models, specifically Tensor models, respectively. In terms of evaluation strategy, offline metrics such as precision and recalls are the most commonly used approaches to evaluate the performance of DRS.