JPE Best Paper (2023)

JPE Best Paper award is given annually to the first author(s) of two to five papers selected by the editors based on the novelty and contributions to the field of plant ecology. A “JPE Best Paper” certificate will be awarded to the authors of JPE best papers. This year, we selected the best papers from all the articles published in JPE in the year of 2023. We are delighted to announce the five winners of a “JPE Best Paper” award and highlight the significance of these papers below.

Extension of the glmm.hp package to zero-inflated generalized linear mixed models and multiple regression (Lai et al. 2023)

This method article makes significant contributions by extending the functionality of the R package glmm.hp, which is designed to evaluate the relative importance of collinear predictors in regression models. The updated version overcomes two major limitations of the original release: it now supports zero-inflated generalized linear mixed models (ZIGLMMs) fitted via the glmmTMB package, and it introduces both commonality analysis and hierarchical partitioning for multiple linear regression models, with support for both unadjusted and adjusted R². By integrating with the flexible modeling framework of glmmTMB, the package enables the decomposition of marginal R² for models with excess zeros—an issue common in ecological count data. Additionally, the inclusion of adjusted R² in decomposition procedures for multiple regression addresses a critical gap left by existing R packages, allowing for more accurate inference and the identification of suppressor variables when negative variance components arise. These methodological advancements enhance the interpretability and applicability of regression models in ecological and environmental research, where multicollinearity and data complexity are frequent challenges. With its new capabilities, glmm.hp emerges as a comprehensive and user-friendly tool for variance partitioning across a broader range of statistical models.

Effects of tree mycorrhizal type on soil respiration and carbon stock via fine root biomass and litter dynamic in tropical plantations (Zhang et al. 2023)

Tree mycorrhizal associations are known to largely influence forest soil carbon (C) stocks. With an approximate three-decade common garden experiment, this study found in tropical tree plantations, arbuscular mycorrhizal monocultures supported significantly higher soil respiration (Rs) and soil C stock, litter turnover rate and fine root biomass than ectomycorrhizal ones，and showed mycorrhizal type directly and indirectly affected Rs and soil C stocks via fine root biomass and litter dynamic. This finding supports the linkages among the mycorrhizal association, plant traits and soil C process, and may also provide insights to future Earth system models’ development and tropical plantation managements.

Plant diversity and ecological intensification in crop production systems (Brooker et al. 2023)

In this paper we consider how Ecological Intensification (EI) – the enhancement of ecosystem services to complement or substitute for anthropogenic inputs – can be supported through increases in plant diversity in cropping systems. EI is dependent on ecological processes, and in turn these can be strongly influenced by biodiversity, including plant diversity. Levels of plant diversity considered include diversity of functional traits, diversity of crops within a field, diversity of non-crop plants in the immediate vicinity of crops, and landscape-scale vegetation mosaics. We discuss how, at all these scales, enhancing diversity can in turn increase delivery of ecosystem services to enable Ecological Intensification, but also outline key knowledge gaps in this quest to link fundamental knowledge with sustainable food production.

Multitrophic biodiversity enhances ecosystem functions, services and ecological intensification in agriculture (Buzhdygan and Petermann 2023)

This systematic review article makes a significant contribution by synthesizing current knowledge on how multitrophic biodiversity supports ecosystem functions and services critical for ecological intensification in agriculture and forestry. The authors examine how factors such as land use, habitat complexity, and management shape biodiversity–ecosystem functioning relationships across trophic levels. Their analysis reveals that positive biodiversity effects are widespread but context-dependent, and highlights key knowledge gaps in underrepresented regions, organism groups, and functional traits. This work provides a roadmap for advancing sustainable resource production through biodiversity-based solutions.

Prediction of potential suitable areas for Broussonetia papyrifera in China using the MaxEnt model and CIMP6 data (Wang and Guan 2023)

Broussonetia papyrifera, a vital native tree in China valued for its adaptability and economic importance, may face distribution shifts under future climate conditions. Using the MaxEnt model and CMIP6 data (2041–2060), its potential distribution was analyzed. Key limiting factors include coldest quarter temperature (11.54–27.11 °C) and driest/wettest quarter precipitation (51.48–818.40 mm; 665.51–2302.60 mm). Currently, highly suitable areas are in Guangdong, Guangxi, Taiwan, and Hainan. Future projections suggest expansion into northern and western China, with highly suitable areas reaching 111.42 × 10⁴ km² (SSP5-8.5) and 87.50 × 10⁴ km² (SSP1-2.6). Excluding farmland, potential planting areas are 212.66 × 10⁴ km² (SSP1-2.6) and 229.32 × 10⁴ km² (SSP5-8.5), providing key insights for ecological restoration planning.

Brief introduction of the first authors:

Jiangshan Lai

Dr. Lai is a full professor at Nanjing Forestry University, specializing in quantitative ecology and biostatistics. He proposed the innovative "average shared variance" theory to address the issue of determining the relative importance of explanatory variables in ecological canonical analysis. Dr. Lai has developed several R packages, including rdacca.hp, glmm.hp, gam.hp, and phylolm.hp, which calculate the contributions of individual explanatory variables in canonical ordination analysis (RDA, CCA, and dbRDA), generalized mixed effects models (GLMM), and generalized additive models (GAMs), phylogenetic generalized linear models (PGLM),respectively. These packages have been widely adopted in ecological and environmental science data analysis.

Guodong Zhang

Dr. Zhang is now a full-time father, who has a broad interest in nature. During his doctoral studies, he investigated the influences of tree plant traits on soil respiration. Upon graduation, he joined an environmental protection company in ShenZhen, leading local biodiversity survey and conservation operations. He obtained his PhD degree in Ecology from Fudan University in 2019.

Rob W. Brooker

Rob is Executive Director of Science at The James Hutton Institute, and was previously Head of Ecological Sciences. He is a plant ecologist with over 25 years’ experience of ecological research. He has extensive experience of ecology in a wide range of environments, including arctic, montane, alpine, semi-arid and arable ecosystems. A central focus of Rob’s research has been facilitative (i.e. beneficial) plant-plant interactions, and his work has contributed to current understanding that beneficial plant-plant interactions occur, and are often important, in virtually all ecosystems containing vascular plants. Underlying all Rob’s work is a strong focus on fundamental ecological theory. Building on initial studies in arctic and alpine environments, he has continued to pursue these fundamental questions through studies based in Scottish ecosystems including Scottish agricultural landscapes. This work has given him a broad understanding of the problems of food security, the challenges of biodiversity conservation in Scotland, and the potential to further link fundamental ecological knowledge to issues of sustainable food production and biodiversity conservation.

Oksana Y Buzhdygan

Dr. Oksana Buzhdygan is a senior scientist in Theoretical Ecology at Freie Universität Berlin. Her research focuses on how environmental change, land use, and habitat complexity shape biodiversity and ecosystem functioning across spatial and temporal scales, with a particular emphasis on multitrophic interactions. She integrates empirical fieldwork, ecological synthesis, and statistical modeling to support sustainable land management and biodiversity conservation.

Meiquan Wang

Dr. Wang is currently working at the Sichuan Academy of Forestry, specializing in research areas including karst forest ecosystem structure, rocky desertification control, and functional trait plasticity under environmental stress. She obtained her Ph.D. in Ecology from the College of Ecology and Environment at Nanjing Forestry University in 2021.