OBJECTIVES: To analyze the positional distances covered above generic and individualized speed thresholds within the most demanding phases of match play. Categorized by position, 17 English Premier League players' match data were analyzed over 2 consecutive seasons (2019-20 and 2020-21). The most demanding phases of play were determined using a rolling average across 4 periods of 1, 3, 5, and 10 minutes. Distance covered in the time above the standard speed of 5.5 m/s was analyzed, with individualized metrics based on the maximal aerobic speed (MAS) test data. RESULTS: Central defenders displayed lower values for high-intensity periods when compared with fullbacks, midfielders, and wide midfielders for both generic and individualized metrics. MAS during 1-minute periods was significantly higher for forwards when compared with central defenders (82.9 [18.9] vs 67.5 [14.8] for maximum high-speed running [HSR] and 96.0 [15.9] vs 75.7 [13.8] HSR for maximum MAS activity). The maximum effect size differences between the central midfielders, wide midfielders, and fullbacks for HSR and MAS measures under the maximum HSR criterion was 0.28 and 0.18 for the 1-minute period, 0.36 and 0.19 for the 3-minute period, 0.46 and 0.31 for the 5-minute period, and 0.49 and 0.315 for the 10-minute period. CONCLUSIONS: Individualized speed metrics may provide a more precise and comparable measure than generic values. Data appear to be consistent across playing positions except for central defenders. This information may allow practitioners to directly compare individualized physical outputs of non-central defenders during the most demanding phases of play regardless of the players' positional group. This may provide coaches with important information regarding session design, training load, and fatigue monitoring.
Athletic Performance , Football , Running , Humans , Fatigue , Geographic Information Systems
In grassland ecosystems, it is well known that increasing plant species diversity can improve ecosystem functions (i.e., ecosystem responses), for example, by increasing productivity and reducing weed invasion. Diversity-Interactions models use species proportions and their interactions as predictors in a regression framework to assess biodiversity and ecosystem function relationships. However, it can be difficult to model numerous interactions if there are many species, and interactions may be temporally variable or dependent on spatial planting patterns. We developed a new Diversity-Interactions mixed model for jointly assessing many species interactions and within-plot species planting pattern over multiple years. We model pairwise interactions using a small number of fixed parameters that incorporate spatial effects and supplement this by including all pairwise interaction variables as random effects, each constrained to have the same variance within each year. The random effects are indexed by pairs of species within plots rather than a plot-level factor as is typical in mixed models, and capture remaining variation due to pairwise species interactions parsimoniously. We apply our novel methodology to three years of weed invasion data from a 16-species grassland experiment that manipulated plant species diversity and spatial planting pattern and test its statistical properties in a simulation study.Supplementary materials accompanying this paper appear online. Supplementary materials for this article are available at 10.1007/s13253-022-00505-2.
Biodiversity and Ecosystem Function analyses aim to explain how individual species and their interactions affect ecosystem function. With this study, we asked in what ways do species interact, are these interactions affected by species planting pattern, and are initial (planted) proportions or previous year (realized) proportions a better reference point for characterizing grassland diversity effects?We addressed these questions with experimental communities compiled from a pool of 16 tallgrass prairie species. We planted all species in monocultures and mixtures that varied in their species richness, evenness, and spatial pattern. We recorded species-specific biomass production over three growing seasons and fitted Diversity-Interactions (DI) models to annual plot biomass yields.In the establishment season, all species interacted equally to form the diversity effect. In years 2 and 3, each species contributed a unique additive coefficient to its interaction with every other species to form the diversity effect. These interactions were affected by Helianthus maximiliani and the species planting pattern. Models based on species planted proportions better-fit annual plot yield than models based on species previous contributions to plot biomass.Outcomes suggest that efforts to plant tallgrass prairies to maximize diversity effects should focus on the specific species present and in what arrangement they are planted. Furthermore, for particularly diverse grasslands, the effort of collecting annual species biomass data may not be necessary when quantifying diversity effects with DI models.
Understanding the biodiversity and ecosystem function relationship can be challenging in species-rich ecosystems. Traditionally, species richness has been relied on heavily to explain changes in ecosystem function across diversity gradients. Diversity-Interactions models can test how ecosystem function is affected by species identity, species interactions, and evenness, in addition to richness. However, in a species-rich system, there may be too many species interactions to allow estimation of each coefficient, and if all interaction coefficients are estimable, they may be devoid of any sensible biological meaning. Parsimonious descriptions using constraints among interaction coefficients have been developed but important variability may still remain unexplained. Here, we extend Diversity-Interactions models to describe the effects of diversity on ecosystem function using a combination of fixed coefficients and random effects. Our approach provides improved standard errors for testing fixed coefficients and incorporates lack-of-fit tests for diversity effects. We illustrate our methods using data from a grassland and a microbial experiment. Our framework considerably reduces the complexities associated with understanding how species interactions contribute to ecosystem function in species-rich ecosystems.
Biodiversity , Ecosystem
