DOI: 10.5061/dryad.sj3tx96hn
title: Doubled density and increased resilience: Consequences of seven consecutive annual dry-season fires to the unburned Cerrado grass layer

description: Tropical grasslands and savannas are dominated by C4 grasses, which are
 crucial for the biodiversity, structure, and functioning of these
 ecosystems. Yet, grasses have been neglected in vegetation science,
 leading to a superficial understanding of their responses to fire and of
 the mechanisms/traits associated with it. This hinders the effective
 management and conservation of the tropical open ecosystems, especially
 because recent anthropogenic fire regimes have raised concerns about the
 long-term effects of repeated dry-season burns. Therefore, we investigated
 how prolonged frequent fires affect the Cerrado grass layer by exploring
 community properties, functional guilds, population responses, and
 persistence mechanisms. We sampled all grass individuals within 120
 permanent 1-m² plots before the first fire in 2015 (single fire recorded
 in the last 30 years) and again after seven annual mid-dry-season fires in
 2022. We also collected data on traits relevant to species’ regeneration
 and persistence. The data were analyzed using an effect size approach to
 assess changes in community properties and population sizes between
 sampling occasions. We also compared shifts in the proportions of
 functional guilds and partitioned the composition of populations to
 demonstrate persistence mechanisms. After seven annual burns, the grass
 layer maintained its composition, increased its homogeneity, increased its
 total richness (from 26 to 30 species), and doubled its density (from 8 to
 17 individuals/m²), but did not change its richness/m² (4 species/m²).
 Functional composition was affected by fires, increasing the prevalence of
 traits related to fire tolerance and vegetative regeneration. Species
 responded differently to burns, with 25% benefited, 25% disadvantaged, and
 50% unaffected. Resprouting, recruitment, and clonality contributed
 differently to species persistence, with clonal reproduction driving
 population growth. Synthesis and applications: the
 Cerrado grass layer benefits from frequent fires and is resilient even to
 an extreme fire regime, such as seven annual burns. Recurrent
 mid-dry-season fires are not harmful to the grass layer and should be less
 concerning than prolonged fire-suppression regimes, which diminish
 grasses’ biomass, cover, diversity, and functioning. Maintaining frequent
 fires or reintroducing them after prolonged fire exclusion (as in our
 study) are suitable management actions for the conservation of the grass
 layer.
description: # Doubled density and increased resilience: Consequences of seven
 consecutive annual dry-season fires to the unburned Cerrado grass layer
 Dataset DOI: [10.5061/dryad.sj3tx96hn](10.5061/dryad.sj3tx96hn) ##
 Description of the data and file structure To explore the effects of
 frequent fires in the grass layer, we selected three burned permanent
 grassland plots, each plot measuring 50 m x 20 m (0.1 ha) with 40 nested 1
 m² subplots arranged in a grid. During the experiment, plots were burned
 every year from 2015 to 2021 (seven annual burns) during the mid-dry
 season (July or August). We counted and identified all grass individuals
 within each subplot (totaling 120 1 m² sampling units) in two surveys:
 before the first fire in 2015 and after the seven prescribed fires in
 2022. ### Files and variables #### File: Grasses_Dataset.csv
 **Description:** Species' abundance data from each of the 120 1m²
 quadrats.  ##### Variables: | Variable | Description | | :-------- |
 :--------------------------------------------------------------------- | |
 Site | Type of site/plot (in relation to all long-term experimental plots)
 | | Block | Block identifier (in relation to all long-term experimental
 plots) | | Treatment | Treatment identifier (in relation to all long-term
 experimental plots) | | Plot | Plot ID (each 20 m x 50 m) | | Subplot |
 Subplot ID (each 10 m x 10 m) | | Sample | Sample ID (each 1m²) | |
 Species | Species accepted name | | Ind_2015 | Number of individuals in
 the first sampling | | Ind_2019 | Number of individuals in the 2019
 sampling | | Ind_2022 | Number of individuals in the last sampling |