Data from: Recommendations for assessing earthworm populations in Brazilian ecosystems
Nadolny, Herlon et al. (2020), Data from: Recommendations for assessing earthworm populations in Brazilian ecosystems, Dryad, Dataset, https://doi.org/10.5061/dryad.4md0s64
Earthworms are often related to fertile soils and frequently used as environmental quality indicators. However, to optimize their use as bioindicators, their populations must be evaluated together with environmental and anthropogenic variables regulating earthworm communities. In this review we identify the earthworm, soil chemical, physical, environmental and management-related variables evaluated in 124 published studies that quantified earthworm abundance (>7300 samples) in 765 sites with different types of climate, soils, land use and management systems in Brazil. Most soil chemical and physical attributes (except pH) were less reported (<50% of studies) than other environmental variables such as sampling date, altitude, temperature, precipitation, climate and soil type and land use (all >50% of studies). Earthworms were rarely identified (24%) and few studies (31%) measured their biomass, although most provided adequate information on sampling protocol. Based on the importance in regulating earthworm populations, we propose a set of variables that should be evaluated when studying earthworm communities and other macrofauna groups. This should help guide future studies on earthworms in Brazil and other countries, optimize data collection and replicability, allow comparisons between different studies and promote the use of earthworms as soil quality bioindicators.
A dataset of earthworm abundance in Brazilian ecosystems was constructed using published literature on the topic. Published studies that evaluated earthworm populations in Brazilian ecosystems were searched in the literature from 1976 up to the year 2017. The literature review included searchable online databases such as Web of Science, Scielo, Lattes-CNPq Platform, Biblioteca Digital de Teses e Dissertações (BDTD- Brazilian digital library of theses and dissertations), Google Scholar and the Alice-Embrapa Repository. As we were aiming to review all studies available and determine which soil, environmental, earthworm and sampling-related factors were evaluated, we also included non-indexed journals, book chapters, and conference proceedings in soil science, zoology, ecology, agroecology and conservation agriculture. We also made personal contact with colleagues who work with earthworms and/or are members of the Brazilian CNPq research group “Biology, ecology and function of terrestrial Brazilian oligochaetes (earthworms and enchytraeids)”, in order to help us expand our data search.
The data on soil, environmental and earthworm sampling variables were extracted from each of the 124 publications and entered into an excel data file. Using the data for all sites, the number of publications with each environmental, earthworm and soil physical and chemical variables was quantified, as well as the corresponding number of points/sampling sites. The data contains information from 765 specific sites, representing over 7300 earthworm samples, from a wide range of soils, vegetation types and management systems in 135 counties in Brazil. Additional environmental and soils data were also obtained from these sites and complemented with information from various sources (e.g. online climate data, municipal GPS coordinates, Köppen climate database), or calculated (e.g. CEC or Sum of bases, pH, H+Al, C:N) and included, when possible. Climate information was corrected also when needed, following the data of Alvares et al. (2013). When not provided, geographic position data used was for the county seats, and when found to be incorrect, the county or state information was corrected. The data thus includes 13 climate and vegetation-related environmental variables (Sampling date, Sampling season, Locality, County, State, Geographical coordinates, Altitude, Mean annual precipitation, Mean annual temperature, Köppen climate, Biome, Soil cover/Vegetation type, Type of native vegetation), 8 management-related variables (Crop type, Soil management, Years in current land use, Previous land use, Pesticide use, Pesticides type, Fertilizers, Fertilizer type), 17 soil-related variables (Soil type, pH, H+Al, K, Ca , Mg, P , C, Sum of bases, CEC, Base saturation, N , C:N, Sand, Clay, Silt, Textural class), and 6 earthworm sampling-related variables (Size of holes dug, Number of holes, Depth, Density, Biomass, Species identification).
Native vegetation types were classified according to standard categoreies (IBGE, 2012), while forest plantations were divided into four main types: Eucalyptus or Pinus spp. trees, Araucaria angustifolia, and Others (contemplating all other tree species). Pasture grasses were separated into only two categories, Brachiaria spp. pastures (some of which are currently in the Urochloa genus), and Others, including all other types or species of pasture grasses. Soil types information was obtained from the publications or using the Brazilian national or state maps available online and from Embrapa databases (IBGE; Santos et al., 2018). Soil were classified according to the Brazilian (Santos et al., 2018) and the FAO (2015) soil classification systems. Soil tillage was classified into four categories, according to decreasing intensity: conventional tillage (CT), minimum tillage (MT), no tillage (NT) and permanent crop with no tillage (PC). Similarly, pesticide and fertilizer use information were searched for in each publication, including types (herbicides, fungicides or insecticides and fertilizer formulation) and active ingredients.
The soil chemical (pH, H+Al, K, Ca, Mg, P, C, sum of Bases, CEC, Base saturation, N and C:N ratio) and physical (sand, clay and silt proportions) attributes included were those generally provided in routine soil fertility analyses in Brazilian soil analysis laboratories (van Raij, 1987), except for C and N by combustion (which are not routine in most laboratories). Soil pH values were transformed to equivalent of pH in CaCl2 or KCl for all samples, using a conversion factor of 0.6 when measured in water (i.e., pH in water was considered to be 0.6 points higher than that estimated in CaCl2 or KCl). Values obtained in CaCl2 or KCl were maintained and considered of similar magnitude for these two methods. All values of H+Al, K, Ca, Mg, sum of Bases and CEC not in standard units (cmolc dm-3) were transformed. Phosphorus values were standardized to mg dm-3 and only studies using the Mehlich-extraction method were included. Carbon and Nitrogen values were in g dm-3 and based on analyses performed using combustion or Walkley-Black digestion. When OM values were given, C was estimated using the “Van Bemmelen” factor, dividing OM by 1.72. Total sand, clay and silt contents were all in g kg-1. Textural soil classes were based on the soil texture triangle of IBGE (2007), similar to that of FAO (2015). When several sources of data were available for the same collection site, or when it was sampled more than once (for example, at different times of the year, or different years), the mean values of chemical and physical attributes of soils were calculated.
Only studies performed using the standard method for collecting earthworms in tropical soil conditions, based on ISO (2018) and the Tropical Soil Biology and Fertility (TSBF) method (Anderson & Ingram, 1993) were included in the database. Information on the size of excavated area to collect the earthworms, the number of samples taken per site, and the depth of the sample were extracted from each publication. Mean earthworm density and biomass data (when available) were entered into the database as well as information on species identification (if performed or not). Earthworm data was entered as number of individuals per m2 (no. indiv. m-2) and biomass expressed as fresh mass in g m-2 (fresh weight in preservative liquid, including intestinal contents). When the data were not expressed in these units, they were obtained by using sample number and the size (area, in m2) at each date and site.
When more than one source of data was available by the same authors for the same site, i.e., if it was sampled more than once (e.g., at different times of the year or different years), mean density and biomass values were calculated for each sample site or treatment evaluated, so that each individual treatment or site was represented by only one line in the database. A preliminary analysis of climate effects showed that earthworm abundance and biomass at the same site could be significantly affected by the sampling date, particularly at sites in climates with pronounced dry and wet seasons. For sites in these climates, dry season sample dates were excluded from the database, as they could not be compared with samples of earthworm populations taken in the wet season. For cases where multiple samples were taken during the wet season, data were combined and means calculated per sample site. For climate types where there is no dry season, data from all sampling dates, irrespective of season were combined and means calculated per site.
All data is provided in excel format, and includes four tabs in the data file: Legend, Data base, count and References. The Legend tab provides a detailed explanation for each variable included in the Data base tab. The counts tab provides a table with the total number and percentage of times each variable was provided in the 124 publications used. The References tab gives the full bibliographic information on each of the 124 studies (148 publications) used, of which most (80%) are in Portuguese.
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 307486/2013-3
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 401824/2013-6
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 400533/2014-6
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 310690/2017-0
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior