Skip to main content

Cloud forests of the Orinoco River Basin (Colombia): Variation in vegetation and soil macrofauna composition along the hydrometeorological gradient

Cite this dataset

Ramirez, Beatriz H. et al. (2023). Cloud forests of the Orinoco River Basin (Colombia): Variation in vegetation and soil macrofauna composition along the hydrometeorological gradient [Dataset]. Dryad.


We present vegetation, soil macrofauna, soil, hydrometeorological and topographical data collected from Tropical Montane Cloud Forests in the Orinoco River basin. Specifically, from the municipality of Chámeza, department of Casanare, Colombia. These data sets were used to evaluate how vegetation and soil macrofauna diversity vary along the 1700–2200 m a.s.l. elevation range. Within this elevation range, we have previously described a hydrometeorological gradient largely driven by a fog incidence increase with elevation. Vegetation data were collected for all individuals with a diameter at breast height (DBH) > 5 cm in four vegetation plots (5 x 50 m; total: 0.1 ha) every 100 m in altitude between 1700–2200 m a.s.l. From each plot, we obtained three soil monoliths from the organic layer and three from the mineral horizon, and manually extracted their soil macrofauna, and soil samples for determining pH, organic matter content, and soil texture, among others in a soil laboratory. Topographical data was inferred from Digital Elevation Models. Hydrometeorological data was collected in a previous study, but it was interpolated to the sampling plots. Here we present the interpolated hydrometeorological data.


Vegetation data

On each transect, we delimited four plots of 5 x 50 m (250 m2) with a minimum distance between plots of 200 m. The four plots per elevation covered 0.1 ha. In each plot, we marked all trees with a diameter at breast height (DBH > 5 cm regardless of their height, with traffic reflective yellow paint and an aluminum numbered tag. At the beginning of the wet season (March 2021) we collected botanical specimens with a telescopic tree pruner. Samples were pressed inside newspaper sheets and piled up. Then, the piles were tied up, bagged, and wet with 70% alcohol. In the Forest Herbarium of the Universidad Distrital Francisco José de Caldas (UDBC) these samples were oven-dried, quarantined, and identified to the most detailed taxonomic level possible.

The data set has species abundance data per plot.

Soil macrofauna data

Soil macrofauna was collected in April 2021, one month after the beginning of the wet season, following the Tropical Soil Biology and Fertility method (TSBF) (Anderson & Ingram, 1993). This method consists of collecting a rectangular volume of soil (monolith) with the help of a metal frame of 25 x 25 x 10 cm. Along each vegetation plot, we aimed to collect three monoliths from the organic layer and three monoliths from the underlying top mineral horizon. The first monolith was extracted from the plot’s center and the other two were collected at a mean distance of 10 m to both sides of the central monolith. This would yield a total of 144 monoliths, however, in the field we extracted 134 monoliths of which 70 were from organic layers and 64 from the top mineral horizon. In two sampled monoliths, we did not find an organic layer, likely due to the steep terrain and water flows that prevented the accumulation of plant debris. In the case of the top mineral horizon, there were eight monoliths in which the organic layer was directly on top of a rock substrate.

Each extracted monolith was placed in a sealed plastic bag properly marked and transported back to a working area where soil macrofauna was manually extracted the same day to prevent desiccation and placed in 80% ethanol on 100 ml plastic vials. Specimens were identified to the order level (following Velásquez & Lavelle, 2019) at the Forest Health Laboratory in the Universidad Distrital Francisco José de Caldas and then deposited at the Entomological Forest Collection (CEFUD- RNC 045). 

Data of soil macrofauna orders was standardized to density per square meter, considering that the monoliths had 25*25 cm of area, and depending on the number of monoliths per plot.

Soil analyses, hydrometeorological and topographical variables

We collected soil samples (~500 g) from the organic layer and the top mineral horizon on the side of each extracted monolith. For each sample, we estimated the volume of the mineral/organic layer that was retrieved. This is an indirect measurement of the mineral/organic layer depth, as in most cases the depth of the layer was not enough to extract a full monolith. In this case, we can account for the studied horizons with less depth than 10 cm, but not for deeper soils. These samples were oven-dried at 70°C for 24 hours, then sieved with a 2-mm metal sieve. We weighed the > 2 mm fraction and the < 2 mm fraction. From the < 2 mm fraction, we extracted 10 g for pH measurements with an APERA SX-823-B pH-meter by dilution in distilled water. Also, to identify soil texture we employed the Bouyoucos method (Bouyoucos, 1962) and organic matter content by combustion in the Soil laboratory in the Universidad Distrital Francisco José de Caldas. The data were averaged to the plot level, to be included in the ordination analyses.

Hydrometeorological data were collected from 12 June 2014, to 13 July 2016, on 10-minute time resolution (see Ramírez et al., 2017a for a detailed description of the setup). Soil volumetric water content data were collected with GS-3 frequency domain soil moisture sensors (Decagon Devices Inc.) at 10-cm depth in the mineral horizon collected from 12 June 2014, to 13 July 2016, in three forested locations within our study area at 1720, 1973, and 2068 m a.s.l. Rainfall data were collected with four Decagon ECRN-100 rainfall gauges at 2 m above the ground. They were located at 2148, 1819, 1729, and 1554 m a.s.l. Fog hours were collected with three cylindrical Juvik type fog collectors (Juvik & Nullet, 1995) each connected to a Decagon ECRN-100 rainfall gauge. They were located at 1819, 1729, and 1554 m a.s.l. Disconnection of the hoses, failures of the tipping buckets during the wet season and poor silicone sealing before the dry season did not allow us to specifically quantify water inputs by fog. However, we used the data to discriminate between hours with and without fog, following Tanaka et al. (2011). With this approach, we established elevation trends in fog frequency, including estimating expected fog incidence at the 2148 m a.s.l. elevation. We estimated potential evapotranspiration with the Penman–Monteith model (Monteith, 1965) assuming a single-layered canopy with zero stomatal resistance, by employing shortwave incoming radiation, relative humidity, air temperature, wind speed, and barometric pressure data collected with two Davis Vantage Pro2-plus automated weather stations (AWS) located at 1819 and 2148 m a.s.l. More detailed information about the hydrometeorological setup can be found in Ramírez et al., (2017a).

The collected and estimated data were interpolated to the focal vegetation and soil macrofauna plots by employing the inverse distance and elevation weighting interpolation method (IDEW) following Masih et al. (2011). We assigned a 50-50 % weight to distance and elevation. From these interpolations mean annual values were estimated for each variable: soil moisture, rainfall, fog hours, and potential evapotranspiration. Ramírez et al., (2017a, 2017b, 2018) showed that the study area and neighboring micro catchments present a hydrometeorological gradient with higher humidity and lower evapotranspiration at the higher elevations compared to the lower elevations.

Topographical data including slope, aspect (nine-parameter second-order polynomial, sensu Zevenbergen & Thorne, 1987), and topographic position index (Guisan et al., 1999; Gallant & Wilson, 2000) were obtained from the Radiometric Terrain Corrected High-Resolution Digital elevation model ALOS PALSAR (ASF DAAC, 2015). Aspect refers to the compass direction that a slope faces, and it can be related to wind and sunlight exposition. We transformed the aspect from degrees to a continuous variable by applying the cosine for the north aspect and the sine for the east aspect. 

Usage notes

All files are in .csv format so they do not need any particular software to be read.


Colombian National Financing Fund for Science, Technology and Innovation, Award: contract No 80740-641-2020