Relationships between plant species richness and grazing intensity in a semiarid ecosystem
Data files
Oct 16, 2023 version files 151.45 KB
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Fulbright_et_al_east_fnd.csv
149.97 KB
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README.md
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Abstract
Plant species richness is an important property of ecosystems that is altered by grazing. In a semiarid environment, we tested the hypotheses that (1) small-scale herbaceous plant species richness declines linearly with increasing grazing intensity by large ungulates, (2) precipitation and percent sand interact with grazing intensity, and (3) response of herbaceous plant species richness to increasing intensity of ungulate grazing varies with patch productivity. During January to March 2012, we randomly allocated 50, 1.5-m x 1.5-m grazing exclosures within each of six 2,500 ha study sites across South Texas, USA. We counted the number of herbaceous plant species and harvested vegetation in 0.25-m2 plots within exclosures (ungrazed control plots) and in the grazed area outside the exclosures (grazed treatment plots) during October and November 2012–2019. We estimated percent use (grazing intensity) based on the difference in herbaceous plant standing crop between control plots and treatment plots. We selected the negative binomial regression model that best explained the relationship between grazing intensity and herbaceous plant species richness using the Schwarz Bayesian Information Criterion. After accounting for the positive effect of precipitation and percent sand on herbaceous plant species richness, species richness/0.25 m2 increased slightly from 0 to ~ 30% grazing intensity and then declined with increasing grazing intensity. Linear and quadratic responses of herbaceous plant species richness to increasing grazing intensity were greater for the least productive patches (<15.7 g/0.25 m2) than for productive patches (≥15.7 g/0.25 m2). Our results followed the pattern predicted by the intermediate disturbance hypothesis model for the effect of grazing intensity on small-scale herbaceous plant species richness.
https://doi.org/10.5061/dryad.2bvq83bww
Description of data:
Column 1 in the data set identifies the geographic location (site) where data were collected and the exclosure number. SAV is the San Antonio Viejo Ranch. There were three 2,500 ha sites on the ranch designated SAV1, SAV2, and SAV3. BV is Buena Vista Ranch, SR is Santa Rosa Ranch, and EELS is East El Sauz Ranch.
Column 2 provides a unique identifier number for each pair of an exclosure and grazed plot.
Column 3 is the year of data collection (data were collected in October/November of the designated year).
Column 4 signifies that data were collected in autumn of the designated year.
Column 5 is the standing crop of herbaceous vegetation in the exclosure in grams per quarter meter squared and,
Column 6 is the standing crop of herbaceous vegetation in the grazed plot in the same units of measure.
Column 7 is the number of herbaceous plant species per quarter meter squared in the exclosure and,
Column 8 is the number of herbaceous plant species per quarter meter squared in the grazed plot.
Columns 9 and 10 provide the rainfall at the location of the exclosure in August and September, respectively, before vegetation sampling was conducted.
Column 11 is the percentage of sand in the soil at each exclosure based on USDA National Resource Conservation Service data.
We sampled vegetation in the study sites during October and November (autumn) 2012-2019 inside and outside 1.5 m x 1.5 m grazing exclosures. Fifty grazing exclosures were randomly allocated with each of six 2,500 ha study sites during January - March 2012. Grazing exclosures were rerandomized each year after vegetation sampling. Vegetation sampling consisted of counting the number of herbaceous plant species with a 0.25 m2 sampling frame in the exclosure and in the grazed area outside the exclosure and then harvesting herbaceous vegetation inside and outside the exclosures. Harvested plant material was dried at 45 C to a constant mass and weighed. Data were entered in an excel spread sheet and statistical analyses were performed using Statistical Analysis Systems (SAS) software.