| Literature DB >> 31964935 |
Rafael Barbizan Sühs1, Eduardo Luís Hettwer Giehl2, Nivaldo Peroni2.
Abstract
Woody encroachment threatens several ecosystems around the world. In general, management of grasslands includes regulation of fire and grazing regimes. Changes in these two types of disturbances are potential drivers of woody encroachment. Here we assessed how the traditional management carried out by local landholders affects a highland grassland ecosystem in southern Brazil. We hypothesized that grasslands converted to protected areas undergo fast woody encroachment. To reconstruct changes in vegetation, we interviewed former and current landholders and coupled their knowledge with an analysis of aerial and satellite images. During the first 11 years without fire and cattle, woody encroachment in grasslands increased exponentially. Woody encroachment occurred mostly by the replacement of grasslands by shrublands. Meanwhile, grasslands under traditional management remained almost unchanged for the last 40 years. The management of fire by local landholders has been part of their traditional practices for decades. Such management prevents large-scale wildfires and maintains natural highland grasslands. The quick pace of shrub encroachment in such grasslands threatens its exclusive diversity, human well-being and regional cultural heritage. Thus, conservation policies are needed to regulate and instruct about the use of fire as a management tool in highland grasslands of southern Brazil.Entities:
Year: 2020 PMID: 31964935 PMCID: PMC6972928 DOI: 10.1038/s41598-020-57564-z
Source DB: PubMed Journal: Sci Rep ISSN: 2045-2322 Impact factor: 4.379
Detail on the evaluated lands and management practices in subtropical highland grasslands in southern Brazil.
| L ID | O ID | Elevation (m) | Land area (ha) | Period (years) | Cattle density (animals.ha−¹) | Fire frequency (years) | Grasslands’ fate | Grassland 1978 (ha) | Grassland 2018 (ha) | WE (%) | P | YWTM |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1*** | a | 1571.4 | 1200 | 116 | 0.3 | 2 | 368.8 | 367.6 | 0.41 | No | 0 | |
| 2 | b | 1650.0 | 340 | 300 | 0.38 | 2 | 146.4 | 145.9 | 0.38 | No | 0 | |
| 3 | c | 1250.0 | 105 | 100 | 0.57 | 2 | 23.6 | 23.6 | 0.38 | No | 0 | |
| 4 | d | 1616.7 | 319 | 100 | NA | 2 | 8.5 | 5.0 | 35.18 | Yes | 8 | |
| 5 | e | 1650.0 | 840 | 30 | 0.24 | 2 | 25.8 | 22.4 | 14.60 | Yes | 11 | |
| 6* | f | 1650.0 | 58.4 | 80 | 0.37 | 2 | 27.4 | 27.2 | 0.56 | Yes | 9 | |
| 7 | f | 1650.0 | 68.5 | 80 | 0.37 | 2 | 60.8 | 59.9 | 1.41 | Yes | 9 | |
| 8 | f | 1625.0 | 114 | 80 | 0.37 | 2 | 37.2 | 31.8 | 11.27 | Yes | 9 | |
| 9 | g | 1650.0 | 17.4 | 130 | 0.57 | 3 | 12.1 | 12.1 | 0.05 | No | 0 | |
| 10 | h | 1650.0 | 68.8 | 200 | 0.5 | 2 | 27.1 | 26.4 | 2.40 | No | 0 | |
| 11 | i | 1650.0 | 65 | 120 | 0.46 | 4 | 17.5 | 17.5 | −0.14 | No | 0 | |
| 12 | j | 1650.0 | 65 | 100 | 0.42 | 2 | 19.6 | 19.5 | 0.43 | No | 0 | |
| 13 | k | 1525.0 | 150 | 80 | 0.26 | 2 | 11.5 | 11.2 | 2.14 | No | 0 | |
| 14 | l | 1516.7 | 190 | 150 | 0.42 | 1 | 28.1 | 11.7 | 63.60 | Yes | 9 | |
| 15 | m | 1400.0 | 148 | 100 | 0.47 | 2 | 45.6 | 43.2 | 3.18 | No | 0 | |
| 16 | n | 1450.0 | 118 | 60 | 0.42 | 2 | 8.3 | 8.3 | −0.44 | No | 0 | |
| 17 | o | 1416.7 | 800 | 241 | 0.37 | 2–3 | 353.2 | 322.2 | 0.15 | No | 0 | |
| 18 | p | 1400.0 | 800 | 62 | 0.31 | 2 | 451.7 | 448.8 | 0.03 | No | 0 | |
| 19 | q | 1700.0 | 550 | 250 | 0.33 | 2 | 146.3 | 145.2 | 0.41 | No | 0 | |
| 20 | r | 1416.7 | 700 | 55 | 0.26 | 2–3 | 330.5 | 330.7 | 0.06 | No | 0 | |
| 21 | s | 1450.0 | 114 | 80 | 0.4 | 1 | 19.4 | 12.6 | 27.34 | Yes | 9 | |
| 22 | s | 1450.0 | 154 | 80 | 0.4 | 1 | 9.1 | 1.1 | 76.74 | Yes | 9 | |
| 23 | t | 1600.0 | 300 | 35 | 0.33 | 2 | 16.5 | 6.6 | 45.94 | Yes | 5 | |
| 24 | u | 1300.0 | 155.8 | 150 | 0.51 | 2 | 54.6 | 50.8 | 4.15 | No | 0 | |
| 25 | v | 1650.0 | 1500 | 150 | 0.27 | 2 | 380.8 | 162.8 | 54.70 | Yes | 8 | |
| 26 | v | 1521.4 | 1000 | 150 | 0.2 | 2 | 37.9 | 37.0 | −0.76 | No | 0 | |
| 27 | w | 1650.0 | 200 | 100 | 0.5 | 1 | 91.9 | 87.8 | 4.31 | No | 0 | |
| 28 | x** | 1550.0 | 169 | NA | NA | NA | NA | 109.2 | 104.7 | 4.51 | No | 6 |
| 29 | x** | 1450.0 | 300 | NA | NA | NA | NA | 132.5 | 103.6 | 21.17 | Yes | 10 |
| 30 | y** | 1450.0 | 135 | NA | NA | NA | NA | 42.9 | 39.6 | 7.43 | No | 6 |
| 31 | z** | 1650.0 | 101 | NA | NA | NA | NA | 56.5 | 45.2 | 19.12 | No | 9 |
| 32 | aa** | 1500.0 | 36.8 | NA | NA | NA | NA | 16.8 | 14.2 | 15.12 | No | 9 |
| 33 | bb | 1220 | 199 | 40 | NA | 2 | NA | NA | NA | No | NA | |
| 34 | cc | 1070 | 218 | 56 | 0.28 | 2–3 | NA | NA | NA | No | NA | |
| 35 | dd | 1300 | 41.5 | 80 | 0.72 | NA | NA | NA | NA | NA | No | NA |
| 36 | ee | 1550 | 120 | 38 | 0.25 | 2 | NA | NA | NA | No | NA | |
| 37 | ff | 1600 | 260.5 | 29 | 0.69 | 2 | NA | NA | NA | Yes | NA | |
| 38 | gg | 1550 | 120 | 150 | NA | 2 | NA | NA | NA | NA | Yes | NA |
| 39 | hh | 1500 | 65 | 250 | 0.38 | 2 | NA | NA | NA | Yes | NA | |
| 40 | ii | 1280 | 150 | 150 | 0.56 | 3 | NA | NA | NA | No | NA | |
| 41 | jj | 1300 | 60 | 200 | 0.67 | 2–3 | NA | NA | NA | No | NA | |
| 42 | kk | 1540 | 67 | 300 | 0.52 | 2 | NA | NA | NA | No | NA | |
| 43 | ll | 1445 | 62.5 | 300 | 0.48 | NA | NA | NA | NA | No | NA |
L ID = land ID; O ID = Owner ID; Period = Period the property is within the owner/family; WE = Woody encroachment; P = Within protected area (cattle and fire are prevented); YWTM = number of years without traditional management. Grasslands’ fate: the main answer of interviewees for the question: “What happens to grasslands if fire and cattle are excluded?”. Grassland 1978: grassland area computed in polygons in 1978. Grassland 2018: grassland area computed in polygons in 2018. *property was removed from the analysis due to a recent fire in grasslands. **owners that were not interviewed. *** land ID 1 has three owners (all interviewed) who use the same management techniques. NA = Data not available.
Set of produced models for evaluating shrub encroachment in relation to elevation and traditional management in southern Brazilian grasslands.
| Int | Elev. | YWTM | Elev. × YWTM | df | logLik | AICc | ΔAIC | wAIC |
|---|---|---|---|---|---|---|---|---|
| −2.965 | — | 0.244 | — | 3 | 58.22 | −109.52 | 0.00 | 0.73 |
| −2.400 | −0.0004 | 0.246 | — | 4 | 58.25 | −106.90 | 2.62 | 0.20 |
| −3.778 | 0.0005 | 0.767 | −0.0003 | 5 | 58.67 | −104.84 | 4.68 | 0.07 |
| −1.801 | — | — | — | 2 | 46.35 | −88.27 | 21.25 | 0.00 |
Int = Intercept; Elev. = Elevation; YWTM = years without traditional management, df = degrees of freedom, logLik = log-likelihood, AICc = Akaike information criteria corrected for small samples, wAIC = Akaike weight. Interaction between predictors is represented by “ × ”. Selected model follows a “*”. Models ordered by increasing values of AICc.
Figure 1Effect of land management on shrub cover (calculated from the difference in shrub cover between the years 2018 and 1978) in southern Brazilian highlands. Solid line represents interpolation and dashed line represents extrapolation. Shaded areas represent 95% confidence intervals built from predictions based on 1000 bootstrap replicates of the original data. Arrows indicate 50 and 99% of shrub cover in grasslands (12 and 30 years, respectively).
Figure 2Location where the study was developed and the evaluated properties in southern Brazilian highlands. More details on evaluated properties can be found on Table 1. Map created with the software QGis platform, version 2.18.20[52].
Figure 3Schematic representation of each step taken before analysis, from interviews to image classification in southern Brazilian highlands. Example figure taken from freely available imagery from Google Earth. Image classification was carried out in MultiSpec software, version 3.4[51] (https://engineering.purdue.edu/~biehl/MultiSpec/). QGis platform version 2.18.20[52] was used for georeferencing, mosaic building and area measurements.