This topic is the core of my PhD and I have a lot to say, so I will let it out slowly and this will be the first of a series of posts.
Livestock grazing has attracted the attention of ecologists for many decades. We have learnt a lot but the story is not complete. The effects of livestock grazing on vegetation remain a controversial and important subject. This is highlighted by the recent controversy in Victoria on grazing in the high country – if you are interested in this issue feel free to read the posts from some fellow QAEcologists here, here and here.
My work does not deal with alpine grazing but is concerned with livestock grazing in native vegetation within the floodplains of the Victorian Riverina. The Victorian Riverina subregion has the highest proportion of cleared vegetation in Australia, with only 5.1% native vegetation remaining in 2001 (NLWRA 2001). Much of the remaining vegetation occurs in narrow strips along roadsides and creeks (Figure 1).
Figure 1. Aerial photo of Broken and Boosey Creeks in the Victorian Riverina
The remnant riparian vegetation is under significant threat from weed invasion and grazing. Many of the riparian remnants are licenced to be grazed by sheep or cattle. Some sites are also fenced from adjacent farms. However, a licence does not necessarily mean a remnant is grazed (or how heavily grazed) and a fence does not necessarily mean a remnant is more heavily influenced by the adjacent farm. I used three separate indicators of grazing for analysis: presence/absence of a fence, presence/absence of grazing licence, and presence/absence of recent heavy grazing.
Based on some of my previous work showing the change in vegetation cover with increasing distance from the creek edge (see my post on sampling for details), I separated remnant sites into two zones – Inner (first 25 m) and Outer (all outside 25 m) – for examining the effect of livestock grazing.
Using the zoned sites, I wanted to firstly determine the specific effect of grazing on each of the vegetation life forms. This is not directly possible with these data as I’m not modelling the specific change of cover at a given site that is grazed or not grazed. Instead I’ve done this by comparing vegetation attributes of sites that have been grazed to sites that have not been grazed. Boosted Regression Tree (BRT) analysis was used to do this, since they are an effective tool for modelling the influence of a particular variable while accounting for effects of other variables. I built models including management and environmental variables and used bootstrapping to perform multiple runs of the model (let me know if you want any more information about the modelling process).
These models estimated the cover of native vegetation life forms when grazed and ungrazed. All but the smallest native life forms had lower cover in grazed sites, as did plant litter (Figure 2). Bare ground was higher in grazed sites. Small native shrubs showed the weakest response to grazing, perhaps due to their prostrate growth form. Exotic life forms were much less sensitive to grazing, and in the case of small herbs increased in cover when grazed (Figure 2). These general findings are well supported in the literature, which highlights that over time, intense grazing will result in vegetation dominated by species with traits suited to grazing, such as short lived (annuals), low-growing perennials, small seeds, high regrowth potential, plasticity in response to grazing, and high fecundity (Landsberg et al. 1999; McIntyre and Lavorel 2001). The data in Figure 2 only include comparisons of inner zones.
Figure 2. The modelled comparison between cover in grazed (grey) and ungrazed (black) sites accounting for variation in other environmental variables. Life forms are: MS: medium shrubs, SS: small shrubs, MTG: medium tufted graminoids, MH: medium herbs, SH: small herbs, MNG: medium non-tufted graminoids, BG: bare ground and LT: litter. Appending letters refer to natives (.N) and exotics (.EX).
So this is great, we can detect correlative effects of grazing on vegetation cover in this system from my data. However, this is not particularly helpful if these effects are much weaker than other factors. For management to be effective it must have a relatively strong influence on the vegetation. This is partially taken into account in the BRT models above, however I did an additional test of this influence by comparing the management variables (grazing, licencing and fencing) against a range of environmental variables using hierarchical partitioning.
This method determined the contribution of each variable to the goodness of fit for the response variable (native vegetation cover). These individual contributions were summed into ‘management’ and ‘environmental’ variables to compare the combined influence. Grazing management was less influential than environmental variables but was higher and more variable in the inner zone (Figure 3). This suggests that the vegetation (and ground layer) cover is more susceptible to grazing management near the creek.
Figure 3. The combined contributions of management (dark) and environmental (light) variables on explained variance of native vegetation cover.
Hopefully I have explained this satisfactorily so that you can see that within this study site I have detected correlative responses of understorey life forms to grazing variables, and that these management indicators appear to be significant drivers of understorey cover.
Feel free to ask if anything is unclear. Also, for a great brief summary of grazing effects see Ian Lunt’s latest post in the Conversation here.
Landsberg J., Lavorel S. & Stol J. (1999) Grazing response groups among understorey plants in arid rangelands. Journal of Vegetation Science 10, 683-96
McIntyre S. & Lavorel S. (2001) Livestock grazing in subtropical pastures: steps in the analysis of attribute response and plant functional types. Journal of Ecology 89, 209-26
NLWRA. (2001) National land & water resources audit: Australian native vegetation assessment. National Heritage Trust