Why big square vegetation plots

Found at: republic.circumlunar.space:70/~johngodlee/posts/2021-12-10-big_plots.txt

TITLE: Why big square vegetation plots are best
DATE: 2021-12-10
AUTHOR: John L. Godlee

I had to write an email recently to persuade someone that using 
bigger permanent vegetation survey plots in savannas was better 
than small plots. In our field of research one of the basic field 
measurements we do is to demarcate an area of savanna, tag all the 
tree stems gt5 cm diameter, and then return every few years to 
track growth and mortality of those trees.

The reasons for constructing small plots are mostly practical. 
Small circular plots are very quick to set up, with a single metal 
pole sunk into the ground at the centre of the plot. Then it's just 
a case of running a tape measure out to the required radius and 
moving round with the tape measure measuring all the trees that are 
within the radius. It's also fairly easy this way to determine the 
spatial distribution of tree stems by using a distance and compass 
angle coordinate system. Big plots on the other hand are quite slow 
to set up, because the trees get in the way of the tape measures, 
and there's more chance of the tape measure drifting, which is 
especially problematic for square/rectangular plots where the 
corners have to be at 90 degrees. After a plot gets to be more than 
about 25 m in radius, I would advocate for square plots instead, as 
it actually becomes easier to set them up as squares at this size.

The problem is that small plots can leaf to un-representative 
estimates of the biomass and species diversity of the landscape 
they are sampling.

Measuring competition effects: There’s been some work in our 
group on miombo woodland plots in Tanzania which suggests that 
tree-tree competition is an important determinant of tree growth, 
and that this competition effect peaks at about ~10-15 m radius 
from the focal tree, on average. In the case of a small plot (e.g. 
20 m radius circle), you will only really be able to understand 
competition effects for a few individuals right at the centre of 
the plot. I’m particularly interested in spatial distribution of 
trees, and it’s something I would like SEOSAW to do more of. But 
we can’t do that with small plots.

  [SEOSAW]: https://seosaw.github.io/

Edge effects and the representative-ness of biomass estimates: The 
woody biomass per area estimates from small plots are much more 
influenced by the presence or absence of large trees than larger 
plots. To gather a representative estimate of the biomass per area 
of a landscape, it can actually require greater effort per unit 
area sampled to set up many small plots, than fewer big plots, as 
you need to sample less area overall from big plots. Additionally, 
edge effects are magnified when measuring smaller plots, as the 
perimeter:area ratio doesn't scale geometrically, thus subjective 
decisions about whether a tree/stem is inside or outside a plot are 
more influential on plot level estimates of abundance, diversity, 
and biomass. Finally, from personal experience there is more chance 
that small plots will be opportunistically sited to include or 
exclude certain features of the woodland, like a thicket area, or 
large impressive trees, introducing bias when estimating landscape 
processes. So, replication is not such an issue as compared to 
sampling a few representative plots.

Matching with satellite data: In our lab group there are people 
working on estimating biomass from L-band radar backscatter 
relationships. Small plots are much worse for matching with these 
remotely sensed data. A previous PhD student found that the R^2 of 
the biomass estimation uncertainty peaked at about 1 ha (100x100 
m), and was almost zero for plots lt0.2 ha (lt~25m radius circle). 
You just can’t fit as many data points in a smaller plot. These 
papers highlight similar issues. https://doi.org/10.3390/rs5031001,