Reference no: EM133044028

ENMPG17 Field Skills - Rapid assessment of the productivity of different forest types

Learning objectives

Introduction

In both forest ecology and commercial forestry, we are often interested in understanding how productive a forest is and how the productivity of different forests compare. The theory that the productivity (i.e. biomass accumulation) of more diverse forests is more stable and robust to a range of challenges - the so called diversity-stability debate - is an old concept in ecology(Mccann, 2000) and the foundation of a large variety of science that has sought to understand the link between biodiversity and productivity in a range of ecosystems, including forests.

There is now a growing interest in understanding if, and how forests with greater tree species diversity can be both more resilient to the challenges posed by climate change (e.g. extreme events such as droughts, floods, storms and outbreaks of pests and diseases) whilst also being more productivethan monocultures (forests comprised of only a single species)(Messier et al., 2021). However, not all tree species get along well, and a growing body of evidence is highlighting how the existence and nature of any positive effects of mixing tree species ishighly context dependant (Gillerot et al., 2021; Grossiord, 2019; Jactel et al., 2017; Van de Peer et al., 2018), meaning we can't assume that purely mixing any old tree species together will automatically be beneficial.

By understanding how productive different forestsareunder a range of conditions, we can make informed decisions about how best to balance a range of management objectives, such as maximising carbon sequestration, the production of timber and the creation or restoration of key habitats for biodiversity. As forest managers and conservation practitioners can directly control what species are planted and what density trees are growing at, these two variables are particularly valuable for us to study.

In this project, we will use standard forest mensuration (measurement) techniques to investigate how forest productivity (in terms of radial tree size) and stand density (number of trees per hectare) vary between three forests with different tree species compositionsto help us investigateif more diverse, mixed-species forests are more or less productivethan forests with only a single species.

Research Questions
Q1: Do more species rich forests have a larger basal area than less diverse forests?
Q2: Do more dense forests have a larger basal area than less dense forests?
Q3: Is average tree size in diverse forests significantly different than in less diverse forests?

Step 1: Data collection
In groups of 2-3, establish at least 3 fixed-radius (circular) plots with a radius of 5.64m in each of the three forest types (so a total of 9+ plots for each group) (Figure 1). These plots should not overlap. Inside each plot, measure the diameter at breast height (known as DBH, and always measured at a height of 1.3m up the tree) of each tree, note which species you think it is (these might include birch, oak, pine and larch species, sycamore and beech, amongst others). You can use leaves and bark in combination with the Seek app to help identify the different tree species. Record all of this information whilst in the field on the provided data collection sheet. Each tree should be a new row of data. One person from each group should enter your groups data digitally into the spreadsheet when you get home (don't forget to delete the example rows), so you should agree who will do this before you finish collecting your data.

Step 2: Data processing & summary stats

Estimating the number of trees per hectare
Using the complete dataset, add up the number of trees in each plot. Now scale each of these plot level estimates up to an estimate of the number of trees per hectare for all three forest types (remembering that the area of your plots is equivalent to 0.01 ha). Next, using these scaled estimates, calculate the mean, standard deviation (SD) and standard error (SE) of the number of trees per hectare for each forest type. You could also estimate the proportion of each of your sampled species per hectare in all the forest types.

Estimating tree basal area
Next, we need to calculate the basal area (BA) of each tree from its DBH. This basal area value will be calculated for you automatically when you enter each tree's DBH into the spreadsheet, as the formula for calculating basal area from DBH has already been entered into the data collection spreadsheet. If it does not calculate it for it for you automatically in some cells, you may need to left click the bottom right corner of a cell which has the formula in it (there is a small square in the bottom right corner of the cell) and drag the cursor to cover all of the cells in the column to apply the formula.

We approximate the basal area of a tree by using the equation for the area of a circle (πr^2) to turn our diameter at breast height measurements into an estimate of basal area. This approach assumes that the tree is perfectly circular where we collected the DBH measurement, which will clearly never be the case, hence why this is an approximation.

Estimating plot and stand level basal area
1. Now calculate the basal area of each plot in the class dataset by summing up the basal area of all the trees in each plot. This will give you a single value (BA) for each plot.

2. As our plots are 0.01ha in size, we now need to scale the estimated BA in each plot up to 1ha, by multiplying it by 100.

3. Next, for each of the two forest types separately, calculate the mean basal area along with the standard deviation (SD) and standard error (SE) of the mean using all of the 1ha estimates of basal area from each forest type. You should report your estimate of the mean basal area and the SE for each forest type in cm2 ha-1 (centimetres squared per hectare).

Step 3: Data analysis
Finally, to address our question of whether average tree size in diverse forests is significantly different from average tree size in less diversewe can perform a one-way Analysis of Variance (ANOVA) test on the tree level BA values. Aone-way ANOVA is used to test the null hypothesis that the means of several populations are all equal, so if we find a p-value < 0.05, we can reject the null hypothesis as we have found some evidence of a difference. A one-way ANOVA is often used when we have three or more categorical, independent groups and is a common statistical test, so a useful one to start to get to know.

A one-way ANOVA can be performed very easily in excel and I recommend this tutorial for a step-by-step guide. Remember to make sure your data structure reflects the layout in the example and make sure that you interpret the results of the ANOVA in relation to your own hypothesis i.e. did you hypothesise there would or would not be a difference inBA between the two forest types?

Step 4: Write the report
You should read this excellent paper which provides a short, gentle and comprehensive introduction on how to write in a scientific style and then present your findings and write your report following the style and layout they recommend.

Your results section of your report should contain at least the following:

- Your estimates of the mean number of trees per hectare for both forest types, along with their SD and SE.
- Your estimates of the mean basal areaof each forest in type in cm2 ha-1, along with the SD and SE of each mean.
- A single figure with correctly labelled axes and an appropriate figure caption that shows the mean basal area ± 1 SE of each forest type.
- The results from your one-way Analysis of Variance (ANOVA) test. This can also be displayed in graphical form with correctly labelled axes and an appropriate figure caption.

Attachment:- Field Skills.rar