Decadal Vegetation Succession Along a Chronosequence Within Eucalyptus Obliqua Wet Forest, Southern Tasmania Christine Dobbin SIT Study Abroad

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Spring 2017 Decadal vegetation succession along a chronosequence within Eucalyptus obliqua wet forest, southern Tasmania Christine Dobbin SIT Study Abroad

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Recommended Citation Dobbin, Christine, "Decadal vegetation succession along a chronosequence within Eucalyptus obliqua wet forest, southern Tasmania" (2017). Independent Study Project (ISP) Collection. 2641. https://digitalcollections.sit.edu/isp_collection/2641

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Decadal vegetation succession along a chronosequence within Eucalyptus

obliqua wet forest, southern Tasmania

Dobbin, Christine

Academic Director: Brennan, Peter

Project Advisor: Penrose, Helen

Rice University

Kinesiology, Ecology & Evolutionary Biology

Australia, Hobart

Submitted in partial fulfillment of the requirements for Australia: Sustainability and

Environmental Action, SIT Study Abroad, Spring 2017 ABSTRACT

Southern Tasmania is home to fire dependent mixed forests, which, if not maintained, will eventually be replaced by the rainforest understorey. Wet eucalypt forest succession after disturbance events was investigated through floristic and vertical measurements of four north facing chronosequence plots with labels describing the age class of each, from regrowth to mature sites. This study was possible due to the establishment of permanent 50m x 50m plots in 2007 for longitudinal monitoring and subsequent illustration of forest dynamics following disturbance, including clearfell burns and wildfire. The contents of this report are the comparative analyses of the findings from the first two installments of the methods, which were employed ten years apart.

The implications of this research include evidence for the protection of carbon dense old growth forests and the production of an empirical successional model by which human-induced landscape treatments can be fashioned after. This project is one of the first of its kind in the country of Australia, as it will ultimately produce experiential data of wet eucalypt succession in real time.

The collected data described no significant floristic change over one decade. More time must be allocated to the decadal interval studies before such change will be evident as forest succession occurs on the scale of centuries. In the same time span, substantial structural change was described by the findings for individual species within each plot. These variations over time were consistent with Clement’s classical successional theory and Gilbert’s climax vegetation progression.

Keywords: forest succession, wet eucalypt, disturbance, Tasmania

i TABLE OF CONTENTS

Ethics Review vi

Acknowledgements vii

Introduction 1

Methods 5

Results & Discussion 10

Conclusion 25

References 27

Appendix 29

ii TABLE OF FIGURES

Figure 1. Plot distribution in the southern forests landscape. 3

Figure 2. Coding and design of core plot and subplots. 6

Figure 3. NMS ordination diagram in which the distance separating subplots provides a relative measure of the floristic dissimilarity between them. 10

Figure 4. The frequency of stems less than 5 cm as a function of time and plot age. 12

Figure 5. Frequency of stems organized by size class in plot 2003CN per visit. 13

Figure 6. Frequency of stems organized by size class in plot 1967N per visit. 14

Figure 7. Frequency of stems organized by size class in plot 1898N per visit. 16

Figure 8. Frequency of stems organized by size class in plot OGN per visit. 18

Figure 9a. Frequency per hectare of all species in plot 2003CN per visit. 22

Figure 9b. Basal area per hectare of all species in plot 2003CN per visit. 22

Figure 10a. Frequency per hectare of all species in plot 1967N per visit. 22

Figure 10b. Basal area per hectare of all species in plot 1967N per visit. 22

Figure 11a. Frequency per hectare of all species in plot 1898N per visit. 23

Figure 11b. Basal area per hectare of all species in plot 1898N per visit. 23

Figure 12a. Frequency per hectare of all species in plot OGN per visit. 23

Figure 12b. Basal area per hectare of all species in plot OGN per visit. 23

Figure 13. Carbon retention in 2017 of eucalypt, all other present species, and total live stems by plot. 24

iii TABLE OF TABLES

Table 1. Matrix of PerMANOVA results (t) of paired comparisons between plots tested. 11

Table 2. Plant species which changed in frequency of occurrence within 25* 10x 10 m subplots between 2007 and 2017 at four long term monitoring plots. 20

iv TABLE OF APPENDICES

Appendix A. Site selection criteria. 29

Appendix B.1. 03CN Access & Site Map. 30

Appendix B.2. 67N Access & Site Map. 31

Appendix B.3. 98N Access & Site Map. 32

Appendix B.4. OGN Access & Site Map. 33

Appendix C. Less than 5cm stem count data collection template. 34

Appendix D. Greater than 5cm DBH data collection template. 34

Appendix E. Species cover data collection template. 34

Appendix F. Species presence/absence data collection template. 34

Appendix G. Results of species indicator analysis for plots and age-classes. 35

Appendix H. Relative abundance species in each plot at each visit. 36

Appendix I. Plant list. 37

ISP Ethics Review

(Note: Each AD must complete, sign, and submit this form for every student’s ISP.)

The ISP paper by Christine Dobbin does conform to the Human Subjects Review approval

from the Local Review Board, the ethical standards of the local community, and the ethical and

academic standards outlined in the SIT student and faculty handbooks.

Completed by: Peter Brennan

Academic Director: Peter Brennan

Signature:

Program: Australia: Sustainability and Environmental Action

Date: 7/5/2017

ACKNOWLEDGEMENTS

My research project would not have been possible without the massive role that Jayne Balmer, a senior ecologist with DPIPWE, played as my mentor. I cannot thank her enough for giving me the opportunity to conduct research with her and for her trust in my abilities. The grace with which she fielded my unceasing questions and the way she contextualized the work helped me grow as a researcher and as an environmental activist. She also contributed greatly towards data analysis and interpretation in the final stages of the research period.

Of course, I also must mention the constant and incredible work that Nick Fitzgerald, a DPIPWE project officer for Resource Management and Conservation, did towards my project both in the field, in the office, and during the completion of this report. His patience, support, and wealth of knowledge are greatly appreciated. Tim Rudman from the Biodiversity Conservation Branch at DPIPWE helped immensely in many respects with data collection at the research sites on several occasions as well as with data retrieval. I am very thankful to Micah Visoiu, a DPIPWE ecologist, for helping the project begin smoothly and efficiently in the field. Thank you to Dr. Perpetua Turner, a research ecologist who began the project in 2007, for orienting the team in the early stages and for all her work within the development of the research. Others who deserve recognition for their contributions in 2007 are Craig Airey, Jayne Balmer, and others at DPIPWE.

I want to say another massive thank you to Dr. Helen Penrose for acting as my ISP advisor throughout the entire process, answering countless questions and offering invaluable advice to formulate my independent research. She remained an active resource during the writing process and narrowing my research aims, which helped shape my report for the better.

Last but not least, I would like to an extend many thanks to Dr. Peter Brennan for his markedly impactful teachings throughout the program as well as for the privileged exposure to the inspirational history of the environmental movement in Australia. I also very much appreciated his guidance during the whirlwind of establishing an accessible and exciting research project

vii INTRODUCTION

Wet Eucalypt Forest Succession

Tasmania naturally hosts three main types of forest, including temperate rainforest, wet eucalypt forest, and dry sclerophyll forests (Wells & Hickey, 1999, p. 225). Wet eucalypt forest is generally defined as mixed forest with rainforest dominated understorey, or forests that contain an understorey of broad-leaved shrubs and ferns (Tirkpatrick et al., 1988, p. 1). Tasmanian wet eucalypt populations are composed of mixed forest, with wildfire as the dominant form of disturbance ranging from once every two decades to once a century (Mount, 1979, p. 184). The eucalypt overstorey must regenerate through fire events once every one hundred to four hundred years, or it will die off over time and allow for rainforest to endure superiorly (Jackson, 1968, p. 11).

Clements (1936) believed that in the absence of catastrophic disturbance, vegetation associations change and develop until they reach a climax community. He described that at this climax, each species in the community is able to maintain itself in equilibrium with the greater environment and climate. In response, Gilbert (1959) proposed that within the cool temperate forest biome in Tasmania, the climax vegetation is cool temperate rainforest. However, rainforest species are fire sensitive and in areas that are burned frequently enough, rainforest is displaced by the colonization of fire tolerant species such as eucalypts and other pioneer species such as Pomaderris apetala and Acacia species (Gilbert, 1959, p. 141). These pioneer species are shade intolerant and cannot regenerate within the shade of the forest understorey CITE. Rainforest species, including Nothofagus cunninghamii, Eucryphia lucida and Atherosperma moschatum, are more shade tolerant and over time, replace the pioneer species community (Gilbert, 1959, p. 135). Shade tolerant stems are able to regenerate within the canopy gaps created when the mature trees die (Gilbert, 1959, p. 135). As pioneer species reach the end of their lives and die, the rainforest species replace them (Gilbert, 1959, p. 136). Eucalypts are quite long- lived (May et al, 2012, p. 318) so they remain the canopy dominants for their life span, but the understorey of the forest changes from being dominated by pioneer species to being dominated by rainforest species (Jackson, 1968, pp. 13-14).

1 Land Management & Conservation

In recent years, human management of fire events for the purpose of controlling ecological demographics has grown globally within forestry industries and private landholder sectors. Suppression of wildfires can lead to homogeneity in regard to forest age, biodiversity, and individual structure (Brown, 1996, p. 285), while induced fires in too frequent of intervals and are at too high intensities can lead to similar simplifications of forest dynamics (Whiteley, 1999, p. 25). Thus, it is increasingly important that maintenance strategies be tailored to wet eucalypt forests such that natural degrees of complexity be supported.

Wet eucalypt forests are also an essential facet of the biosphere in that they are often characterized as some of the most carbon dense landscapes (May et al, 2012, p. 318). This label arises out of the physical attributes of the dominant Eucalyptus species, such as diameter and height, which are, by nature, of greater magnitude than the same features of rainforest species (May et al, 2012, p. 318). Their long life spans (May et al, 2012, p. 318) as well as the nutrient cycling that occurs when stems die and decompose are other measures of the continual service that they provide with respect to carbon sequestration (Attiwill, 1979, p. 453).

For the purposes of this project, sustainability refers to the “capacity of ecosystems to maintain their essential functions and processes, and retain their biodiversity in full measure over the long-term” (Business Dictionary, 2017, para. 1). The research contained within this report contributes to a global mission of sustainability such that it provides scientific support for successional biomimicry by means of land management as well as for conservation of carbon dense forest communities. Creating strategies, based on longitudinal survey research, to maintain fire- dependent wet eucalypt populations in a way that fosters native biodiversity composition will promote the ecosystem’s health and long-term existence. Protecting natural areas which extract carbon from the atmosphere and hold sturdy supplies of this greenhouse gas, serves to counteract the urgent threat of climate change in a stable and organic way.

2 Chronosequence Establishment

The Department of Primary Industries Parks, Water, and Environment (DPIPWE) was founded in 2009 under the Government of Tasmania and now facilitates the Wildfire Chronosequence Project (WCP), which was created to research the wildfire management and treatment strategies of southern wet eucalypt forests. Dr. Simon Grove and Dr. Perpetua Turner coordinated the plot establishment along with the collaborative efforts of Forestry Tasmania, the Bushfire CRC, and the School of Plant Science within the University of Tasmania (Turner, 2007, p. 6). The permanent plots created for the study are located in the Huon forest district (Figure 1) approximately 60 km southwest of Hobart, Tasmania (Turner, 2007, p. 16). The WCP plots were initially located on state forest land, but the extension of the Tasmanian Wilderness World Heritage Area boundary in 2013 now covers many of the plots. The twelve 50m x 50m permanent plots that were specified in 2007 include a north and south facing slope for each of the different age classes (Turner, 2007, p. 16). Plots were designated midslope on Jurassic dolerite, within the altitude range of 0m to 500m above sea level, and at minimum were 100m from a forest edge to establish a buffer around the plot’s perimeter. See Appendix A for full list of site selection criteria.

Gaps in Current Knowledge

Past research initiatives focused on fire-dependent biodiversity management have been conducted on varying field sites throughout the state, causing comparative analysis to be difficult across the results from each project (Turner, 2007, p. 8). The mapping of the Wildfire Chronosequence Project plots provides a common region for permanent data collection, which bridges the previous gap in this specific field of study. In this way, empirical information can be gathered to formulate substantiated successional models. The only other study that employed this scheme in the region thus far was Serong & Lill (2008) in the Victorian Central Highlands. Researching these concepts at decadal intervals over prolonged periods of time to mimic natural disturbances may provide insight on management strategies and conservation value, thus supporting the survival of wet eucalypt forests in southern Tasmania.

Figure 1. Plot distribution in the southern forests landscape. Source: (Turner, 2007, p. 24)

Study Aims

According to classical succession theory proposed by Clements (1936), is there evidence after 10 years of changes in floristic composition of four age classes that would provide support for Gilbert’s (1959) theory of the successional dynamics within wet eucalypt forest communities in Tasmania?

Is there evidence that the structural complexity of wet eucalypt forest at four different ages changes significantly over a ten-year scale. Is the type of change dependent on age?

4 METHODS

Site Description

The specific WCP sites that were studied for this research report were a plot that was clearfell burned and sowed in 2003 (03CN), a plot affected by wildfire in 1967 (67N), an 1898 wildfire site (98N), and an old growth site (OGN). The aforementioned sites were on north facing slopes. The plots were accessed through one meter wide tracks outlined with blazed trees and flagging tape, which was further aided by GPS waypoints taken at stakes in the center of each plot. During this round of data collection, waypoints were also taken at the outer stakes in each corner of the plot. Appendix B i – iv contains specific directions and landscape maps for each site. See the introduction for plot establishment information.

Plot Design

The 50m x 50m core plot of each site including its 100m buffer is 6.25 ha with the landscapes contour parallel to the top and bottom rows of the plot (Figure 2). Each plot consists of 25 subplots, which are each 10m x 10m (Figure 2). Within every subplot, there are four sectors coded A – D (Figure 2). The four outside corners and the center subplot of each core plot are marked by iron star-pickets (Turner, 2007, p. 44). There are galvanized steel fence-posts marking the rest of the subplots (Turner, 2007, p. 44). These posts were placed so that in the event of a fire, the sites can be relocated (Tuner, 2007, p. 44). String was placed between every post and picket to mark the subplots (Turner, 2007, p. 44). The 03CN site was not strung because this young site hosts rapidly changing flora which may get caught on the string and pull the posts out, and due to the fact that stronger ultraviolent radiation is present in the plot and may degrade the string at a quicker pace (Turner, 2007, p. 45).

The subplots care categorized by letters (A – E) along the perimeter perpendicular to the slope and numbers (1 – 5) along the perimeter, which is parallel to the slope (Figure 2). Therefore, row E is present at the highest point of the slope, with row A at the lowest. As the plots were created on contours, the perimeters were corrected for

5 the slope to represent a horizontal map plane. Thus, the perimeters may not be exactly 50m x 50m, but the plots are comparable in area.

Figure 2. Coding and design of core plot and subplots. Each 50m x 50m plot is divided into 25 subplots, which are each 10m x 10m. Source: (Turner, 2007, pp. 43- 44, 46)

Measurement Standards

The following methods were applied to each site individually and were replicated from the original WCP report (Turner, 2007) over four weeks. For rows A through B, the presence of species within each subplot was noted for each subplot by Jayne Balmer, Nick Fitzgerald, Tim Rudman, and Micah Visoiu. The cover of present species was estimated for each subplot in rows D through E by Jayne Balmer, Nick

6 Fitzgerald, and Tim Rudman, and Micah Visoiu. See Appendix E & F for cover and presence/absence data collection sheets.

Due to time constraints and consistency in methodology, the subsequent measurements were only taken for rows D through E by myself, Nick Fitzgerald, Jayne Balmer, Tim Rudman, and Dr. Perpetua Turner. The diameter at breast height (DBH) was recorded for all stems with a diameter greater than 10cm in sectors B – D within each subplot. In sector A of each subplot, all stems with a diameter greater than 5cm were measured. Further, in sector A stems with diameters less than 5cm were counted and categorized by species (Appendix C). This data was collected by firstly laying out 50m transects at the top and bottom edge of the row being surveyed. Two 30m transects were laid out within a subplot such that they crossed in the center and outlined the sectors A, B, C, and D. The DBH data collection sheets (Appendix D) hosted columns for 2007 DBH, live/dead status, and comments as well as analogous columns for 2017. Along with this information, each individual stem over 5cm in sector A or over 10cm in sector B – D, was recorded with its species identification, a unique plant ID, and its x, y coordinates in meters. The latter set of information aided in relocating these stems for measurement.

Breast height was standardized at 1.3m for diameter recordings. Stems could not be leaning more than forty-five degrees from the vertical in order to have their diameters recorded. If a stem recorded in 2007 was identified as fallen down in 2017, its 2017 DBH was not considered for the study. In regards to DBH and stem counts, individuals had to be above 1.3m in height to be recorded. When stems were located that passed the appropriate diameter threshold in 2017, but had not in 2007, they were added to the sheet, along with the species and coordinates. In the case that stems could not be located, they were noted as ‘gone’ and their 2017 status was left blank.

These measurements were taken by different teams of people in 2007 and 2017. Different team members varied in what type of data they collected within each year as well. This may have introduced some deviances in measurements and estimations due to the lack of consistency. Further, on many field trips, one individual would measure the DBH of stems and say the readings aloud to a scribe, which

7 could have added another layer of possible error in hearing or perhaps in reading the writing later. These errors were reduced through consistent review of the methodology and is not likely to have caused significant skewing of the data.

Statistical Analysis

The software package PCOrd 6.0 (McCune & Mefford 2011) was used by Jayne Balmer to ordinate the floristic cover data using non-metric multidimensional scaling (NMS). The measure of dissimilarity used was Relative Sorensen and the slow and steady default options were chosen. Floristic assemblage differences between plots was tested using Permanova in PCOrd 6.0.

The greater than 5cm DBH data for all of the plots was stacked by Jayne Balmer and then the 2007 and 2017 diameters were fused into one column by adding a new column that specified visit, with Visit 1 referring to 2007 and Visit 2 referring to 2017. Then, basal area was calculated for each stem using the formula (Keith et al, 2000): BA = 0.00007854 x DBH2 The basal areas were summed for each species in each plot and visit, then multiplied by a factor of ten to simulate a hectare. The frequency of stems for all species in each plot was also extrapolated to a hectare for comparability. Following this, each stem was placed into a size class from 5 to 40 and above.

From these combined data sets, pivot tables were created to display the relationship between various variables. For instance, the number of individual stems within each size class was summed to create a frequency, that was then plotted against size class and separated by visit. The same stacking procedure and pivot table inserts were applied to the less than 5cm stem counts.

For each species within each plot a tally was made of the number of subplots in which the species had changed from — (b) being absent in 2007 to being present in 2017; and (c) being present in 2007 to being absent in 2017. McNemar's test was then used by Jayne Balmer to test the null hypothesis that there was no difference between the number of subplots in which the species had changed to being present and the number where it had changed to being absent. The single tailed Chi-square

8 distribution for one degree of freedom, was used to determine the probability of obtaining the result. If the p-value was less than the alpha level of 0.05 the result was considered unlikely to have occurred by chance. McNemar Test: Chi-Square (Yates corrected) = (|(b-c)|-1)2/(b+c). Tests were applied for subplots in which presence and absence data were collected from both visits (i.e. 25 subplots for three plots and 21 subplots at WCP 12). Only species which were recorded from at least 8 different subplots within a plot in either 2007 or 2017 were analysed. Ephemeral geophyte species (all orchids) were excluded from analysis because surveys were undertaken at different times of the year, and seasonal changes were not the focus of this study. Cryptic species for which observer differences may have led to the observed levels of change were also excluded from comparison.

The biomass of each stem was calculated through the formulas (Keith et al, 2000): Eucalyptus B = e(-1.598 + 2.283 * (ln(DBH))) Other species B = e(-1.8957 + 2.3698 * (ln(DBH))) Gifford suggests that a valid estimation of carbon retention is biomass multiplied by a factor of 0.5 (2000, p. 15). Thus, this was applied to the resulting biomass as well as a multiplication by 10 to generate carbon storage in tonnes per hectare for the four plots.

This research received ethics approval from the Local Review Board under the exempt category as it was not conducted on humans or animals.

9 RESULTS & DISCUSSION

Floristic Dynamics

One ordination axis (final stress = 10.2%) is sufficient to explain most of the floristic variation (86%) in the data set of the four plots. This axis was highly correlated with the variable time since the last fire (R2= 0.84), although this gradient appears to be driven by the dramatic separation between the floristic composition of the old growth site and that of the regrowth plots which shared relatively little in common. The distribution of regrowth subplots did not show a strong association with the first axis with almost all of the subplots clustered at the lower end of Axis 1 (Figure 3). Floristic similarity among regrowth communities when studied alongside mature forest populations was also exhibited in Brown (1988, p. 671).

Figure 3. NMS ordination diagram in which the distance separating subplots provides a relative measure of the floristic dissimilarity between them.

Subplots were relatively well clustered within their plot groups, with minimal separation between the two visits (Figure 3). The similarity of subplots in each core plot is a reinforcement of the relative homogeny of the plots chosen for each age class. In turn, the choice to survey only two rows within the core plots as a representation of each age class is supported and allows for valid extrapolation of data from this sample to larger areas. Moreover, a related study asserted that a

10 more prolonged project would be necessary in order to see significant difference in floristic characteristics within a specific age class (Brown, 1988, p. 671).

Table 1. Matrix of PerMANOVA results (t) of paired comparisons between plots tested.

Plot 03CN 67N 98N OGN TSLF 3 13 50 60 73 83 160 170 03CN 3 X 1.86 3.26 3.32 4.48 5.14 7.30 7.99 13 * X 2.81 3.22 3.57 3.74 5.66 6.03 67N 50 *** *** X 1.78 2.93 3.19 6.96 7.33 60 *** *** * X 3.39 3.81 5.59 5.77 98N 73 *** *** *** *** X 1.66 6.35 6.49 83 *** *** ** *** ns X 6.39 6.51 OGN 160 *** *** *** *** *** *** X 0.92 170 *** *** *** *** *** *** ns X Note P-values not adjusted for multiple comparisons. ns= not significant (P>0.05), * 0.05> P>0.1, ** 0.1>P>0.001, *** 0.001>P>0.0001

Regardless of the survey year, there were significant differences in floristic assemblage between the four plots (Table 1). This shows that the communities chosen for each plot are different enough to validate an appropriation of separate age classes. The floristic change recorded over the ten-year period within each plot was relatively small compared to the differences between plots, and the size of the change reduced with increasing age of the plots (Table 1). The latter result is further investigated through stem frequencies in the subsequent sections.

Species Incidence & Turnover: Less than 5cm stems

The frequency of stems with diameters less than 5cm shows a bimodal trend over both visits and all four age classes (Figure 4). Attiwill (1979, p. demonstrated that following fire there is dense regeneration of pioneer species within wet eucalypt communities (03CN). There is rapid growth in height and competition for light as the canopies of individual plants expand, resulting in a large mortality rate of the initial germinants (67N). Most of the change in the first few decades of forest growth is manifested through the thinning of trees unable to compete with their neighbors. This is evident in Figure 4 by the dramatic reduction in total stem numbers over the ten- year period and the relatively small increase in number of stems entering the larger diameter classes as is exhibited in Figure 6 - 8.

11 STEMS 2007 STEMS 2017 2500

2000

1500

1000

STEM STEM FREQUENCY 500

0 2003CN 1967N 1898N OGN PLOT

Figure 4. Frequency of stems less than 5cm as a function of time and plot age. The most common species was Pomaderris apetala in 2003CN, Pomaderris apetala in 1967N, Olearia argophylla in 1898N, and Eucryphia lucida in OGN.

The frequency inclines slightly from 98N to OGN demonstrating a more sustainable equilibrium (Clements, 1936) of stems for this sized plot. The numbers of stems in OGN from 2007 to 2017 remained exactly the same, creating the appearance of no change. However in reality, this visual was created by an almost complete turnover of present species in the plot. The change in the most common species from Pomaderris apetala in the first two plots, to two distinct species in the last two plots furthers this concept of accelerating species turnover. These patterns affirm studies, such as Williams (2012, p. 12), that indicate increasing diversity of species with increased age.

The youngest regrowth plot, 03CN, harbored the fewest number of species with diameters greater than 5cm as shown in Figure 5, while the old growth plot (Figure 8) contained more than twice as many. The 67N (Figure 6) and 98N (Figure 7) plots contained similar numbers of species in between the above two values, which solidifies the aforementioned diversity concept.

Species Incidence & Turnover: Greater than 5cm stems

Pomaderris apetala and Eucalyptus species are the only species that are present in significant frequencies across all four plots (Figure 5 – 8).

12 20 18 16 14 12 10 8 6 4 2 41 0 5 10 15 20 25 30 35 40 ACACIA

20 18 16 14 12 10 8 6 4 2 0 5 10 15 20 25 30 35 40 EUCAL

20 18 16 14 12 10 8 6 4 2 0 5 10 15 20 25 30 35 40 POMAD

Figure 5. Frequency of stems organized by size class in plot 2003CN per visit.

13 20 18 16 14 12 10 8 6 4 2 0 5 10 15 20 25 30 35 40 ACACIA

20 18 16 14 12 10 8 6 4 2 0 5 10 15 20 25 30 35 40 DICKS

20 18 16 14 12 10 8 6 4 2 0 5 10 15 20 25 30 35 40 - 80 EUCAL

Figure 6. Frequency of stems organized by size class in plot 1967N per visit.

20 18 16 14 12 10 8 6 4 2 88 44 89 88 0 5 10 15 20 25 30 35 40 POMAD

20 18 16 14 12 10 8 6 4 2 0 5 10 15 20 25 30 35 40 RAINF

Figure 6. Frequency of stems organized by size class in plot 1967N per visit.

These are both pioneer species (Appendix I), thus it is sensible that they exist from the beginning to the end of the successional process.

Apart from OGN (Figure 8), eucalypt is the most evenly distributed across the range of size classes. As eucalypts are fire tolerant, they are likely to survive as stems or through their woody capsuled seeds through cool, infrequent fire events (Mount, 1979, p. 184). Therefore, persistence through disturbance would create the size evenness described.

Acacia species started with high frequencies in 03CN (Figure 5) before declining to complete absence in the OGN plot. This set of species are also classified as pioneers and are related by Gilbert (1959) to be present with high fire frequency (p.143). This matches with the decreasing correlation through the four plots as the

15 20

0 5 10 15 20 25 30 35 40 ACACIA

0 5 10 15 20 25 30 35 40 - 105 EUCAL

0 5 10 15 20 25 30 35 40 NEMAT

5 52 46 0 5 10 15 20 25 30 35 40 POMAD

Figure 7. Frequency of stems organized by size class in plot 1898N per visit.

16 time since last disturbance is increasing. Dicksonia is classified as an “occasional species” (Gilbert, 1959, p. 141), which explains its narrow presence confined to the 67N plot (Figure 6). Nematolepis is classified as on the border between a pioneer and rainforest species (Appendix I), which would connect well to its late appearance in the succession model within the old growth plot (Figure 8). Eucryphia stems were only present in the OGN plot, as they are more attuned to lower frequency of fire (Gilbert, 1959, p. 143) and thus prefer undisturbed communities.

Anodopetalum occurred solely in the OGN plot as well, which was atypical. This representation is an underestimate, as many of the Anodopetalum stems violated the angle from the horizontal parameter as they were parallel to the ground. Moreover, typical mature wet forest species such as Northofagus and Atherosperma were not present in this plot as was expected. This is explained by the fact that there are different paths through forest succession and the three regrowth plots happen to be on a trajectory towards a different old growth composition than the old growth plot surveyed, However, old growth, regardless of present species, have characteristically slow change dynamics (Jackson, 1968, p. 16). For this reason, the comparison across the four plots as a model for the rate of structural transformation should not be affected by this deviation in direction of succession

The significantly lower frequency in 03CN of Pomaderris stems greater than 5cm when compared to less than 5cm stems, displays the fact that few of the original stems in the dense first stage matured into the next stem class in 2017. Their strong reappearance in plot 67N would suggest that after the initial round of competition, this species is quite successional, however the subsequent plots do not have significant levels of this species. From this data, the rate of species turnover appears to be an exponential regression, with 98N marking the beginning of the plateau.

The OGN plot has higher incidences of relocated stems as it hosts much slower change than more recently disturbed wet eucalypt communities (Jackson, 1968, p. 16). It is well documented in scientific literature that in this late stage within the successional process, more structural consistency would be prevail (Jackson, 1968, p. 16). Support for this is evident through fewer instances of disappeared or new stems, or in other words, a smaller difference between 2007 and 2017 frequencies.

17 20

0 5 10 15 20 25 30 35 40 ANODO

0 5 10 15 20 25 30 35 40 - 225 EUCAL

5 21 40 38 23 0 5 10 15 20 25 30 35 40 - 55 EUCRY

0 5 10 15 20 25 30 35 40 NEMAT

Figure 8. Frequency of stems organized by size class in plot OGN per visit.

18 20

0 5 10 15 20 25 30 35 40 POMAD

0 5 10 15 20 25 30 35 40 - 45 RAINF

0 5 10 15 20 25 30 35 40 OTHER

Figure 8. Frequency of stems organized by size class in plot OGN per visit.

These expressions of species change are consistent with Gilbert’s (1959, p.129) theory of Eucalyptus species replacing more ephemeral pioneer species after reasonable periods without disturbance. This species formulates what Clements (1936) would categorize as the climax structure of the equilibrated forest community.

For most species which experienced a change in their presence in subplots between the two survey dates, the direction of change was inconsistent, and there was

19 insufficient evidence to conclude that these changes were the result of anything except chance variation. However, for six species, sufficient and consistent change occurred between 2007 and 2017 and the possibility of these changes being the result of chance variation was rejected (alpha = 0.05). Five of these species reduced in frequency over the ten-year period, while only one, Coprosma quadrifida, increased in frequency within the subplots in which they were present (Table 2). Only two of the long-term monitoring plots included species with significant change in their level of frequency: 98N and 67N (Table 2).

Table 2. Plant species which changed in frequency of occurrence within 25* 10x 10 m subplots between 2007 and 2017 at four long term monitoring plots.

Present Absent 2007 Present 2007 Chi-square Plot Species 2007 & 2017 Present 2017 Absent 2017 (P-value) 98N Clematis aristata 15 0 7 5.14 (0.02) 98N Gonocarpus sp. 0 1 9 4.90 (0.03) 98N Histiopteris incisa 7 1 8 4.00 (0.05) 67N Grammitis billardierei 16 0 7 5.14 (0.02) 67N Olearia argophylla 10 0 8 6.13 (0.01) 67N Coprosma quadrifida 6 10 1 5.82 (0.02)

The fact that several species recorded changes in both directions between visits demonstrates that community composition is somewhat dynamic within the subplots. In the OGN site, Drymophila cyanocarpa, was present at both survey dates in only three subplots but changed from absent to present or vice versa in 15 other subplots. This species is evidently short lived, but its bird-dispersed fruits enable it to readily colonize new sites (Kirkpatrick, 1999, p. 109).

The large subplot size is likely to obscure some changes in species mortality and establishment rates. This is due to the possibility that species which die over the ten- year span may be replaced by new germinant within the subplot and are thus recorded as present in both visits, creating the impression of no change. Additionally, many species were excluded that may be the ones which are more likely to change within this time frame. This includes ephemeral species, such as orchids (McCormick, 2013, p. 398), and cryptic species, which are difficult to determine in the field, as well as species that were not common enough to test. The presence of seedlings and young plants demonstrate that plots are being colonized, although in many cases these seedlings may not survive to maturity.

20 Allometric Relationships

In 03CN (Figure 9a), there was a massive influx of individuals into greater stem classes from 2007 to 2017. The frequency grew by a factor of more than 10 for two out of the three species present. This rapid change is juxtaposed with the seemingly stable stem frequencies in the other three plots. Again, this exhibits the occurrence of immense change in the beginning stages of succession, before equilibrium (Clements, 1936) is reached. The less than and greater than 5cm stems run parallel in this respect, with 67N (Figure 4 & 10a) showing the most stability over ten years.

The common trend throughout all four plots is that in both survey years, eucalypt species contributed the most to basal area per hectare (Figures 9b, 10b, 11b, 12b). When compared to the frequency graphs for the relevant plot, it is apparent that the species with the greatest frequency is not necessarily the species with the most basal area. In fact, eucalypt species were never the highest counted species in any year for any of the age classes. Figure 12b presents a plot in which there were only three eucalypt individuals identified, but due to their large DBH’s, their combined basal area in 2007 is greater than eucalypt basal area in any other plot.

From this information, it can be deduced that eucalypt species provide the greatest amount of the total biomass compared to other individual species contributions. Literature considering biomass comparisons between eucalypt and rainforest species found this to be heavily supported as well (May et al, 2012, p.323). As biomass is closely related to carbon stores (Figure 13), these findings carry implications for the importance of this genus for carbon sequestration. Even as the plots begin to display rainforest understorey restoration, namely in Figure 10, 11, & 12, the basal area of these species combined remains less than that of eucalypt. This is largely due to characteristic differences in the two species types such that eucalypt tend towards taller and wider stand structures than rainforest species of similar ages (May et al, 2012, p. 318).

In light of these results, it seems as though the ecological role of eucalypt trees could quantitatively be given high conservation priority, though understorey dominated by the rainforest remains a crucial component of mixed forests.

21 1000

800

600

400

200

0 ACACIA ANODO DICKS EUCAL EUCRY NEMAT OTHER POMAD RAINF

Figure 9a. Frequency per hectare of all species in plot 2003CN per visit. V1 V2

70 60 50 40 30 20 10 0 ACACIA ANODO DICKS EUCAL EUCRY NEMAT OTHER POMAD RAINF

Figure 9b. Basal area per hectare of all species in plot 2003CN per visit. V1 V2 visit. 1000

800

600

400

200

1900 1550 0 ACACIA ANODO DICKS EUCAL EUCRY NEMAT OTHER POMAD RAINF

Figure 10a. Frequency per hectare of all species in plot 1967N per visit. V1 V2 visit. 70 60 50 40 30 20 10 0 ACACIA ANODO DICKS EUCAL EUCRY NEMAT OTHER POMAD RAINF Figure 10b. Basal area per hectare of all species in plot 1967N per visit. V1 V2

visit.

22 1000

800

600

400

200

0 ACACIA ANODO DICKS EUCAL EUCRY NEMAT OTHER POMAD RAINF

Figure 11a. Frequency per hectare of all species in plot 1898N per visit. V1 V2

70 visit. 60 50 40 30 20 10 0 ACACIA ANODO DICKS EUCAL EUCRY NEMAT OTHER POMAD RAINF

Figure 11b. Basal area per hectare of all species in plot 1898N per visit. V1 V2

1000visit.

800

600

400

200

0 ACACIA ANODO DICKS EUCAL EUCRY NEMAT OTHER POMAD RAINF

V1 V2 Figure 12a. Frequency per hectare of all species in plot OGN per visit. visit.70 60 50 40 30 20 10 0 ACACIA ANODO DICKS EUCAL EUCRY NEMAT OTHER POMAD RAINF

Figure 12b. Basal area per hectare of all species in plot OGN per visit. V1 V2 visit.

23 Carbon Evaluation

3000000

2500000

)

1 -

2000000

1500000

1000000 Carbon storage (t ha storage Carbon 500000

0 03CN 67N 98N OGN

Eucalyptus Other species Total

Figure 13. Carbon retention in 2017 of eucalypt, all other present species, and total live stems by plot.

The data in Figure 13 illustrates the positive and linear correlation between plot age and total carbon storage of live stems. The old growth plot harbors around forty percent more carbon than the youngest 03CN plot. Further, Eucalypt species contribute approximately the same amount of carbon storage as all other species combined in every individual plot. These findings suggest that old growth forests have significantly higher capacities for carbon retention and thus, are superior in regard to atmospheric carbon reduction. These findings of greater carbon stock magnitudes in old growth forests relative to younger aged communities are consistent with Carmona (2002, p. 271). However, this data set only includes carbon held by live trees, thereby underestimating the one quarter of carbon contained within old growth leaf litter, soil, and dead woody debris (Sohn, 2007, p. 17) Moreover, Eucalyptus obliqua and Eucalyptus regnans are shown as key elements in total carbon retention of the wet forests studied. These findings support the conservation of mature eucalypt trees and surrounding wet forest for the purpose of carbon sequestration detailed in Williams (2012, p. 20). Though heights were not utilized to calculate carbon stocks, the equations used to arrive at these carbon values are widely accepted in ecological research (Keith, 2014).

24 CONCLUSION

The null hypothesis to be tested in regard to the first study aim was that succession does not happen over a ten-year time scale and that there is no evidence of floristic change. The results do, in fact, show little change over a decade in the floristic composition of any of the four plots, irrespective of initial time since the last fire event. There is no significant evidence in this report to reject the null hypothesis.

The second study aim facilitated the discovery that there was significant change in the structural vegetation of some of the plots in the chronosequence. Specifically, the frequency of stems within each plot, when considered for each species individually, did reflect the main successional principles laid out by Clements (1936) and Gilbert (1959) over the two survey years.

This structural change was translated into species specific and total carbon retention, which followed the expected increase of stores from regrowth plots to mature wet forest plots. The relationships from these calculations provide evidence towards the protection of an entity—old growth forests—which naturally combats climate change.

Further studies could include height data of the located stems, either through field estimates, utilization of a vector device, or through Light Detection and Ranging (LiDAR) technology. This could be combined with the DBH measurements to create a more accurate quantification of carbon stocks within the forests. An ideal future study would also involve complete measurements of all rows in all twelve WCP plots.

This project, by nature, should continue at regular ten year intervals over longer brackets of time in order to paint a more holistic picture of wet forest succession, as this study was too short to extract enough significant floristic change. If possible, a long-term research team could be assigned, or at the very least, instill a protocol for each individual to collect a constant data type within one survey. To improve upon efficiency in the future, the plots could be restrung between all subplots. This would save time in identifying subplots and having a marker to lay the transects out straight.

25 REFERENCES

Adams, D. L., J. D. Hodges, D. L. Loftis, J. N. Long, R. S. Seymour, and J. A. Helms. (1994). Silviculture Terminology with Appendix of Draft Ecosystem Management Terms. Silviculture Instructors Subgroup of the Silviculture Working Group of the Society of American Foresters.

Attiwill, P.M. (1979). Nutrient cycling in a Eucalyptus obliqua (L’Herit.) forest. III. Growth, biomass, and net primary production. Australian Journal of Botany. 27(4). 439-458.

Brown, M.J. and Podger, F.D. (1982). Floristics and re regimes of a vegetation sequence from sedgeland-heath to rainforest at Bathurst Harbour. Australian Journal of Botany 30:659-676.

Brown, M.J. (1996). Benign neglect and active management in Tasmania's forests: a dynamic balance or ecological collapse? Forest Ecology and Management 85: 279- 289.

Commonwealth of Australia. (2013). Guidelines for detecting orchids listed as ‘threatened’ under the Environment Protection and Biodiversity Conservation Act 1999.

Ecological sustainability. [Def. 1]. (2017). In Business Dictionary, Retrieved March 26, 2017, from http://www.businessdictionary.com/definition/ecological- sustainability.html.

Gilbert J.M. (1958) Forest succession in the Florentine Valley, Tasmania. Papers and Proceedings of the Royal Society of Tasmania 93: 129-151.

Jackson, W.D. (1968) Fire, air, water and earth - an elemental ecology of Tasmania. Proceedings of the Ecological Society of Australia 3: 9-16.

Keith, H., D. Barrett, and R. Keenan. (2000). Review of allometric relationships for estimating woody biomass for New South Wales, the Australian Capital Territory, Victoria, Tasmania and South Australia. National Carbon Accounting System Technical Report Number 5b. Australian Greenhouse Office, Canberra, ACT, Australia.

Kirkpatrick, J.B., Peacock, R.J., Cullen, P.J., Neyland, M.J. (1988). The wet eucalypt forests of Tasmania. Tasmanian Conservation Trust Inc. Hobart.

Keith, H., D. Lindenmayer, B. Mackey, D. Blair, L. Carter, L. McBurney, S. Okada, and T. Konishi- Nagano. (2014). Managing temperate forests for carbon storage: impacts of logging versus forest protection on carbon stocks. Ecosphere. 5(6):75. 1-34.

Kirkpatrick, J.B. (1999). The characteristics and management problems of the vegetation and flora of the Huntingfield area, southern Tasmania. University of Tasmania. 133(1): 103-113.

26 May, B., Bulinski, J., Goodwin, A., & Macleod, S. (2012). Tasmanian forest carbon study full report. CO2 Australia Limited. 323-365.

McCormick, M.K. and Jacquemyn, H. (2013). What constrains the distribution of orchid populations? New Phytologist Trust. 202(2). 392-400.

McCune, B. and M. J. Mefford. (2011). PC-ORD. Multivariate Analysis of Ecological Data. Version 6.08 MjM Software. Gleneden Beach.

Mount, A.B. (1979) Natural regeneration processes in Tasmanian forests. Search. 10: 180-186.

Turner, P., Grove, S., Airey, C., McElhinny, C., Scanlan, I., Power, J., Strutt, O., Sohn, J., Smith, T., Clark, S. (2007). Establishment of wildfire chronosequence benchmark plots in southern Tasmania. The Wildfire Chronosequence Project: Forestry Tasmania, Bushfire CRC, University of Tasmania (School of Plant Science). Hobart.

Serong, M., Lill, A. (2008). The timing and nature of floristic and structural changes during secondary succession in wet forests. Australian Journal of Botany. 56. 220- 231.

Scanlan, I. (2007). A methodology for modelling canopy structure. An exploratory analysis in the tall wet eucalypt forests of southern Tasmania. Unpublished Hons. Australian National University, Canberra.

Sohn, J.A. (2007). Variation in coarse woody debris attributes in Tasmanian tall wet Eucalyptus obliqua (L’Herit.) forests and implication for its monitoring. Unpublished Hons. Albert-Ludwig University, Freiburg. Germany.

Turner, P.A.M., Grove, S.J., and Airey, C.M. (2007). Wildfire Chronosequence Project Establishment Report. Division of Forest Research and Development, Forestry Tasmania. Hobart.

Wells, P., Hickey, J.E. (1999). Wet sclerophyll, mixed and swamp forest. In 'Vegetation of Tasmania'. Australian Government Printing Service. Canberra pp. 224-243.

Whiteley, S.B. (1999). Calculating the sustainable yield of Tasmania's State Forests. Tasforests 11, 23-34.

Williams, J. (2012). The evolutionary significance of Tasmania’s rainforests and associated vegetation types. IVG Forest Conservation Report 5C: Evolutionary significance.

27 APPENDICES

Appendix A. Site selection criteria. Source: (Turner, 2007, p. 15)

• Meeting the fire history. (1898/1906 wildfire, 1934 wildfire, 1966/67 wildfire) • Clearfell burn and sow (1966-or-1967, post 2000) • Old growth mixed forest - containing at least 5% eucalypt old growth • Aspect: north to westerly is defined as between WSW (247 )̊ and NNE (22.5 ).̊ South to easterly aspect is defined as between ENE (67 )̊ and SSW (202.5 )̊ • Located preferably within the Warra, Picton, Weld or Arve Forest Blocks. • On State forest or within the World Heritage Area • Altitude within the range 0 – 500 m • Site potential (E2 preferred but E1 and E+3 sites can be included) • Eucalyptus obliqua is the dominant tree species but up to 50% of the stocking of co- dominant strata by E .regnans is acceptable. • Underlying bedrock (dolerite is preferred). Disregard sites on sandstone, quartzite and conglomerates. • Position on slope: midslope preferred. • Exclusion from timber harvesting schedules (preferably outside provcoupes) • Ease of access (the easier the better). but least 100 m and preferably no more than 1km from a road edge. • Former research history. Consider current or dropped Continuous Forest Inventory (CFI) plots /Permanent Inventory Plots (PIP's); sites already included in trials (Silvicultural Systems Trial (SST), Baseline Altitudinal Monitoring Project plots (BAMP's)) at Warra; and sites used in other ecological studies such as by John Hickey, Sue Baker, Perpetua Turner, Gemma Woldendorp). • At least 100 m from hard edges (main roads, buttongrass plain). Each 50m x 50 m plot will have a 100m buffer, therefore the site will be 250m X 250m = 6.25 ha. • Any site(s) found which meet all the criteria and have cut stumps with the evidence of ‘shoes’ (the area where boards/planks were inserted for logging) will be included in a short- list. Sites with cut stumps but no evidence of shoes should be disregarded.

28 Appendix B.1. 03CN Access & Site Map. Source: (Turner, 2007, pp. 41, 48)

29 Appendix B.2. 67N Access & Site Map. Source: (Turner, 2007, pp. 36, 48)

30 Appendix B.3. 98N Access & Site Map. Source: (Turner, 2007, pp. 32, 48)

31 Appendix B.4. OGN Access & Site Map. Source: (Turner, 2007, pp. 29, 48)

Appendix C. Less than 5cm stem count data collection template.

Record Plant No. Plot Subplot Species binomial Status Stems 07 Stems 17 Note 07 Note 17 0017 OGN 17 live 3 A1 0018 OGN 18 live 1 A1 OGN live A1 OGN live A1

Appendix D. Greater than 5cm DBH data collection template.

Record Plant No. Plot Subplot Quadrant x y Species Status DBH 07 DBH 17 Note 07 Note 17

OGNA 0017 1 17 live 3

OGNA 0018 1 18 live 1

OGNA 1 live

Appendix E. Species cover data collection template.

E1 E2 PLOT E3 E4 E5 07 17 07 17 07 17 07 17 07 17 0.5 0.5 Acacia 0.5 6 15 30 15 Acacia 30 15 20 15 30 35 0.5 0.5 1 0.5 0.5 0.5 0.5 0.5 1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

Appendix F. Species presence/absence data collection template.

A1 A2 PLOT A3 A4 A5 07 17 07 17 07 17 07 17 07 17 0.5 0.5 Acacia 0.5 6 15 30 15 Acacia 30 15 20 15 30 35 0.5 0.5 1 0.5 0.5 0.5 0.5 0.5 1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

Appendix G. Results of species indicator analysis for plots and age-classes. Plot in which TSLF in which Species the species Indicator p-value the species p-value Abbreviation has its Value (plot) had its (TSLF) greatest (plot) greatest importance importance Leptglau 03CN 15 0.048 - ns Acacmela 03CN 25 0.009 - ns Acacdeal 03CN 37 0.015 3 0.072 Pterescu 03CN 41 0.003 3 0.064 Zierarbo 03CN 45 0.001 3 0.001 Billlong 03CN 79 0.001 3 0.001 Acacvert 03CN 94 0.001 3 0.001 Pomaapet 03CN 39 0.001 13 0.001 Gahngran 03CN 62 0.001 13 0.001 Hymecupr 67N 20 0.023 - ns Hyporugo 67N 25 0.001 - ns Histinci 67N 33 0.001 50 0.039 Dickanta 67N 84 0.001 50 0.001 Senesp 67N 16 0.043 60 0.009 Micrpust 67N 25 0.004 60 0.005 Hydrhirt 67N 30 0.001 60 0.007 Hymeflab 67N 36 0.001 60 0.048 Rumoadia 67N 65 0.001 60 0.001 Polyprol 67N 69 0.001 60 0.001 Gonocarp 98N 20 0.008 73 0.041 Eucaregn 98N 29 0.006 73 0.048 Coprquad 98N 37 0.004 73 0.039 Clemaris 98N 44 0.001 73 0.004 Oleaargo 98N 45 0.001 73 0.025 Drymcyan 98N 48 0.001 73 0.003 Pimedrup 98N 52 0.001 73 0.001 Nemasqua 98N 64 0.001 73 0.002 Eucaobli 98N 38 0.006 83 0.001 Hymepelt 98N 42 0.001 83 0.001 Ctenhete 98N 54 0.001 83 0.001 Proslasi OGN 26 0.004 160 0.02 Tasmlanc OGN 40 0.001 160 0.013 Monoglau OGN 41 0.001 160 0.078 Hymeraru OGN 45 0.001 160 0.007 Prioceri OGN 55 0.001 160 0.002 Cyatglau OGN 70 0.001 160 0.001 Pittbico OGN 75 0.001 160 0.001 Athemosc OGN 76 0.001 160 0.001 Blecwatt OGN 87 0.001 160 0.001 Phylaspl OGN 94 0.001 160 0.001 Cenaniti OGN 100 0.001 160 0.001 Troccunn OGN 100 0.001 160 0.001 Noteligu OGN 39 0.001 170 0.014 Pimecine OGN 43 0.001 170 0.005 Anodbigl OGN 95 0.001 170 0.001 Eucrluci OGN 100 0.001 170 0.001 Nothcunn OGN 20 0.015 170 ns Grambill OGN 29 0.05 50 0.005 Arispedu ns - ns Galiaust ns - ns Uncitene ns - ns

34 Appendix H. Relative abundance species in each plot at each visit.

TSLF 3 13 50 60 73 83 >160 >170 Plot 03CN 03CN 67N 67N 98N 98N OGN OGN (visit) (V1) (V2) (V1) (V2) (V1) (V2) (V1) (V2) Acacdeal 7 3 6 8 1 + Acacmela + + + + Acacvert 20 11 1 + 1 + + + Anodbigl 9 12 Arispedu + + + + Athemosc + + 2 1 Billlong 1 + + + + Blecnudu 1 Blecwatt + 6 3 Cenaniti 13 13 Clemaris + + + 1 + Coprquad + + 1 + 1 1 Ctenhete + + + + Cyatglau + + + + 2 1 Dickanta + 7 6 1 1 Drymcyan 1 + + + Epacimpr + Eucaobli 7 14 22 27 26 39 5 8 Eucaregn + 1 2 1 Eucrluci + 29 33 Gahngran 6 11 1 1 5 3 Galiaust + + + + Gonotetr + + Grambill 1 + 1 + + + Grammage + + + Grammarg + Histinci + + + + + Hydrhirt + + Hymeaust + + + Hymecupr + + + + + Hymeflab + + + + + + Hymepelt + + + Hymeraru + + 1 + 1 + Hyporugo + + Lasthisp + + Leptjuni + + Leptglau + + + 1 1 Micrpust + + Monoglau + + + + 1 1 Nemasqua 1 1 1 1 15 13 3 5 Noteligu 1 + + 1 Nothcunn + 1 Oleaargo 2 3 3 3 Parsbrow + Phylaspl + + 11 9 Pimecine + + + + Pimedrup + + + + 1 + + + Pittbico + + 2 2 Polyprol 1 1 + Pomaapet 54 55 57 50 41 38 1 2

35 Prioceri 1 1 Proslasi + + 1 + Pseugunn + Pterescu 1 + + + Rumoadia 1 1 Senesp + + Sticurce + + Tasmlanc + + + + Tmessp + + Troccunn 3 2 Uncitene + + + Urtiinci + Zierarbo + + + +

(+ indicates species with a mean cover of <0.5%)

Appendix I. Plant list.