Natural value assessments – can they predict the species richness of red listed and bioindicator fungi in Fennoscandian coniferous forests?

Bachelor thesis

Natural value assessments – can they predict the species richness of red listed and bioindicator fungi in

Fennoscandian coniferous forests?

Author: Louise Samuelsson

Supervisors: Börje Ekstam & Anna Norberg

Term: VT17 Subject: Biology

Level: First level, Bachelor thesis Course code: 2BI01E

Institution: Linneaus University Nr: 2017: Bi4

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Abstract

The thesis aims to examine how well two standardised methods in nature conservation, namely natural value assessment and key biotope inventory, perform in terms of recognising an area with a high amount of red listed and bioindicator fungi. The two methods are compared to see if any method is superior to find areas with high presence of red listed and bioindicator fungi and if a larger area automatically means a larger number of species. A natural value assessment inventory is conducted on study sites with a performed key biotope inventory, with an addition of an inventory on fungi.

Statistical tests are completed to give information about occurring correlations. The results display that the key biotope inventory and the natural value assessment do not differ in their evaluations of an area. Analysis also shows that there is no relationship between the forest sites area and the assessment performed by the natural value

assessment. However, if the assessment is performed by the key biotope inventory there exist a relationship with the forest sites area. Further on, there is no correlation between the area and its number of species. The tests also indicate that neither of the methods predict presence or the number of red listed and bioindicator species in a forest. Based on the results, the presence of bioindicator and red listed species alone are not good indicators of forest nature value. In order to give more credible answers to these questions, more studies with increased number of replicates should be conducted.

Abstrakt

Avhandlingen syftar till att undersöka hur bra två standardmetoder inom naturvården (Skogsbiologernas naturvärdesbedömning och Nyckelbiotopsinventering) är på att upptäcka ett områdes förekomst av rödlistade svampar och signalarter. Metoderna jämförs för att upptäcka om någon metod är överlägsen den andra angående att finna områden med stor förekomst av rödlistade svampar och signalarter samt om ett större område automatiskt innebär fler arter. Detta undersöks genom att Skogsbiologernas naturvärdesbedömning utförs på områden som redan inventerats utifrån en

Nyckelbiotopsinventering med tillägg av en svampinventering. Statistiska analyser genomförs därefter för att ge svar om förekommande samband. Analyser visar att Nyckelbiotopsinventeringen och Skogsbiologernas naturvärdesbedömning inte skiljer sig angående bedömningen av skogsområden. Utförda statistiska testerna tyder även på att det inte förekommer något samband mellan bedömda områdens areal och dess

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bedömning enligt Skogsbiologernas bedömning, däremot finns det ett samband mellan områdenas areal och Nyckelbiotopsinventeringens bedömning. Det förekommer dock inget art-area samband. Utförda tester indikerar även att ingen av metoderna kan förutsäga mängden rödlistade svampar samt signalarter i ett skogsområde. Baserat på detta resultat ger förekomsten av signalarter samt rödlistade svamparter ingen bra indikation på en skogs naturvärde. För att ge mer trovärdiga svar på dessa frågor krävs mer studier med en ökad mängd replikat.

Keywords

Nature conservation, key biotope inventory, natural value assessment, bioindicator, red listed species

Acknowledgements

Magnus Wadstein & Mikael Hagström Swedish Board of Forestry Börje Drakenberg

Sveaskog

Anna Norlund and Börje Ekstam Family and friends

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Index

1 Introduction _____________________________________________________________ I 1.1 The Natural value assessment __________________________________________ III 1.2 Key biotope inventory ________________________________________________ IV 1.3 Bioindicators _______________________________________________________ IV 1.4 Red Listed species ____________________________________________________ V 1.5 Key biotope ________________________________________________________ VI 1.6 Aim of study & Research questions ______________________________________ VI 2 Method and Material _____________________________________________________ VII 2.1 Selecting study areas _________________________________________________ VII 2.2 Fieldwork & inventory _______________________________________________ VIII 2.3 Analysis ___________________________________________________________ IX 3 Results ________________________________________________________________ XI 3.1 Research question 1: Equally assessment (NVA & KBI) _____________________ XI 3.2 Research question 2 & 3, Assessment value (NVA & KBI)– species richness of fungi

XII

3.3 Research question 4, Species – area relationship ___________________________ XIII 3.4 Research question 5, Assessment value – area relationship (KBI & NVA)_______ XIV 4 Discussion _____________________________________________________________ XV 4.1 Research question 1: Equal assessment (NVA & KBI) _______________________ XV 4.2 Research question 2 & 3: Assessment value (NVA & KBI) – species richness of fungi

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4.3 Research question 4: Species–area relationship ____________________________ XVI 4.4 Research question 5: Assessment value – area relationship (KBI & NVA) ______ XVII 4.5 Further Discussion _________________________________________________ XVIII 5 Conclusion ___________________________________________________________ XVIII 6 References _____________________________________________________________ XX 7 Appendix ____________________________________________________________ XXIII 7.1 Natural value assessment form _______________________________________ XXIII

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1 I NTRODUCTION

During the last decades, the environment has gone through drastic changes, ensuing in disappearance of species, biotopes and whole ecosystems (Persson & Persson 2015;

Hooper et al. 2012; Beardsley 2012). This has resulted in decrease of earth’s biological diversity, also called biodiversity (Hooper et al. 2012; Beardsley 2012). Biodiversity is important for maintaining the stability and functioning of ecosystem processes, and an asset that needs to be preserved for the future (Loreau et al. 2001). Therefore, finding and establishing nature protection areas on forests with the highest natural values is important for stopping the ongoing depletion of the nature (Forsberg 2012).

Due to the aforementioned, the United Nations (UN) formed the convention of biological diversity in 1992, aiming to preserve biological diversity on three different levels: genetic, species and ecosystem level (United Nations 1992). According to the UN convention, biodiversity may be defined as the variety among all the living

organisms on earth and the systems and environments that they are living in. In Sweden, the Parliament stated 16 goals to improve the environmental quality (Naturvårdsverket 2009). One of the goals was to enlarge the protection of forests and water areas to ensure the longevity of these valuable areas (Forsberg 2012). In order to reach the national environmental quality goals, as well as the UN convention of biodiversity, it is important to find areas in the forest that are undisturbed and that holds high natural values. After identifying these areas, action may be taken to protect these areas and their natural values.

The identification of valuable forests is of great importance since it is the first step in being able to protect them. Protection of forest are of upmost importance since the biodiversity is declining. The contributing factors include modern agriculture and forestry practices (Simberloff 1999) as well as a shift in land use, which distorts and even destroys species habitats (Forsberg 2012). For instance, the Fennoscandian forest landscape has changed massively during the recent decades (Ericsson et al. 2005), mostly due to increased forest management (Magnusson et al. 2014). Modern forestry practices have drastically reduced the amount of dead wood in the forests, leading to

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increased numbers of threatened wood-inhabiting fungal species (Magnusson et al.

2014). Several studies have shown the effect of modern forestry practices on wood- inhabiting fungi (Magnusson et al. 2014), and e.g. Siitonen (2001) showed that a decline of dead wood and increased habitat loss have negative effects on wood-inhabiting fungi.

Some wood-inhabiting fungi as well as other fungal species, plants and animals can be used as a tool in the nature conservation work. These species, called bioindicators, indicate if a forest have high natural values and are worthy of protection (Nitare &

Hallingbäck 2000). Red listed species can also be used to discover areas that are worthy of protection (Nitare & Hallingbäck 2000) and have specific habitat requirements (Berg et al. 2002). However, since these species may be hard to find and identify, they are often left out and not used in the same way as bioindicators at a natural value inventory (Nitare & Hallingbäck 2000). Henceforward, species richness of fungi is referring to species richness of red listed and bioindicator fungi.

For preserving biodiversity in the forest, different measures such as protection,

certifications, voluntary provisions and general consideration should be taken. In order to protect an area with high natural value, an inventory or evaluation of the area must be done to recognise such valuable areas (Appelqvist 2005). Studies have been done to compare methods in nature conservation (Cameron et. al. 2008). In Sweden, there are several methods to evaluate if a forest has a high natural value, and in this thesis the focus will be on two of them: 1) key biotope inventory (KBI) and 2) Forest-biologist natural value assessment (henceforth referred to as ‘natural value assessment’ or ̓NVA).

These two inventory methods may lead to establishment of key biotopes which is a common nature protection used in Sweden. Key biotopes are areas with high natural values that aims to preserve biodiversity (Skogsstyrelsen 2014).

Areas that become protected because of a nature conservation work can have different sizes. Often a larger area is considered better than a small one, since they tend to hold more species and support larger populations of species than a smaller area (Götmark &

Thorell 2003). A study comparing the size of nature reserves, however, showed that small forests of conservation can also have great values for biodiversity (Götmark &

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Thorell 2003). There are also several studies made to discover if there exist relationship between the amount of species in an area and the size of that area (Newton & Haigh 1998; Peay et al. 2007; Connor & McCoy 1979).

1.1 T

N

The NVA is a method primarily intended to provide a general natural value assessment of different forest areas and not specific key biotopes or other particularly valuable areas (Skogsbiologerna 2000). The approach is to assess relevant forest properties, provide increased survival for the forest's flora and fauna (Skogsbiologerna 2000) and estimate the environments qualification to accommodate a rich biodiversity

(Drakenberg & Lindhe 2004). The method should serve as a tool that frugally can present an overview of the selected area, so further decisions regarding increased inventories or disposing of the area due to high conservation values may be taken.

Assessed properties are for instance forest age, topography, structure, fertility and cultural influences. The assessment is done through a scoring system where the score may be converted into a rough measurement of the area's biodiversity (Skogsbiologerna 2000). However, no specific inventory directed against occurring species, neither red listed nor bioindicator species are conducted even though presence of red listed and bioindicator species are noted (Drakenberg & Lindhe 2004).

The method is applicable to all forests in Sweden regardless of the age and other

characteristics of the target. The assessment is altered for four different regions and one general nationwide version (Drakenberg & Lindhe 2004). The assessment is appropriate to apply to sites with a maximum area of 10 hectares, but it should not be used on smaller, point-like areas or edge habitats (Skogsbiologerna 2000). In the hemiboreal version there are five different described habitat groups used. Since the inventory method for the assessment is based on knowledge and terms that are fundamental for people active in forestry, no prior knowledge is required except some regarding elementary forest biology (Drakenberg & Lindhe 2004).

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For further reading and more detailed information regarding the different habitat groups used in the hemiboreal version, see Drakenberg & Lindhe (2004). For more detailed description of the forms appearance and implementation in the field, the assessment and how the method is used today and by whom, see Appendix 7.1, Drakenberg & Lindhe (2004) and Skogsbiologerna (2000).

1.2 K

A “key biotope inventory” is an inventory where mainly the forest structure and elements are evaluated, e.g. the age and tree composition of the forest. Presence of bioindicators is another important part in the assessment but due to limited time, species are not explicitly searched after but they are registered upon encounter (Skogsstyrelsen 2014). The inventory can be used as a tool and base of knowledge to illuminate

environments and habitats that are considered valuable in terms of biodiversity. The KBI methods and definitions are the nationally consistent, hence an area should be similarly assessed regardless of the location (Skogsstyrelsen 2014).

For a forest to be assessed as a key biotope, large areas of the site should indicate high conservation values and the site should also include or have possibilities to include red listed species. (Skogsstyrelsen 2014). Those conducting the inventory should be

proficient in identifying species, conservation biology and inventory methodology since the assessment is mostly based on the person’s own judgement of the area

(Skogsstyrelsen 2014). For more detailed information of the forms appearance and implementation in the field, the assessment and how the method is used today and by whom, see Skogsstyrelsen (2014).

1.3 B

The biggest goal for nature conservation is to preserve the biodiversity of forest

ecosystems (Lindenmayer & Margules 2000). The Swedish forest should be managed in a way that secures the biological diversity — in a way that secures the presence of animal and plant species that naturally belong there, as well as protects species and

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habitats that may be threatened. To ensure this, knowledge regarding our forests and the species living there is needed. An important tool in today’s nature conservation is bioindicators, sometimes referred to as indicator species. A bioindicator may be defined as a species that indicates areas with high natural values (Nitare & Hallingbäck 2000).

With the help of bioindicators, it is possible to evaluate the quality of biotopes and different environments and also find areas that are worthy of protection. A good bioindicator should be easy to identify in the field and be linked to areas with high natural values (Nitare & Hallingbäck 2000), as well as quite sensitive, so it reacts to changes in the environment (Halme et al. 2009). According to Halme et al. (2009) no indicator species can fulfil all the criteria needed for an ideal indicator, hence different indicators are needed for diverse purposes. Nevertheless, the usage of bioindicators has shown to be both timesaving and quality assurance (Nitare & Hallingbäck 2000). Plants, lichens, mosses and fungi are all used as bioindicators, but in this study the focus will be on ground growing fungal indicator species (Nitare & Hallingbäck 2000).

1.4 R

L

Some species among the bioindicator species are also classified as red listed in the IUCN (International Union for Conservation of Nature and Natural Resources) red list (IUCN 2017). The Swedish red list is produced yearly by the Artdatabank of the Swedish University of Agricultural Science and is established by the Environmental Protection agency in Sweden (Naturvårdsverket). The red list categorize species based on how threatened they are, which is decided by criteria’s as the species current and predicted future population size and the currently known distribution (Nitare &

Hallingbäck 2000). Species are departed into six different red list categories according to the IUCN red list (2016). These are Least concerned (LC), Near threatened (NT), Vulnerable (VU), Endangered (EN), Critically Endangered (CR), Extinct in the Wild (EW) and Extinct (EX). In the Swedish version in the Artdatabank (Artdatabanken 2017) the categories are Data Deficient (DD), Near threatened (NT), Vulnerable (VU), Endangered (EN), Critically Endangered (CR) and Regionally Extinct (RE). The IUCN Red list of threatened species serve as a tool to assess the world’s species conservation status (IUCN 2017). Red listed species might be used as indicator for discover areas worthy of protection and conservation. However, red listed species are often hard to

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identify and discover in the field and therefore they are habitually left out and not used as bioindicators at natural value inventories (Nitare & Hallingbäck 2000).

1.5 K

The concept ‘key biotope’ has been used in Sweden since October 1990 and its implication was decided by the Swedish National Board of Forestry (Skogsstyrelsen 2014). The main goal with key biotopes was to preserve biodiversity and nowadays they indeed represent a notable part of the biodiversity in the Swedish forest (Skogsstyrelsen 2014). The key biotopes may sometimes be referred to as ‘key habitats’ (Tomppo et al.

2009) but in this study the term ‘key biotope’ will be used. According to the Swedish National Board of Forestry, a key biotope is defined as an area which indicates a high natural value and therefore should be protected. The evaluation is conducted on e.g. the history and structure of the forest, amount of dead wood or the bioindicators and red listed species that has been found (Skogsstyrelsen 2014).

1.6 A

& R

The aim of the thesis is to examine if two standardised methods for the evaluation of the naturalness (Winter 2012) of a focal area (NVA and KBI) are coherent in their

evaluations and if any correlation exist between the assessments and the presence of red listed and bioindicator fungi. The specific research questions that this thesis aims to answer and their exposition are the following:

1) Equal assessment (NVA & KBI): Is a forest equally assessed regardless of the assessment method?

Exposition: Does the KBI give a forest the same evaluation as the NVA?

2) Assessment value (NVA) – species richness of fungi: Can the evaluation level in the NVA predict the richness of fungi?

Exposition: Does a high (low) natural value evaluation according the NVA entail a high (low) presence of red listed and bioindicator fungi?

3) Assessment value (KB1) – species richness of fungi: Is there a correlation between the KBI evaluation level and the richness of fungi?

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Exposition: Does a high (low) natural value evaluation according the NVA entail a high (low) presence of red listed and bioindicator fungi?

4) Species-area relationship: Does the number of species depend on the forest area?

Exposition: Does the number of species living in an area depend on its size and/or quality?

5) Assessment value-area relationship: Is there a correlation between the natural value classification performed by either of the assessment methods and the size of the area?

Exposition: Does the sites area influence the sites evaluation according to the NVA or the KBI?

To answer the thesis research questions, 12 forests were visited and a natural value assessment was conducted according the NVA form. These results were thereafter compared to previously completed KBI. Since the fieldwork for this study will be conducted in Östergötland the forest region that will be analysed is the hemiboreal zone.

According Ahti et al. (1968) the hemiboreal zone is a transition area between the boreal zone in northern Sweden and the Nemoral zone in southern Sweden.

2 M ETHOD AND M ATERIAL

2.1 S

12 areas were selected in Östergötland where the presence of red listed and bioindicator fungi has previously been carefully inventoried. The areas had already been assessed by a Key biotope inventory (KBI) with an addition of a fungi inventory. The KBI classify all areas in one of four different categories based on their value. Three areas from each category were selected for analysis in this study. In order to reduce interacting effects of area, all selected forests had similar size (0.8-5.0 hectare). The selected areas were all from Linköping county, in the province Östergötland. Our collaborators Mikael Hagström and Magnus Wadstein at the Swedish National Board of Forestry provided help to select the areas. Artportalen, (Artportalen 2017) is a website where all

observations of fungi, plants and animals in Sweden are reported by scientist, private individuals as well as people working in the nature conservation sector. The site was used as a tool to detect if any new discoveries of red listed or bioindicator fungi had

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been found in the selected areas after the inventories had been carried out. The data collected from this site was a complement to one of the sites inventories:

Taggfingersvamp (Ramaria karstenii), found at Stenstugan, Ulrika, Ög 30/8–30/9- 11 by Mikael Hagström.

2.2 F

&

The fieldwork was performed during weeks 8 and 9 in the end of February and beginning of March 2017. In the beginning of each inventory, the whole area was walked through and the numbers of elements such as trees with a diameter > 60 cm or the proportion dead trees, were recorded with the help of a GPS device. After seeing most of the area, the type of environment was determined and thereafter the questions in the form (Appendix 7.1) were answered. Questions were answered with “yes” or “no”

depending on whether different features and properties were present. There were totally 80 questions but not all was answered during each inventory since they were adapted to different regions. Each “yes” contributed with one score while a “no” gave no score. In the end the scores was summarised, giving a total score that may be converted into a rough measurement of the area's biodiversity (Skogsbiologerna 2000). The maximum score was 50, and a rule of thumb was that a score over 30 indicates high natural values.

If a site scored 15-20, it held relatively high natural values, whereas if it scored 5-10 points or even lower, it held low natural values (Skogsbiologerna 2000). All

encountered red listed or bioindicator species were listed and after the inventory a short description of the site was written. Photos were taken of deviating and interesting elements in the forest, such as uprooted trees or bioindicator species. To ensure the accuracy and good reliability of the inventories, I received a lecture by the founder of the method, Börje Drakenberg.

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2.3 A

All analyses were carried out in the R environment (R Core Team 2017). Appropriate statistical test was performed to answer the research questions (Table 1).

Table 1.Description of the statistical test performed to answer the research questions.

Research question Performed statistical test

Implementation

1. Equal assessment (NVA & KBI)

Spearman rank correlation

The dataset includes the assessment results from twelve sites. The data from the NVA assessment consists of continuous values, while the data from the KBI consists of categorical data.

A dataset was formed including the results from each assessment.

The test will show if the methods assessed the forests alike or if the assessments differ and there was a significant difference between the methods (Figure 1).

2. Assessment value (NVA) – species richness of fungi

Spearman rank correlation

The dataset includes the number of species in the area, which is continuous data. As well as the twelve sites score in the Natural value assessment (NVA), also continuous data. Data was log- transformed to normalize data.

Zero values where set as 0.01 to enable log-transformation (Figure 2a).

X 3. Assessment value

(KB1) – species richness of fungi

Kruskal-Wallis test The dataset includes the number of species in each of the twelve sites, which is continuous data and the four different assessment levels (A 4 in the KBI

corresponds to low level, 3 corresponds to medium, 2 corresponds to high and 1 to highest) which is categorical data.

(Figure 2b).

4. Species–area relationship

Linear regression The dataset includes log- transformed data of the twelve sites area and number of species.

Both variables consist of

continuous data. The zero values were put as 0.01 to enable the log-transformation (Figure 3).

5. Assessment value – area relationship

Spearman rank correlation

Two datasets, one for each assessment method where both includes data of the twelve sites areas and the assessment of each method. The data for the sites area includes continuous data for both datasets. The variable for the assessment according the NVA include continuous data, while the assessment according the KBI includes categorical data (Figure 4a and 4b).

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3 R ESULTS

3.1 R

1: E

(NVA & KBI)

The Spearman rank correlation for analysing the equality between assessment methods (NVA & KBI) showed a p-value below 0.01 (3.6e-06), meaning it is statistically

significant (Table 2). The direction of the correlation was negative, -0.94, and showed a monotonic relationship (Table 2 and Figure 1).

Table 2. Result for performed spearman rank correlation for research question 1, Equally assessment (NVA & KBI) showing p-value, direction of correlation and interpretation.

Research question Performed test

p-value Direction of

correlation

Interpretation Related figure 1. Equally

assessment (NVA &

KBI)

Spearman rank correlation

3.6e-06 -0.94 A monotonic negative relationship with a p-value below 0.01, meaning it is statistically significant

Figure 1

1.0 1.5 2.0 2.5 3.0 3.5 4.0

101520

NBI

SKB

Figure 1. The relationship between the evaluations performed by the Key biotope inventory (KBI) and the Nature value assessment (NVA). Continuous data on the y-axis and categorical data on the x- axis. Unit: evaluation level. Y-axel: Score in NVA (0-50); X- axis: Evaluations level in KBI (1-4) (p- value<0.01; rho=-0.94)

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3.2 R

2 & 3, A

(NVA & KBI)–

SPECIES RICHNESS OF FUNGI

The Spearman rank correlation for testing the relationship between the assessment value (NVA) and the species richness of fungi showed a p-value above 0.05 (0.052), meaning no significance (Table 3). The direction of the correlation was positive, 0.49 (Table 3 and Figure 2a). The Kruskal-Wallis test for testing the relationship between the

assessment value (KBI) and the species richness of fungi showed a p-value above 0.05 (0.72), meaning no significance (Table 3 and Figure 2b).

Table 3. Result for performed spearman rank correlation for research question 2, Assessment value (NVA)– species richness of fungi, showing p-value, direction of correlation and interpretation and performed Kruskal-Wallis test for research question 3, Assessment value (KB1) – species richness of fungi showing p-value and

interpretation.

Research question Performed test

p- value

Direction of

correlation

Interpretation Related figure 2. Assessment

value (NVA)–

species richness of fungi

Spearman rank correlation

0.052 0.49 A p-value above 0.05, meaning no significance

Figure 2a

3. Assessment value (KB1) – species

richness of fungi

Kruskal- Wallis test

0.72 A p-value

above 0.05, meaning no significance

Figure 2b

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Figure 2. a) Scatterplot of Spearman rank correlation test. Logarithmic continuous data on the y-axel for the number of species and continuous data on the x-axel for the score in Natural value assessment (NVA). Y-axel: Log Number of species; X-axel: Score in NVA (0-50) (p-value>0.05; rho= 0.49); b) The correlation between the different assessment levels in the Natural value assessment (KBI) and the number of red listed and bioindicator fungi species in each level.

Y-axel: Number of species; X-axel: Assessment in KBI (1-4) (p-value>0.05) (Log= Logarithmic).

3.3 R

4, S

–

The linear regression for species–area relationship showed a p-value above 0.05 (0.076), meaning no significance (Table 4 and Figure 3).

Table 4. Result for performed linear regression for research question 4, Assessment value (KB1) – species richness of fungi showing p-value and interpretation.

Research question

Performed test

P-value Interpretation Related figure 4. Species –

area relationshi p

Linear regression

0.076 A p-value

above 0.05, meaning no significance

Figure 3

Figure 3. The species-area-relationship. Logarithmic continuous data on the y-axel for the number of species and logarithmic continuous data on the x-axel for the sites area. Y-axel: Log Number of species; X-axel: Log Area (ha) (p- value>0.05) (Log= Logarithmic).

0.4 0.6 0.8 1.0 1.2 1.4

-4-202

logarea

logspecies

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3.4 R

5, A

–

(KBI & NVA)

The performed Spearman rank correlation for testing the relationship between the assessment value (KBI) and area showed a p-value below 0.05 (0.046), meaning it is statistically significant (Table 5). The direction of the correlation was negative, -0.59 (Table 5 and figure 4a). Performed Spearman rank correlation for testing the

relationship between the assessment value (NVA) and area showed a p-value above 0.05 (0.053), meaning no significance (Table 5). The direction of the correlation was positive, 0.57 (Table 5 and figure 4b).

Table 5. Result for performed spearman rank correlation for research question 5a,Assessment value – area relationship (KBI) showing p-value, direction of correlation and interpretation and research question 5b,Assessment value – area relationship (NVA) showing p-value, direction of correlation and interpretation.

Research question

Performed test

p-value Direction of

correlation

interpretation Related figure 5a.

Assessment value – area

relationship (KBI)

Spearman rank correlation

0.046 -0.59 A p-value

beneath 0.05, meaning statistical significance

4a

5b.

Assessment value – area

relationship (NVA)

Spearman rank correlation

0.053 0.57 A p-value

above 0.05, meaning no significance.

4b

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Figure 4. a) The relationship between the objects area and the Key biotope inventory (KBI) level. Y-axel: Evaluations level in KBI (1-4: 1 is most valuable); X-axel: Area (ha) (p-value<0.05; rho=-0.59); b) The relationship between the objects area and the Natural value assessment (NVA) level. Y-axel: Score in NVA (0-50); X-axel: Area (ha) (p- value>0.05; rho=0.57).

4 D ISCUSSION

4.1 R

1: E

(NVA & KBI)

There was a strong significant correlation between the assessments of a forest natural value performed by the key biotope inventory (KBI) and the nature value assessment (NVA) (Table 2 and Figure 1). The relationship is negative because a high natural value gives a large number in latter and a low number in former assessment. The relationship was monotonic and statistically significant, meaning that the study areas were assessed alike by both methods, and hence the two different assessments methods do not differ in their evaluations. However, it is not certain to say that the methods always assess alike, as different situations with changed conditions might give another result. For instance, sufficient prior knowledge and experience of the person performing the survey plays an important part and may have a great impact on the result. In this case, the person

performing the KBI, had a lot of experience and sufficient knowledge, resulting in a trustworthy evaluation. The NVA was performed by myself and due to a practical training with instructions of the methods founder, Börje Drakenberg, these assessments should as well be reproducible. Thus, based on the result from this thesis, the areas were assessed alike regardless of method.

A study conducted in New Guinea also compared methods in conservation planning (Cameron et al. 2008). The study compared three different methods on total amount of protected biodiversity and their cost efficiency. According to the study all three methods captured resembling amounts of the biodiversity estimated in the area, but they differed in cost efficiency. The overall conclusion for the study was however, that different methods are appropriate and preferred in different scenarios, to minimize cost and maximize biodiversity conservation (Cameron et al. 2008). The results from the study in New Guinea have some similarities with this thesis results since all compared methods in the New Guinea study, assessed the areas to hold similar amounts of biodiversity.

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This was the same as for this thesis result except for that the evaluation was based on the natural value of an area instead of the biodiversity it held. The New Guinea study also brings up that a different situation with changed conditions may give another result.

4.2 R

2 & 3: A

(NVA & KBI) –

SPECIES RICHNESS OF FUNGI

No significant correlation was found between the assessments NVA of sites and the number of red listed and bioindicator fungi species (Table 3 and Figure 2a). Neither was there any significant difference in richness of fungi between the four KBI groups (Table 3 and Figure 2b). Therefore, based on this study, a high natural value according the NVA or KBI does not always mean a high occurrence of red listed and bioindicator fungi.

If we look at the habitat requirements that fungi have to occur and compare them with the criteria’s that the KBI and NVA are evaluating an area on, it is possible to see if the assessment methods evaluate factors that are important for the occurrence of fungi. A study performed in Sweden (Berg et al. 2002), showed that the availability of substrates with high quality, as logs and old living trees, are important for the occurrence of red listed fungi. Other contributing factors were the ground conditions, forest composition and historical land use (Berg et al. 2002). The aforementioned criteria are all assessed in the NVA, while the KBI assess all except the historical land use. The NVA and KBI are therefore according the study of Berg et al. (2002) evaluating factors that are of major importance for the occurrence of red listed fungi. A high value in either of the

assessments should thence mean that above mention criteria are fulfilled. Therefore, it is hard to say what the occurring zero values in the fungi survey for areas evaluated to high natural value may depend on. To answer this question, further studies are required.

4.3 R

4: S

–

There is no significant correlation between the area of a site and richness of fungi (Table 4 and Figure 3). However, the areas selected in this study was quite similar, and hence an increased number of replicates would be needed to determine small scale

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effects ranging from one to five hectares. Nevertheless, it is also possible that the number of red listed and bioindicator species are not dependent on the area of a site.

Two studies, both analysing the species-are relationship for ectomycorrhizal fungi presented that there was a strong species-area relationship (Newton & Haigh 1998; Peay et al. 2007). With these results in mind, a study with increased amounts of replicates would help to determine if there exist any species-area relationship in the type of forests studied in this thesis. However, according Connor & McCoy (1979) the species-area relationship does not present an unequivocal justification: a larger area does not always hold a larger amount of species although many studies indicate that. This report

therefore presents information indicating that it is possible that this thesis result may be given even after more studies. However, to provide a final answer, further studies are required.

4.4 R

5: A

–

(KBI & NVA)

There was a negative, statistical significant correlation between the area of a study site and the four assessment levels in the KBI (Table 5 and Figure 4a). Hence the area of a site affects the assessment it receives in the KBI and a larger area receives a higher assessment than a smaller area. However, performed test also showed that there was no correlation between the area of a study site and the assessment levels in the NVA (Table 5 and Figure 4b). The difference between the p-value received for each of the two methods was quite small (0.007), which indicates that there might exist a correlation for the NVA as well, but further studies with more replicates are required to determine that.

Larger areas are often considered better than small areas since they tend to hold more species and support larger populations of species than a smaller area (Götmark &

Thorell 2003). However, a study comparing the size of nature reserves, showed that small forests of conservation also have great values for biodiversity (Götmark & Thorell 2003). A small area does therefore not automatically mean that the area has low natural value.

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Based on this thesis result it is possible to say that the area of a site may correlate with an assessment of its natural value (KBI), but it could also be no correlation between the size of the area and the assessment of its natural value (NVA).

4.5 F

D

Based on the results, neither of the two natural value assessments are good indicators on how high the presence of bioindicator or red listed fungi are. This is mostly due to that areas with or hardly no presence of fungi has been evaluated to both low natural values and high natural values. This demonstrates that there is no clear pattern, indicating that a low presence of red listed and bioindicator fungi correlates with a low natural value.

Even though all sites that were evaluated to low natural values in both methods showed a low presence of fungi, (1-3 species), it does not mean that there is a relationship. For instance, one of the sites that per the nature value assessment had the second highest score of all sites, also presented one of the lowest presence of red listed and bioindicator fungi. Therefore, the results indicate that there exists no correlation between low and high natural value and low respectively high occurrence of bioindicator and red listed fungi species.

5 C ONCLUSION

The results from this thesis displays that the key biotope inventory (KBI) and the natural value assessment (NVA) do not differ in their evaluations of an area. Both method should therefore be able to be conducted and provide the same evaluation of a forest. Performed analysis also indicates that none of the assessment methods can predict presence or number of red listed and bioindicator species in a forest. A larger sample size with more replicates are demanded to prove a possible relationship between the richness of fungi and a forest natural value. In addition, there is no correlation between the number of species and the area of a site, indicating that the number of red listed and bioindicator species are not size dependent. This is important since it entails that the results in research question 2 and 3 are not affected by any species-area relationship. Also, the area of a site may correlate with an assessment of its natural

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value (KBI), but it could also be no correlation between the size of the area and the assessment of its natural value (NVA).

As mentioned, more studies with a larger sample size is required to give more credible answers. However, if those tests show the same result, further studies are demanded to find another better way to detect presence of red listed and bioindicator fungi. If areas including red listed fungi species were detected more easily, the amount of protected areas probably would increase. This would have a positive effect on the biodiversity decline. Also, if more areas were protected, as mentioned in the introduction, it would contribute to reach the UN convention of biological diversity (United Nations 1992) and the Swedish environmental goals (Naturvårdsverket 2009).

Why do some of the most valuable areas in this thesis have low abundance of red listed and bioindicator fungi? The underlying cause could be that the environment was not suitable or the amount of dead wood was not sufficient (Berg et al. 2002). Modern forest management have had an immense impact on the amount of threatened wood- inhabiting fungi (Magnusson et al. 2014). Therefore, another way to decrease the biodiversity loss and fulfil the UN convention of biological diversity as well as the Swedish environmental goals, is to modify the forest management into a more ecologically sustainable way. Since dead wood has a major role for wood-inhabiting fungi, an increased amount of dead wood left in the forests should be favourable.

It is possible that the presence of bioindicator and red listed species alone are not good indicators of the natural value of a forest. A forest natural value (according KBI and NVA) is therefore not a good option to detect sites with abundant occurrence of red listed and bioindicator fungi. Instead, further studies are required to find ways to detect valuable sites regarding fungi that thereafter may be protected to stop the depletion of earth’s biological diversity.

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