Volume changes of deadwood in southern Sweden

Transcription

1 Linköping University Department of Physics, Chemistry and Biology Bachelor thesis, 16 hp Biology programme: Physics, Chemistry and Biology Spring term 2017 LITH-IFM-G-EX--17/3391--SE Volume changes of deadwood in southern Sweden Factors driving volume changes and prediction models for estimating the change Olof Widén Examinator, Anders Hargeby, IFM Biologi, Linköpings universitet Tutor, Per Milberg, IFM Biologi, Linköpings universitet

2 Avdelning, institution Division, Department Department of Physics, Chemistry and Biology Linköping University Datum Date Språk Language Svenska/Swedish Engelska/English Rapporttyp 1 Report category Licentiatavhandling Examensarbete C-uppsats D-uppsats Övrig rapport ISBN ISRN: LITH-IFM-G-EX--17/3391--SE Serietitel och serienummer ISSN Title of series, numbering URL för elektronisk version Titel/Title Volume changes of deadwood in southern Sweden Factors driving volume changes and prediction models for estimating the change Författare/Author Olof Widén Sammanfattning/Abstract It is well known that deadwood, such as snags and logs, is an important part of forest ecosystems. For status evaluation, it is important to know which factors affect the volume of snags and logs found in forests. In the present study, data on 9000 deadwood objects from permanent plots and four different time points were analysed to produce prediction models for change in volume of individual deadwood objects. The study was conducted in southern Sweden. Species were tested individually with the dependent factor being Change in volume. The variables that were significant most often were Canopy cover and County, while Shrub cover and Stem diameter were not significant for any of the species. Prediction models the four most abundant species were then constructed to enable estimating the volume changes. In the species-wise models produced, only 2-4 of 8 independent variables were selected. The factors selected were Cause of death, Log position, County, Soil moisture, Forest fertility and Shrub cover. Predictions can be useful for modelling volume changes and a better understanding of the turnover of deadwood in production forest. Nyckelord/Keywords Volume changes, AIC, SNFI, deadwood, prediction model

3 Content 1 Abstract Introduction Material and methods Area of study Data collection Data management Statistical analyses Results Discussion Social & ethical aspects Conclusion Acknowledgement References... 11

4 1 Abstract It is well known that deadwood, such as snags and logs, is an important part of forest ecosystems. For status evaluation, it is important to know which factors affect the volume of snags and logs found in forests. In the present study, data on 9000 deadwood objects from permanent plots and four different time points were analysed to produce prediction models for change in volume of individual deadwood objects. The study was conducted in southern Sweden. Species were tested individually with the dependent factor being Change in volume. The variables that were significant most often were Canopy cover and County, while Shrub cover and Stem diameter were not significant for any of the species. Prediction models the four most abundant species were then constructed to enable estimating the volume changes. In the species-wise models produced, only 2-4 of 8 independent variables were selected. The factors selected were Cause of death, Log position, County, Soil moisture, Forest fertility and Shrub cover. Predictions can be useful for modelling volume changes and a better understanding of the turnover of deadwood in production forest. 2 Introduction In forest ecosystems, deadwood plays an important role as habitat for a lot of different species. Snags (standing dead trees) are important habitats for a lot of species. For example, insects use the snags as habitat and food source. When insects are present, snags also provide an important food source for many birds that feed on those insects. Logs (downed deadwood) also provide food and habitat for various insects and birds. An important factor for this is that the logs often are moister than the surrounding environment every season of the year (Kimmins 2004, p. 62). The importance of deadwood was also studied by Milberg et al. (2016). They focused on downed oaks (Quercus robur) and found that the saproxylic beetle fauna where much different than the fauna of insects present on standing living oaks. According to their study it is important to maintain the amount of downed oak trunks because of the variation in species between living and dead oaks. There is a positive trend that deadwood is increasing, especially in the period, compared to the period (Länsstyrelsen Norrbotten 2011). Even so, there is a risk that habitats will be lost for many species which could threaten the biodiversity in our forests. The reason for this is today's production forests and forest management. Nowadays, a forest can be certified if considering biodiversity when 2

5 conducting forestry. This leads to better help in the conservation work in our forests. Voluntarily set aside areas can provide important factors and broaden the network of protected forests (Simonsson et al. 2016). That this kind of consideration works is shown in an article by Santaniello et al. (2016). Their study about partial cutting showed that in higher levels of retention, the deadwood diversity was greater than in lower levels. And as said above, the diversity in deadwood favour biodiversity. Earlier studies about deadwood have been done with data from SNFI. Fridman and Walheim (2000) used data from sample plots ( deadwood objects). They studied the amount of deadwood in different parts of Sweden and measured the distribution of decay class and species. They found that the volume of deadwood in southern parts of Sweden is less than in the north. Another study using deadwood data from SNFI is Jonsson et al. (2016), who studied the amount and type of deadwood in Swedish forests. They found that the amount of deadwood has increased in Sweden but that the increase probably is because of storms and other weathers and not because of, e.g., certification. They also report low heterogeneity in variables like diameter, decay class and species. What these two studies did not show was the changes in volume of logs and snags. With prediction models for different species it is possible to predict the changes in volume for objects found in forests. This could be of help when analysing the status of ecosystems. In this study, I analysed the change in volume of individual logs over a 20 years period. The aim was to find the relative importance of different factors and the volume and of deadwood and, in addition, to make prediction models for changes in deadwood objects. 3 Material and methods 3.1 Area of study I chose to study the area beneath the Limes Norrlandicus which is the southern part of Sweden. The forests in the southern parts are more heterogenous, more species-rich and less dominated by coniferous trees than the northern parts. I wanted the diversity in types of forests to get useful data over different factors effect on deadwood. I also expected the milder climate in southern Sweden to speed up the decomposition of deadwood. 3

6 3.2 Data collection All data for this project originated from inventories by SNFI. The Swedish National Forest Inventory (SNFI) has conducted inventories of Swedish forests since 1923 and today annual inventories are undertaken by field workers with handheld field computers (SNFI 2016). The inventories are done in tracts (clusters with sample plots) with different sizes depending on location. There are both permanent and temporary tracts, the permanent tracts are visited every five year and the temporary tracts are visited only once (Inventory design 2016). For this study, I chose to focus only on permanent tracts and the time between 1994 and 2016 (i.e. during the period when deadwood had been inventoried). The permanent tracts are inventoried every five year and this resulted in different time intervals; years + 10 years + 15 years + 20 years years + 10 years + 15 years + 20 years years + 10 years + 15 years years + 10 years + 15 years years + 10 years + 15 years 3.3 Data management Before any statistical tests some sorting and structuring was conducted in Excel. First, I excluded deadwood objects that were found only once. Second, by using the slope function in Excel, I got the average change in volume per year of each object. Third, I selected a number of explanatory variables to be included in the statistical analyses. I used the logarithms of deadwood volumes to get a more normal distribution of the factor Change in volume. In addition, I ln(x)- transformed the Stem diameter variable and ln(x+1)-transformed the Shrub cover category to get more suitable data for the study. 4

7 Table 1. The independent continuous variables used for the study. Canopy cover and basal area have missing values. Variable Descriptive Statistics N Mean Minimum Maximum Std. Dev. Change in volume (m 3 ) Forest fertility (m 3 sk/ha) Canopy cover (%) Soil moisture Basal area (m 2 /ha) ln(shrub cover) (%) ln(stem diameter) (mm) SNFI records many different species of deadwood, some of which were rare in data. Hence, before analyses, I merged some species into groups. Ulmus glabra, Fraxinus excelsior, Carpinus betulus, Prunus avium, Sorbus intermedia, Tilia cordata and Acer platanoides were put together in the category Other hardwood. Betula pubescens and Betula pendula were put together in Betula spp. Deadwood objects of unknown identity were excluded from the study. Additional grouping was made in the Cause of death variable. Here I put Human, Beaver and Other mammals together in Other not felled. Gremmeniella, Other fungus and Cronartium flaccidum were merged into the Fungus category. Bark beetle and Other insects were merged into Insects. 3.4 Statistical analyses The first step of this study was to investigate the relative importance of different factors on changes in deadwood volume while in the second step, I selected prediction models for the most abundant tree species. I used the statistical program Statistica to conduct multiple regression between the change in volume of deadwood objects and the independent factors. Before the multiple regression I considered the correlations between the independent factors. The highest correlation found was between canopy cover and basal area (r = , N Canopy cover =9010, N Basal area =8771). Because of this I included only one of them in the analyses. Canopy cover was measured through subjective judgement and the basal area was measured with an angle gauge (SLU 2016) which I think made basal area a more reliable factor to use than canopy cover. Therefore, in the multiple regression, I only used basal area for study. 5

8 Finally, I performed model selection using AIC to identify the combination of explanatory variables that most efficiently explained changes in volume of deadwood objects. I then only considered four of the most common species. AIC (Akaike Information Criteria) compare different models and then shows what factors could be used to predict outcomes (in this case change in volume of snags and logs) (Snipes & Taylor 2014). 4 Results In the Wald test, I looked at the relative importance of the explaining variables. There I found that the variables significant for most species (4 each) were Canopy cover and County. Other seemingly important variables in this test was Log position and Forest fertility that were significant for three species each. Ln(stem diameter) and ln(shrub cover) were not significant for any species. The most common species in used data were Picea abies, Pinus sylvestris and Betula spp. These three species were also the ones having the largest number of significant factors (Table 2). Table 2. Wald test statistic for all independent factors from multiple regression. The sample size (n) is specified for each species. Values in bold indicate significant factors. Independent factors Picea abies (n=3339) Pinus sylvestris (n=2819) Betula spp. (n=1525) Other hardwood (n=89) Wald Stat. Salix caprea (n=197) Fagus sylvatica (n=70) Alnus spp. (n=358) Sorbus aucuparia (n=100) Quercus robur (n=284) Populus tremula (n=244) Forest fertility (m 3 sk/ha) Canopy cover (%) Soil moisture Basal area (m 2 /ha) ln(shrub cover) (%) ln(stem diameter) (mm) Log position County Cause of death

9 Prediction models for change in deadwood volume were constructed for four of the most common species in the study. The County variable was compared to Jönköping County, located in the center of the studied area, Cause of death was compared to Insects and Log position was compared to Continuous standing because the other two have ground contact. Log position and ln(shrub cover) were selected factors for three of the four species. Forest fertility, Soil moisture and Cause of death were only selected in two prediction models. County was only selected as a factor for one of the species (Table 3). Independent factors Table 3. Four of the species in relation to different factors from the AIC. Different species had different independent factors that effects the changes in volume, hence the empty cells. Values in bold were significant. Level of effect Species Pinus sylvestris Picea abies Betula spp. Quercus robur Estimate Std. Error Estimate Std. Error Estimate Std. Error Estimate Std. Error Intercept Forest fertility Soil moisture ln(shrub cover) Log position Cont cumbent Log position Changed County Skåne County Blekinge County Halland County Kronoberg County Kalmar County Gotland County Östergötland County Västra Götaland County Södermanland County Stockholm County Västmanland County Uppsala Cause of death Other not felled Cause of death Felled Cause of death Wind/snow Cause of death Fungus Cause of death Crowding

10 5 Discussion As expected, the most abundant species in the data, i.e. the ones with the greatest statistical power, were the ones with the most significant values. More surprisingly, there was little consistency among species. The reason for this could be the difference in location where the species grow or differences in wood structure which could make some of them harder to decompose. I was also surprised that log size had little to do with the change in deadwood volume. As data had been ln-transformed, this suggests a constant rate of decay, irrespective of size of the initial log. Important to remember is that there is a possible bias in the results due to inventory methods. For example, Basal area were not inventoried the same way in 1995 as in 2005 which resulted in missing values in my data. Even so, the methods in general are the same which, together with the vast amount of data collected, gives the results high transferability and applicability. The results showed that logs lying down have greater loss in volume over time than standing snags. When lying down the log is also more affected by other factors like Shrub cover, Soil moisture and different decomposers, for example insects. These interactions could be the reason for the increased volume loss of logs compared to snags. Shrub cover was another variable selected in three of the four prediction models. Shrub cover can be important for biodiversity of invertebrates living on deadwood, and some species only lives in shaded areas (Jonsell et al. 1998). Hence, I hypothesize that shaded environments have downed deadwood that is more moist and with a different and possibly larger assembly of saproxylic beetles. Together, that would increase the decay. Out of three different species, one (Quercus robur), however, showed positive estimate value (Table 3). It is unclear why more shrubs would lead to slower decay in this species. The Cause of death variable was selected for prediction models in 2 out of 4 species. Possible reasons why this is the case are many. The category Other not felled contains all damage done by larger animals for example. Trees damaged by large sized animals could be more sensitive to decomposers and also, the damaged parts may have more variation in structure. This leads to different niches where several insects or fungus can thrive. 8

11 The County variable was significant for 4 different species in the Wald test (Table 2) but it was only selected for Quercus robur (Table 3). It is important to note the relatively small sample size (n=284) compared to the other tree species (Table 2). It is also important to note, that although the oak is distributed throughout the study are, there may be regional differences in managements of oak forests, with some focusing more on oak habitat conservation than others. Looking at the study of Milberg et al. (2016), I think the small amount of dead oaks could mean lesser variation in habitats for some saproxylic beetles which threatens the biodiversity because of the missing niches. Soil moisture and Cause of death were only selected for the prediction models for the coniferous species. A contributing factor for this could be that difference in lignin composition between coniferous and broadleaved trees. Coniferous trees, in most cases, contain larger amounts of lignin (Sjöström & Westermark 1998). The lignin makes the coniferous trees more resistant to decomposers (Stokland et al. 2012), and could be a major driver for differing fungal assemblages. Results on Forest fertility, i.e. how well the forest produce timber, suggest that an increase in the fertility is associated with larger negative changes in volume (Table 3). The reason is probably that high production equals high presence of different decomposing and saproxylic animals because of the increased presence of food and habitat. To know if that is the case, more studies about habitat heterogeneity in forests are needed. A possible way to do this is check for correlations between the number of habitats and the amount of saproxylic animals present in a specific area. 5.1 Social & ethical aspects Every year the SNFI is doing inventories of Swedish forests. It is important that the data collected is used so that the gathered information helps in improving our knowledge of forest dynamics and guidelines for management. In the present case, my results may help improve conservation efforts in production forests. There are no perceived ethical aspects in the study. 5.2 Conclusion The factors most decisive for the change of volume of deadwood were Canopy cover and County. In contrast, when selecting prediction models for four of the species, ln(shrub cover) and Log position variables were often selected. Also, differences among species were often substantial. 9

12 The next step is further investigations about today s forestry and its effects on deadwood objects. Together with my results that could be of great importance in forest conservation. 6 Acknowledgement First, I like to thank my supervisor Per Milberg for his invaluable help and guidance during this project. Second, I would like to thank Jonas Fridman, the program director of SNFI, who provided me with data from SNFI and Johan Bergstedt for his help with data selection and the introduction to the work of SNFI. 10

13 7 References Fridman J, Walheim M, (2000) Amount, structure, and dynamics of dead wood on managed forestland in Sweden. Forest Ecology and Management 131, Jonsell M, Weslien J, Ehnström B (1998) Substrate requirements of redlisted saproxylic invertebrates in Sweden. Biodiversity & Conservation 7, Jonsson BG, Ekstöm M, Esseen PA, Grafström A, Ståhl G, Westerlund B (2016) Dead wood availability in managed Swedish forests Policy outcomes and implications for biodiversity. Forest Ecology and Management 376, Kimmins JP (2004) Forest Ecology. A Foundation for Sustainable Forest Management and Environmental Ethics in Forestry. 3rd edition. Pearson Education Inc, Upper Saddle River Länsstyrelsen Norrbotten (2011) Uppföljning av miljötillståndet I skog baserat på Riksskogstaxeringen. Länsstyrelsens rapportserie nr 3/2011 Milberg P, Bergman KO, Sancak K, Jansson N (2016) Assemblages of saproxylic beetles on large downed trunks of oak. Ecology and Evolution. 6, Santaniello F, Djupström BL, Ranius T, Rudolphi J, Widenfalk O, Weslien J (2016) Effects of partial cutting on logging productivity, economic returns and dead wood in boreal pine forest. Forest Ecology and Management 365, Simonsson P, Östlund L, Gustafsson L (2016) Conservation values of certified-driven voluntary forest set-asides. Forest Ecology and Management 375, Sjöström E, Westermark U (1998) Chemical composition of wood and pulps: basic components and their distribution. In: Sjöström E, Alén R (eds.) Analytical Methods in Wood Chemistry, Pulping, and Papermaking. Springer-Verlag, Berlin, 1-35 Snipes M, Taylor DC (2014) Model selection and Akaike Information Criteria: An example from wine ratings and prices. Wine Economics and Policy. 3,

14 Stokland JN (2012) Host-tree associations. pp in: Stokland JN, Siitonen J, Jonsson BG (eds) Biodiversity in Dead Wood. Ecology, Biodiversity and Conservation. Cambridge University Press, NY The Swedish National Forest Inventory, SLU (2016) About us. Accessed: The Swedish National Forest Inventory, SLU (2016) Inventory design. Accessed: