Integrative analysis of ecological patterns in an untended temperate woodland utilising standard and customised software

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Abstract

The integration of a comprehensive monitoring programme conducted in a temperate woodland through statistical analysis and simulation modelling is presented here. The Heron Wood Reserve in Peebleshire, Scotland is a 7.5-ha wood, left untended to facilitate investigation into a natural Scottish Woodland Ecosystem. Data on fungal fruiting have been collected since 1994, and since November 2000 physico-chemical and biotic experiments on soil and woodland litter have also been conducted with the aim to construct and integrate a number of simulation submodels of the various ecological systems present in an untended Scottish woodland. Important systems for modelling include patterns of fungal succession, habitat characteristics and fungal biochemical patterns. A large volume of data has been collected so computer software is used for the storage and handling of data. Microsoft Access was the database used for storage; integration of functions available in Microsoft Excel with a customised software written in Microsoft Visual Basic and Microsoft VBA allowed various statistical modelling tests (e.g. Scheffé analysis of contrast) to be carried out, and the quick display of data in a suitable manner. Model Maker software is used to construct and test simulation models formulated from the data and ultimately to facilitate the running of various simulations. Integration of the separate lines of investigation is demonstrated on the example of a case study describing differences in the dynamics of decomposers in beech- and birch-dominated habitats. Integration of the information obtained allowed us to put forward the following hypothesis. The leaf litter produced by beech trees is of lower quality compared to birch, and decomposition in beech-dominated habitats is delayed (i.e. in comparison to birch-dominated) because of the differences in the extent to which these plants create and maintain a litter and humus layer (sensu Wardle, Communities and Ecosystems. Linking the Aboveground and Belowground Components, Princeton University Press, Princeton, 2002). Hence, the decomposition in beech-dominated habitats mainly occurs somewhat deeper in the soil profile, and may be carried out with some considerable involvement of ectomycorrhizal fungi. The correct understanding of the site-specific peculiarities of the ecological patterns is important for the improvement of biodiversity of woodland and forest ecosystems both in Scotland and worldwide.

Introduction

Complex integrative modelling studies help to enhance our knowledge of natural environment and are, therefore, an important prerequisite for sustainable management of ecosystems. Here we present an overview of a Scottish temperate woodland ecosystem study, integrating a comprehensive monitoring programme with the examination of data obtained through analysis of a database, and statistical and simulation modelling.

Half of Britain’s forest and woodland area is located in Scotland with forest and woodland covering an area of 1.2 million ha—15% of Scotland’s land area. Over 17,000 ha of land are planted with trees every year to replace those felled and to establish new woodlands, while employment in the forestry and primary wood processing sectors exceeds 10,000. Around 40% of Scotland’s woodlands are managed for recreation and include important conservation habitats such as the Caledonian Forest Reserves (Forestry Commission, http://www.forestry.gov.uk).

Analyses of biological interaction patterns and modelling of key ecosystem dynamics within an untended woodland may be used to improve management, forecasting, sustainability and conservation of Scotland’s woodlands. To obtain suitable and substantial data required for such analyses, a number of ecosystem variables and processes are under investigation, including, e.g., bacteria and fungal dynamics, decomposition of plant material, abiotic factors such as pH and soil composition, fungal fruiting and local weather conditions.

To analyse the dynamics of a complex ecosystem, the information obtained in different lines of investigation may be integrated by means of statistical and simulation modelling. Particularly useful in this respect may be a multiple simultaneous application of the Scheffé analysis of contrast presented here. In particular, Scheffé’s analysis of contrasts is used to compare site plots, which can be characterised using many different attributes. Scheffé’s analysis of contrasts is a very flexible technique that can be used in a variety of investigations (e.g. see Fernie and Staines, 2001, where this technique was used to form a taxonomy of European grocery markets). However, it is not widely used in ecology, perhaps because it is not available in the most commonly used software (e.g. Excel, Minitab). For example, in one application found (Danielson and Visser, 1989) this technique was used for pairwise comparisons only when Tukey’s method would have been more powerful. The software developed in this investigation allows ad hoc, and a library of regularly used contrasts to be tested very efficiently.

Standard techniques used to analyse samples have been adapted and may be beneficial in future woodland ecosystem studies. Computer software has been utilised to integrate the information obtained throughout this research, from data storage, calculations for different dynamic systems, displaying of data, statistical calculation and ultimately constructing simulation models. These may all be adapted for studies of other temperate woodlands.

Section snippets

The Heron Wood Reserve

The Heron Wood Reserve (Peeblesshire, Scotland) is part of the Dawyck Botanic Garden situated on the Silurian rock system characteristic of the Scottish Borders. It lies on a NW slope of a hill covered with shallow stony soils, which are acidic and almost lime free. The climate is characterised by low winter temperatures between November and mid March (e.g. December temperature has dropped as low as −18 °C). The maximum temperature recorded in recent years was 28.5 °C at the end of June.

Systems under investigation

A monitoring programme to observe and record fungal fruiting bodies was established in 1994 (see, e.g., Watling, 2002, Krivtsov et al., in press c). Soil and litter samples regularly collected since December 2000 were analysed for the following:

  • Estimation of total microbial biomass through differences in Abs 280 nm levels between fumigated and control samples.

  • Estimation of fungal biomass utilising the biomarkers glomalin and ergosterol.

  • Estimation of bacteria numbers.

  • Estimation of

Objectives

The main objectives of this investigation are:

  • To collect data from the woodland ecosystem.

  • To utilise customised software to collate quantifiable interactions.

  • To construct a simulation model that can represent various aspects of the ecosystem.

  • To integrate the results of the database examination, and statistical and simulation modelling in order to explain the ecological patterns observed in the Heron Wood Reserve.

  • To put forward novel hypotheses (e.g. as regards ecological interactions between

Application of computer software

The standard Microsoft Office Suite of programmes was chosen to handle most of the data due to the predominance of the software and importantly, the facility to integrate the different applications (e.g. database, spreadsheet) without altering data.

The programmes are also fully programmable by Microsoft Visual Basic thereby facilitating fully compatible software programming. Microsoft Access was used for the storage of data, and Microsoft Excel for data manipulation. Visual Basic was used

Database of observed fungal fruiting

The database for observed fungal fruiting bodies currently stores over 5000 records across four tables. Fig. 2 displays the relationships between the four tables in Microsoft Access.

  • 1.

    MainData contains records on the sporomes recorded including date, location and any vegetative associations.

  • 2.

    FungiFamily stores family and organism data for each fungal record. This table was added after 3000 records to reduce duplications and consequently the overall size of the database.

  • 3.

    TreeID stores species and

Statistical modelling—Scheffé’s analysis of contrast

The automation of a number of tests and data displaying options are carried out in a stand-alone programme. The correct data are taken from Microsoft Access (using Structured Query Language) and operated upon in Microsoft Excel. The results from laboratory investigations are already in the correct format for statistical testing and the Scheffé algorithm is implemented as Macros. A database is currently being tested to allow future rapid selection of data should further testing require it.

As an

Customised software

The Scheffé statistical modelling test is not widely available in commercial software, which prompted the creation of customised software. This is currently implemented both in VB6 and VBA (i.e. linked to Excel). First, standard ANOVA calculations are performed, which includes calculation of ȳi, ni, s, k and Fα;(k−1),(Nk) which is also needed for Scheffé’s test.

The user then specifies which variables to test (e.g. plots 1 and 8 against plots 2 through 7) and the programme completes this

Simulation modelling

A model of fungal fruiting patterns has been developed and further submodels are being constructed to test hypotheses on the other interactions investigated. The seasonal pattern of fungal fruiting may be shown with a relatively simple model utilising algebraic and differential equations. The model (Fig. 6) simulates fungal biomass and fruiting peaking twice in a year as was found during the investigation.

Equations

A number of processes have been integrated in the model of fungal fruiting. Some of the major equations used in the model are explained below.

Season limitation function (Jorgensen, 1994, Krivtsov et al., 1998) accounts for the seasonal dynamics of total (i.e. in soil and litter), fungal biomass, which has a tendency to have two peaks per yearSeasLim=exp(−2.3×Abs(SeasonFunc−a)/b),where a and b are adjustable coefficients.

The season function is a sin curve with positive values at the middle of

Integrating models

The model of fungal fruiting is only a module (i.e. a submodel) of the larger model that is to be constructed encompassing all the variables under investigation. Preliminary models under construction must be integrated together. For example, Fig. 8 displays a draft model of litter levels in the plots.

Litter levels are determined by a number of primary factors including decomposition and litter fall from the trees as the main input. This varies seasonally and is therefore dependent on a seasonal

Integration of information: case study of beech and birch plots

This section gives an example how the integration of information obtained by the separate lines of investigation was used in a case study of the dynamics of decomposers. Differences in the ecosystem dynamics observed in beech and birch sampling plots were examined using a combination of statistical and simulation modelling. Firstly, we have applied the computerised Scheffé analysis of contrast (implemented in our customised software—see above) to test a number of important physico-chemical and

Further investigations

There are now a large amount of data from the physico-chemical analysis which are undergoing further testing with the customised software. A number of other non-commercial statistical modelling tests may be implemented along with a few standard tests already found in commercial software. This would enable the bulk of the testing to be carried out within the Microsoft Excel platform as opposed to extracting data to other programmes (e.g. Minitab) as is currently the case. More simulation

Discussion and conclusions

In general, the integration of the standard commercial and customised software has been very useful in the investigations of the woodland ecosystem dynamics presented here. Our integrative monitoring and modelling approach included collection of a wide range of data, examination of these data using a database, and analysis by statistical and simulation modelling.

Many areas under investigation in a complex ecosystem study are also beneficial in their own right. For example, glomalin is a protein

Acknowledgements

Alexey Voinov, Sandy Gilmore, Lachlan Newham, and two anonymous referees are thanked for their suggestions as regards style and content of the paper. This work has been supported by SHEFC and ESF funding.

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