SNOWMAN NETWORK
Knowledge for sustainable soils
Breaking ecotoxicological restraints in spatial planning
(BERISP)
Manual for the BERISP-DSS
1
Contents
3
1
Introduction
6
1.1 Objectives of BERISP-DSS
1.2 Applicability and restrictions of the BERISP-DSS
1.3 Quality of the required data.
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6
8
2
BERISP approach
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3
Your first steps into the BERISP DSS
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3.1 Describing and defining the system
3.2 Analysis of the effects of contamination on the target species
3.3 Comparison of the results of the scenarios
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4
Application manual
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4.1
4.2
4.3
4.4
4.5
4.6
4.7
General outline of the working process
Project
Base scenario
Alternative scenarios
Exposure and Risks
Compare Scenarios
Viewing and assigning maps in Berisp
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27
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5
Maps needed by BERISP
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5.1 Format
5.2 Input maps
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6
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BERISP on disk
6.1 The Projects directory
6.2 The <Your Project> directory
6.3 The Scenarios directory
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36
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7
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Case study: Demo Holme
7.1 Setting up the project
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4
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1
Introduction
This manual describes the decision support system (BERISP-DSS) that has been developed in the
BERISP project (BERISP: Breaking Ecotoxicological Restraints in Spatial Planning). For details on the
BERISP project see www.berisp.org. In this manual the use of the BERISP-DSS will be explained, in
terms of both its technical application and how it can be utilised within spatial planning processes. The
scientific underpinning of the DSS will not fully be supported by this manual, this will be published in
peer review papers. If any information is required please contact Nico van den Brink
(nico.vandenbrink@wur.nl)
The goal of the BERISP decision support system (BERISP-DSS) is to aid spatial planners in dealing
with contamination patterns, in order to reach their objectives. The method presented here is 1)
scientifically sound, 2) it incorporates stakeholders of the spatial planning process, and 3) its results can
be directly used in the spatial planning process.
1.1
Objectives of BERISP-DSS
Due to changes in demography, economic prosperity and time expenditure of people, there is an
increasing demand for areas for nature conservation/development and for recreational use. An extra
complicating factor in this process may be the presence of environmental pollution in these areas. The
contaminants could pose a risk to organisms, and may hamper the development of the areas. Effects
may affect the occurrence or functioning of organisms or complete ecosystems, and may even have
impact on humans. Risks of environmental pollution therefore need to be assessed. Currently the
methods used to assess risks are complex, and for the general public too specific. Hence, decisions
based on such methods may lack the support of the general public. Other disadvantages of the current
methods are that they do not seize upon the relevant parameters and do not rely on the participation of
stakeholders. For spatial planning purposes the current methods often miss the necessary spatial
information that spatial planners need in their planning process. Contamination patterns often vary at a
very small scale (smaller than the planners scale), while larger animals (often a protection goal) may
range over a larger scale (about the planners scale). Small scale contamination patterns are directly
coupled to exposure of larger animals through food uptake so effects of contaminants may therefore
have relevancy on scales that are important for spatial planners.
The objective of the BERISP project is to develop a so-called decision support system (DSS) that aids
spatial planners in their process to optimise their plans in relation to the occurrence of pollutants in the
area of concern. Within the DSS, the risks of pollutants to wildlife will be assessed and the results
presented in a spatially explicit way.
1.2
Applicability and restrictions of the BERISP-DSS
The BERISP-DSS is an instrument to facilitate the redevelopment of contaminated areas (derelict) for
natural or recreational use. The DSS is developed to compare risks of the present situation and
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alternative planning scenarios, using computer models that execute a scientific, spatially explicit risk
assessment of the contaminant in the planning area.
The DSS plays a specific role in the spatial planning process as shown in the flowchart (figure 1).
Figure 1. Conceptual outline of the role of the BERISP-DSS within a planning process.
The spatial planning process starts with political or management objectives concerning a certain area or
region. This process begins by making plans of alternative scenarios for the area in cooperation with
relevant stakeholders. In such a plan all options, possibilities, interests, objectives, impossibilities and
problems of the individual stakeholder can be taken into account. Spatially explicit information of the
area, like the physical, chemical and biological properties are the input for the DSS. The consultation
between stakeholders and risk assessors takes place in an iterative process. When risks are considered to
be acceptable (as will be shown in maps resulting from the DSS) a new spatial structure for the area is
ready to be implemented. When unacceptable risks occur, a new consultation round with the
stakeholders needs to be performed with the goal of minimizing the risks of contaminants. This leads
to a new (adapted) proposed land-use plan in which the views and goals of each stakeholder are still
reflected. This plan is fed into the DSS again and the previously described process is repeated until a
proposed land-use plan leads to a new spatial structure for the area with acceptable risks that can be
implemented.
This spatial planning process is described within the BERISP manual, including a description of the
collection of the necessary spatially explicit information that is required for using the DSS. The DSS is
designed in such a way that current land-use and proposed land-use can be compared with regard to
spatially explicit risks of contaminants. In this way the DSS helps to design plans with acceptable
ecotoxicological risks (scientifically sound) and an acceptable spatial configuration (stakeholders).
There are some restrictions to the use of the BERISP-DSS and its results. It is more or less a quick
scan tool, which uses site specific information on abiotic conditions, but generic information on the
species that are modelled in the DSS. The results should therefore be regarded as an assessment of
potential risks, based on modelling, and not an assessment of actual risks based on measurements of
contaminant levels of effects in organisms. This is both a restriction and an advantage, because this
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approach allows the user to assess risks a priori to plan development. Nevertheless, the results of the
DSS should be treated in relation to the quality of the input data (see later). This system has been
developed to support the decision making processes of the planners, not to make the decisions – the
output does not recommend a course of action, it instead provides comparative risks of various
planning scenarios.
1.3
Quality of the required data.
The data required in spatial planning processes is mostly general information about the current use, the
management (and policy) objectives and future plans, but also information about interests and
viewpoints of all relevant stakeholders. Basic knowledge about the problems regarding contamination is
needed here. The design of a proposed land-use plan should be a creative process that is not hindered
by detailed information.
The information required for running the BERISP-DSS is far more explicit. This information should
be very detailed about species, contamination, physical, chemical and biological properties of the area
(as detailed as possible). Moreover this information should be available on a spatially explicit level. This
means that the spatial distribution of all these characteristics has to be available (GIS). The qualitystandard of the data is high and additional research maybe needed to gather the necessary information.
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BERISP approach
The DSS approach is based on two concepts. The first concept is the spatial habitat use and foraging of
the organisms. At the spatial scale planners are interested in (i.e. the landscape scale), most top
predators do not distribute their foraging efforts homogeneously over the area of concern. Instead they
may exhibit preferential foraging in certain landscape elements, e.g. where prey is numerous or easily
encountered. The second concept refers to the food web and the way trophic relationships determine
the accumulation of contaminants in the food resources of the top predator.
Spatial foraging occurs on all trophic levels. At the landscape scale, however, we have both a known,
heterogeneous, distribution of contaminants in soil as well as clear spatial patterns in the resource
exploitation of top predators. This matching of scales of contaminant and foraging effort distribution
makes it mandatory to be spatially explicit when estimating e.g. daily uptake of potentially deleterious
substances.
Spatial processes
For a given landscape we thus estimate, for an individual predator, where we expect it will spend its
foraging effort, assuming that this distribution of foraging effort will be affected by the relative
abundance and availability of food resources as well as by the specific preferences for foraging in
landscape types, e.g. for certain prey, near to the centre of the home-range, etc.
For the top predator species we explicitly model spatial foraging. Conceptually, the same approach is
followed for the prey species. However, for several of these species (e.g. earthworms), the resolution of
the spatial representation (grid cells with a dimension of 10 by 10 metre) is such that they can be
assumed to spend most of their life within a single grid cell. For these species a spatial foraging
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approach is not required. Note that they have to cope with spatial heterogeneity as well, but at a scale
smaller than the landscape scale chosen for the modelling.
Temporal variability
The modelling approach deals with spatial variability. However we don’t take into account temporal
variability. This means that all densities and biomass values represent long-term averages. Seasonal
fluctuations are ignored. For some of the prey groups (e.g. carabid beetles) age- or stage-structure of
the population is taken into account in the estimate of average individual age and/or weight. For the
top-predator we assume that it is resident in the area and year-round present.
For more detail on the modelling approaches see appendix 2.
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3
Your first steps into the BERISP DSS
This chapter will guide you through your very first BERISP-session. Hence, this
chapter is not meant to cover everything - see chapter 4 for a more detailed
description of the DSS. On your first use of the DSS just closely follow the steps
given below and within five minutes you will have completed your first risk
evaluation. Do not try anything beyond this tutorial yet at the risk of getting lost
in the details of the system. To find out what all the options and buttons are for,
please refer to the Application manual in chapter 4 of this document. For your
orientation, it may be helpful to browse through the introductory paragraphs of that
chapter outlining the general plan of the application. In the following paragraphs an
example project will be presented, with the goal of getting used to working with the
application. The example used is pre-installed with the program and is called
‘Afferdsche en Deestsche Waarden’. This is a real floodplain in the Netherlands
along the river Rhine, with contamination problems. This project is only in the DSS
for demonstration use only.
3.1
Describing and defining the system
In this first part we will guide you through the steps of the DSS that define the input
for the analysis. For any case study to be analysed, the habitats, the soil properties,
and the concentrations of one or more contaminants have to be defined.
Start the Berisp-application.
The first introductory screen displays the
general introductory text of the Projectchapter. Generally the screens of the
DSS are split in three parts: (a) across the
top the banner shows the project name,
(b) on the left the main function buttons
can be used to navigate through the
different steps of the project, (c) on the
right a larger “working screen” is
available in which options and
information can be entered in the DSS.
The screens will change according to the
information needed, or produced by the
DSS.
No project has yet been loaded, as can be
easily seen from the label in the green
banner at the top of the screen, which
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says “(no name)”. In the next steps we
will load a project.
a) BERISP banner shows project name
b) Menu bar
c) Working screen
Click on the Explorer-tab in the
Project-menu.
When you click on the explorer tab a list
of previously defined projects is
displayed.
Click on the name to select a project.
Clicking on the word “delete” will
permanently remove the corresponding
project. Don't do this right now!
A new project can be started by clicking
on the link “Create a new project” in
the upper right corner. Don't do this
right now!
Now open the Project ‘Afferdsche en
Deestsche Waarden by clicking its name.
Project
Define a project
The name of the selected project appears
in the green banner at the top of the
screen.
The Project-Properties page is displayed.
Here you can enter essential information
about the project, as well as descriptions
that only serve documentary purposes.
The essential fields are marked by a little
black square, the documentary ones by a
white one.
At this page you also define the project
area by selecting two maps: a map with
topographic information, which can be
used for orientation purposes and a
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mask-map, which defines the boundaries
of the project area. The former is nonessential, as the DSS can be run without
this map, however the latter is essential
and the DSS will not run without it.
Click on the Properties-tab to further
define the project.
Project: Present Situation
The present situation of the Project is
defined in this screen. A more detailed
description can be made of the present
situation of the project area, and of the
plan objectives. Also a general
description of the contamination status
of the area can be presented These are
for documentation purposes only and
will not be processed by the application.
Click on the Base Scenario item in the
menu bar at the left to define the Base
Scenario.
Base scenario
Define the Base Scenario
The Base Scenario is the reference
situation with which alternatives will be
compared. Often this is the present
situation, but it is also possible to define
a future scenario as a reference Base
Scenario.
Other scenarios are defined as deviations
from the Base Scenario.
In the tabs in the “working screen” the
Base Scenario can be further defined.
Read the introduction and click on the
Habitat-tab in the “working screen”.
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Define the Habitat of the Base
Scenario
Defining the habitat is a crucial step in
the process as it defines the distribution
of the prey species and the behaviour of
the predator.
The habitat map is defined as an ASCIIgrid (this is a text file representing spatial
data, also called a map) that can be
imported into the application. Values in
the map correspond to the Berisp-habitat
classes (see appendix 5 for details on
map definitions, formats and properties).
Expert users can specify the occurrence
of the prey species per habitat type. Press
the Expert button to see this and then
Cancel to close the Prey-abundance
Definition Window.
Now click the Contaminants-tab to
define the spatial distribution of the
contaminants.
Define the contaminants in the
project area
Defining the contamination is a two step
action. First, check which contaminants
you would like to evaluate.
The contaminants which are supported
by Berisp are listed here. Note, that when
you (un-)check any of them, the
corresponding
item
in
the
contamination-sub menu (dis-)appears.
When the selection is complete, click on
each of the items in the contaminationsub menu to define the spatial
distribution of that contaminant.
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Define the contaminant patterns of
the Base Scenario
As an example, we show here the page
that is displayed when you click the
Cadmium-tab from the sub menu.
Like the habitat, the contamination is
defined by importing a map (ASCII-grid)
by the import button. Values in the map
define the concentration of contaminant
within each cell.
A map can also be imported in a file in
which x- and y-coordinates are listed
with a measurement value. Such data can
be imported, and in the DSS a method is
available to interpolate the data, based on
Inverse Distance Weighting.
Note: when a map does not show well, it may be
that the legend is not in accordance with its
values. Double-click on the legend to open its
definition. “Auto detect” will help you find
appropriate bounds.
Define soil properties
The final step in the definition of the
Base Scenario of the Project is to specify
the Soil properties.
The best way to do this is to supply maps
for pH, Clay Content and Organic
Matter, similar to the contamination
map. When such maps are not (all)
available there are two alternatives:
- enter a mean value in the table for the
whole project-area
- enter a mean value for each habitat.
The Soil Properties-tab opens with
this Substitute screen where values may
be entered or by using the menu bar,
maps for pH, clay content and organic
matter can be entered.
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Clay-map construed from substitute
values
In the previous step we defined the Clay
Content-map as a fixed percentage for
each habitat. The DSS then creates a
map using this data.
The result is shown here. You could still
import a real map here. If you do so, the
substitute table will be reset for this
component.
Note that both Acidity and Organic
Matter are two additional input maps
defined exactly the same way.
Alternative scenarios
Define Alternative Scenarios
The next step may be to define
Alternatives Scenarios, although you can
also run the models on the Base scenario
alone. These are scenarios in which
factor(s) are changed compared to the
Base Scenario, e.g. a different Habitat, or
(part of) the contamination is
remediated. These changes are a result of
the planning process, and the required
maps can be completely redesigned by
the stakeholders, as long as the technical
format is not changed.
Defining a scenario starts out much the
same as defining a project - with an
explorer screen.
Try this and check the different
Alternative Scenarios. In this tutorial we
will skip this step for it is similar to the
definition the Base Scenario.
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Species, Exposure and Risks
3.2
Analysis of the effects of contamination on the target species
In section 3.1 the (physical) outline of the project area has been defined. Next, the
risk assessment needs to be further defined and then executed. Firstly the species to
be used in the assessment, including its associated, predefined food web is chosen.
Exposure to the contaminants and the associated risks for this species are calculated
and will be illustrated on a map. Exposure and risks are specific to the selected
species, as each has its own tolerances and exploit different food chains and habitats.
Selection of the target species is therefore an important step in the process.
Execute a Risk assessment
Press the Exposure and Risks menu
option and then the Exposure-tab.
To start an assessment of a Scenario, the
Scenario, the Contaminant and the
Predator species must be selected.
Then click the Show-button. The models
start running, and after a while the result
appears instead of the empty map at the
bottom.
In this tutorial not all option are
explored, for details on the other
buttons, please see the Application
Manual in chapter 4.
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Exposure
After pressing the Show-button the
result map area is updated.
This map shows the exposure of the
predator to the contaminant as it is taken
up through the food chain in µg
contaminant per day. Not used means
that the habitat is not used by the
receptor, not calculated means there is
no data, insufficient data means that not
all data is available, which may differ
between species
To view the same results expressed as
risks, click on the Risk-tab.
Risk
Risk evaluation is essentially the
reclassification of the Exposure results.
Exposure values are grouped into three
classes, relative to the threshold level,
specific to the selected risk.
Each combination of contaminant and
predator species has its own risks
defined. They may differ in name and/or
values.
The final step in the evaluation process is
to compare inputs and results from
different scenarios. To do so, click the
Compare Scenarios menu option.
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Compare scenarios
3.3
Comparison of the results of the scenarios
In this final step, all the maps produced during the previous steps can be compared
with each other. A combination map is produced which presents the difference
between the maps in terms of increase or decrease.
Compare data inputs and Results
In this screen, any two maps can be
compared. When the maps show the
same theme, a combination map is
constructed and shown at the bottom.
To change the selection, click on the title
of the left or the right map (green,
underlined). Calculations will be started
whenever necessary.
4
Application manual
4.1
General outline of the working process
In order to assess the risks that contaminants may pose to target species in a specific
case study, case specific information on the habitat and contaminants is needed from
the user of the DSS, which will be combined with generic information on species
properties (already available in the DSS). The case-specific information describes the
spatially explicit landscape properties of the case, including habitat patterns and
spatial patterns of contamination and soil properties. In the DSS, information is
available on the habitat specific food web of the target species, its sensitivity towards
exposure, its foraging behaviour etc. In figure 4.1 the different types of information
and their use are illustrated in a flow-chart.
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Figure 4.1 Flow chart of different types of information needed in the DSS
The application is organised into five stages, each represented by its own main menu
entry (see figure 4.2). The first three of them (Project, Base Scenario, and Alternative
Scenarios) define and describe the cases and their scenarios. The next one (Exposure
& Risks) takes the previously defined cases and scenarios and performs the risk
assessment. The last menu entry (Compare Scenarios) is a post-analysis step to
compare the results between scenarios or the input data of the various scenarios. To
allow maximum flexibility, the choice of species and contaminant can be made at any
moment during the analysis and post-analysis phase and additional model runs will
be initiated automatically wherever required.
Note: technical descriptions of the input maps can be found in
chapter 5 on the Maps needed by BERISP
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Figure 4.2 Flow chart of the different stages of an assessment procedure with the BERISP-DSS
Project
4.2
Project
The Project defines the case study area, its topography, its background, the soil
concentrations of the pollutants, habitat, etc. Furthermore, the Project defines a
number of possible developments in the area, which are called scenarios. One of
these scenarios, the Base Scenario, defines the reference situation. Its data
requirements must be fully completed before exposure or risks can be evaluated. All
other scenarios are referred to as Alternative Scenarios (see figure 4.2).
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A project in Berisp is stored in a self-contained directory. All definitions, maps and
scenarios are stored within this directory. For more information about the way
information in Berisp is stored, see chapter 6 “BERISP on Disk”.
Explorer
Multiple projects can be managed within Berisp. The Explorer-page enables the
management of the Berisp projects. Here you can open a project, delete a project, or
create a new project.
Click on a project-name to open it. The Explorer-page closes and the Project
Properties-page is opened.
Click on delete to remove an existing project. The project directory with all its
content will be erased from disk.
Click on new project to create and open an empty project. The Explorer-page
closes and the Project Properties-page is opened. The first time a project is saved, a
subdirectory in the folder Projects\ of the Berisp installation directory will be created
with a name derived from the project name at that moment.
When the check box at the bottom (“next time load this project automatically”) is
checked before opening a project, the project of your selection will become default
and at program start-up its Properties-page will be opened without having to select it
in the Explorer.
Properties
■ Project name:
This is the display name of the project. Every project needs a name. The first time a
project is saved, a subdirectory in the folder PROJECTS\ of the Berisp installation
directory will be created with a name derived from this name. The project name can
be changed at anytime, but the directory name will remain as it was.
□ Location:
Optional, this information is not used in the calculations of the DSS. Enter a
description of the whereabouts of the project area.
□ Basic topography / ■ Project area mask:
The project area outline is defined by the project area mask. This is a map (ASCIIgrid file) of the project area specifying for each cell whether it is included in the area
or not. No calculations will be performed outside the area defined by the mask,
although the effective area can be further limited when required data are not available
for the full area positively included by the mask.Within the mask grid, cells of which
the value corresponds with the grid's NODATA_VALUE are excluded, whereas cells
with any other value are included. For the sake of human readability, an inclusion
value of one (1.) is advised.
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The Basic topography is not needed by the models or the application, but helps to
interpret the maps by providing boundaries and other topographic symbols. Basic
topography is read from shape files (ESRI, see chapter 5 on details on maps, and
map-formats).
Both maps are designated for use within the project simultaneously. The Importbutton activates the following dialog:
Press the Select-buttons to open a file selector dialog. The blue-shaded area is the
area excluded by the mask file. Any point outside the extent of the mask file (the blueshaded rectangle) is excluded by default.
□ Objective:
Optional. Describe the objectives of the project. This information is not used in the
calculations of the DSS, but can be used to describe the project.
Present Situation
In this screen a more detailed description is provided of the present situation of the
project area as well as of the plans for this area in the future. Also a general
description is made of the contamination at present. Both fields are for
documentation only and not used in the DSS.
□ Present land use:
Optional. Describe in words the current land use in the area of concern.
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□ Present contamination:
Optional. Describe in words the current contamination patterns.
Save
Saves the project and everything included in it. Using the save command is optional
as many choices are saved automatically. Not saving a project does not imply it won't be
changed.
Base scenario
4.3
Base scenario
The Base Scenario is the starting point with which alternatives will be compared.
Often this will be the present situation, but it could also be any future option. The
Base Scenario defines those properties of the project that can be changed in
Alternative Scenarios, including the Habitat, Contamination and Soil. Other
scenarios are defined as deviations from the Base Scenario with regard to any of
these factors.
Habitat
Defining the habitat is a crucial step in the process as it defines the distribution of
the prey species and the behaviour of the predator. The habitat is defined by
importing an ASCII-grid (a map) into the application. Values in the map correspond
to the Berisp-habitat classes (see Chapter 5: Maps required by Berisp).
Expert users can specify the distribution of the prey species. Abundances are given in
numbers per square meter, and the list may vary between predators. Negative
numbers and zeros are interpreted as the prey being absent in that particular habitat.
Changes in this table are saved in PREYABUNDANCE.DAT in the Project directory.
Although the number of prey items may vary between predators, prey abundance is
not.
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Detailed information on the file defining the habitat can be found in Appendices 3-5.
Contaminants
In this section the contaminants are defined as along with their spatial distributions
(concentration maps). The Contaminants-page has a submenu that always includes a
first option Define, and a varying number of tabs corresponding with each selected
contaminant.
Contaminants supported by Berisp are listed; the ones relevant to the project should
be checked. Make sure at least one item is checked.
Once complete, each selected contaminant must be assigned a distribution map.
Click on the corresponding tabs to open the respective maps.
See chapter 5: “Maps in Berisp” for the general map options.
Soil properties
The simulation models underlying BERISP need three soil parameters: Acidity, Clay
Content and Organic Matter. Each of them must be assigned a distribution (value)
map. When one or more of these maps are unavailable, substitute maps can be
generated with a single value for the whole project area, of different values for each
habitat type.
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The Soil properties-page has a submenu that always includes a first option
Substitute, and three tabs corresponding with each soil parameter. Use the check
boxes in the top rows of the Substitute table to specify whether soil maps are
available, or if one of the substitute maps should be generated. Click on the
corresponding tabs to open the respective maps.
Space utilisation
One of the ecological receptors in the BERISP-DSS is the larger grazer (bovine). in
contrast to the other receptors (little owl and blackbird) are the grazers normally
confined to a specific area, fenced off. Hence, risks need to be analysed at the level
of the specific areas. This can be defined in the tab on space utilisation. In this map,
the project-area is divided into different sub-areas, simply by assigning a number to it
(e.g. 1 is sub-area 1, 2 subarea 2 etc.). Exposure is calculated spatially explicit, but the
risks are averaged over the sub-areas.
In the space utilisation tab it is also possible to define the period in which the large
grazers are actually in the area. Other receptors are expected to be in the area yearround, large grazers may be managed, and only by in the area in specific periods of
time. Depending on the occurrence of vegetation this may affect the exposure, and it
is therefore needed to define this. The calculated risks however are for the specific
period, per sub-area. It is not possible to include exposure from the other periods
within the year. For each compartment it is possible to define the specific period that
the grazers will be in the areaq.
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Alternative scenarios
4.4
Alternative scenarios
Alternative Scenarios define alterations to the Base Scenario that could have an effect
on the exposure of the predator species to the contaminants. Such alterations include
changes to the habitat, to the contaminant concentrations, or to the soil parameters.
Explorer
The Explorer-page facilitates the management of the Alternative Scenarios. Here you
can open the definition of a scenario, delete a scenario, create a new scenario, or
copy an existing scenario to create a variant.
Click on a scenario-name to open it. The Explorer-page closes and the
Description-page is opened.
Click on delete to remove an existing scenario.
Click on new scenario to create and open an empty scenario. The Explorer-page
closes and the Scenario Description-page is opened.
Click on copy to create a copy of an existing scenario and open it.
Description
Scenario name:
This is the display name of the scenario. Every scenario needs a name. The first time
a scenario is saved, a scenario-file in the folder SCENARIOS\ in the project directory
will be created with a name derived from this name. The scenario's name can be
changed at any time, but the file name will remain as it was.
Measures:
Optional. Enter a description of the measures (to be) taken that characterise this
scenario.
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Habitat
Define a new habitat map when the new scenario involves changes in the habitat
configuration. The habitat is defined by importing an ASCII-grid (a map) into the
application. Values in the map correspond to the Berisp-habitat classes (see Chapter
5 “Maps needed by Berisp”).
It is not necessary to define a new map here. When it is, the new map overrides the
settings of the Base Scenario. When it's not, the map of the Base Scenario will be
used. To revert to using the Base Scenario's map, press the Reset-button.
Contaminants
The Scenario Contaminants-page has a submenu that, contrary to that of the Base
Scenario, does not include an option to define contaminant species, but only a
varying number of tabs corresponding with each selected contaminant.
Each contaminant may be assigned a distribution map. Click on the corresponding
tabs to open the respective maps.
It is not necessary to define new maps here. When they are, the new maps override
the settings of the Base Scenario. When they're not, the maps of the Base Scenario
will be used. To revert to using a Base Scenario's map, press the Reset-button.
Soil properties
The Soil properties-page has a submenu that includes three tabs corresponding with
each soil parameter. Click at the corresponding tabs to open the respective maps.
It is not necessary to define new maps here. When they are, the new maps override
the settings of the Base Scenario. When they're not, the maps of the Base Scenario
will be used. To revert to using a Base Scenario's map, press the Reset-button.
However, at least one of the above three maps must be changed for the scenario to
be different from that of the base scenario.
Species, Exposure and Risks
4.5
Exposure and Risks
Exposure
Select the desired Scenario, Contaminant
and Predator species. Then click the
Show-button to update the map display.
Reliability indicates the data quality of
the models' input. A total of 5 input
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maps are required, but some can be substituted by fixed values. More details about
the input maps can be seen after clicking the Reliability-link. This generates a
report showing all maps and for each map the coverage, i.e. the number of cells for
which the map defines a value.
The i-buttons on the right side of the Contaminant and Species drop down lists
open windows with static information about the selected items.
Models start running once you press the Show-button. Depending on the overall
speed of your computer system, these may take about 15 seconds to complete. After
completion the map will be updated to reflect the latest calculations.
Three more buttons at the right of the window offer additional options, these are the
‘expert’, ‘settings’ and ‘report’ buttons.
Expert-button
The models produce a number of intermediate results that are normally deleted.
However, you may want to inspect these temporary data in order to better
understand the results you've obtained. Press the Expert-button to retain the
intermediate results. Once this option is chosen, intermediate results will be kept as
ASCII-grid files in the subdirectory INTERMEDIATE RESULTS\ in the Berisp
installation directory.
To rerun the models, it may be necessary to check the Force Recalculation-option
underneath the Show-button.
Stored ASCII-grids cannot be inspected from within Berisp. However, an ASCII-grid
viewer is provided alongside the Berisp software.
The following options appear when the expert button is clicked, allowing you to view
these intermediate files.
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Settings-button
A Berisp model-run depends on a large number of parameters. A change in any of
them may provide a different result. Berisp keeps track of many changes in these
parameters and detects when there is a need for recalculation, but it cannot detect
every parameter change. For example, changes in the data files can easily go
unnoticed. Therefore, a structured log file is created with each recalculation
specifying that runs particular parameter and data environment. The value of every
parameter is included, the maps listed, and for each map a fingerprint value is
generated based on the content of the file.
This log file is stored in the subdirectory LOG\<species>-<Contaminant><Scenario>\ of the project directory. E.g. the log file for an evaluation of the effects
of Cadmium to the Little Owl in the scenario named “Future” will be written to the
directory LOG\OWL-CD-FUTURE\. The file name of the log file is based on the date
and time of the run. File name and location can be found in the reference-line at the
bottom of the displayed result maps.
Report-button
A summarising report can be generated and displayed by clicking the Report-button.
It can be saved to a self-contained html-file.
Risk
Risk evaluation essentially is the reclassification of the Exposure results. Exposure
values are grouped into three classes, relative to the No Observable Effect Limit
(NOEL) specific to the selected risk. To update the risk map, select a risk and press
the Show-button (when the exposure has already been calculated, the updating will
be near instantaneous).
The rest of this screen is identical to the Exposure screen.
Compare scenarios
4.6
Compare Scenarios
Maps
In this screen, any two maps available to Berisp can be compared. The lay-out of the
screen is self-adapting so that it will always fit on the screen. When two maps of a
different theme (e.g. habitat and pH) are compared they will be displayed side by side
each with its own legend. When a pair of maps of the same theme (e.g. pH and pH)
are compared, they'll share their legend, and additionally a generated map will be
displayed highlighting the differences.
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To select a map to show, click on the green links on top of either of the maps. Then
a window pops up allowing you to choose various selection parameters. Click the
Copy-button to copy the settings from the other map. Click OK to close the window
and implement your new choices, or anywhere outside the window to close it
without changing the map.
4.7
Viewing and assigning maps in Berisp
Maps in Berisp can be either dynamically generated or based on an existing file. In
the latter case (as in the example below) a location bar is present showing the current
file name and an import-button. Selecting a file results in copying that file into the
project directory so the project directory will be completely self-contained. However,
files with the same file name imported from different locations do conflict, so
beware of using maps with similar names.
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Toolbuttons in the toolbar allow to return to the full map's extent, to switch to the
zoom-in mode, zoom out, save the map as bitmap file, copy the map to the
clipboard, and open the cell value inspection window.
The legend at the right can be moved out of the way by pressing the  to close it.
Double click on a category to open its definition (change colour etc.).
Notice the information bar at the bottom where important information about scale,
position and identity of the map is displayed.
5
Maps needed by BERISP
5.1
Format
All maps except the general topography map should be provided in the ASCII-grid
format. Three variants of this format are accepted:
1
2
3
5.2
the standard ASCII-grid format (see description below); the file should have the
file extension .ASC
the float grid format: this format uses the same header as the previous, but the
data are stored in a separate file as a binary sequence of NRows x NCols 4-byte
reals. The text file with the header data is designated by the file extension .HDR
and the binary data file by the file extension .FLT.
a proprietary interpolation format called points grid, which is especially suitable
for coordinate-based measurement data (see description below). The file
extension should be .PTS.
Input maps
Habitat map
Habitat maps must be provided using BERISP's simplified land use typology:
value
1
2
3
4
5
6
7
8
9
type
avoided
unused
arable land
coniferous plantation
long grass
orchard
short grass
shrubs
woodland no understory
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10
11
12
13
woodland with understory
heath
moor
inland marsh
The habitat map is required. The interpolation format is not suited for the habitat
data, which are essentially integers. To reclassify an existing map, a conversion utility
is provided.
Contamination maps
For each contaminant in the project area to be evaluated, a concentration map must
be provided.
The soil maps pH, Organic Matter & Clay Content
Three maps with soil parameters are required by BERISP to estimate the exposure to
contaminant(s). However, when not available, the parameters may be supplied using
a scalar value for the whole project area, or scalar values for each habitat. These
values can be set at the base scenario's definition.
Definition of the project area (Mask)
Each map describes a rectangular area. To define which points are within the project
area and which are out, a mask-map is mandatory. This is an ASCII-grid in which the
points outside the project area must have the NODATA-value whereas the points
within the project area may have any other value.
Topography
The topography is not used by the models and therefore is for presentation only. The
topography should be supplied as a shapefile. The ESRI Shapefile is a popular
geospatial vector data format for geographic information systems software. It has
been developed and regulated by ESRI as a (mostly) open specification for data
interoperability among ESRI and other software products.
The ASCII-Grid format
The ASCII file must consist of header information containing a set of keywords,
followed by cell values in row-major order. The file format is
<NCOLS xxx>
<NROWS xxx>
< XLLCORNER xxx>
< YLLCORNER xxx>
<CELLSIZE xxx>
{NODATA_VALUE xxx}
row 1
row 2
.
.
.
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row n
where xxx is a number and the keyword NODATA_VALUE is not optional and the
default value of -9999 is preferred. Row 1 of the data is at the top of the raster, row 2
is just under row 1, and so on. For example:
ncols 480
nrows 450
xllcorner 378923
yllcorner 4072345
cellsize 30
nodata_value -9999
43 2 45 7 3 56 2 5 23 65 34 6 32 54 57 34 2 2 54 6
35 45 65 34 2 6 78 4 2 6 89 3 2 7 45 23 5 8 4 1 62 ...
The NODATA_VALUE is the value in the ASCII file assigned to those cells whose
true value is unknown. In the raster, they will be assigned the keyword NODATA. In
deviance of the standard ASCII-grid format by ESRI, this value is not optional.
In deviance of the standard ASCII-grid format by ESRI, xllcenter and yllcenter are
not supported.
Cell values should be delimited by spaces. No carriage returns are necessary at the
end of each row in the raster. The number of columns in the header determines
when a new row begins.
The number of cell values must be equal to the number of rows times the number of
columns, or an error will be returned.
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The points grid format
The points file must consist of exactly the same header information as is used by the
ASCII-grid format. The data are an arbitrary number of x-y-value sequences. Header
and data may be separated by one or more blank lines.
ncols
502
nrows
130
xllcorner 169860
yllcorner 433160
cellsize
10
NODATA_value 0
170570.00000,434292.00000,0.700
170890.00000,434167.00000,5.000
171075.00000,433844.00000,0.500
The Nodata_value parameter will be ignored.
Make sure the decimal format corresponds with your computer's locale settings.
6
The points and values will be interpolated to get a more
complete area coverage.BERISP on disk
This section describes how Berisp and its subdirectories are laid out on your hard
disk. All paths here are relative to the directory in which BERISP.EXE has been
installed (the Berisp-installation directory). Typically this will be C:\PROGRAM
FILES\BERISP\, but individual computers systems may differ. Also it is possible to
have more than one copy of the installation directory.
The directory structure is outlined below, with some explanation to the content of
the subdirectories. Underlined directories will be explained in more detail.
<BERISP INSTALLATION DIRECTORY>
CONTAMINANTS
contains contaminant definitions
INTERMEDIATE FILES
here intermediate files produced by the
model recalculation can be found.
PROJECTS
in this directory your projects are stored,
each in its own subdirectory
AFFERDENSCHE AND DEESTSCHE WAARDEN
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this is Berisp's example project
LOG
log files of the model run are stored here;
see the 'reference'-line at the bottom of the
exposure maps
MAPS
contains copies of the maps you've
imported
35
GENERATED
soil maps that have been generated from
fixed substitute values
REPORTS
(reserved)
SCENARIOS
contains the alternative scenario definitions
<YOUR PROJECT>
6.1
LEGENDS
(reserved)
MAPS
s. above
REPORTS
(reserved)
SCENARIOS
s. above
RESOURCE
contains texts used by the application
SPECIES
contains species definitions
TEMPLATES
contains report templates
The Projects directory
A project in Berisp is stored in a self-contained directory. That means, to copy a
project from one Berisp-installation to another, only this directory needs be copied.
You can send it by mail to a co-worker abroad who then, after copying it into his
Projects-directory, can work with it in Berisp.
6.2
The <Your Project> directory
Project definition, maps and scenarios are all saved into a directory named after the
project's original epithet. (The names may differ to some extent because a directory
name must conform to the operating system's rules for valid file names). When a
new project is created and saved, a new directory in the Projects directory is created.
Conversely, when a project is deleted its directory is removed from disk.
6.3
The Scenarios directory
7
Each of the alternative scenarios consists of a single
definition file in the Scenarios directory, situated within the
project directory. Berisp can not stop the removing or
copying of scenario files by hand, provided the project is not
presently loaded in the application, however, it is the user's
own responsibility that scenarios manipulated in such a way
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still make sense and that file references in the scenario
definition file remain valid.Case study: Demo Holme
In this chapter we will illustrate how to set up a completely new project and to realise
useful evaluations with it. It is assumed that you have at least completed the tutorial
in the Getting Started with Berisp-section of this document, and that you know to
find your way around in the Application Manual and the Maps-section (Chapter 5).
7.1
Setting up the project
Start a new project
Click in the side menu on Project and then go to the Explorer. Do not select an
existing project, but click on the link Create a new project. You will arrive now in
the Project|Properties screen, which is almost empty.
First go to the Project name, which is still labelled “(no name)”, and change it into
something useful. In this case study we change it to “Demo Holme”. Then continue
to Location and Objective and complete the fields with information about the
project.
Define the project area
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The next step is to provide the system with information about the limits and
whereabouts of the project area. All maps are geo-referenced by absolute
coordinates. Although it is not important which coordinate system is used, provided
coordinates can be expressed in a x, y-format, maps within a project must use the
same system and be aligned over the same area.
The Project Area Mask defines the location of the project area (which is a rectangle)
and which cells to use within that area. Basically, the Mask is an ASCII-grid with
zeros and ones for exclusion and inclusion respectively.
The optional Basic Topography does not define anything, but provides topographic
symbols that make the maps easier to read when presented in the application.
Click on the Import-button to open the basic maps dialog.
Getting the Basic Topography map file is straightforward, for we have an ESRIshape file of the area available. Using the file selector, this file is selected and
immediately displayed in the dialog.
The mask file is not pre-available though. So one has to be created. This can not be
done within Berisp, however, any GIS-package can be used. An alternative way is to
use the Berisp Habitat Reclassification Tool.
Leave the dialog open and start FILE WIZARD.EXE, which is in the BERISP program
directory. Go to the AreaMask section and select the option that is most appropriate.
If we have a ASCII grid file of the project area, for instance a habitat map, this can
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be used to create the Mask file. For each class it is possible to include or exclude it in
the Mask. If you have a shape file then the next step is highly dependent on the
shape file itself. We need to identify an attribute that helps us to distinguish “inside
the area” form “outside the area”. The distinction can be made either by the
attribute's value, or by its existence. Some trial and error will help you here.
The converter utility can be closed now and we'll return to Berisp. Here the newly
created mask file can be loaded and the dialog is closed.
Define the habitat
The habitat map can be included in the project conform the description earlier.
However, the program FILEWIZARD.EXE may also be used to reclassify the habitat.
This is rather simple, and can only be done to reclassify one habitat type in another
for all locations e.g. all long grass may be changed into short grass.
For doing so open FileWizard.exe, and select the habitat map option. Then you can
open an existing habitat map in your data directory or another existing ASCII-map
fitting the area or a shape file. Normally, an existing habitat will need to be selected.
The original habitat map will open with the original data values in the map. This may
not directly by the actual habitat classes, this depends on the classification used to
produce the original habitat map. With the drop-down menus it is now possible to
assign a specific habitat type to each class.
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Define the contaminants
Next, click the Contaminants link to define which contaminants are present in the
project area and will be evaluated. Then assign a map to each contaminant. Currently,
risks can be assessed for cadmiu, copper, lead and zinc
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Define the contamination
Contaminant data can be entered in two formats, either as an ASCII file with already
integrated values, or as a data file with coordinates and measurement values. The
procedure to inport ASCII file is similar to the habitat map. Here we will explain
how to use measurement data.
The contamination data
need to be available as a
series of measurements
and stored in Microsoft
Excel.
This
should
be
converted in a format
that could be read by
Berisp. This procedure
is quite straightforward.
Save your data as CSV
(Comma Delimited) file.
However,
you
file
should also contain a
heading,
which
is
available in the mask
file.
Open the Project Area
Mask file (MASK.ASC or
something similar) into
notepad. Select the
header lines and copy
them to the clipboard.
Open the newly created CSV-file into notepad. Paste the header lines above the data:
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make sure no column names remain and there's at least one blank line between
header and dat
Save to e.g. CADMIUM.PTS
The result should look similar to this:
ncols 483
nrows 137
xllcorner 169966.59375
yllcorner 433150.125
cellsize 10
nodata_value -9999
170570,434292,0.7
170890,434167,5
171075,433844,0.5
171928,434094,0.5
171610,433672,1.5
171972,433585,1.9
etc.
Saved to a .PTS-file, this can be loaded into Berisp directly instead of an ASCII-grid.
The area between the data points will be interpolated. It should be noted that the
interpolation method used is quite simple (Inverse Distance Weighing). We'll
continue to the Cadmium tab to load the file.
This is how it looks like:
The black dots represent the locations of the data points. Circular boundaries caused
by the limited range of the interpolation may be visible. By default, the interpolation
is inverse distance weighed with a range of 40 (m). The range can be changed by
clicking on the Expert-button and entering a new range. In order to cover the whole
area, in this map the range is increased to 48 m.
Define the soil properties
For the soil properties we have constructed .PTS-maps just like the one described
above. Please refer to the manual section when such data are not available and need
be substituted by general values.
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Define an alternative scenario
From the exposure and risk maps of the base scenario it is evident that most
cadmium uptake is occurring at patches of arable land. To investigate would what
happen if those areas were taken out of use and planted with trees, an alternative
scenario is constructed. In this alternative scenario the habitat map is reloaded and
substituted with one where all cells of arable land are replaced by Woodland with No
Understory.
Compare present situation with the alternative
Then, in Compare Scenarios, the effect of the intervention is examined. In the topleft pane the exposure map of the Base Scenario (cadmium, owl) is selected, in the
top-right pane the exposure map of the alternative scenario (ditto) is selected. The
generated map at the bottom shows where both maps differ in exposure to
cadmium. It is clear that this habitat change reduces the contamination uptake by the
little owl.
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Evaluation Overview
When pressing this button a short overview of the results will be given on the input maps and
the results of the calculations (the results given here may not match the ones from the Del
Holmo case)..
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Detailed Model descriptions
Overview
The modelling approach is based on two concepts. The first concept is spatial foraging. At the spatial
scale we are interested in (the landscape scale), most top predators do not distribute their foraging
efforts homogeneously over their home range. Instead they may exhibit preferential foraging in certain
landscape elements, e.g. where prey is numerous or easily encountered. The second concept refers to
the food web and the way trophic relationships determine the accumulation of contaminants in the
food resources of the top predator.
Spatial foraging occurs on all trophic levels. At the landscape scale, however, we have both a known,
heterogeneous, distribution of contaminants in soil as well as clear spatial patterns in the resource
exploitation of top predators. This matching of scales of contaminant and foraging effort distribution
makes it mandatory to be spatially explicit when estimating e.g. daily uptake of potentially deleterious
substances.
For a given landscape we thus estimate for a predator individual where we expect it will spend its
foraging effort, assuming that this distribution of foraging effort will be affected by the relative
abundance and availability of food resources as well as by the specific preferences for foraging in
landscape types, for certain prey, near to the centre of the home-range, etc.
Spatial processes
For the top predator species we explicitly model spatial foraging. Conceptually, the same approach is
followed for the prey species. However, for several of these species, the resolution of the spatial
representation (grid cells with a dimension of 10 by 10 meter) is such that they can be assumed to
spend most of their life within a single grid cell. For these species a spatial foraging approach is not
required. Note that they have to cope with spatial heterogeneity as well, but at a scale smaller than the
(landscape) scale chosen for the modelling.
Temporal variability
The modelling approach deals with spatial variability. However we don’t take into account temporal
variability, besides the possibility to define periods of exposure for the large grazers. For the prey items
of the little owl and the blackbird, all densities and biomass values represent long-term averages.
Seasonal fluctuations are ignored. For the vegetation, food for the large grazers, a seasonal biomass is
calculated per vegetation type, see later.
Notation
All weight and biomass values refer to fresh weight, unless explicitly stated otherwise. For clarity’s sake
the names of the maps are capitalized; in shorthand notation we may omit the x,y subscripts that indicate
a specific location on these maps.
Prey species of the little owl and blackbird
Prey species densities
Density of prey species in the landscape is defined in the biomass maps Ni (ind m-2) or Bi (g m-2). With
bi the average individual (fresh) weight of species i, these two maps relate to each other as Bi = bi * Ni.
Contamination in prey species
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The transfer of (heavy metal) contaminant from the soil to all prey items is accounted for in two
different ways. For the mammalian prey (vole and shrew) a single-compartment uptake model is used.
For invertebrate prey and vegetation linear-regression models are used.
Earthworm
The availability of heavy metals for uptake by earthworms depends on the concentration of free metal
ions present in the dissolved soil solution phase. Typical relevant site-related variables determining the
uptake include soil pH, clay and organic matter fractions, cationic exchange capacity, and certain
macro-element concentrations. For cadmium, a regression equation linking soil cadmium concentration
to the total body burden in earthworms from Ma (2004) is used, with the values listed in table 1 (OM
refers to the organic matter percentage of the soil):
log
C

a

b

log
C

c

log
OM

d

pH(mg kg-1 DW)
earthworm
soil
For zinc and arsenic, the only significant site-related variable in the available data was found to be pH
(Sample et al. 1998). Thus the basic equation for these metals is:
ln
C

a

b

ln
C

d

pH
earthworm
soil
Where m, n and c are constants:
Table 1: constant values for the worm model of zinc and arsenic bioaccumulation.
Constant
a
b
c
d
Cadmium
2.92
0.747
-0.5336
-0.2101
Zinc
4.453
0.234
0.0
0.12845
Arsenic
0.341
1.0908
0.0
-0.41611
Vegetation
The heavy metal concentration in vegetation (grass) is given by the following regression equation :
log
C

a

b

log
C

c

log
OM

d

pH

e

log
Clay
veg
soil
(mg kg-1 DW)
metal
Cd
Cu
Zn
Pb
a
0.17
1.4
2.06
1.018
b
0.49
0.83
0.41
1.287
c
-0.28
-0.65
1.09
-0.609
D
-0.12
-0.18
-0.09
-.021
e
0
0
-1.05
-1.533
Carabid
Equations for the beetles are similar to the ones for vegetation and earthworm, although the influence
of the soil properties have not been included.
metal
Cd
Cu
Zn
Pb
a
-1.0
0.8
1.5
-1.9
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b
0.6
0.31
0.24
0.98
47
Dry-fresh weight conversions
For Earthworms, Slugs and Carabids, contamination is calculated in mgkg-1 dry weight. To convert to a
fresh weight-based value of contamination the following experimentally determined equations are used.
For earthworms:
W

W
6
.25
fresh
dry
For carabids:
W
W
2
.5
fresh
dry
For grass:
W fresh  Wdry  9.09
For herbs, trees and shrubs:
W fresh  Wdry  9.
Vole and shrew
For mammalian prey, contaminant uptake is related to food intake. Heavy metal body burden is
calculated using a simple single-compartment model (Gorree et al. what year?) describing the amount of
contaminant in the body, and dividing this amount by average individual weight (b):
food

C

c
_
up
1
food

c
_
out

a


C


1

e
(mg kg-1)
b c
_
out
With c_up referring to the assimilation efficiency of food (dimension-less), c_out the excretion rate of
food (d-1), a the average age (days) of the species. Initial concentration at birth C0 is assumed to be zero.
The different small mammals have a specific diet, based upon literature and own data.
grass
bank vole
common vole
Wood mouse
shrew
herbs
20
42
6
berries
seeds
earthworm beetle
soil
20
26
24
4
4
41
15
6
12
58
8
8
49
49
2
2
2
2
Top-predators: little owl and black bird
Home-range
Spatial foraging takes place within the home-range of the individual. The home-range is represented as
a square area of species-specific and fixed size. For a given (grid-based) landscape representation the
home-range consists of a fixed number of grid cells. Spatial foraging within the home-range depends on
the landscape, both directly by certain landscape elements not being used or preferentially being used to
forage for certain prey, and indirectly, through the landscape-specific density of food items.
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Functional response
We assume a type II functional response (Holling, 1959) adapted to the situation where multiple prey
are available and (simultaneously) searched for. The number of prey items of species i consumed when j
prey species are present in densities Nj amounts to
e
iN
i
FR
i
(ind d-1)
1

hjejN

j
j
In the equation e refers to the attack or encounter rate (m2 d-1) and h to the handling time (d) per prey
item. The prey intake is converted into g d-1 by multiplying it with the average (fresh) weight of the
prey. In the table the coefficients max and coef are used belonging to a different notation of the same
functional response equation; max = 1/h and coef = 1/he. The functional response equation is applied
per grid cell, and thus FRi represents the consumption rate (ind d-1) for each prey when foraging in this
cell. When applied to all cells of the landscape we obtain the spatial distribution of the local
consumption rate per prey i. These maps we coin the “availability” map for species i (shorthand Ai):
A
FR
i,x,y 
i,x,yb
i
(g d-1)
It should be noted that, by using a multiple prey functional response equation, these are not
independent of each other, for the different species.
Spatial foraging behaviour
Spatial foraging behaviour of the species results in a distribution of foraging effort over the homerange. For a mobile species like the Little owl, the actual movement path is irrelevant (when acting as a
central place forager however distance to nesting or resting place might play a role). We assume that it
flies directly to the part of the home-range where it wants to forage.
For a ground-dwelling species like the hedgehog the actual movement behaviour may be important. We
may thus need to estimate the distribution of the fraction of foraging time spent in each cell of the
home-range (coined the “utilization” or “effort” map) in different ways. Note that the sum of all values
Ux,y 1

in this map equals one ( 
).
x y
Utilization depends on the way landscape composition and structure affect foraging decisions. We
account for 4 general situations, in the following referred to as foraging strategies. Firstly, uniform
utilization where equal time is spent in every cell (the “uniform” strategy). Secondly, availability-driven
utilization where the time spent in a cell depends on the (total) availability of prey in this cell (the
“availability” strategy). Thirdly, distance-driven utilization where the time spent is related to the
nearness of the cell to the home-range centre (the “distance” strategy). Fourthly, preference-driven
utilization where the time spent is related to the preference for foraging in specific landscape elements
(the “preference” strategy). We also account for two mixed strategies: the “availability-distance” and
“preference-distance” strategy. Note that in “uniform” and “distance” there is no influence of the
landscape on the choice of foraging locations.
Uniform
In each cell of the home-range equal time is spent. Thus,
cs
Ux, y 
hs
with cs the cell size (m2) and hs the home-range size (m2).
Availability
The time spent in a cell is weighted by the amount of biomass effectively available in the cell. This
equals the sum of local availability for each prey, divided by the total availability within the home-range:
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49
A


A



i,x,y
U
x,y
i
i,x,y
x
y
i
Distance
The time spent in a cell is weighted by the nearness to the home-range centre following a negative
exponential relationship:

dx,y
e
U
x,y 
e


x

dx,y
y
Here dx,y denotes the distance from the cell to the home-range centre and  defines the rate of decline
(m-1).
Preference
The time spent in a cell is weighted by the preference value v associated to the landscape type contained
in the cell:
vx,y
U
x,y 
vx,y


x
y
Availability-distance
A combination of both the weighting by availability and by distance:


d
x
,y
e
U
x
,y

A
i,x
,y
i


d
x
,y
(
e



A
)
i,x
,y
x y
i
Preference-distance
A combination of both the weighting by preference and by distance:


d
x
,y
U
x
,y
e
v
x
,y


d
x
,y
x
,y
(
e


v )
x y
Food uptake
The amount of food-items of each prey species consumed at each location within the home-range
amounts to:
Ux, y  Ai,x, y
(g)
With Ux,y (d) being the utilization map defining how much time is spent at each location and Ai,x,y in (g
d-1) is the availability of prey species i at each location after accounting for a (multiple prey) functional
response.
Depending on the prey species, a fraction of the intake is assumed to be digested (fi, Table top
predator). For instances, owls return a part of the voles and shrews ingested, i.e., the hair and the
bones, in the form of pellets (. The intake is scaled to the required individual net daily biomass intake
(ndbi, see table) using a scaling factor for this particular home-range of:
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50
ndbi
f
U



i
x
,yA
i
,x
,y
s
h
i
(-)
x y
The (gross) consumption of prey i from each location x,y in the home-range thus amounts to
DI
s
U
i,x
,y
h
x
,yA
i,x
,y
The total (gross) consumption over the whole home-range amounts to:
tbi

s

U
A



h
x
,y
i
,
x
,y
x y i
(g d-1)
(g d-1)
The total net biomass consumption over the whole home-range is, after application of the scaling
factor by definition equal to the net daily biomass intake:
s
f
U

A

ndbi



h
i
x
,
y
i
,
x
,
y
xyi
Contaminant uptake
The amount of contaminant taken up daily depends on the amount of prey biomass digested and the
body burden in the prey. The latter depends on the location where it was obtained. Thus, net daily
contaminant uptake from each location in the home-range equals:
DCU

DI

f

C

s

U

A

f

C
i
,
x
,
y
i
,
x
,
y
i
i
,
x
,
y
h
x
,
y
i
,
x
,
y
i
i
,
x
,
y
(g d-1)
The total daily contaminant uptake (tdcu) of the individual in the home-range amounts to:
tdcu

DI

f

C

s

U

A

f

C






i
,
x
,
y
i
i
,
x
,
y
h
x
,
y
i
,
x
,
y
i
i
,
x
,
y
(g d-1)
x
y
i
x
y
i
Risk assessment
Within home-range
The total daily contaminant uptake tdcu (see above) estimated for a specific home-range can be used to
model the internal concentration build up in the predator, e.g. assuming a single-compartment model.
In such case the internal concentration would reach equilibrium at:
1
tdcu

c
_
up
c
_
out

a
C

1

e
(g g-1)
bc
_
out
where c_up is the fraction of ingested contaminant that is taken up by the intestines, c_out is the
excretion rate, a is the age of owls (averaged), and b is the total body weight of owls (averaged).


For cadmium, Pascoe et al. found that several bird species may face no adverse effects for a body
burden of up to 0.8 mg/kg. Recalculation of this value to Little owls with an average weight of 0.175 kg
and an excretion rate of 0.005 day-1, results in an uptake rate of 710-4 mg/day. This rate is considered
the lowest observed adverse effect level (LOAEL). The total daily cadmium uptake for a specific home-range
can be compared to this LOAEL in a spatially explicit risk assessment at the home-range level.
Risk maps
To move the risk assessment from home-range to the landscape level, home-ranges are projected as a
moving window over the whole landscape, with each cell in the landscape map acting as the centre of a
potential home-range. For each home-range, projected in this manner, the uptake of contaminant (tdcu)
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is estimated. The resulting value, per home-range, is assigned to the centre cell of the home-range.
From the spatial distribution of this total uptake over the landscape several statistics are obtained in a
landscape-level spatially-explicit risk assessment, a major one being the number or fraction of cells
where the LOAEL is exceeded.
Large ruminant herbivores,
Model description
Input maps
Additional maps are needed in case large herbivores are modelled which define the compartments in
which they graze (they cannot cross fences or ditches). Compartment map. An asciigrid defining the
area that is accessible to a single group of large herbivores. Often certain parts of a nature reserve are
fenced off, or the area is divided into several compartments with no possibility of exchange. In the map
each cell has a compartment number (integer, value of 0 means inaccessible). The distribution of
foraging time and effort will be calculated for each compartment separately.
Resources
In each cell of the landscape 4 different resources for herbivores may be present, characterized by their
standing crop (g DW m-2) and overall digestibility (kJ kg-1 DW):
- grass
- herbs
- shrubs
- trees (leaves, twigs and bark within reach of the animal)
Depending on the herbivore species, a subset of these resources can be consumed. For cattle, grass,
herbs and shrubs are available; for horses all 4 resources.
Foraging process
The spatial distribution of energy intake is assumed to be proportional to the total amount of available
(digestible) energy in forage (kJ m-2) in a grid cell, Ei. Following (Prins et al., 2008c) but summing over
resources instead of over vegetation types and taking Xi as the standing crop of resource i instead of
amount eaten of resource i, total energy obtainable from a cell is given by:
r
E   G E  % DOM  cGD  X i
i 1
Where GE represents the energy content of the resource (kJ kg-1, in the following kJ g-1 will be used),
%DOM the digestibility of organic matter of resource i, cGD the fraction of the digestible energy in the
resource that can be converted into metabolic energy, and r is the number of resources. Note that we
thus ignore the functional response for feeding on the resource and assume that intake rate is not
limited by resource density. To further simplify, we take the first three terms together, roughly
-1
DW) or nutritional quality (Wilmshurst et al., 2000), and
assume this is constant during the year. Standing crop (g DW m-2) on the other hand fluctuates
throughout the year. Input values for standing crop can be corrected for seasonal variation in
digestibility: at the end it is only the product of both that matters (in the model). Some habitat types can
be excluded from foraging: although forage might be available there, these habitats are not used by the
animals (Ei = 0).
Available energy in each cell in the compartment is used as a weight, to define the fraction of the
required daily energy intake, NDE (kJ), taken up in each cell:
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wi  (1   ) 
Ei
(-)
n
E
j
j
with n representing the total number of cells used in the compartment. When animals receive
supplementary feeding, the weight is reduced by 1energy intake met by supplementary feeding. Energy intake is converted into biomass intake (g DW),
by multiplying with digestibility (kJ g-1 DW) of the resource. Within cell i with multiple resources, the
intake is divided over the resources present in proportion to the relative amount of energy represented
by each resource. Thus, in terms of energy, intake of resource r in cell i is
Ei ,r
I i ,r  wi 
 NDE (kJ day-1 of resource r)
Ei
while in terms of biomass it is
I i ,r
(g DW day-1 of resource r)
Bi ,r 
r
Exposure
-1
The amount of contaminant (mg kg-1
) in the ingested plant material is calculated from the soil
concentration using the regression equation:
Intake of the contaminant from a cell is calculated by multiplying biomass intake per resource in each
cell i with contaminant concentration in the resource at this location, and summing this over the
resources r:
m
CI i   Bi ,r C i ,r
r
with m representing the total number of resources in the cell.
An animal is assumed to have a fixed soil intake per day (DSI, g DW). The fractions of the total soil
intake consumed in each cell of the compartment, are given by the same weight wi as used to distribute
energy intake by grazing:
SI i  wi  DSI
Input data
-
Digestibility of resource, per resource (kJ g-1DW)
Resource biomass (standing crop, per month) in g DW m-2
Conversion table CORINE land cover classes to BERISP habitat types
Table defining the resources used in each BERISP habitat type (basically 0/1)
Table defining net daily energy requirement (NDE, in kJ) in each month
Daily intake of soil (g DW)
Fraction of net daily energy requirement met by supplementary feeding, per month
References
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Gordon, I.J. and Prins, H.H.T., 2008. The Ecology of Browsing and Grazing. Ecological Studies 195.
Springer, 330 pp.
Prins, H.H.T. and Langevelde, F.v., 2008a. Resource Ecology: Spatial and Temporal Dynamics of
Foraging. Wageningen UR Frontis Series 23. Springer, 306 pp.
Prins, H.H.T. and Langevelde, F.v., 2008b. Prospects for further development of resource ecology. In:
H.H.T. Prins and F.v. Langevelde (Editors), Resource Ecology: Spatial and Temporal Dynamics
of Foraging. Springer, pp. 267-271.
Prins, H.H.T. and Langevelde, F.v., 2008c. Assembling a diet from different places. In: H.H.T. Prins
and F.v. Langevelde (Editors), Resource Ecology: Spatial and Temporal Dynamics of Foraging.
Springer, pp. 129-158.
Wallis de Vries, M.F., 1994. Foraging in a landscape mosaic : diet selection and performance of freeranging cattle in heathland and riverine grassland, Wageningen University, Wageningen.
Wilmshurst, J.F., Fryxell, J.M. and Bergman, C.M., 2000. The allometry of patch selection in ruminants.
Proc. R. Soc. B Biol. Sci., 267: 345-349.
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