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SID 5
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Research Project Final Report
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SID 5 (Rev. 3/06)
Project identification
IF0103
Evaluating ecosystem models as tools for policy
development on biodiversity
Contractor
organisation(s)
University of Warwick
Warwick HRI
Wellesbourne
Warwick
Warwickshire
CV35 9EF
54. Total Defra project costs
(agreed fixed price)
5. Project:
Page 1 of 23
£
60,034
start date ................
01 April 2006
end date .................
31 March 2007
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Executive Summary
7.
The executive summary must not exceed 2 sides in total of A4 and should be understandable to the
intelligent non-scientist. It should cover the main objectives, methods and findings of the research, together
with any other significant events and options for new work.
The aim of this project was to evaluate whether ecosystem scale modelling approaches could be used as
tools to develop policy to meet biodiversity targets. The project focussed on producing a framework for the
selection and development of models that can be used to predict how plausible changes in land-use might
impact on biodiversity.
We initially categorised and reviewed existing models and modelling approaches according to trophic
level, separately considering models for vegetation, invertebrates and farmland birds. From these reviews
we identified that most existing models considered only single species, often at inappropriate spatial
scales for the wide-scale modelling of changes in land-use, and that existing models rarely included
interactions between trophic levels. Thus there is a need to develop models that include multiple species
and trophic levels and the interactions between them, based on quantitative data.
To allow the incorporation of multiple species and trophic levels, there is a need to be able to integrate
data and models across spatial, temporal and ecological scales. A wide diversity of mathematical and
statistical modelling approaches exist to tackle the integration of data in this way, with the specific
approach required being dependent on the focus of the modelling exercise and the available data.
Modelling approaches used in existing studies of the impact of land-use changes on biodiversity were
reviewed, most being based on expert opinion rather than quantitative data, and rarely incorporating data
across several spatial scales
We therefore needed to ensure that our proposed modelling framework assisted both in the
selection of appropriate techniques and the identification of available and necessary data sources,
according to the outcome information required to address any particular policy question.
Proposed Modelling Framework
Our proposed modelling framework, to develop models to determine how plausible changes in land-use
might impact on biodiversity, is defined by a set of five key questions:
1) What is the current spatial distribution of the species of interest?
2) What is the potential spatial distribution of the species of interest given the changes in land-use?
3) How much of the potential spatial distribution can the species of interest achieve (within a defined
time scale), taking account of habitat and landscape permeability?
4) Are there sufficient resources to allow the species of interest to establish in the new locations (within
a defined time period)?
5) Will the species of interest establish, and if so how will it affect the current community?
SID 5 (Rev. 3/06)
Page 2 of 23
(ecological interactions)
The modelling approaches required to answer these questions will need to be selected according to the
policy question being asked, the level of spatial detail required, and the level of detail of the available data.
The progression through the questions requires increasing detail to be included within the model.
Questions 1 and 2 can be answered using quite broad scale data, and for a relatively large geographical
region, though models at this scale will only be able to assess potential changes in biodiversity on this
large spatial scale. Question 3 requires more localised spatial data, as it requires knowledge of the
potential movement of species and will therefore be subject to variation caused by habitat composition
(although it should be possible to aggregate the information to provide summaries over wider spatial
scales). Questions 4 and 5 certainly require information at a smaller spatial scale, since they involve
questions about species interactions with the local environment.
Answers to all 5 questions will enable the development of models to provide the most useful and detailed
information on how changes in land-use will impact on biodiversity. Predictions from these models of the
outcomes associated with ranges of plausible future scenarios can then be used to inform or develop
policy on particular issues of interest.
Having defined the framework within which ecosystem modelling approaches could be applied to address
policy questions, there was a further need to define a clear process within which to apply the modelling
framework. This includes the definition of clear, unambiguous targets or hypotheses (under specified
land-use scenarios and timescales) to be evaluated using modelling.
The key stages in the process are:
1. Define and clarify the policy problem or question to be addressed:
a. What is the precise question to be answered, specified both at an overall policy level and at a
practical implementation level?
b. What definition(s) of biodiversity (species richness, community composition, density of key
indicator species) will be used, and how should multiple measures be combined?
c. What spatial scale is of interest, over what geographical region?
d. Over what time scale should land-use changes and biodiversity impacts be considered?
e. How are changes in biodiversity to be measured and presented?
2. Develop plausible future land-use scenarios:
a. What are the most likely land-use changes?
b. What are the likely spatial distributions of these changes?
3. Select modelling approaches (based on data availability) to answer the framework questions, allowing
for any required species interactions:
a. What is the current spatial distribution of the species of interest?
b. What is the potential spatial distribution of the species of interest after land-use changes?
c. How much of the potential spatial distribution can the species achieve (within a defined time
period)?
d. Are there sufficient resources to allow the species to establish in the new distribution (within a
given time period)?
e. Will the species establish, and if so how will it affect the current community?
4. Simulate the plausible future scenarios and analyse model outputs.
5. Make recommendations based on analysis of model output.
a. Provide list of model assumptions.
b. Provide measures of uncertainty on outputs.
c. Rank scenarios in terms of being most beneficial (or least harmful) to biodiversity.
Together the modelling framework and the above process provide a pragmatic structured approach to
producing models to inform and assist in the development of policy on land-use changes, with regards to
their impacts on biodiversity. This approach fits well with the ALMaSS modelling approach developed in
Denmark, which consists of a landscape and weather simulator, combined with farm management
information. Models of individual species are then linked to the landscape simulator, providing a modular
approach to developing models of the impacts of landscape change on species abundance, movement
and distribution.
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We recommend that Defra adopt both the framework and modelling process for projects that
aim to examine the impact of land-use changes on biodiversity, and embed it into a modular
modelling approach.
Consultation with Defra provided a number of example policy questions. These questions focussed on
specific Public Service Agreement and England Biodiversity Strategy targets. It is possible to use the
SID 5 (Rev. 3/06)
Page 3 of 23
approach described above to provide potential distributions for individual UK priority species under a
range of land-use change scenarios, using existing data (where it is available). It will not be possible to
determine achievable distributions unless more data on species dispersal and habitat permeability are
collected.

We recommend that Defra considers funding research to determine the effect of the
permeability of the landscape mosaic on the distribution of species of interest and to collect
data on species dispersal through a range of habitat types.
For biodiversity conservation, there would almost certainly be a need to consider multiple species, rather
than individual species. Although the modelling techniques and tools exist to incorporate species
interactions into models, there is a lack of data on interactions between different species.

We recommend that Defra considers funding research into the interactions between multiple
species in a range of habitats.
The availability of appropriate data will be a particular problem that Defra may have to address when
considering future projects to model how changes in land-use impact on biodiversity. Where appropriate
data are not available, either data will have to be collected as part of the project, or the project will have to
develop the model in the absence of data, possibly based on expert opinion. Such projects should,
however, also provide clear recommendations on the data that need to be collected to fully parameterise
the developed model, and the data collection and subsequent use of the model for decision making should
incorporate the concepts of adaptive resource management, allowing iterative improvements in models
and decisions as greater information and data become available.
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We recommend that Defra collate and co-ordinate the collection of data on habitat types and
land-use, combined with monitoring of the effects of policy to provide a dataset for use in
models to predict the effects of land-use changes on biodiversity.
We recommend that Defra revise models and their predictions on a regular basis (annually or
bi-annually), using data from monitoring, so that policy can be adjusted to account for the
most up-to-date information.
Project Report to Defra
8.
As a guide this report should be no longer than 20 sides of A4. This report is to provide Defra with
details of the outputs of the research project for internal purposes; to meet the terms of the contract; and
to allow Defra to publish details of the outputs to meet Environmental Information Regulation or
Freedom of Information obligations. This short report to Defra does not preclude contractors from also
seeking to publish a full, formal scientific report/paper in an appropriate scientific or other
journal/publication. Indeed, Defra actively encourages such publications as part of the contract terms.
The report to Defra should include:
 the scientific objectives as set out in the contract;
 the extent to which the objectives set out in the contract have been met;
 details of methods used and the results obtained, including statistical analysis (if appropriate);
 a discussion of the results and their reliability;
 the main implications of the findings;
 possible future work; and
 any action resulting from the research (e.g. IP, Knowledge Transfer).
Evaluating ecosystem models as tools for policy development on biodiversity
Aims and objectives
The aim of this project was to evaluate whether ecosystem scale modelling approaches could be used as tools to
develop policy to meet biodiversity targets. The objectives for the project are listed below:
1.
Identify modelling approaches that can be used for predictive ecosystem scale modelling, including food web
and vegetation change models.
SID 5 (Rev. 3/06)
Page 4 of 23
2.
Critically review selected ecosystem scale modelling approaches to determine those that can predict policy
effects on habitat and biodiversity changes and their impact on farmland birds, using the criteria defined by an
expert steering group.
3. Determine how to integrate data across a range of geographical scales to allow ecosystem modelling
approaches to predict changes at regional and national scales.
4.
Prioritise the future research necessary to develop ecosystem scale modelling approaches for policy
development on farmland biodiversity.
Project progress and outcomes of research
Objective 1: Identify modelling approaches that can be used for predictive ecosystem scale modelling,
including food web and vegetation change models
The aim of this objective was to identify the models and modelling approaches that were available in the scientific
literature to provide information on the current status of the modelling of biodiversity and land-use, and to define
the scope of the project and identify the definitions to be used for biodiversity.
In consultation with the Steering Group, it was decided that the project should consider biodiversity at a range of
trophic levels (plants, insects and birds), and that modelling approaches could be developed to address the
differing needs of scientists, implementers of policy and policy makers, as each of these user groups would
require different degrees of complexity and information within a modelling structure (a potential framework for this
is summarised in Appendix 1).
The Steering Group also identified the following topics as important areas that would need to be included within
ecosystem models for the modelling of biodiversity:
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Soil type
Vegetation type and vegetation structure
Spatial aspect between as well as within fields
Weather and environmental variables
Dispersal of organisms
Factors linked to population dynamics, such as food availability, breeding habitat availability, predation
Pesticide usage
It was noted by the Steering Group that the majority of these factors require detailed biological and environmental
information that would be difficult to collect in sufficient detail to incorporate into a model. They would be useful in
the context of developing a model that aims to determine the mechanisms which are influencing changes in
biodiversity from a scientific viewpoint, but would perhaps be less useful for models aimed at understanding larger
scale trends for the development of policy. The Steering Group did however feel that it would be very important to
include both multiple species and spatial heterogeneity within any models to be used for the development of
policy.
The steering group suggested that the key to setting the scope of the project was to talk directly to those people
involved with the development of policy to determine the information and level of detail that was required, and the
geographical scale at which the model would need to predict.
A meeting was arranged with Defra policy makers, and held on 24 th November 2006. A wide range of topics
relating to modelling were discussed at the meeting, and it was suggested that although mechanistic models
would be helpful from a policy point of view, there were limitations due to the costs and time required for the
collection of sufficient data with which to parameterise the models. This meant that the development of
mechanistic models was often uneconomic and unfeasible from a policy point of view. It was suggested that there
were many existing approaches used to model biodiversity, but these approaches, implemented in projects such
as MONARCH and BEETLE, tended to result in a high degree of uncertainty associated with outputs. One useful
output of the project could be to examine ways to reduce the uncertainty of models, and to address areas relating
to the permeability of landscapes for particular species. It was felt by Defra policy makers that the models should
provide generic information that was applicable to a wide geographical area (regional and national). Although
knowing what was happening within a farm or field was useful for the delivery of policy, it was the effects at the
landscape scale that were of greatest interest in terms of determining policy, particularly in relation to the
interactions between multiple species and different trophic levels. The key issues agreed at the meeting, in
relation to this project, were:
SID 5 (Rev. 3/06)
Page 5 of 23
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Ecosystem models should capture the effects of land-use changes on biodiversity (defined either as the
population dynamics of key indicator species, as species richness or as species dominance).
Landscape permeability (i.e. the dispersal capability of species between habitats) is an essential
component to be included in any ecosystem modelling approach.
Ecological interactions between species should be included in future modelling projects, as this is a major
omission that limits existing models.
Ecosystem models should generate predictions that provide information that is better than expert opinion.
An ecosystem modelling framework should identify crucial information gaps and suggest how they can be
filled.
An ecosystem modelling framework should determine current data availability and the priorities for further
data collection.
Following these meetings, the project team finalised the scope and aims of the project. It was felt that a wide
range of modelling approaches that could be used for modelling the effects of land-use on biodiversity already
existed (described and reviewed in Objective 2), and that these could be integrated and selected to provide the
required approaches to solve specific problems. However, selecting the right approach would depend on the
specific problem or question that needed to be addressed. It was decided that the project should focus on
developing a generic framework to select and develop models to determine the effect of land-use changes
on biodiversity.
Having provided a clear focus for the project, the literature was examined to determine the existing models and
modelling approaches for the three trophic levels, identified at the first steering group meeting, along with issues
and modelling approaches for integrating land-use changes with species distributions. The results of the survey of
the literature and a critical review of the approaches are described under Objectives 2 and 3 below.
Summary of Objective 1
Following discussions with the steering group and with Defra policy makers, the scope of the project was defined
as developing a generic framework to select and develop models to determine the effect of land-use changes on
biodiversity, where biodiversity can be defined either as species richness, species dominance or in terms of the
population dynamics of key indicator species.
Objective 2: Critically review selected ecosystem scale modelling approaches to determine those that can
predict policy effects on habitat and biodiversity changes and their impact on farmland birds, using the
criteria defined by an expert steering group.
The aim of this objective was to determine the current state of play with regards to existing models and
approaches that are used to model the dynamics of key components of biodiversity. The reviews were done on a
trophic level basis, dealing with vegetation, insects and birds, respectively, as these are the major trophic levels
within farmland ecosystems (as suggested by the steering group), particularly in relation to the farmland bird
indicator species.
Vegetation dynamics
Published models to describe vegetation dynamics currently fall into three broad categories.
1. Local scale, species specific or life stage focussed models
2. Regional scale species specific models
3. Regional and global generic models
Local scale, species-specific or life stage focussed models
The first category of models usually describes a particular stage in the life cycle of the species of interest. Life
stages may include the germination and emergence response to biotic variables, the growth and competitiveness
of a species (either in single or multiple species mixes) or the fecundity of a species. By their very nature these
types of vegetation models are very focussed on specific biological or ecological questions. The parameters for
these models are often derived from detailed experimentation and can be very specific in their relevance not only
to a single species, but sometimes to sub-populations within that species.
Several attempts have been made to link together life stage models to develop true population dynamic models
for a species of interest. The species may warrant such detailed effort for example because of its damage to a
habitat as a threatening invasive species, because of problems (economic, harvesting or contamination) caused
within a particular cropping system or even because the species itself has some intrinsic beneficial role within a
system or some rarity value worthy of conservation. Linking together detailed life-stage sub-models to develop
population dynamic models can entail the inclusion of a great deal of parameters as reviewed by Holst et al.
SID 5 (Rev. 3/06)
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(2007). Whilst these models undoubtedly offer the most detailed insight to the processes within the development
of a plant population over time, their complexity is also their downfall. Introducing possible errors within each
component sub-model can lead to over-parameterisation and a confounding of errors within the dynamic model
(Grundy, 2003).
Many detailed population dynamic models remain largely an academic exercise to aid the development of a better
understanding of how a species behaves. However, in some cases the development of these models has been
specifically triggered by an issue questioning the sustainability of a vegetation management regime and has had
industry application and uptake. For example, the response of an economically problematic weed species to a
rotational system (Buhler et al. 1997; Heggenstaller & Leibman 2005). Alternatively they may be used to predict
the evolution of herbicide resistance within a given control regime and to devise tactics for avoidance – for
example the “RIM” model developed for the management of herbicide resistance in Lolium rigidum (Pannell et al.,
2004). Detailed population dynamic models for specific species are often targeted at the field scale, but
sometimes even within a sub-division of a field (Buckley et al 2002). For these models to be manageable and
user friendly, certain assumptions and pragmatism are often necessary where detailed parameterisation is
unavailable. However, cases where these detailed, targeted and largely species-specific models have included
biotic factors, such as soil type and nutrient status or interactions with other trophic levels and climatic variables
are extremely rare within the literature (the only reference found being Buckley et al 2002).
Regional scale species-specific models
Some plant species are important on not just a local but a regional scale. These species might include invasive
species. Vegetation dynamic models may be developed to predict the invasion success of exotic species or the
stability of a wetland species within a given landscape. These models usually include some spatial element such
as Geographic Information Systems (GIS). (e.g. Townsend Peterson et al., 2003)
Two contrasting approaches to these types of models are often seen in the literature. The first approach is to
develop vegetation dynamic model predictions based on climatic variables using logistic regression to identify
environmental envelopes and hence potential ranges in novel, yet to be colonised, areas. Unfortunately, these
reaction-diffusion (R-D) models often fail to consider spatial heterogeneity or other ecological processes other
than dispersal (Higgins et al., 1996).
The second approach is to develop individual-based models that account for competition between native and
introduced species. This approach makes some classification of vegetation using plant functional types to
describe behaviour traits. These models tend to place greater emphasis on a wider range of ecological
interactions than simply dispersal. Hence they may have some potential in predicting biodiversity outcomes on a
trait-based, if not species-specific level (Higgins et al. 1999, Goslee et al. 2006; Grime and Hillier 2000).
The spatial grain (scale) of both of the approaches used in these regional scale models is critical to their success.
It is imperative that the spatial grain of the models must be compatible with the spatial processes being modelled.
In other words, if the information is to develop such a model is only available at a very coarse level, then it is
inappropriate to attempt to use this information to make predictions on a much finer spatial scale and vice versa.
Regional and global generic models
The final broad category of vegetation dynamics models are those developed to predict vegetation patterns on a
regional or even global scale. These models are again spatially explicit and often referred to as Dynamic Global
Vegetation Models (DGVM). They are appropriate to describe broad-scale patterns in vegetation with for example
climate change (Arora & Boer 2006; Hughes et al., 2006). These models frequently use bioclimatic parameters to
determine the distribution of plant functional types (Woodward & Lomas, 2004) within climatic specific envelopes.
They may use a combination of models to assess changes in ecosystem vegetation types (for example BIOMEUK) with models to assess changes in the distribution of dominant species or species with particular biodiversity
interest (for example SPECIES, Berry et al., 2002).
SPECIES, and models like it, use neural networks to “train” the model on existing data on species distribution to
make predictions. For example, detailed data collected on the distribution of a species in one country could be
used as a training data set to then make predictions of the distribution of that species in another country. The
advantage over the previous types of models described, is that detailed physiological data on individual species
are not required. Because of this they have great potential to make predictions regarding vegetation dynamics on
a generic level. The main disadvantages are that these models by default require very large data sets to train and
optimise the model before they can be used to make predictions. In addition, neural networks are something of a
“black-box” approach, and allow no insight into the importance of the individual variables that are driving the
dynamics of the populations.
SID 5 (Rev. 3/06)
Page 7 of 23
The main advantages and disadvantages of these models and modelling approaches are summarised in the table
below.
Model categories
Local scale
species-specific
models
Advantages
 Provide an excellent
understanding of the
biological and
ecological processes
and interaction driving
vegetation
Disadvantages
 Limited by
sufficiently detailed
data
 Danger of overparameterisation
 Specificity of
parameters restrict
the wider
applicability
Regional scale
species-specific
models
 Provide a level of
scale appropriate for
looking at how
biodiversity may
change over a
Landscape scale
Regional and
global generic
models
 Able to help guide
global policy decisions
 Limited by not
having data
available at correct
scale
 Few account for
natural biotic
interactions and
are mainly driven
by dispersal alone
 Limited by lack
of detail for looking
at biodiversity
 No transparency
of ecological
processes
Ideal

Generic element to the modelling
approach to allow broad application to
multiple species.

Data available at sufficient level of
detail suitable for application at a regional
or landscape scale accounting for
heterogeneity.

Some insight to natural or biotic
interactions

Avoidance of over parameterisation
Insect dynamics
There are few models describing insect species interactions in large heterogeneous systems (Brewster & Allen,
1997). Most of the modelling effort has been devoted to understanding the dynamics of insect populations in
cropping systems and the majority of these insects have been crop pests. Studies have focused on single species
rather than insect assemblages. For example, Cocu et al. (2005) used multiple linear regression to identify the
effects of geographical locations, climate and land use on the abundance and phenology of the pest aphid, Myzus
persicae. Potting et al. (2005) used simulation modelling to investigate the factors in agro-ecosystem
diversification that influence pests. Behavioural factors that influenced the spatial dynamics of herbivore
populations were the colonisation pattern, movement speed and sensory mode of finding host plants. Levine &
Wetzler (1996) undertook simulation modelling to determine the effect of habitat architecture (host-plant
dispersion, density and abundance) on herbivorous pest species with different searching strategies. Bhar &
Fahrig (1998) built a stochastic, individual based simulation model of a specialist insect in a landscape of a
number of crop fields. Brewster & Allen (1997) developed a framework for a spatiotemporal model that can be
used to study insect dynamics in regional (heterogeneous) systems. It integrated ideas from spatially explicit
population models, coupled map lattices and integro-difference equations. In the spatially explicit population
models, information on the environment of the organisms is incorporated explicitly in the model, which combines
an insect population simulation model with a habitat map.
There are relatively few studies that have attempted to consider the effects of land management on insect
assemblages. Using Principal Component Analysis, Benton et al. (2002) showed that there was linked temporal
change between farmland birds, invertebrate numbers (from suction trap data) and agricultural practice (in
Scotland). Multivariate measures of arthropod numbers were associated with multivariate measures of farming
practice in the current and previous year.
Studies explicitly considering the relative roles of plant species composition and habitat area in determining the
structure of insect assemblages found in different habitat fragments are relatively recent (Crist et al. 2006). Crist
et al (2006) used a multiple regression approach to analyse data on the effects of habitat area, edge and plant
community composition on the spatial structure of insect species richness and composition. Collinge & Forman
(1998) developed a conceptual model of landscape change that focuses on habitat spatial arrangement and
tested its predictions with a field experiment on grassland insects.
SID 5 (Rev. 3/06)
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Walters et al. (2000) used ecosystem modelling to evaluate management policies in the Grand Canyon. They
concluded that it was possible to make fairly accurate predictions about some components of ecosystem
response to policy change, including insect communities, but that it was less possible for other components of the
ecosystem.
The main advantages and disadvantages of these models and modelling approaches are summarised in the table
below.
Model categories
Simulation models
Advantages
 Provide an
excellent understanding
of the biological and
ecological processes
and interaction driving
insect dynamics
Disadvantages
 Limited by
sufficiently detailed
data
 Danger of overparameterisation
 Limited to crop
pests
Statistical
modelling
 Demonstrate
linkages
between
trophic levels or habitat
and
environmental
variables
 Difficult
to
extrapolate beyond
data collected
 Mainly limited to
crop pests
 Lack of suitable
data
Ideal

Generic element to the modelling
approach to allow broad application to
multiple species.

Data available at sufficient level of
detail suitable for application at a regional
or landscape scale accounting for
heterogeneity.

Some insight to natural or biotic
interactions between species and other
trophic levels

Avoidance of over parameterisation

Need to consider all insects and
not just economically important species
(insects as consumers of all plants or as
food resources for bird species)
Farmland bird dynamics
The majority of work on farmland birds has been focused on auto-ecological studies, monitoring and experimental
work, with only a small number of papers that focus on utilising modelling to integrate and synthesise the large
body of data on farmland bird ecology. Underlying this to some extent, is the fact that the majority of work has
been done on single species in specific locations, which makes it difficult to generalize the findings to a wider
geographical area.
Stephens et al (2003), reviews 7 different approaches to the modelling of farmland bird populations. The
modelling approaches are split into two categories: phenomological models and behavioural models. The
advantages and disadvantages of these two categories of modelling are described below:
Phenomological models
This modelling approach develops models based on empirical relationships between populations and their
environment. As long as the environmental conditions remain within the range observed experimentally then
predictions can be made, but these models are unable to make predictions outside the range of data observed.
The first type of models in this category are aggregative models that relate the abundance of birds to food
density, and can provide reasonable predictions about habitat use for bird species that show a clear aggregative
response to food, as has been done for skylarks (Watkinson et al., 2000). The major problem with aggregative
models is that it can be difficult to demonstrate aggregative responses empirically due to problems in establishing
exact food availability, and ensuring that the dominant food source is actually consumed by the bird species.
Aggregative models explain only the presence of a species within a habitat, and provide no link to the
demography of the species, which means that these models provide only limited inference about the responses of
populations to food availability and cannot be used to predict the effect of a habitat change on the population of a
species.
The second types of model within this category are population models (Pennycuik, 1969; Potts & Aebischer,
1991). These models are based on experimentally determined rates of fecundity, mortality and, to a limited
extent, dispersal. However, for population models to be used to predict the effect of changes to land-use, the
models need to link demography to the availability of food, which is not often done, but was done in the case of
the grey partridge (Potts & Aebischer, 1991). A crucial limitation to population models is the availability of
appropriate data that links demographic parameters to food availability, and Stephens et al. (2003) state that “for
farmland birds there are very few published examples of such work”. However the main limitation is that
population models cannot be extrapolated outside of the range of data used to parameterize the model with
confidence, which means that they should not be used to predict the effect of habitat or land-use changes.
Behavioural models
SID 5 (Rev. 3/06)
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Behavioural models are focused around describing the behaviour of individuals within a population and therefore
have a much greater potential to be used to make predictions about the effect of changes in habitat or land-use
on population dynamics.
Depletion models focus on the interactions between individuals and their food supply, and there are many
approaches used for this type of model, which gives prediction on the use of habitat (Atkinson, 1998), but does
not link habitat use to demography. Due to the need to have a good understanding of the use of resources,
behavioural models are best developed for species that use a limited set of resources. The current limitation to
the development of these models, which can be extremely useful tools, is the lack of sufficient data to develop
and parameterise the models.
The main problem with all of the models described in Stephens et al. (2003) is that they are species-specific,
describing the population dynamics of a single species, and do not consider the interactions between species,
which limits their use within a biodiversity context. In addition the phenomological models, being empirical, may
not be representative of the general behaviour of populations, outside of the area for which the data were
collected. It is also pointed out by Stephens et al. (2003), that the major limitation for all of the models is that they
do not link the demography of the bird populations to the food availability, which makes use of these models to
predict the impact of land-use changes on bird populations problematic, as changes in land-use will undoubtedly
affect food availability, and possibly also nest site availability. Stephens et al. (2003) clearly state that “Available
data severely limit the range of approaches for predicting the response of farmland bird populations to changing
food supply.”, which is a major problem for the development of models to predict the effect of land-use changes
on biodiversity, as without sufficient robust data to develop the assumptions about movement and resource use,
there will be an unacceptable level of uncertainty within the models.
The models described above fall into the scientific category of models, in that they are being used to explain
behaviour and population dynamics in detail. For the development of policy or the implementation of policy a
more simplistic type of modelling could potentially be used. There are very few examples of models being
developed for policy development or implementation, but those that do exist are reviewed below.
Siriwardena & Vickery – Defra project (BD1618), Chapter 1.
This project was concerned with predicting the response of farmland birds to agricultural change, and chapter 1
describes a population modelling approach to predict future population trends of farmland indicator species to
determine the changes in population demography required to reduce or reverse the decline of the species.
Although chapter 1 deals with all farmland indicator species, a separate model for the grey partridge is provided in
chapter 2, and is not considered within this review, which relates solely to chapter 1.
The models are simplistic, using multiple regression to develop models to predict the historic changes in
populations of each species separately. Because the initial fits did not match the trends appropriately, the models
had to be modified to provide better fits. The need for these changes was suggested to be due to biased data
from the common bird census, and mis-matched data types. Therefore, the model predictions have a large
degree of uncertainty linked to them, which is not stated, meaning that the models should be used with caution
when predicting the changes needed to alter the demography of bird species, to reverse or reduce decline.
The model is based on data from a wide range of habitats, and does not take any account of habitat use, or have
any link to the availability of food or nesting resources. The lack of geographical consideration is another potential
problem, since populations in different geographical locations may be responding in different ways, and by
averaging this out in the model, through the use of census data, significant errors could be introduced into the
predictions. This model does provide predictions across a wide range of species, but is limited to only describing
demography, and would not be suitable for describing the effects of land-use changes on bird populations.
Swetnam et al. (2005) – Designing lowland landscapes for farmland birds: scenario testing with GIS
This paper develops models to examine habitat variation based on predictor variables, which are then used to
determine the effect of changes in land use on territory density.
Models were developed for two species, the skylark (Alauda arvensis) and yellowhammer (Emberiza citrinella).
For the skylark, the model is based on data recorded in 100 fields over 3 years, and takes the form of a
generalised linear model (GLM) for counts, which has been optimised using backwards selection to identify the
minimum adequate model. The model for yellowhammer was developed using the similar approach (described in
detail in Whittingham et al. (2005)) of binary logistic regression. The models predict either the number of
territories expected in a field (skylark) or the probability of finding a territory in a field boundary (yellow hammer).
Several potential land-use change scenarios were examined using the models, and the models were able to
identify which scenarios would have either a positive or negative impact on the two species concerned. The
advantage of these models is that they link use of landscape to the presence of the bird species. However, their
major disadvantage is that there is no link between territory occupancy and demography. The models are also
SID 5 (Rev. 3/06)
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based on real data, and so they cannot predict beyond the range of landscape conditions found within the data
used to parameterise the model, limiting their ability to be used predictively.
In addition, the models predict the potential number of territories, based on habitat conditions and not actual
territory occupancy. There are many possible reasons why a suitable habitat would not be occupied, including:
inability to disperse to the area, lack of suitable feeding sites (the models use mainly landscape architectural
features as predictor variables), and predation pressure. The authors recognize the limitations of the model and
state “Ideally, habitat association models would be combined with data on species’ demographic rates (e.g.
reproductive success and survival) and their density dependent relationships to create population dynamics
models, and with data on dispersal behaviour and response to landscape-scale environmental variation to
generate spatially explicit population models.”
As mentioned previously, the problem in doing as the authors say, and developing spatially explicit population
models, is the lack of suitable data on demography, resource use and dispersal.
Butler et al. (2007)
This paper is the most recent and most interesting attempt to develop a model that can be used to predict the
effect of land-use changes on bird populations. The model uses a matrix of ecological requirements covering diet,
foraging habitat and nesting habitat for each species. The assumption is that land-use change will affect a
species if it leads to change in food abundance and/or a change in nesting success. In this regard, the model
includes the components that are necessary to describe the impacts of land-use changes on a bird species. The
output of the model is a risk score for the species, which can be related back to the annual population growth
rate, and as such predicts the impacts of land-use change on population growth rate. The ability to predict the
impact of land-use change on annual population growth rate is ideal for determining the effects of policy on
biodiversity.
However, there are some drawbacks to this model. The first is that the risk score is based on expert opinion and
ranking, rather than on actual data, and so is effectively a subjective estimate of the effect of a particular change
on the bird species. Secondly, the model predicts only for population growth at a national level, and does not
consider potential regional differences. The model does not distinguish between different types of habitat, and
therefore averages across all habitat types, which could mask potential local and regional differences in
population growth rates, which are affected by particular local/regional conditions.
As a risk assessment framework, this model is a valuable resource to policy makers, but for predicting the
impacts of land-use changes on biodiversity, the model does not fully account for the vital link between habitat
and resources, and the fact that the majority of species use a wide range of habitat types. The model could be
improved by including greater detail about habitat types and the link between these and resource availability. It
would also be beneficial to include more local and regional level predictions.
Overall, the current status of modelling of bird populations can be summed up as being severely limited by data
availability. Many model approaches exists that could be utilized to develop models of bird biodiversity, and the
impact of land-use changes on biodiversity. However, to fully understand how land-use changes may affect bird
biodiversity there is a need to move towards models of multiple species that link habitat use to demographic
parameters. The most promising approach would be to link several of the models types together to remove the
disadvantages of a single model type, but to keep the advantages of the different types so that all factors affecting
bird biodiversity are included in the model, e.g. linking habitat association models with population or behavioural
models, so that the effect of habitat type on dispersal and movement is explicitly considered within the models.
The main advantages and disadvantages of the bird models and modelling approaches are summarised in the
table below.
Model categories
Aggregative
models
Population models
SID 5 (Rev. 3/06)
Advantages
 Link food abundance
to demography
 Based on
experimentally
determine data
(fecundity, dispersal,
mortality)
Disadvantages
 Limited by
sufficiently detailed
data
 Difficult to prove
link between food
resource and
demography
 Poor linkage of
demography to
food availability
 Cannot be
extrapolated
outside range of
Page 11 of 23
Ideal
 Generic element to the modelling
approach to allow broad application to
multiple species.
 Data available at sufficient level of
detail suitable for application at a regional
or landscape scale accounting for
heterogeneity.
 Some insight to natural or biotic
interactions between species

Avoidance of over parameterisation
Behavioural
models
 Focusses on
individual behaviour
 Include link between
behaviour and resource
availability
Habitat association
models
 Link landscape
factors to potential
presence of a species
Risk framework
 Links changes in
habitat to demography
 Provides predictions
across a wide spatial
scale
data
 Limited data
availability
 Limited links
between resource
use and
demography
 Lack of sufficient
data for
parameterisation
 No inclusion of
demography or
dispersal
 Only potential
presence, not
actual presence
 Cannot predict
beyond range of
observed data
 Based on
subjective expert
opinion
 Does not
account for
landscape
heterogeneity
 No data-based
link between
habitat use and
demography
 Linkage between habitat use, resource
use and demographic parameters

Inclusion of dispersal of individuals
Summary of Objective 2
From the reviews of vegetation, insect and bird dynamics models, it is clear that there is a lack of suitable and
sufficient data across the three trophic levels, which limits the scope and extent of modelling work that can be
done. The majority of models are based on single species, and do not consider multiple species interactions,
which are important in the context of biodiversity. There is a need to integrate across the trophic levels, and to
develop models that are able to integrate the spatial heterogeneity of the landscape, and link this to resource
availability, resource usage and the demography of a range of species.
A wide diversity of modelling approaches exist to tackle the modelling of specific aspects relating to the different
trophic levels, but a framework is needed to ensure that the appropriate approaches are combined and used to
determine how land-use affects biodiversity. The choice of modelling approach will depend very much on the
question being asked, and the level of detail required to answer the question. To that end the framework must
provide a clear set of questions that need to be answered (using appropriate modelling approaches) to determine
how land-use affects biodiversity.
The integration of modelling approaches and data from a range of scales will be essential in providing a useful
framework to address questions relating to land-use and biodiversity, and the potential approaches for this
integration are described in Objective 3 below.
Objective 3: Determine how to integrate data across a range of geographical scales to allow ecosystem
modelling approaches to predict changes at regional and national scales.
The aim of this objective was to determine the approaches that exist for integrating either existing models or
modelling approaches across a range of geographical scales.
In considering this objective we have identified four distinct types of integration that need to be considered in the
development of ecosystem models: spatial integration, trophic integration, temporal integration, and data
integration. Below we define what we mean by each type, and identify the challenges and problems associated
with each.
Spatial integration
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Models describing the population dynamics of a particular plant, invertebrate or bird species (or even a
community of different species) will often be limited in their application and appropriateness to a particular
location and habitat, usually within a defined physical area (e.g. a single field, a catchment, or a region). Where
interest is in the detailed scientific understanding of a particular ecosystem, prediction of the effects of
management changes on the biodiversity within this defined area may be all that is required, but for the purposes
of assessing the impacts of policy changes or for determining the effects of different implementation approaches,
the integration or aggregation of information across a number of adjacent spatial locations will be necessary.
Where data and models are available to predict the response of a particular species in each spatial location, then,
at a simplistic level, integration to predict the response over a collection of neighbouring spatial locations may just
require the aggregating of the responses across the appropriate set of individual locations. However, this does
not take account of the effects that neighbouring spatial locations might have on the response in each location –
 How should we incorporate the effects of the habitat in these neighbouring locations, or of changes in
population dynamics of the species being modelled in these neighbouring locations?
 Over what distance do neighbouring locations have an impact?
The answers to these questions will of course depend on the plant, invertebrate or bird species that we are
modelling, and also on the spatial scale for which the model has been constructed and is appropriate.
Spatial integration of responses becomes more complicated where a community of several species is being
considered, and where some summary measure of biodiversity is the response of interest. Here the biodiversity
response over the larger spatial area may not be just some simple summary (e.g. arithmetic mean) of the
biodiversity responses across each of the constituent areas. One form of integration would require the
aggregating of information about each individual species across the individual spatial areas, followed by the recalculation of the biodiversity measure, to provide a single summary for the larger spatial area. But this loses
information about the (spatial) variability in biodiversity within the larger area. An alternative approach might be to
consider the mean and variance (or some other summary statistics) of the values across the set of individual
spatial area, possibly weighted by the size of each area, or taking account of the spatial location of each area.
Again, the integration may also need to take account of how neighbouring locations affect the response in a given
location.
The issue of spatial scale and spatial integration should also influence decisions made in developing the
ecosystem model:
 On what spatial scale should we collect data? This may vary between trophic levels, and also be affected
by the purpose of the modelling exercise, the type of model being developed, and the ease of data
collection. It will affect the precision of predictions produced by any model developed from the data.
 On what spatial scale should we develop the models? Again this will be affected by the purpose of the
modelling exercise, as well as by the required precision of response, and the availability of data.
 On what spatial scale should we interpret or predict the response to changing management? This is
primarily affected by the purpose of the modelling exercise, but will also be influenced by the availability
of data, and the spatial scale at which we are able to build models.
Trophic integration
Whilst most existing models only describe the population dynamics of single species, to be able to predict
changes in biodiversity ecosystem models must include multiple species and the interactions between them.
Some existing studies use empirical modelling approaches to directly model the value of some biodiversity index
in terms of a range of potential explanatory variables, but this approach causes serious problems if trying to
integrate the results over a larger spatial area (see above). A more ideal, but certainly more time consuming and
intensive, approach is to develop detailed models for each individual species, incorporating interactions between
species, and then to combine the predictions from these individual models to produce a combined measure of
biodiversity.
These interactions between species take a number of different forms. Within trophic levels they will often
primarily be competitive – plant-plant competition for light, water, nutrients; competition between invertebrate
species for food; competition between birds for food and nesting locations – and will often be both within and
between species. Other forms of interaction include the predation (or feeding) of one species on another, usually,
but not exclusively, between trophic levels. Of course these different interactions operate at different spatial
scales, so that the combination of species included in an ecosystem model will influence the choice of spatial
scale for data collection, modelling and prediction – ideally the chosen spatial scale for the model needs to
encompass the largest spatial interactions between species, whilst being based on data at the smallest possible
spatial scale.
Temporal integration
By their very nature, population dynamic models must consider changes in the modelled population over time.
Therefore, in integrating such models to predict the effects of management changes, it is important to consider
the time course over which the ecosystem model operates and that over which predictions will be made. Key
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elements include information about the time course over which management changes are implemented, and the
time period during which changes in outcome (biodiversity, species richness, or individual species density) will be
considered to be important. Of course not all biological processes operate over the same time scale, though
many of the processes of interest will have an annual cycle. Thus it will be important to understand the time
course over which each species completes a cycle when combining individual species models and assessing the
interactions between species.
A second key area of temporal integration relates to the prediction of outcomes, whether biodiversity, species
richness or individual species density. Most observations of these measures will be at a single point in time, and
so it is likely that constructed models will produce predictions on the same basis. However, these measures are
likely to change between different times of the year, and so it will be important to consider some sort of
aggregation of the measure of interest over a given period of time (the choice of this period may depend strongly
on the question being addressed).
Data integration
In building models to address policy issues, it is likely that data from a wide range of sources will be used. As
already indicated under “spatial integration” above, different data components will often have been recorded at
different spatial scales or levels of spatial precision. Incorporating these different data sources will therefore
require either the aggregating of data from a smaller spatial scale to provide an appropriate value for modelling at
a larger spatial scale, or some assumption about the distribution of a variable across all sub-units contained within
the larger unit at which the data were recorded.
A second form of data integration becomes apparent when considering the building of large scale models using
individual species models as components. Whilst many individual species models have been constructed at a
fairly detailed level, it is likely that the incorporation of such models into a more general model will require these
models to be simplified and generalised to require fewer estimated parameters. An important process in the
development of the large scale models will then be to assess the sensitivity of the overall outcomes to these
simplifications.
Related to this idea of assessing the sensitivity of overall outcomes to the choice of individual species models, is
the concept of including stochastic elements in the model. This may be as simple as drawing individual
parameter values from statistical distributions for each simulation run (Monte Carlo simulation), or as complex as
allowing a probabilistic choice of the form (mathematical function) used for difference model components. These
stochastic model elements are likely to be driven by the analysis of data using empirical modelling approaches,
with the incorporated stochasticity reflecting the observed variability in parameter values or input variables, or the
range of possible model forms for individual components. Incorporation of such stochasticity allows model
outcomes to be presented with associated measures of uncertainty, and hence to allow an assessment of the
risks associated with the plausible scenarios evaluated using these models.
Modelling approaches
Having identified the need for the integration of models and data in these four different ways, a wide diversity of
mathematical and statistical modelling approaches were identified that have been used to tackle such integration,
with the specific approach required being dependent on the focus of the modelling exercise, and, more
importantly, the available data (the lack of appropriate data generally appears to be the major constraint on any
model building exercise). There is little value in listing the range of approaches that could be used, though these
range from highly empirical statistical modelling methods to techniques based on a detailed knowledge of the
mechanisms driving observed processes. Instead we choose to define a modelling framework which would assist
both in the selection of appropriate modelling techniques and in the identification of the available and necessary
data sources, according to the outcome information required to address any particular policy question.
Thee proposed modelling framework, which we believe should form the backbone of any modelling work to
determine how plausible changes in land-use might impact on biodiversity, is defined by a set of five key
questions:
1) What is the current spatial distribution of the species of interest?
2) What is the potential spatial distribution of the species of interest given the changes in land-use?
3) How much of the potential spatial distribution can the species of interest achieve (within a defined time
scale), taking account of habitat and landscape permeability?
4) Are there sufficient resources to allow the species of interest to establish in the new locations (within a
defined time period)?
5) Will the species of interest establish, and, if so, how will it affect the current community?
(ecological interactions)
Whilst the questions above all refer to a single target species of interest, they could equally be applied to a
community of species, summarised using some measure of biodiversity.
SID 5 (Rev. 3/06)
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The precise modelling approaches used to answer these questions are not defined, as these will depend very
much on the question being asked, and the assumptions that are being made within the model. It is not essential
to answer all 5 questions, since the usefulness of each question will depend on the information required to inform
policy development. The progression through the questions requires increasing detail to be included within the
model. Questions 1 and 2 can be answered using quite broad scale data, and for relatively large geographical
regions. Question 3 requires more localised data to provide useful answers, as it is concerned with determining
the movement of species and will therefore be subject to variation due to the localised habitat composition
(although it may be possible to generalise the approaches to provide information over wider scales). Questions 4
and 5 will certainly require information specific to particular locations, since they involve questions about how a
species interacts with the local environment.
Providing answers to all 5 questions listed above will provide the most useful and detailed information on how
land-use changes will affect biodiversity, which can then be used to inform or develop policy on particular issues.
Existing approaches
Approaches exist to answer the first two questions of the framework, within the context of conservation biology,
and these are described below:
Prediction of species distributions
The main approach used to predict species distributions is that of a multiple regression using either Generalised
Linear Models (GLM) or Generalised Additive Models (GAM), and is described as landscape modelling. A large
amount of work on characterising species distributions has been done in Australia, where a number of suitable
datasets already exist to allow this form of characterisation. Ferrier et al. (2002b) developed a species-level
approach which combined mapped and modelled layers within a Geographical Information System (GIS)
describing topography, climate, substrate (soil type), vegetation cover and disturbance. The information was
mapped at a range of scales from 1:25000 to 1:100000. This information was combined with floral and faunal
surveys.
The models used GAM-based logistic regression to describe the species distributions. From the statistical
relationships, the distribution could then be extrapolated spatially to regions outside the areas where the surveys
were done. The models predicted three probability surfaces within the GIS: the probability of occurrence together
with upper and lower 95% confidence limits for this probability. The upper limit is relevant for areas where there is
a need to have high confidence that a species exists, whilst the lower limit is relevant for areas where there is a
need to have high confidence that a species is absent. This modelling approach is fine where datasets record
presence and absence of a species, though it can be difficult to reliably record the absence of a species. If a
dataset only consists of presence data, then pseudo absences need to be generated, and these need to be
appropriately weighted within the regression to reflect the fact that they are not recorded absences.
This basic modelling approach does assume that the presence of a species is driven solely by environmental
characteristics or habitat suitability. It does not account for the heterogeneity of the landscape, assuming that two
areas of the same habitat type can support the species linked to that habitat type, irrespective of their geographic
separation. It should be noted that many species exhibit a spatial pattern in their distribution, not predicted simply
by variation in habitat.
The models were then adapted to incorporate the spatial configuration of habitat in cells neighbouring the suitable
habitat cell, using a set of ‘contextual indices’, with an inverse distance weighting (similar to the approach used to
measure habitat neighbourhoods in metapopulation models). The approach provided better estimates of species
distribution, but did rely heavily on expert knowledge rather than actual data in determining the effect of
neighbouring habitats. This is potentially problematic – Pearce et al. (2001) showed that incorporating habitat
indices defined by expert opinion did not improve the predictive accuracy of models of species distributions. They
also showed that models based solely on expert opinion were less accurate than models based on the statistical
modelling of quantitative data.
The approach described above for the species level was then extended to the community level (Ferrier et al.,
2002a), the premise being that the community level could be used to derive coarse-filter biodiversity surrogates
for use in regional conservation planning. They used two approaches to develop models and predictions of
communities, a ‘classification-then-modelling’ approach and a ‘modelling-then-classification’ approach. For the
former, the data are classified to derive groups of sites containing similar species or groups of species. These
derived data are then modelled using a GLM or GAM approach and the fitted models extrapolated for the wider
group of species. The latter approach requires that each individual species distribution is first modelled using a
GLM or GAM approach, and then groups of grid cells with similar predicted species or groups of species can be
identified.
SID 5 (Rev. 3/06)
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It was shown that species-groups provided better surrogates than site-groups, and that the species-group
modelling approach provided similar results to individual species modelling.
The approaches described above require large amounts of data, which may not be available within the UK.
However, Ferrier et al. (2002a) recognised this potential problem, and suggested that rather than modelling
individual species, approaches could focus on modelling collective properties of biodiversity. Species richness is
one such property, but has limitations, since conserving areas with the highest species richness may have limited
value. Additional information is required in the form of compositional dissimilarity (how dissimilar two areas are in
terms of the species they contain). Ferrier et al. (2002a) discuss a matrix regression approach that can be used to
predict the dissimilarities between sites, and call the approach Generalised Dissimilarity Modelling (GDM). This
approach may well be useful in assessing how land-use changes could affect species distributions and hence
biodiversity, by considering the similarity of the effects of land-use changes at different sites.
Virkkala et al. (2004) used a GLM approach to study the effects of landscape composition on birds in a boreal
agricultural-forest mosaic in Finland. The model explained 49% of the variation in the total density of farmland
birds. They showed that a grid-based approach is effective in modelling bird species distributions as it can take
account of variation in habitat structure and other topographical factors.
Landscape and environmental variables have been used to predict species distributions in the UK as part of the
MONARCH project (Berry et al., 2005). The SPECIES model used in MONARCH uses an artificial neural network
(ANN) to characterise bioclimatic variables through a climate-hydrological process model. This is used to identify
climatic regions within which species can exist, and to predict changes to their distribution based on these climatic
envelopes. A GAM approach is used to model changes in key vegetative land cover variables and the system
then uses a cellular automata modelling approach to predict species dispersal based on the climatic and
vegetative characteristics of neighbouring cells. This model is then used to predict the probability of occurrence of
each species in each location. The main problems with this species modelling approach are the lack of any
spatial autocorrelation when considering the dispersal of species and the lack of suitable data, but the approach
provides a possible framework for the modelling of the effects of land-use changes on biodiversity.
The use of landscape and environmental variables as surrogate ecological indicators is currently being
questioned. It is suggested that the surrogate landscape measures lack a generic applicability to a wide range of
species and may not have biological or ecological significance (Lindemayer et al., 2002, Austin, 2007).
Lindenmayer et al. (2002) suggested that the definition of any landscape surrogate measures should be qualified
by an explicit statement about the question being addressed and the biology and ecology of the target organisms
being modelled. Austin (2007) presented a similar message, but came up with a set of rules that should be
applied to any modelling of species distributions, to make the models as robust as possible. These rules
essentially ensure that the assumptions being made are explicitly defined, and that ecological knowledge is used
when selecting the predictor variables from landscape and environmental surrogates.
Prediction of movement, based on habitat permeability
The models described above generally produce potential distributions for individual species or groups of species,
but, apart from SPECIES, they take no account of the patterns of dispersal of the different species. To model the
effects of land-use changes on biodiversity, it is essential that these dispersal patterns are taken into account, so
that the current and potential distributions can be combined to provide realistic estimates of the actual species
distributions under a range of plausible scenarios, including the likelihood of a species attaining that distribution
within a defined time scale.
Currently there is little available information about linking dispersal patterns with current species distributions.
One possible approach is based on habitat permeability, defined as the ability of a species to move through a
particular habitat. There is a large ecological literature on the permeability of boundaries, but this is largely
theoretical, and, although a useful resource, of limited applicability to biodiversity modelling. There are two key
published sources that link landscape permeability with species distributions, and these are described below.
The first example investigates the occurrence of two amphibian species in Switzerland (Ray et al., 2002). A
landscape map was used to derive permeability estimates for each species for a range of habitat types between
the breeding ponds of toads and newts. The approach used was to generate a friction map, a matrix of cells that
define the energy cost for crossing each cell. A conversion table is then used to assign a migration cost to each
habitat type in the landscape. In this example, migration costs were linked to both energy expenditure and the risk
of mortality in crossing a habitat. If the organism is given a set budget for crossing habitats, those routes where
the cost exceeds the budget can be omitted, and hence migration zones can be defined. Based on these
migration zones, the probability that a species can move from its present location to a new location, through the
habitat types encountered on the way, can be calculated. This allows a realistic actual distribution to be
estimated, with those areas of the potential distribution omitted that could not be reached. Ray et al. (2002)
compared the friction map approach with the standard circular migration zone approach, and showed that the
friction map approach led to better predictions of the occurrence and dispersal of newts and toads.
SID 5 (Rev. 3/06)
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Another example where potential distributions have been modified by dispersal models is the set of BEETLE
(Biological and Environmental Evaluation Tools for Landscape Ecology) tools, developed by Forest Research
(www.forestry.gov.uk/fr/infd-69pla5) (Watts et a.l, 2005). This set of tools is aimed at assessing the effect of forest
fragmentation on species distributions and currently models ecologically representative generic focal species. The
approach uses the habitat area requirements and dispersal characteristics of the species to determine how
changes in fragmentation associated with land-use affect the species distribution. The model is based on using
functional connectivity, defined as a functional attribute of the landscape related to ecological processes such as
movement and dispersal. The functional connectivity is a combination of the dispersal ability of a species and the
permeability of the landscape (similar to the friction maps of Ray et al. (2002)).
The ALMaSS Model
Topping et al. (2003) have developed a modelling approach that addresses many of the problems mentioned
under objective 2 and in the section above. The modelling approach uses a modular modelling system that is
specifically designed to be “a predictive tool for answering policy questions regarding the effect of changing
landscape structure and management on key animal species in the Danish landscape”. The model consists of a
core landscape model that provides information to be used by the animal modules included in the model. The
data on landscape is stored as landscape type (e.g. arable field, coastline, quarry, road, river, etc) for each gridsquare, and the model can represent an overall area of 10km by 10km at a 1m 2 resolution (100 million grid
squares). The model incorporates weather information, and also includes standard management practices for six
farm types.
The animal models have been developed for five species: roe deer, field vole, skylark, ground beetle and a
spider, which represent key ecological types. The animal models are agent-based models of behaviour and use
rules for transition between behaviours, based on certain conditions being met (using information from the
landscape, weather and farm management modules). The movement of species is incorporated into the model,
and the rate of movement is weighted by habitat, so landscape permeability is accounted for within the animal
models.
The model has been successfully used to model the impact of land-management, through scenarios of reduced
pesticides on all the species in the model (Jepsen et al., 2005), producing predictions that illustrate the
interactions between land-management and species abundance. For the skylark (Topping, 2005; Topping &
Odderskær, 2004), the model predicted that skylark abundance would decline under a reduced pesticide scenario
due to associated changes adopted by farmers in the cropping regime, with the alternative crops being less
suitable for skylarks. However, the model also showed that the greatest benefit to skylarks was caused by an
alteration of the structure of the crop to provide access for nesting and feeding.
The approach used in the ALMaSS model is limited by data availability, as with all the other models discussed in
this report. Currently ALMaSS does not include interactions between multiple species, due to a lack of suitable
data. In addition the models are usually run for time periods of 60 or more years, with the first 20 or so years
being discounted as being the time required for populations to reach equilibrium. This effectively means that
effects occurring during the transitional time period between different land-uses are not considered, but these
may be extremely important in determining the future abundance and distribution of species. Also, the ALMaSS
model wraps the boundaries of the geographical region being simulated, so there is no immigration or emigration.
These are certainly important processes that would need to be considered when assessing the impacts of landuse changes on biodiversity in a UK context. The use of weather data means that to simulate future behaviour, it
is either necessary to use historical weather data, which can be looped if insufficient data is available, or to
generate plausible future weather scenarios. Another limitation is the high resolution of the model, which not only
constrains the geographical extent of the model, but also means that a high level of detail about the vegetation
spatial distribution and the animal behaviour and movement is required. The model does not currently include
models for vegetation and the interactions between vegetation and land-use. However, Warren and Topping
(1999, 2004) have developed models to examine the effect of environmental heterogeneity on the competition
between plants, and these models could easily be included within the ALMaSS modelling approach due to the
modular structure used.
Despite these limitations, the ALMaSS model remains the most suitable existing approach for modelling the effect
of land-use changes on biodiversity, and for providing predictions that can be used to develop policy on land-use
changes. The ALMaSS approach could be adapted for use in the UK, subject to sufficient data on vegetation,
weather, farm management practices, and animal biology and behaviour being available. Whilst the limited
geographical coverage of the model would probably need to be addressed, the key concepts of having a
landscape simulator, with farm management and weather modules linked to agent-based models of specific
species, could be retained.
Adaptive Resource Management
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Another useful concept is the approach referred to as “Adaptive Management” or, in the area of ecological
modelling and monitoring, as “Adaptive Resource Management”. The basic idea behind Adaptive Resource
Management is that the implementation of management decisions is adapted or modified as additional data,
either independent or from monitoring observations following the earlier implementation of management
decisions, is collected and incorporated into the model. With reference to the use of ecosystem modelling
approaches to support the development and implementation of policy, the Adaptive Resource Management
approach implies the need for continuous dialogue between the model builders and policy makers, and a
medium- or long-term commitment to the monitoring of, and collection of data from, sites both associated with and
independent of the implementation of particular policy management decisions. It must be realised that any model
is only as good as the data on which it is based. Thus as model realisations are observed following the
implementation of policy, quantitative observations provide a validation or otherwise of the developed models.
Whether or not the observations and models are in agreement, the collected data allows the models to be
improved, and so better inform future model predictions and the future development and implementation of policy.
Summary
Integration will be a major issue when developing models to predict the effect of land-use changes on biodiversity.
It will be necessary to have clearly defined spatial and temporal scales, taking account of data availability and
model aims, to ensure that appropriate modelling approaches are selected. The aims of the modelling and the
hypotheses being tested will also need to be precise and unambiguous to ensure that appropriate techniques are
used for integrating data. The modelling framework addresses these issues through a set of structured questions
that assist in defining the spatial, temporal and ecological information required within a model, together with
identifying the required level of detail of this information,. The questions are structured so that progressively more
detail and data on landscape and species biology are required to answer each question. This allows policy
makers to decide how much information to include in the model, depending on the question or questions they
wish to answer.
A lot of research has already been devoted to the use of landscape surrogates to predict species distributions
across large areas, and, when this is linked to ecological parameters and landscape permeability, some highly
useful and sensible predictions about the effect of land-use changes on species can be made, although it may be
necessary to consider incremental changes in land-use over longer timescales, rather than just considering the
start and end points. The approaches described above could form a set of tools to be utilised within the
framework to assess the effect of land-use changes on biodiversity, and to examine how biodiversity can be
conserved at a regional or national level. The only aspect missing from the existing modelling approaches is the
impact of a species moving into a new area on those species already resident in that area. To ensure a robust
and joined-up approach to biodiversity conservation or protection, there is a need to consider multiple species
and their interactions, rather than focussing on individual target or indicator species, since although a land-use
change may benefit the target species, it could be detrimental to other species. Consideration of multiple species
would require models for the ecological interactions between species. Currently there are few if any modelling
tools that cope with interactions between species, although possible modelling approaches have been presented
within the ecological modelling literature. There is certainly a paucity of data on the interactions between species,
and so a significant research effort is needed to collect data on inter-species interactions.
Of the existing approaches, the ALMaSS model (Topping et al., 2003) is the most advanced approach,
addressing the majority of the issues raised as limitations in this report. The model does have its own limitations,
namely a limited geographical range, and the requirement for a high level of detail on farm management, weather
and vegetation distribution as inputs to the model. Despite this, the concept of having a modular set of models,
where models for different species can be linked to a landscape simulator, is highly desirable, and reduces the
cost and time involved in developing a series of models.


We recommend that Defra consider adopting an approach similar to ALMaSS for the development of
models to address policy questions about the impacts of land-use changes on biodiversity.
We recommend that Defra incorporate adaptive resource management concepts into projects where
management decisions and modelling are being integrated, so that improved predictions and
decisions can be made as more data becomes available.
Objective 4: Prioritise the future research necessary to develop ecosystem scale modelling approaches
for policy development on farmland biodiversity.
At the meeting with Defra policy makers, described in Objective 1, a set of four key questions were identified that
the project should address. These questions were:
1) How will land-use changes associated with biomass/bioenergy crops affect biodiversity?
2) How do ELS incentives need to change to promote biodiversity?
3) What would be the most effective strategy for targeting habitat/resource protection?
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4) How should habitat creation efforts be focussed?
Addressing each of these questions individually is problematic, since there are multiple potential scenarios that
could be modelled to assist in answering each question. It is necessary to further refine these questions to
provide a set of scenarios and targets/hypotheses (with defined spatial and temporal scales) that can then be
evaluated or tested using a modelling approach. To define a model specification for each scenario, complete with
estimated costs and data availability would be extremely difficult. Therefore, we provide a generic specification
that can be used by Defra to develop projects to answer each of these questions. The generic specification will be
applicable to any policy question about the impact of land-use changes on biodiversity.
The generic model specification
This specification consists of a set of stages that should be followed to develop models to answer policy
questions on biodiversity. It does not recommend any particular modelling approach, as that will be dependent
upon the question or questions being addressed. It provides a set of questions that need to be considered when
developing models to answer questions about the impact of land-use changes on biodiversity.
Stage 1: Defining and clarifying the problem/question
The first step is to clarify the question. There are several things that need to be considered in this stage:

What is the question?
What are the essential pieces of information that Defra policy makers require?
The question will need to be defined and refined to provide a set of specific and unambiguous questions or
hypotheses, with defined outputs, that can be answered using modelling. This will ensure that any model
developed will provide useful output.

What definition of biodiversity is being used?
Is biodiversity being defined as overall UK biodiversity or is it specific to a geographical region or habitat?
Is the interest in species richness, community composition, species dominance or the population dynamics of
one or more key indicator species?
Which trophic levels are of interest: microbes, plants, insects, mammals, birds or some combination of these?
Are multiple species being considered, or is the focus on a single species?
The aim here is to define the level of detail that will be required within the model, and this will assist with the
selection of the modelling tools and techniques to be used.

What geographical scale is of interest?
Is it sensible to have a case study for a particular geographical region or habitat?
Will the land-use changes be applied on a broad-scale or in restricted regions?
Here we are determining the level of integration that will be required within the model, and this will aid the
selection of the modelling tools required to integrate between different levels of detail within the model.

What time scale needs to be considered?
Will any land-use changes be immediate or will they be phased in over time?
Over what time-scale should the impact of change be simulated – short, medium or long-term?
When considering impacts on biodiversity, impacts may be evident over a range of time scales, particularly
when there is a need to consider the interactions between species or trophic levels and/or the movement of
species. It will therefore be extremely important to ensure that the time-scale over which the impacts need to
be determined is explicitly stated, so that the models will provide worthwhile predictions.

What format does the output need to be in?
The models developed will necessarily be complex, and capable of producing output in a wide variety of
formats. It is essential that the precise format of outputs required from the model is defined in advance to
ensure that information is communicated in the most useful form for policy development. It may be necessary
to have more than one format, so that informed decisions can be made for policy development.
In this first stage, consultation between Defra policy makers and the model developers is highly recommended
(probably essential) as this will ensure that the models provide the exact type of information that Defra policy
makers require to make decisions, rather than relying on the modellers to choose appropriate outputs.
Stage 2: Development of plausible futures
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The aim of this stage is to develop a set of plausible future land-use scenarios, which will form the basis of the
model simulations. Plausible future scenarios should be based on clearly stated assumptions about changes that
will probably occur in the future.
By clearly stating the assumptions used in developing the scenarios, it is possible for policy makers to select
those scenarios that have assumptions that most closely fit with what they believe will occur, enabling them to
examine the model and select the outputs that they feel will be of greatest value in determining policy.
We would recommend that consultation between Defra policy makers and the model developers is maintained
during this stage to ensure that the scenarios used fit closely to those envisaged as likely to occur by policy
makers, including the most likely best and worse case situations.
Stage 3: Selection of modelling approaches, data availability and model development
In this stage of the model, the five questions that form the modelling framework need to be considered. Based on
the questions being asked, and the plausible future scenarios, the model developers will need to determine which
of the framework questions need to be answered, and then the modelling tools and techniques to be used to
answer the framework will need to be selected.
In selecting the modelling techniques it will be essential to have an overview of the quantity and format of data
available to parameterise the models. By combining knowledge on data availability with the selection of modelling
techniques, the most appropriate modelling approach can be identified. In addition, knowledge and data gaps will
be identified that may have to be filled before a suitable model can be developed.
In the absence of suitable or sufficient data, assumptions or expert knowledge can be substituted, if applicable.
However we would recommend that, additionally, a plausible data scenario is developed. The plausible data
scenario would provide criteria for the format of data that would be required to fully parameterise the model. Using
this latter strategy, it is then possible to develop the model in the absence of data, so that once the data have
been collected, in the specified format, the model can immediately be used to make predictions. Having selected
the techniques and collected/collated the data, the models can then be developed.
Stage 4: Simulating the plausible future scenarios and analysis of the outputs
The plausible future scenarios would be used to provide inputs into the model, probably in the form of GIS data
on land-use, with the current land-use as a baseline for comparison. Once the simulations have been completed,
the data would then need to be analysed to determine the impacts of each scenario relative to a baseline
scenario of no change.
Where several impacts are possible within a particular future scenario, a measure of the uncertainty associated
with the occurrence of particular impacts should be provided. This information can then be used to assist in
assessing the likelihood of the occurrence of a particular impact, and then used to determine risk levels
associated with particular scenarios.
Stage 5: Recommendations and presentation of the model output
The model developers should present their recommendations based on the model outputs to Defra policy makers,
and discuss the assumptions used within the model. The assumptions made in the model, including the implicit
assumptions of the individual techniques used to develop the model, should be explicitly stated, along with the
implications of the assumptions not being met. This will assist Defra policy makers in their interpretation of the
recommendations, and allow them to use their judgement about the usefulness of the model predictions.
Can the modelling framework be used to address Public service agreement (PSA) targets?
The key PSA target we consider here is:
To reverse the long-term decline in the number of farmland birds by 2020, as measured annually against
underlying trends.
It is possible to use the modelling framework described here to assess the impact of land-use changes on the
ability to meet this target. With current distribution and habitat data from the BBS, it will be possible to predict the
potential distribution of key indicator species of farmland birds (on an individual species basis) using techniques
similar to those used in MONARCH and SPECIES. This is equivalent to answering question 2 in the framework.
If there was a need to predict the achievable distribution for key indicator species (questions 3 and 4 of the
framework), this is more difficult as there is a lack of data on the dispersal and landscape permeability for bird
species, and on the food values of different habitats for farmland bird species. This information could be derived
using expert opinion, but this adds a large potential error to the predictions, which would need to be considered
when interpreting the model predictions.
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Predicting the distribution of key indicator species of farmland birds, accounting for interactions with other key
indicator species (question 5 of the modelling framework), would be extremely difficult due to lack of data. To
include the interactions between species will require a large data collection effort, as expert opinion would
probably not be reliable enough if modelling at this level of detail. In addition, it would be useful to revise the
models based on annual breeding bird survey data and other research into bird behaviour and life cycles, using
the adaptive resource management concepts. This would enable management decisions to be adapted annually
to any changes in bird distribution or abundance, subject to the availability of suitable habitat and landscape
information.
Can the modelling framework be used to address England Biodiversity Strategy (EBS) targets?
There is the potential to use the modelling framework to assess how land-use changes will affect individual
species targeted in the England Biodiversity Strategy. However, lack of suitable data on the current distribution of
many of the species, may limit the applicability of the modelling framework. Also, many of the priority species
have very restricted habitat requirements and ranges, which means that examining land-use change on a UK or
regional scale may not be appropriate. Also, if land-use changes are examined over a long time scale (15 or more
years), it would be necessary to look at how land-use changes annually over this time scale, rather than just
considering the start and end points. This would enable the determination of whether species ranges would shift
incrementally, allowing the movement of species over time to occupy altered habitat ranges under a range of
scenarios. This would require information on the permeability of different habitat types for each individual species,
data which do not currently exist.
The most valuable way to use the framework for EBS targets would probably be to model local scale land-use
changes, examining how habitat creation would need to be targeted to best benefit the species with restricted
ranges. This would require the collection of data on habitat permeability to assess whether the species are able to
move to the newly created habitats.
For both the PSA and EBS targets, the modelling framework could be used with present data to assess shifts in
distribution, but would not be able to determine whether species would be able to move to the new distributions.
To gain the greatest benefit from the modelling framework, it would be necessary to collect data on the
permeability of the landscape for the species of interest. The ALMaSS concepts combined with adaptive resource
management could be used to address both PSA and EBS targets, through the development of a single
landscape simulator. Individual or multiple species models could then be linked to the landscape simulator to
assess how changes in landscape would affect biodiversity, abundance or dispersal. Management decisions
could then be made, and the results of these decisions fed back into the models at a later date to improve the
predictions and management decisions.
The development of an ALMaSS type of landscape simulator will require large-scale detailed information on land
use and habitat types, the data for which is limited in England at present. There is a need for a central repository
for all landscape and land-use information, and a co-ordination of land-use data collection efforts. This is a role
that Defra could have, and ti would ensure that Defra have sufficient land-use information for modelling the
impacts of land-use changes on biodiversity and the ability to meet PSA and EBS targets.



We recommend that if Defra wish to use the modelling framework to assess the impact of land-use
change on the ability to meet PSA and EBS targets, research is funded to develop a modular
modelling approach, with a central landscape simulator linked to individual or multiple species
models.
We recommend that Defra fund research into the movement and dispersal of species, so that the
permeability of different habitat types, and the movement of species through these habitats can be
determined for use in models assessing the impacts of land use changes on biodiversity.
We recommend that Defra co-ordinate the collection of data on land-use and habitat types within the
UK, as the availability of suitably detailed data on land-use will be a severe limitation to any modelling
of the impacts of land-use change on biodiversity in the UK
Summary of Objective 4
The modelling framework described in Objective 3 provides a useful tool for answering questions about the
impact of land-use changes on biodiversity. Using this framework to answer policy questions will require
consultation between modellers and policy makers to ensure that a clear set of future scenarios and/or
targets/hypotheses are developed that can be tested using modelling approaches. To assist in this process we
have developed a generic model specification, including a set of questions to assist in ensuring and development
of testable scenarios and hypotheses that address the needs of policy makers.
The framework will be useful in the development of models to determine the effect of land-use changes on the
ability to meet PSA and EBS targets. With existing data it should be possible to predict future potential
distributions of individual species, as long as sufficient data on current potential or actual distributions and habitat
types for the species of interest are available. However, the modelling framework would only be able to predict
potential future distributions, as there is a lack of data on the dispersal of species through the landscape. If robust
SID 5 (Rev. 3/06)
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predictions of the future actual distribution of species are required, then there would need to be research to
determine the effect of the landscape mosaic on the movement and re-distribution of the species of interest.
For biodiversity conservation, there would almost certainly be a need to consider multiple species and the
interactions between them, rather than modelling effects on individual species. Although the modelling techniques
and tools exist to incorporate species interactions into models, there is a lack of data on interactions between
different species. Therefore, there would need to be research into the interactions between multiple species in a
range of habitats.
References to published material
9.
This section should be used to record links (hypertext links where possible) or references to other
published material generated by, or relating to this project.
References cited in the report.
Vegetation dynamics references
Arora, V.K. & Boer, G.J. (2006). Simulating competition and coexistence between plant functional types in a dynamic
vegetation model. Earth Interactions 10: Paper 10, 30 pp
Berry, P. M., Dawson, T. P., Harrison, P. A. & Pearson, R. G. (2002). Modelling potential impacts of climate change on the
bioclimatic envelope of species in Britain and Ireland. Global Ecology and Biogeography 11(6) 453-462.
Buckley YM, Briese DT, Rees M. (2003). Demography and management of the invasive plant species Hypericum perforatum.
II. Construction and use of an individual-based model to predict population dynamics and the effects of management
strategies. Journal of Applied Ecology 40 (3): 494-507
Buhler, D.D., King, R.P., Swinton, S.M., Gunsolus, J.L. & Forcella, F. (1997). Field evaluation of a bioeconomic model for
weed management in corn (Zea mays). Weed Science, 45, 158-165.
Goslee, S.C., Peters, D.P.C., & Beck, K.G. (2006). Spatial prediction of invasion success across heterogeneous landscapes
using an individual-based model. Biological Invasions, 8: 193-200.Grime, J.P. & Hillier, S.H. (2000). The contribution of
seedling regeneration to the structure and dynamics of plant communities, ecosystems and larger units of the landscape.
In: Seeds: The Ecology & Regeneration of Plant Communities 2 nd Edition. (ed M. Fenner). 361-374.
Grundy, A.C. (2003) Predicting weed emergence: a review of approaches and future challenges. Weed Research 43: 1–11.
Heggenstaller, A H. & Leibman, M. (2006). Demography of Abutilon theoprasti and Seteria faberi in three crop rotation
systems. Weed Research, 46: 138-151.
Higgins, S.I., Richardson, D.M. & Cowling, R.M. (1996). Modeling invasive plant spread: the role of plant-environment
interactions and model structure. Ecology, 77:2043-2054.
Higgins, S.I., Richardson, D.M., Cowling, R.M. and Trinder-Smith, T.H. 1999. Predicting the landscape distribution of invasive
alien plants and their threat to native plant diversity. Conservation Biology 13: 303-313 Hughes, J.K., Valdes, P.J. & Betts,
R. (2006). Dynamics of a global-scale vegetation model. Ecological Modelling 198:452-462/
Holst, N. Rasmussen, I.A. & Bastiaans, L. (2007). Field weed population dynamics : a review of model approaches and
applications. Weed Research, 47: 1-14
Hughes , J.K., Valdes, P.J. Betts, R. (2006) . Dynamics of a global-scale vegetation model. Ecological Modelling. 198: 452462
Pannell, D.J., Stewart, V., Bennett, A., Monjardino, M., Schmidt, C. and Powles, S.B. (2004). RIM: A Bioeconomic Model for
Integrated Weed Management of Lolium rigidum in Western Australia. Agricultural Systems 79(3): 305-325.
Townsend Peterson, A., Papes, M. & Kluza, D.A. (2003). Predicting the potential invasive distributions of four alien plant
species in North America. Weed Science, 51: 863 – 868.
Woodward, F.I. & Lomas, M.R. (2004). Vegetation dynamics – simulating responses to climate change. Biological Reviews
79:643-670
Bird dynamics references
Atkinson, P. W. (1998) The wintering ecology of the twite Carduelis flavirostris and the consequences of habitat loss. PhD
thesis, University of East Anglia, Norwich, UK.
British Trust for Ornithology (2005) Predicting the Response of Farmland Birds to Agricultural Change. Defra Report for
project BD1618.
Butler, S. J., Vickery, J. A. & Norris, K. (2007) Farmland biodiversity and the footprint of agriculture. Science 315: 381-384.
Pennycuik, L. (1969) A computer model of the Oxford great tit population. Journal of Theoretical Biology 22: 381 – 400.
Potts, G. R. & Aebischer, N. J. (1991) Modelling the population dynamics of the Grey Partridge: conservation and
management. In Bird Population Studies. Relevance to Conservation and Management (Eds. C. M. Perrins, J.-D.
Lebreton & G. J. M. Harris), pp. 373-390. Oxford University Press, Oxford.
Stephens, P. A., Freckleton, R. P., Watkinson, A. R. & Sutherland, W. J. (2003). Predicting the response of farmland bird
populations to changing food supplies. Journal of Applied Ecology 40: 970-983.
Swetnam, R. D., Wilson, J. D., Whittingham, M. J. & Grice, P. V. (2005) Designing lowland landscapes for farmland birds:
scenario testing with GIS. Computers, Environment and Urban Systems 29: 275-296.
Watkinson, A. R., Freckleton, R. P., Robinson, R. A. & Sutherland, W. J. (2000) Predictions of biodiversity response to
genetically modified herbicide-tolerant crops. Science 289: 1554-1557.
Whittingham, M. J., Swetnam, R. D., Wilson, J. D., Chamberlain, D. E. & Freckleton, R. P. (2005) Habitat selection by
yellowhammers Emberiza, citrinella on lowland farmland at two spatial scales: implications for conservation
manangement. Journal of Applied Ecology 42: 270-280.
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Insect dynamics references
Benton, T.G, Bryant, D.M., Cole, L. & Crick ,H.Q.P (2002). Linking agricultural practice to insect and bird populations: a
historical study over three decades. Journal of Applied Ecology 39: 673-687.
Bhar R. & Fahrig, L. (1998). Local vs. Landscape effects of woody field borders as barriers to crop pest movement.
Conservation Ecology 2, http://www.consecol.org/vol2/iss2/art3/
Brewster, C.C. & Allen, J.C. (1997). Spatiotemporal models for studying insect dynamics in large-scale cropping systems.
Environmental Entomology 26: 473-482.
Collinge, S.K. & Forma, R.T.T. (1998). A conceptual model of land conversion processes: predictions and evidence from a
microlandscape experiment with grassland insects. Oikos 82: 66-84.
Crist, T.O., Pradhan-Devare, V. & Summerville, K.S. (2006). Spatial variation in insect community and species responses to
habitat loss and plant community composition. Oecologia 147: 510-521.
Levine, S.H. & Wetzler, R.E. (1996). Modelling the role of host plant dispersion in the search success of herbivorous insects:
Implications for ecological pest management, Ecological Modelling 89: 183-96.
Potting, R.P.J, Perry, J.N. & Powell, W. (2005). Insect behavioural ecology and other factors affecting the control efficacy of
agro-ecosystem diversification strategies. Ecological Modelling 182: 199-216.
Walters, C., Korman, J, Stevens, L.E. & Gold, B. (2000). Ecosystem modelling for evaluation of adaptive management
policies in the Grand Canyon. Conservation Ecology 4, article no. 1. http://www.ecologyandsociety.org/vol4/iss2/art1/
Conservation and integration References
Austin, M. (2007) Species distribution models and ecological theory: A critical assessment and some possible new
approaches.
Berry, P.M., Harrison, P. A., Dawson, T. P. & Walmsley, C. A. (2005) Modelling Natural Resource Responses to Climate
Change (MONARCH) UKCIP.
Ferrier, S., Drielsma, M., Manion, G. & Watson, G. (2002) Extended statistical approaches to modelling spatial pattern in
biodiversity in northeast New South Wales. II. Community-level modelling. Biodiveristy and Conservation 11: 2309-2338.
Ferrier, S., Watson, G., Pearce, J. & Drielsma, M. (2002) Extended statistical approaches to modelling spatial pattern in
biodiversity in northeast New South Wales. I. Species-level modelling. Biodiversity and Conservation 11: 2275-2307.
Jepsen, J. U., Topping, C. J., Odderskær, P. & Andersen, P. N. (2005) Evaluating consequences of land-use strategies on
wildlife populations using multiple-species predictive scenarios. Agriculture, Ecosystems and Environment 105: 581-594.
Lindenmayer, D. B., Cunningham, R. B., Donnelly, C. F. & Lesslie, R. (2002) On the use of landscape surrogates as
ecological indicators in fragmented forests. Forest Ecology and Management 159: 203-216.
Pearce, J.L., Cherry, M., Drielsma, S. Ferrier, S & Whish, G. (2001) Incorporating expert opinion and fine-scale vegetation
mapping into statistical models of faunal distribution. Journal of Applied Ecology 38:412-424.
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