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4 Modelling impacts of drivers on biodiversity and
ecosystem functioning
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Coordinating Lead Authors: Lluís Brotons, Villy Christensen & N H Ravindranath
Lead Authors: Olivier Maury, Vania Proença, Pablo Peri, Mingchang Cao, Baris Salihoglu,
Jung Hwa Chun.
Contributing Authors: Rajiv kumar Chaturvedi, Nicolas Titeux…
This chapter describes how to use models that represent ecosystems and biodiversity and
account for the impacts of human activities. The key messages from the synthesis are:
1. Ecosystems are open systems that are far too complex to be understood and predicted
without formal representations (i.e. models).
2. A diverse range of drivers impact global biodiversity and influence ecosystem stability.
Modelling biodiversity requires the consideration of relevant drivers and ecological
processes to generate projections that are credible.
3. Clarifying the role of modelling to assess the impacts of changes in direct drivers on
biodiversity and ecosystem processes might assist policy making over a range of scales.
4. Biodiversity models rest on processes and assumptions. These must be clearly and
transparently communicated in order to be of use in informing decision-making.
5. Ecosystems are complex in nature and interact in complex ways with direct drivers.
Models should balance complexity and simplicity considering both the decision-making
context and an explicit prioritization of processes and feedbacks to be explicitly
accounted for.
6. While important gaps remain in the link between ecological theory and models,
different approaches are available to conduct assessments and contribute to scenarios
development.
4.1 Introduction
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Biodiversity and ecosystem dynamics are complex, and environmental drivers, both of natural
and anthropogenic origin, induce changes that are commonly translated into changes in
ecosystem services and human wellbeing. The present chapter focuses on the approaches and
methods currently available to explicitly link these changes in the environment with
biodiversity responses from community composition and structure to biogeochemistry fluxes
and ecosystem function descriptors. The aim is to identify the range of tools available to
unravel mechanisms of change and incorporate this knowledge in models allowing the
projection of future biodiversity conditions.
The chapter first provides a comprehensive introduction to the context in which biodiversity
and ecosystem models are to be developed, and stresses how the decision-making processes
should be used to guide model building. Secondly, a description is provided of broad modelling
types and drivers to be included in the quest towards better biodiversity projections. Thirdly,
we continue with a detailed description of available modelling approaches, and explanations of
how environmental drivers of both natural and anthropogenic drivers are introduced into the
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models, and connected with the respective biological components. Fourthly, we describe the
main pros and cons of the different approaches in different contextual applications.
Since feedbacks between biodiversity responses and environmental drivers may be important
and complexity in biodiversity modelling requires careful integration of the spatial scale and
contexts, we illustrate current challenges in these ambits. The issues associated with the
sources of uncertainty in model projections are of outmost importance in the context of
biodiversity projections for IPBES, and we describe this topic in depth in the context of
biodiversity and ecosystem modelling. We finally identify the major challenges for biodiversity
projections in the context of the IPBES programme, and highlight major avenues for
development to make biodiversity projections more useful for policy makers at a range of
administrative scales.
4.1.1 Zooming inside the general IPBES scheme. Links to human drivers
and ecosystem services
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Currently we are in an era that has been called the Anthropocene where human activities have
become global geophysical forces, and major drivers of global environmental change (Steffen
et al., 2007). According to Rockström et al. (2009), humanity has transcended the “safe
operating space” of the planet with respect to biodiversity loss that play an important role as
regulator of ecosystem processes. Traditionally, the discipline of ecology studies aspects
related to the ecosphere, and the discipline of economics focus on such flows in social
systems. However, within the IPBES framework, there is a need of cross-disciplinary fields such
as ecological economics and industrial ecology to understand biodiversity and ecosystem
services changes. Thus, environmental problems at the global or regional level may reanalyse
the perspective of human-environment systems where social and ecological aspects are
interacting at multiple temporal and spatial scales (Clark, 2010). In this context, industrial
ecology has in the last few decades emerged as a field aiming at a sustainable development of
the anthroposphere, which is the interface between the ecosphere and society.
Biodiversity plays multiple roles in the conceptual chain linking direct drivers to nature’s
benefits to people. Specifically, it may regulate the ecosystem processes that generate final
ecosystem services. Indeed, biodiversity itself may constitute a final ecosystem service, or
even a good that is directly enjoyed by people (Mace et al. 2012). In the first case, biodiversity
attributes affect the development of ecosystem processes (Cardinale et al. 2012), such as
nutrient cycling (Handa et al. 2014), primary productivity (Cardinale et al. 2007) or water
infiltration (Eldridge and Freudenberger 2005), which in turn give rise to final ecosystem
services, including material (e.g., medicinal plants) and non-material (e.g., water regulation)
outputs. In the second case, biodiversity elements are themselves material outputs, with direct
use value, such as medicinal plants or fish, but which require human capital inputs (e.g.,
labour, transport) before being enjoyed by society. Finally, biodiversity elements are a good if
directly enjoyed by people without any additional input, which is the case of charismatic
species and ecosystems. Although biotic and abiotic ecosystem components interact and are
both essential to ecosystem functioning the focus of this chapter will be on the biotic
components, as framed by the IPBES conceptual framework (Figure 4.1).
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Figure 4.1. Role of biodiversity and ecosystems in the IPBES conceptual framework. The present chapter
concentrates on the impact of drivers of environmental changes (stressing the role of anthropogenic
drivers, Chapter 3) on biodiversity and ecosystem processes as determinants of ecosystem goods and
services (Chapter 5)
The role of biodiversity as a regulator of ecosystem processes or as a material output (either a
final service or a good), defines the variables of interest when assessing and projecting the
impacts of direct drivers. For instance, community data such as species diversity (Cardinale
2007, Mace et al. 2012) or habitat structure (Eldridge and Freudenberger 2005) may be
particularly important to assess the impact of drivers when biodiversity has a regulatory role,
while population data, such as species distribution (Gaikwad et al. 2011), or population
structure (Berkeley et al. 2004) would be more adequate when biodiversity elements have a
direct use value. The acknowledgement of the different roles of biodiversity follows an
anthropocentric perspective that has ecosystem services, the material and non-material
benefits generated by nature, as its main end. In parallel to utilitarian values biodiversity has
its own intrinsic value, which is independent of human demand or appreciation.
4.1.2 Decision making context in biodiversity models
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Models allowing the assessment of how environmental drivers of change induce changes in
biodiversity or ecosystem functions are essential tools to support decision-making. To be
effective, models should be able to capture the problem motivating their use and to answer
information needs. A formal and accurate definition of the decision-making context and
objective is therefore essential in this process (Guisan et al. 2013). A precise definition of the
decision-making context will guide the decision of the modelling framework including model
complexity, spatial and temporal scales or response variables and data requirements.
Response variables should be sensitive to the pressures underlying alternative management
scenarios or addressed by policies, and, if possible, be responsive in temporal and spatial
scales that are relevant for policy strategies. For example, small farmland birds are responsive
to agro-environmental schemes implemented at the field scale whereas large farmland birds
are not, being more affected by the conditions found at the larger spatial scales (Concepcíon
and Díaz 2011). Moreover, response variables should also be representative of the biodiversity
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attributes underpinning the benefits of nature that are valued in a given decision-making
context (see Section 4.1).
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Regarding model scope, models should be adjusted to the specific requirements of the
decision-making context. Models could rely on observed data to describe the relationship
between pressures and response variables, explicitly describe the processes linking those
variables, or follow an intermediate approach. The explicit inclusion of mechanisms in
modelling approaches will be relevant whenever the understanding of the underlying
dynamics is necessary to guide management and where changing environmental conditions
call for a mechanistic approach (Gustafson 2013; Collie et al. 2014). The use of correlative
approaches, on the other hand, will be adequate if there is limited knowledge about the
underlying mechanisms or when model outputs are able to capture the dominant response
patterns that are needed to inform policy, such as the evaluation of large scale conservation
initiatives (Araújo et al. 2011; Dormann et al. 2012). Regarding model complexity, input data
requirements should be balanced against data availability and quality, namely the spatial and
temporal resolution of available data, as the lack of or the use of inadequate input data may
compromise model feasibility and results quality (Collie et al. 2014).
4.1.3 Why and for what purpose do we need models?
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Ecosystems are open systems that are far too complex and multidimensional to be understood
without formal abstraction (e.g., models). As humans, we do experience reality but we can
only know it through representations. Models are such representations of reality, expressed in
scientific terms, most often using mathematical formalisms. Models are tightening together
scientific concepts (e.g., energy, population, biomass) using functional hypothesis and the
logical grammar of mathematics. They are useful to document the ecological world
scientifically, communicate it, and discuss it. Beyond, models are necessary to conceive and
think complex scientific objects such as ecosystems and biodiversity, just as words and
sentences allow saying but are also the raw material of thinking. Models are therefore at the
core of ecological sciences, and ecologists build and use them to represent, explore, share, and
discuss their understanding.
Ecological models can have various levels of formalisation, from purely qualitative to fully
mathematical. The model’s formalisation provides the logical consistency that is needed to
manipulate our representations, to make idealized thought experiments that are not possible
in real ecosystems, or predict the behaviour of the modelled system beyond the range of
observed conditions.
Biodiversity is an abstract concept that refers to highly complex living objects (genomes,
organisms, populations, communities, ecosystems, Earth system) that are often difficult to
observe comprehensively. For that reason, models are useful to formalize the contextdependent scope of biodiversity according to objective definitions, and therefore facilitate
discussion and communication. They furthermore allow testing the theoretical consistency of
hypothesis on ecosystem functioning and biodiversity response to specific drivers, and their
compatibility with observations. They are therefore essential for improving our understanding
of ecological patterns and biodiversity.
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Ecological observations often reflect the interaction of multiple processes and convoluted
factors acting at various scales. By formalising the ecological complexity at stake, ecological
models allow disentangling the factors and processes involved to interpret the data. Revealing
theoretical inconsistencies, models stimulate the development of theory. Highlighting
observation gaps, models can also be used to guide data collection and stimulate new field and
experimental studies. Finally, through their capacity to integrate the effects of various drivers
and major interactive processes, and predict their future dynamics, ecological models provide
strong basis to build biodiversity scenarios and projections, according to possible evolutions of
drivers. They can therefore assist policy makers to understand cause-effect relationship
between drivers and the impacts and design efficient policies.
4.1.4 Drivers of environmental change and associated scales
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The world has in recent decades experienced global environmental change due to human
impact, and this has encouraged research to study the new challenges that biodiversity is
exposed to (Pereira et al. 2010). The assessments of the established links between these
drivers and biodiversity responses are central to the IPBES and determined by both changes in
the environment and to the ecological and physiological processes contributing to the
dynamics of these ecological systems even in environmentally static systems (e.g., adaptation
can induce range expansion; Lavergne et al. 2010). Thus responses to environmental changes
derived directly or indirectly from human activities may be either related to changes in the
environment itself, to the biological processes acting within ecosystems or, more frequently,
to the combination of both (de Chazal and Rounsevell 2009; Leung et al. 2012). It is therefore
important to distinguish between indirect and direct drivers of change in ecosystems and their
services, and how changes in ecosystem services have affected human wellbeing. Although
biodiversity and ecosystem services experience change due to natural causes, these
anthropogenic indirect drivers increasingly dominate current environmental changes.
The human sources of these changes lie in two major groups of production and consumption
activities: (i) resource extraction with associated processing, use and disposal, and (ii) habitatimpacting measure, e.g., in form of land-use and land-cover change (Ayres, 1989). These
activities cause the main direct drivers of environmental change for which we are interested in
assessing the responses of biodiversity and ecosystem services. Specifically, temporal and
spatial habitat modification in marine and terrestrial habitats, land degradation, the impact of
invasive species, climate changes, pollution and exploitation and use of resources (see Chapter
2) induce changes in biodiversity and ecosystem function that are not trivially related to the
magnitude of the environmental change recorded.
Human impacts on the global environment are operating at a range of rates and spatial scales.
Scaling issues are particularly important to assess impacts on biodiversity and ecosystem
services because drivers have different impact at different scales. For example, while climate
change is a driver that acts at the global scale, habitat modification has an impact on
biodiversity and ecosystems services at regional and local scales. In this context, at least half of
the ice-free surface of the Earth has already been substantially altered for a variety of human
uses (Kates et al., 1990), the methane content of the troposphere has doubled, and the level of
carbon dioxide increased by 25%, since preindustrial times (Graedel et al., 1990). Similarly,
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humans impact all parts of the world ocean, with a large fraction being strongly impacted by
multiple drivers (Halpern et al. 2008).
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A better understanding of habitat modification is of crucial importance to the study of global
environmental change. Land uses (agriculture, livestock grazing, settlement and construction,
reserves and protected lands, and timber extraction) have cumulatively transformed land
cover at a global scale. The main direct drivers affecting this process are external input, (e.g.,
fertilizer use, pest control, irrigation), technology adaptation and use, resource exploitation,
pollutions, species introduction and habitat changes. For marine systems, the main drivers are
fisheries (Halpern et al. 2008), notably through bottom-impacting fishing gears (Watling 2005).
The consequences of habitat modification have been significant for many aspects of local,
regional, and global environments, including climate, atmospheric composition, biodiversity,
soil condition, and water and sediment flows. However, global-scale assessments typically
mask critical sub-global variations. Local and regional case studies can provide the spatial and
temporal resolution required to identify and account for major variations in cause-to-cover
relationships and the consequence on biodiversity. But, single-factor explanations, at the
macro or the micro scale, have not proven adequate. Many models assessing the impact of
environmental drivers on terrestrial ecosystems and biodiversity elements, including those
dealing with climate and trace-gas dynamics, require projections of land-cover change as
inputs. In this context, Loreau et al. (2003) highlighted that knowledge of spatial processes
across ecosystems is critical to predict the effects of landscape changes on both biodiversity
and ecosystem functioning and services.
Changes in ecosystem connectivity after fragmentation or other anthropogenic and natural
perturbations may substantially alter both species diversity and ecosystem processes at local
and regional spatial scales. Both increasing and decreasing connectivity can either increase or
decrease species diversity and the average magnitude and temporal variability of ecosystem
processes, depending on the initial level of connectivity and the dispersal abilities of the
organism considered.
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4.2 Definition of main elements determining ecosystem and
biodiversity dynamics
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Scientists and stakeholders in decision-making processes are always faced with the challenge
of simplifying reality and selecting key processes and drivers most relevant for their study
object (Guisan et al. 2013). Decisions in how and what to include explicitly and what can be
simplified or ignored are crucial. The prediction of ecological responses to environmental
changes should start with the specification of the major model conceptual components
relevant for the case and their critical relationships. In the description of any model of this
type, three major conceptual components should be identified:
1. Structural elements describing ecosystem. Target state variable(s) used to describe the
biological component of interest, such as biomass, species richness, or habitat
heterogeneity (Figure 4.2). State variables should be included based on their sensitivity to
pressures and the stability of their response pattern, but also the costs and feasibility of
data collection.
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2.
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Environmental and biotic factors whose spatial dynamics have a direct or indirect effect
on the biological component of interest (e.g., climate changes inducing species
extinction). In a context of environmental change, changes in the value of environmental
and biotic factors will affect the value of state variables.
Ecological processes relevant in determining changes in the biological component (e.g.,
species distribution dynamics such as colonization and extinction).
Figure 4.2. Summary of some of the predicted aspects of climate change and some examples of their
likely effects on different levels of biodiversity. From Bellard et al. 2012.
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Biodiversity models, as other mathematical models in environmental sciences, are made of a
set of components, namely, state variables, external variables, mathematical equations, and
parameters (Jørgensen 1994; Smith and Smith 2007, Soetaert and Herman 2009). State
variables correspond to the biodiversity variables of interest, the ones that describe the state
of the system, such as biomass, species richness, or habitat heterogeneity (Figure 4.2). State
variables should be included based on their sensitivity to pressures and the stability of their
response pattern, but also the costs and feasibility of data collection (Dale and Beyeler 2001).
External variables (or ‘drivers’) correspond to the external factors that influence the state of
the system, such as climate or nutrient inputs. In a context of environmental change, changes
in the value of external variables will affect the value of state variables. In biodiversity models,
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mathematical equations establish the link between environmental factors (or external
variables) and state variables by describing the relationship between them. Models may
feature equations that explicitly describe the processes controlling that relationship
(mechanistic models), or describe the shape of the response curve by fitting observed data to a
predefined function, which is assumed to represent well that process or use a pure statistical
approach to derive a relationship from measured values. Parameters are constants in the
mathematical equations, which are specific for the system being modelled.
The links between external and state variables, and between state variables, as well as the
type of processes involved, should be identified at the early stages of the modelling process,
namely through the development of a conceptual model (Jørgensen 1994, Smith and Smith
2007). The conceptual model, which can take the form of a diagram, helps to clarify the links
between variables but also to define model assumptions – including space and time
boundaries (Jørgensen 1994).
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The nature of driver impact on the biological process is key in determining the nature of the
model choice and the inclusion of multidisciplinary expertise in the model building process
(Guisan et al. 2013). In complex approaches, the impact of drivers on the biodiversity
component is compacted through correlative approaches (e.g., SDMs) making the model
building process very easy. However, this kind of shortcuts should be performed with caution
and full recognition of the assumptions associated to the decision (e.g., ignoring indirect
pathways and feedback between element). In a context of environmental change, the effect of
environmental pressures on state variables can be direct, (e.g., loss of tree cover after
deforestation) or mediated by biological processes, (e.g., ocean acidification and warming
affecting coral recruitment and growth, and hence coral abundance and reef structure). In
addition, processes also mediate interactions among state variables, (e.g., trophic cascades).
The choice of the state variables and processes to figure in a model requires some previous
knowledge about the system dynamics. The more realistic a model is, the more complex it will
be as realism tends to require description based on solid knowledge of the multiple ecosystem
components and processes involved. There is a trade-off with regard to a models predictive
capability involved here, where increased complexity leads to a decrease in predictive
capability (Walters 1986). Because biodiversity response to environmental change can assume
many forms as a consequence of its inherent complexity, there is a large diversity of potential
indicators or state variables. A way of addressing this diversity is to reduce it to a few
meaningful dimensions.
4.2.1 Describing biodiversity and ecosystem functioning
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Biodiversity indicators or state variables can be arranged along two dimensions representing
key aspects of biodiversity complexity, that is, biological organization (species, populations,
ecosystems, etc.) and biodiversity attributes (composition, structure, and function). These two
dimensions define a conceptual space that can be useful for the identification of state
variables and processes (
Table 4.1). More specifically, state variables correspond to composition and structural
elements, such as species richness or biomass, and processes to function elements, such as
primary productivity, herbivory or competition. Composition and structure emerge from
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processes but also affect them (Dale & Beyeler 2001). From an ecological perspective,
composition and structure variables describe the structural elements of ecosystems, while
processes describe the fluxes of energy and matter and the interactions within and between
organization levels.
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Table 4.1 Examples of compositional, structural, and functional biodiversity variables (Noss 1990, Dale
and Beyeler 2001). Based on the representation of the key characteristics of composition, structure and
function (from Dale & Beyeler 2001, based on Franklin 1988 at represent the many aspects of
biodiversity that warrant attention in environmental monitoring and assessment programs.
Individuals
Species/
Populations
Community/
Ecosystem
Landscape/
Region
Composition
Structure
Function
Genes
Presence,
abundance, cover,
biomass, density
Species richness,
evenness and
diversity,
similarity
Genetic structure
Population structure,
range, morphological
variability
Canopy structure,
habitat structure,
Habitat richness
Spatial heterogeneity,
fragmentation,
connectivity
Genetic process
Demography, dispersion,
growth, metabolism,
phenology
Species interactions
(herbivory, predation,
competition, parasitism)
Succession,
decomposition
Landscapes processes
(hydrologic processes,
geomorphic processes),
disturbances
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Ecosystems are open systems. They harness solar energy and transfer it through their various
structural elements and organization levels, via different biological and ecological processes.
At the biosphere level, water and nutrients, such as carbon, nitrogen and phosphorus, are key
structural elements of all living components, and key abiotic components of ecosystems. Their
flux across the Earth system is described by the biogeochemical cycles. This flux of energy
permits life on Earth and fuels the ecological functions that are useful for societies, (e.g.,
ecological services). To model the dynamics of biodiversity, the major ecological processes
involved in the transfer of energy through ecosystems must be taken into account. These
include production, consumption, respiration, and recycling. Other processes such as
regulation and evolution are critical to the maintenance of ecosystems over time and also
involve biodiversity.
Broadly speaking, mechanisms determining ecosystem dynamics can be divided into three
broad groups (Lavergne et al. 2010). First, we find mechanisms related to the ecology of the
species populations, such as habitat selection, dispersal, and population dynamics. Those
processes are primarily determined by species traits expressing the capability of the target
species to deal with environmental variability in space and time, (e.g., Hanski et al. 2013;
Thuiller et al. 2013). At this level of biological organization, the main structural elements,
besides individuals, are the cohorts or life stages, such as seedlings and mature trees. Primary
production and respiration are major ecological processes, occurring at the organism level and
underlying population dynamics. Organic matter from primary production is at the basis of all
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life on Earth. Numerous factors such as light, the availability of inorganic nutrients, water and
temperature influence primary production and have to be considered explicitly in models.
Respiration, which comprehends all the living processes using oxygen, is at the core of
metabolism. Respiration can be considered at every organization levels, from mitochondria to
ecosystems. At the organism level, respiration processes are influenced by many factors
including the species considered (body-size scaling rules imply that many metabolic processes
vary with the maximum size that a species can reach, Kearney et al. 2010a), the size of
individuals, their condition, the availability of food, oxygen and temperature. At the population
level, respiration integrates the metabolism of all individuals. It is therefore highly dependent
of the size- and state-structure of the population. At the community level, respiration
integrates the metabolism of all populations and is therefore controlled by their relative
abundance and the structure of the community. Modelling the variability and environmental
influences on metabolic biomass production and dissipation therefore requires integrating
over several organisation levels, from individuals to communities (Maury and Poggiale, 2013).
In the second place, species interactions can restrict or expand the set of places that the
species is able to inhabit (Davis et al. 1998). Competition, facilitation or trophic relationships
are site and species-specific and account for a great deal of variability in the species capability
to survive in a given environment. At the community level, the main structural elements of
interest are related with the trophic dynamics (e.g., producers, consumers, detritivores) and
other species interactions (e.g., plant-pollinator interactions). Consumption and recycling are
main processes associated to trophic interactions. Consumption constitutes a major process of
ecosystem dynamics that transfers solar energy along food chains, from primary producers up
to top predators. Trophic interactions are influenced by various factors, including by spatialtemporal co-occurrence of grazers/predators and their food/prey, which is often constrained
by environmental features. In the oceans, both horizontal and vertical distributions and
movements of organisms control trophic interactions, but the vertical distributions are
especially dynamic and can change over short time scales. Alternative modelling approaches
considering explicit modelling of vertical distributions through three-dimensional models with
the complexity this entails have to be explored along with implicit modelling of vertical
distributions.
Recycling is a major component of the dynamics of ecosystems that must considered in models
for closing the system-level mass-balance. Recycling has a critical importance in the
biogeochemistry of the Earth System. It is in particular responsible for the storage of carbon in
soils and its export to the deep marine sediments via the biological carbon pump (Sarmiento
and Gruber, 2006).
Finally, evolutionary processes including contemporary microevolution and macro
evolutionary trends, allow for critical changes in species traits (Lavergne et al. 2010). Any
biological process of interest should have an explicit link with the components formulated in
the model. However, this link does not need to be one-to-one.
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4.3 Modelling approaches for assessing the impact of drivers
on biodiversity and ecosystem functioning
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Various types of ecological models are used for assessing the impacts of large-scale
environmental drivers on the ecosystem. Ecological models can be classified in two broad
categories: phenomenological and mechanistic. Amongst the phenomenological approaches,
purely statistical models, that only rest on correlations between variables, must be
distinguished from phenomenological process-based models that explicit some relevant
processes in a phenomenological way. Mechanistic approaches attempt to derive ecosystem
dynamics from first principles, essentially like theoretical physics attempts to describe the nonliving universe. Practically, since the development of a purely mechanistic theory of
ecosystems is still to come, existing approaches can be classified along a gradient from purely
phenomenological to purely mechanistic.
Correlative models use statistical methods to establish relationships between environmental
variables and biodiversity data, such as species richness, abundance or distribution (Morin and
Lechowicz 2008). These models deliver information on biodiversity patterns and responses to
drivers based on empirical observations, but do not explain the mechanisms underlying those
patterns and responses (Rahbeck et al. 2007). Model performance and realism is therefore
dependent on a careful choice of environmental predictors and of the spatial resolution and
extent of the data, but also on the judicious use of the model given model assumptions (Elith
and Leathwick 2009, Araújo and Peterson 2012).
Figure 4.3. A simple schematic showing the relationship between two observations of a species
distribution in the ‘real world’, ‘statistical models’ and ‘dynamic, process based models’. From McInerny
& Etienne 2012.
Process-based phenomenological models are empirical constructions. They use the inductive
scientific method, going from empirical observations to likely generalizations. Unlike purely
statistical models, process-based phenomenological models explicitly consider some relevant
processes that they represent using mathematics as a tank of flexible (potentially non
parametric) shapes that are fitted to data to mimic the observations. That is providing them
with some functional capacity and the possibility to interpret the data within a formal
theoretical framework. However, process-based phenomenological models may have limited
cognitive capacity (they describe rather than they explain) and they are limited by (1) the
sensitivity of the system dynamics to the arbitrariness of the mathematical form used to
represent the process, (2) the sensitivity to the data used to estimate the parameters and the
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impossibility to predict beyond the range of observed conditions, (3) the disconnection of
processes that leads to a lack of internal consistency and over-parameterization.
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At the other end of the formalization gradient, pure mechanistic models also termed as
theoretical models are axiomatic constructions (Gallien et al. 2010). Like in theoretical physics,
they use the hypothetico-deductive scientific method, starting from hypothesis (the axioms) to
deduce predictions that can be tested empirically, to falsify or conversely to corroborate the
hypothesis made (but never to prove it or “validate” it). Mechanistic models are based on few
basic rules and general principles in which processes are explicitly accounted for and from
which their dynamics can be deduced logically and mathematically. They consist in
mathematically derived parameterizations where parameters are meaningful constants. They
are based on clear processes and causality chains formulated from “first principles”.
Mechanistic models of biodiversity have to be internally consistent with both biological theory
and mathematical rules. They must represent the biological and ecological processes,
respecting dimension rules and verifying the principle of energy and mass conservation. In the
literature, the term mechanistic is often used in a wider perspective to include models
explicitly accounting for processes in their internal structure (i.e. correlative mechanistic
models, Kearney et al. 2010). In this chapter we use the term process based in the context of
explicit implementation of ecological processes in the model and mechanistic to describe
models with stronger foundations on ecological theory.
Mechanistic models of biodiversity may help improve our understanding, interpret
observations, guide data collection, and stimulate new field and experimental studies.
Mechanistic models have a high cognitive capacity (they explain more than they describe).
However, they are often very complex, empirically undetermined, and difficult to use
practically.
4.3.1 Phenomenological correlative modelling approaches
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Phenomenological correlative models are frequently used to assess the impacts of human
activities on biodiversity, forecast future impacts of environmental changes, support human
productive activities (e.g., enhance agricultural production) and conservation actions (e.g.,
identify sites for translocations and reintroductions, predict the location of rare and
endangered species), understand species’ ecological requirements, among other uses
(Peterson, 2006; Muñoz et al. 2011). Correlative models have the advantage of offering greater
interpretability as to causation of phenomena, and permit predictability of phenomena that
depend on the differences between components—e.g., the invasive potential of a species
depends on the difference between potential and actual distributional areas (Peterson, 2006).
Correlative models can be applied at all spatial scales. The choice of the appropriate spatial
scale should consider the scale of variation of relevant environmental predictors (Elith and
Leathwick 2009). For instance, the effect of climate variables is better assessed at large spatial
extents, such as regions, and coarse resolution data may be acceptable, whereas the effect of
soil nutrients requires fine resolution data, to cover fine-scale variations, and is usually
modelled at smaller extents, such as landscapes. When the selected environmental predictors
act at different scales, hierarchical models with nested sub-models can be used (Elith and
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Leathwick 2009). Similarly, advances in modelling marine ecosystems will require coupling of
different trophic levels that may have different resolution. A way in representing biodiversity
in complex marine systems would be to concentrate the detail of representation at the target
species. Regarding temporal scales, correlative models are often static (i.e., assume a constant
time frame) failing to capture species or community dynamics, such as species dispersal.
Nevertheless, temporal predictors, such as variability of food resources, may be added to
models to capture variation in the state of biodiversity variables. More complex dynamics,
such as species dispersal, can be integrated in modelling approaches, such as Dynamic
Bioclimate Envelope models (DBEM), where an envelope niche model is combined with
dynamics of species dispersal (Cheung et al. 2008).
An important caveat of correlative models, concerns extrapolation to new spatial and time
frames, namely hind-casting and forecasting applications, since the conditions associated to
training data (i.e., the data used to fit the model), may not be constant over time (Elith and
Leathwick 2009, Araújo and Peterson 2012) or be inadequate to represent the conditions
found outside their area of distribution. For instance, the data informing niche models are not
drawn from the entire abiotic niche or even from the potential distribution, but from the
actual area of distribution, which calls for caution when interpreting results (Araújo et al. 2005;
Pearson and Dawson 2003). Alternative approaches based explicitly on mechanisms are likely
to be robust under novel environmental combinations but are limited by the availability of
data to build the models. In contrast, data required to fit correlative models is readily available
across a range of scales and the models can implicitly capture many complex ecological
responses. Because of this, Elith et al. (2010) anticipate ongoing use of correlative models for
biodiversity projections.
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Species occurrence and abundance are common state variables in correlative modelling
approaches at the species level whereas variables of community diversity and structure are
common in community-level approaches (Iverson et al. 2001). Environmental datasets often
comprise climatic variables (e.g., mean annual temperature, annual precipitation, and annual
solar radiation), topographic variables (e.g., average elevation, elevation coefficient of
variation, elevation range), and habitat variables (e.g., habitat type, habitat extent). For
instance, a study to assess the impacts of climate change on the distribution of Pinus densiflora
in South Korea (Chun and Lee 2013) utilised 27 environmental variables from 4 categories of
climate characteristics, geographic and topographic characteristics, soil and geological
properties, and MODIS EVI, and about 4,000 species occurrence and abundance records from
National Forest Inventory sites across the whole country.
4.3.1.1 Species-level modelling
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Species distribution models (SDMs), also known as niche models or bioclimatic envelope
models, have been widely used to model the effects of environmental changes on species
distribution across all realms (Pearson & Dawson 2003, Elith and Leathwick 2009). A
bioclimatic envelope can be defined as a set of physical and biological conditions that are
suitable to a given species, it is generally identified through the relationship between species
observed distribution and environmental attributes using statistical methods (e.g., generalized
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additive model, Luoto et al. 2005) or artificial intelligence models (e.g., artificial neural
network, Pearson et al. 2002).
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Main critiques to bioclimatic envelope models relate to their omission of biotic interactions,
evolutionary change and species dispersal processes (Pearson & Dawson, 2003). Still, examples
exist where bioclimatic envelope models were developed to include some of these processes.
The Dynamic Bioclimate Envelope Model developed by Cheung et al. (2008) simulates changes
in the relative abundance of marine species through changes in population growth, mortality,
larval dispersal and adult movement following the shifting of the bioclimate envelopes induced
by changes of climatic variables. The model does not account for species interactions and
potential food web changes. A new dynamic bioclimate envelope model is being developed to
account for effects of ocean biogeochemistry such as oxygen level and pH on the ecophysiology and distribution of marine fish (Stock et al. 2011).
SDMs often use species presence-absence data, however, in some situations there is only
access to presence data (e.g., museum data). In those situations other models, such as GARP
and MaxEnt, can be used (Elith and Leathwick 2009). GARP (Genetic Algorithm for Rule set
Production) is a machine-learning system that has shown predictive ability in delineating
species’ ecological niches and predicting geographic distributions (Peterson et al., 2002). At an
initial stage, GARP produces four types of rule sets (atomic, range, negated range, and logistic
regression) explaining the relationship between the distribution of species presence data and
explanatory environmental variables. Then it uses an iterative process of rule modification and
selection until it reaches the most appropriate set of rules, which can then be used to predict
species distributions. MaxEnt is a presence/background method, that is, model fitting uses
presence-only data and data on environmental variation across the study area (i.e.,
background data) (Peterson et al. 2011). The algorithm estimates the species range according
to a probability distribution of maximum entropy (i.e., closest to uniform) constrained by
information on environmental data at presence sites (Araújo et al. 2007, Phillips and Dudík
2008, Merow et al. 2013). Maxent has been used for several purposes, such as, to assist
conservation planning, to predict potential distributions for invasive species, and to predict
climate and land use change impacts (Elith et al. 2010).
Case study: Conservation of Iberian lynx populations (Fordham et al. 2013)
Decision-making context: Iberian lynx populations have suffered severe declines during the
last century due to habitat change, changes in abundance of preys (due to habitat change and
overexploitation), direct persecution and road killing. Today, the Iberian lynx is the world’s
most endangered cat. On-going conservation actions include habitat protection and
restoration for the species and its preys (mainly rabbits), ex-situ breeding and reintroductions.
Policymakers are considering the establishment of new Iberian lynx populations in areas within
its recent historical range, namely in different autonomous regions in the southern half of
Spain. However, this solution may be compromised by climate change due to impacts on
habitat suitability and barriers to dispersal. An alternative would be to perform reintroductions
in habitat-favorable areas across all Iberian Peninsula, even if outside the recent historical
range.
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Modelling approach: The future distribution of Iberian lynx populations was assessed using
coupled ecological niche models (ENM) of Iberian lynx and European rabbit range dynamics
under different climate change and conservation scenarios. Climate change scenarios include a
high-CO2 stabilizing reference scenario and a greenhouse gas mitigation Policy scenario.
Conservation scenarios, included a
business as usual scenario, an extramanagement
scenario
(no
reintroductions), and two reintroductions
scenarios, within the recent historical
range and Peninsula-wide. ENM were
coupled to metapopulation simulations of
source-sink dynamics to account for
stochastic changes in Iberian lynx
populations driven by changes in prey
abundance, due to disease, climate and
land-use changes, and by changes in
habitat suitability, due to climate and landuse changes.
Main results: Probability of extinction is
high for the business as usual and extra
management
conservation
scenarios
(>85%) for the two climate-change scenarios. Planned reintroduction programs, on the other
hand, can reduce extinction risk (<5%). Planning reintroductions in habitat-favorable areas
across all Iberian Peninsula may be more successful than the restricting reintroductions to the
recent historical range.
4.3.1.2 Species traits approaches
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Trait based ecological risk assessments (TERA) is a trait based approach to assess ecological
responses to natural and anthropogenic stressors based on species characteristics related to
their functional roles in ecosystems (Baird et al. 2008). In case there is enough information on
functional characteristics and roles of taxonomic species or communities in the ecosystems,
TERA has potentials in ecological risk assessment adding trait attributes to map onto existing
taxonomic entities but more cooperative effort is needed to address the challenges such as
task of trait definition, linkage of traits to specific environmental drivers, and the extraction of
trait information by observation, experiment, and literature data mining (Baird et al. 2007).
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4.3.1.3 Community-level modelling
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Community-level models are used to produce information at the collective level (Ferrier,
2002). The community metric of interest can take a number of forms, such as predictive
mapping of community types (groups of locations with similar species composition), species
groups (groups of species with similar distributions), axes or gradients of compositional
variation, levels of compositional dissimilarity between pairs of locations, and various macroecological properties (e.g., species richness) and even phylogenetic diversity (Ferrier & Guisan,
2006). Community-level modelling offers an opportunity to move beyond species-level
predictions to predicting broader impacts of environmental changes (e.g., Hilbert & Ostendorf
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2001; Peppler-Lisbach & Schröder 2004), which may be relevant in certain decision-making
contexts. Examples are when time and financial resources are limited, when existing data is
spatially sparse or the knowledge on individual species distribution is limited (Ferrier, 2002),
and when species diversity is beyond what can feasibly be modelled at the individual species
level.
Three community-level modelling approaches have been proposed (Ferrier & Guisan, 2006):
(1) ‘Assemble first, predict later’, whereby species distributions are first combined with
classification or ordination methods and the resulting assemblages are then modelled using
machine learning or regression-based approaches, (2) ‘Predict first, assemble later’, whereby
individual species distributions are modelled first and the resulting potential species
distributions are then combined (i.e. the result is in fact the summation of individualistic
models), and (3) ‘Assemble and predict together’, whereby species distribution models are
fitted using both environmental predictors and information on species co-occurrence. Reviews
on usefulness of community-level modelling can be found in Ferrier et al. (2002) and Ferrier &
Guisan (2006)
Table 4.2. The six main type of spatial output that can be generated using community level modeling
(Ferrier and Guisan, 2006)
Community
metrics
Description
Structure of derived grid layers
Individual species
Predicted distribution of multiple species, as for
species-level modelling
Community types
Each’ community type’ defined as a group of locations
(grid cells) that closely resemble one another in terms
of predicted species composition.
Grouping normally achieved through some form of
numerical classification
Species groups
Each ‘species group’ defined as a subset of species with
similar predicted distributions. Grouping again
achieved through numerical classification, but in this
case the objects classified are species rather than
locations
A set of continuous axes (or gradients) representing
dimensions of a reduced space that summarizes the
compositional pattern exhibited by multiple species.
These axes most commonly derived through some form
of ordination
The predicted level of dissimilarity in community
composition between all possible pairs of grid cells in a
region
A separate layer for each species, indicating the predicted
probability of occurrence or abundance of that species in
each cell
Either (i) a single layer with each cell assigned exclusively
to one community type (depicted as a map with different
colours indicating different types) or (ii) a separate layer
for each community type, indicating the probability of that
type occurring in each cell (depicted as multiple grey-scale
or colour-ramp maps)
A separate layer for each species group, indicating the
predicted prevalence or abundance of that group in each
cell (depicted as multiple grey-scale of colour-ramp maps)
Axes of
compositional
variation
Levels of
compositional
dissimilarity
between pairs of
cells
Macroecological
properties
Most commonly modelled property is species richness,
either of a whole group (e.g. all vascular plants) or of a
functional subgroup (e.g. annuals and trees). Many
other macro-ecological properties (e.g. mean range size
and endemism) can potentially be modelled
A separate layer for each axis, indicating the predicted
score for that axis in each cell (depicted either as multiple
grey-scale or colour-ramp maps, or as a single map by
assigning each of the first three axes to a different colour
dimension, e.g. red, blue, green)
In theory a complete matrix of pair-wise dissimilarities, but
in practice these values are usually predicted dynamically
as required by the application of interest (difficult to
depict spatially without prior conversion to community
types or axes of compositional variation)
A separate layer for each macro-ecological property
(depicted as a grey-scale or colour-ramp map)
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Also important at the community-level are species-area relationship (SAR) models, which use
habitat area as a predictor of community species richness. SARs have been applied to a wide
range of taxa and across all scales, from local to global. SAR functions are often used to predict
the impacts of changes in habitat availability, driven by land use change (e.g., van Vuuren et al.
2006, Desrochers et al. 2011) or climate change (e.g., Malcom et al. 2006, van Vuuren et al.
2006), on community richness, but also to assess the impacts of direct exploitation on
community parameters, such as species-turnover rates (e.g., Tittensor et al. 2007). Reviews on
the use of SARs can be found in Rosenzweig 1995, Drakare et al. 2006, and Triantis et al. 2012).
4.3.2 Process-based and mechanistic models
Considering the relevant processes provides process-based phenomenological models with
some functional capacity and the possibility to interpret the data within a formal theoretical
framework. However, process-based phenomenological models have poor cognitive capacity
(they describe rather than they explain) and they are limited by (1) the sensitivity of the
system dynamics to the arbitrariness of the mathematical form used to represent the process,
(2) the sensitivity to the data used to estimate the parameters and the impossibility to predict
beyond the range of observed conditions, (3) the disconnection of processes that leads to a
lack of internal consistency and over-parameterization.
Let’s look at a few illustrative examples to clarify these limitations:
1. Even though they can theoretically be derived from ecological mechanisms, functional
response curves are usually used as flexible and practical regression functions linking the
amount of food eaten by a predator to the density of prey, disregarding the underlying
mechanisms that control their shape. Yet, when embedded in ecosystem models, the
choice of the functional response’s form (e.g. type I, II or III) leads to profound differences
in the modelled ecosystem dynamics (Fulton, 2003; Anderson et al., 2010).
2. Other classical examples of phenomenological representation include the allometric
equations that are typically used for representing the various processes of metabolism
(e.g., respiration is proportional to some power – named the allometric coefficient – of
individual weight). Because allometric functions are stationary and do not explicit the
influence of environmental factors on metabolism, any change of environmental
conditions is absorbed by a corresponding change in parameter value. Changing the
dataset used to estimate the allometric coefficients therefore leads to different estimated
values and associated responses of metabolism to weight (e.g. Bonnet et al., 2003). As a
consequence, parameters are meaningless system- and state-specific “constants that vary”
in space and time to capture the observations.
3. Because metabolism includes very different processes with specific dynamics, fitting
allometric regressions to different processes of metabolism (e.g., respiration, reproductive
output, age at maturity, aging mortality) leads to as many allometric coefficients as
processes considered. Using independent allometric curves to represent the various
metabolic processes at stake in an ecosystem model therefore leads to overparameterization and do not consider the interactions between the processes
represented.
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Process-based models allow a more explicit representation than correlative approaches of
ecological processes mediating biodiversity responses to environmental drivers. However, and
despite their wide use in biology and ecology, process-based phenomenological ecosystem
models, as all model developments suffer from fundamental and practical limitations that
should be explicitly acknowledged.
Examples
Various strategies and levels of process formalisation can be distinguished amongst the
available process-based phenomenological models of ecosystem:
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1. Box models. This is the simplest and most developed category. It describes the
ecosystem structure as a set of bulk functional groups that are connected together by
fixed uptake/predation terms based on given functional responses, and that dissipate
energy through simple respiration terms that do not explicit the various components
of metabolism. The main advantage of these models is that they balance the mass and
energy fluxes at the scale of the system represented. The counterpart is that they
neglect important phenomenon such as the importance of size in controlling
metabolism, predator–prey interactions and life-history omnivory (i.e. diet changes
when organisms grow). Examples include nutrient-phytoplankton-zooplanktondetritus (NPZD) type models (e.g. PISCES ref, MEDUSA ref, ERSEM ref, TOPAZ ref, etc…)
that represent 3D marine biogeochemistry in climate models, mass-balanced trophic
models (e.g. EwE Polovina, 1984; Christensen and Pauly, 1992; Christensen and
Walters 2004) that represent marine communities in 0-D (Ecopath, Ecosim) or in 2-D
(Ecospace), often with a focus on upper trophic level organisms.
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2. Age/stage-structured models. These models are box models that are structured along
a dimension (usually age or stage) that is supposed to be functionally important. They
explicit some processes of metabolism such as growth, reproduction and the agedependence of respiration. This group, among others includes dynamic global
vegetation models (DGVM) that represent the dynamics and biogeochemistry of the
global vegetation cover, the Atlantis model (Fulton et al. 2005) that represents the
trophic interactions and biogeochemistry of marine trophic networks in 3-D boxes, and
the dynamic EwE models, which incorporates age-structured population models
(Walters et al. 2000; Walters et al. 2010). Spatial population dynamics models (e.g.
Lehodey, 2008) use habitat-preference functions to describe population movements
with advection-diffusion equations.
3. Size-structured models. Some approaches emphasize the interest of size to structure
ecosystem models. In marine ecosystems, size rather than taxonomic identity controls
predation (e.g., Shin and Cury, 2004) and metabolism (e.g., Gillooly et al., 2001). Also,
size-based models are easier and cheaper to parameterize than food-web models. A
wide variety of size-structured models exist. This include 0-D marine ecosystem sizespectrum models, where ecosystem dynamics is projected along the size dimension
“from bacteria to whales”, disregarding the metabolic and physiological differences
between species (e.g., Platt and Denman, 1978; Benoit and Rochet, 2004; Maury et al.,
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2007). In the same perspective, Madingley (Harfoot et al., 2014) represents the 2-D
energy fluxes through size-structured functional groups with a lagrangian approach, in
both terrestrial and marine environments. Recently, the need to consider explicitly the
functional role of species diversity in the dynamics of functional groups has emerged.
This has led to the development of trait-based approaches (e.g., Andersen and Beyer,
2006; Hartvig et al., 2011), where the community-level dynamics emerges from the
interactions of a large number of species that are distinguished solely on the basis of
their maximum size, which captures most of the inter-specific differences of
metabolism and life history (Kooijman, 1986). Other approaches, like the 2-D
lagrangian models, Osmose (Shin and Cury, 2004) and size-based Ecospace model
(Walters et al. 2010), represent explicitly a set of key size-structured species and
emphasize the importance of size and spatial co-occurrence in controlling trophic
interaction and the emergent structure of marine communities.
Dynamic Global Vegetation Models (DGVM)
The most advanced tool to estimate the impact of climate change on vegetation dynamics at
global scale are Dynamic Global Vegetation Models (DGVMs). These are process-based models
that simulate various biogeochemical, biogeophysical and hydrological processes such as
photosynthesis, heterotrophic respiration, autotrophic respiration, evaporation, transpiration
and decomposition. DGVM models simulate shifts in potential vegetation and the associated
biogeochemical and hydrological cycles as a response to shifts in climate. DGVMs use time
series of climate data and, given the constraints of latitude, topography, and soil
characteristics, simulate monthly or daily dynamics of ecosystem processes. DGVMs are most
often used to simulate the effects of future climate change on natural vegetation and its
carbon and water cycles.
DGVMs integrate biogeochemistry, biogeography, and disturbance sub-models, and
disturbance is generally limited to wildfires (for example in IBIS wildfires are represented by
static values). DGVMs are usually run in a spatially distributed mode, with simulations carried
out for thousands of “grids” or geographic points, which are assumed to have homogeneous
conditions within each grid. Under DGVM framework, simulations are carried out across a
range of spatial scales, from global to landscape.
With a growing interest in the biodiversity changes in an ecosystem, these ESM’s generally
comprise of the coupling of DGVMs with AOGCMs. Basic structure of a DGVM is given in Figure
4.4.
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Figure 4.4. Structure of DGVMs. Source: http://seib-dgvm.com/oview.html
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Dynamic models capture the transient response of vegetation to a changing environment using
explicit representation of key ecological processes such as establishment, tree growth,
competition, death, nutrient cycling (Shugart 1984, Botkin 1993). Plant functional types (PFTs)
are central to DGVMs as, on the one hand, they are assigned different parameterizations with
respect to ecosystem processes (e.g., phenology, leaf thickness, minimum stomatal
conductance, photosynthetic pathway, allocation, rooting depth), while on the other, the
proportion of different PFTs at any point in time and space defines the structural
characteristics of the vegetation (Woodward and Cramer 1996).
Input and outs: DVMs use spatially explicit time series of climate data (e.g., temperature,
precipitation, humidity, sunshine days, wind) and take into account the features of topography
and soil characteristics in order to simulate monthly or daily dynamics of ecosystem processes.
Plant species are represented as groups with similar physiological and structural properties,
termed Plant Functional Types (PFTs), which are designed to represent all major types of
plants (Sitch et al. 2008). DGVMs are most often used to simulate the effects of future climate
change on natural vegetation and its carbon and water cycles. DGVM provides policy relevant
outputs with respect to ecosystems such as shifts in vegetation types, NPP and carbon stock
changes, biomes and habitat change.
Advantage: Key advantages of using DGVMs include its capacity to simultaneously models the
transient responses related to dynamics of plant growth, competition and, in a few cases,
migration. As such it allows the identification of future trends in ecosystem function and
structure and these models can be used to explore feedbacks between biosphere and
atmospheric processes (Bellard et al 2012).
4.3.2.1 Mechanistic models
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Mechanistic models of biodiversity are tools to improve our understanding, interpret
observations, guide data collection, and stimulate new field and experimental studies. Their
consistency and structural stationarity (e.g., mechanistic models keep valid in other
environments, including beyond the range of observed conditions) provide a basis to build and
analyse scenarios and projections. Mechanistic models have a high cognitive capacity (they
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explain more than they describe). However, they are often very complex, empirically
undetermined and difficult to use practically. Purely mechanistic models and theories in the
fields of biodiversity are rare. In practice, they are often incomplete and articulate mechanistic
descriptions of the components under focus with phenomenologic parameterizations of the
less known or less important phenomenon. However, in recent years there has been growing
effort to better place existing approaches into more available ecological theories allowing
wider use and greater potential of the models derived from this approach. Some of these
examples are the theory based biodiversity model introduced by Thuiller et al. (2013), which
used metapopulation theory as the cornerstone for a new generation of biodiversity models.
This kind of approaches has the advantage of being explicit about the processes involved and
provides interpretable parameters while at the same time has solid foundations in population
dynamics. Furthermore, the new developments build on the recent addition of environmental
heterogeneity, dispersal limitation, and biotic interactions into the incidence function and
include the addition of an evolutionary perspective as a new challenge.
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Another theory used in mechanistic modeling is the Dynamic Energy Budget (DEB) theory (e.g.,
Kooijman 2010) which is a mechanistic theory aiming at capturing the quantitative aspects of
metabolism at the organism level from a small set of key assumptions (Sousa et al., 2008a).
The DEB theory allows accounting for environmental variability effects on organisms through
food and temperature changes. It captures the diversity of all possible living forms on earth in
a single mechanistic framework. This allows representing the energetics and major life history
traits of all possible species in a community with the same set of unspecific taxa-dependent
DEB parameters. The existence of inter-specific scaling rules is a fundamental property of the
DEB theory. It emphasizes the fundamental importance of the maximum size of the species
and implies that functional biodiversity (in terms of energetics and life history) has actually far
less degrees of freedom that one would expect given the high number of species on Earth. This
major finding opens the way to a mechanistic understanding of functional biodiversity and
ecosystems dynamics. DEB-based individual bioenergetics can indeed be scaled up to build
physiologically-structured mechanistic population dynamics models that can in turn be scaled
up using interspecific scaling rules to derive size-based mechanistic models of the dynamics of
communities (Maury and Poggiale, 2013).
The APECOSM model adopts this strategy to represent the dynamics of marine ecosystems in a
mechanistic way (Maury, 2010). Embedded in a 3-D mechanistic framework representing
organisms movements (Faugeras and Maury, 2007) and environmentally-driven behaviours,
the trait-based dynamics of canonical DEB-structured marine communities is modelled from
regional to global scales. APECOSM can be coupled to an Ocean General Circulation Models
(OGCM) and an Ocean General Biogeochemical Model (OGBM) that provide the timedependent and tri-dimensional environmental forcing variables, both for hind- and forecasting (Lefort et al., 2014; Dueri et al., 2014).
4.3.2.2 Other approaches
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Deterministic Models: In these models one cause is modeled to have one or many effects. The
term “forest dynamics” spans a huge range both in time and space. For example the enzymatic
reactions of photosynthesis operate within fractions of a second; foliage development takes a
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few weeks, while tree growth lasts decades to centuries, and the dynamics of soil organic
matter span millennia (Bugmann 1994). On the other hand, the germination of a seed takes
place on a few square cm3, a sunfleck moving over the forest floor covers a few m2; a dominant
tree in the canopy occupies 0.01-0.1 ha, and the quasi-equilibrium of a forest landscape may
be reached on the scale of several hectares (Shugart and Urban 1989). There have been two
basic approaches to modeling vegetation response to changing climates, static (timeindependent) and dynamic (time-dependent) models.
Static or equilibrium models: Static models assume equilibrium conditions in both climate and
terrestrial vegetation in order to predict the distribution of potential vegetation by relating the
geographic distribution of climatic parameters to the vegetation. The equilibrium approach is
implicitly large scale in nature as it ignores dynamic processes. It generally requires far less
information and provides estimates of potential magnitude of the vegetation response at
regional to global scales. The equilibrium models are restricted to the estimation of the steadystate conditions. Prominent examples of static models include BIOME4 (Kaplan 2003) and
Mapped Atmosphere Plant Soil System (MAPPS; Neilson 1995).
Biogeochemistry Models: Biogeochemistry models project changes in basic ecosystem
processes such as the cycling of carbon, nutrients, and water. These models are designed to
predict changes in nutrient cycling and primary productivity. The inputs to these models are
temperature, precipitation, solar radiation, soil texture, and atmospheric CO2 concentration.
The plant and soil processes simulated are photosynthesis, decomposition, soil nitrogen
transformations mediated by microorganisms, evaporation and transpiration. Common
outputs from biogeochemistry models are estimates of net primary productivity,
evapotranspiration fluxes and the storage of carbon and nitrogen in vegetation and soil. Some
of the popular models include BIOME-BGC (Running and Hunt, 1993), CENTURY (Parton et al.
1993), and the Terrestrial Ecosystem Model (TEM; McGuire et al. 1992).
Biogeography Models: Biogeography models simulate shifts in the geographic distribution of
major plant species and communities. They analyze the essential environmental conditions
over entire continents, to estimate the type of vegetation that is most likely to cover a given
area. These types of models are best suited for assessing broad-scale changes in vegetation.
They are based on ecophysiological constraints, which determine the broad distribution of
major categories of woody plants, and response limitations, which determine specific aspects
of community composition, such as the competitive balance of trees and grasses. Examples of
biogeography models include the Mapped Atmosphere Plant Soil System (MAPSS; Neilson,
1995), BIOME3 (Haxeltine and Prentice 1996), and IBIS (Foley et al. 1996). Input datasets for
biogeography models mainly include latitude, mean monthly temperature, wind speed, solar
radiation, and soil properties such as texture and depth.
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Knowledge-based systems generally refer to expert system approaches to prediction or
decision support. An expert is someone who has achieved high knowledge on a subject
through her/his life experience (Kuhnert, Martin, & Griffiths 2010), therefore being a reliable
source of information in a specific domain. Expert knowledge-based species-habitat
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relationships are used extensively to guide conservation planning, particularly when data are
scarce (Iglecia et al. 2012). Expert knowledge is quite commonly utilized in conservation
science (Janssen et al. 2010). Ecological expert knowledge has been frequently incorporated in
aquatic habitat suitability model to link environmental conditions to the quantitative habitat
suitability of aquatic species (Mouton et al. 2009). Research on species distribution modelling,
which incorporated expert knowledge into modelling process is relatively limited. In a study on
“finessing atlas data for species distribution models”, Niamir et al. (2011) incorporated existing
knowledge into a Bayesian expert system to estimate the probability of a species being
recorded at a finer than the original atlas data to predict a bird species distribution. They
noted that knowledge-based species distribution maps of finer scale using a hybrid
MaxEnt/expert system had a higher discriminative capacity than conventional approach even
though such an approach might be limited to well-known species. In a study on “tradeoffs of
different types of species occurrence data for use in systematic conservation planning”,
Rondinini et al. (2006) noted that the geographic range data of species generated by expert
knowledge had advantage of avoiding the potential propagation of errors simplifying data
processing steps.
However, eliciting expert information involves dealing with multiple expert judgements, with
different sources of biases in the elicited information and with uncertainty around expert
estimates (Martin et al. 2012). For instance, expertise may vary geographically, with relevant
information restricted to the region of interest of the experts (Murray et al. 2009).
4.3.4 Hybrid modelling
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Hybrid models combine multiple modelling approaches to represent complex, integrated
systems of human and biophysical components (Parrot et al. 2011). These models are highly
generally data driven and serve to aid in pattern extraction and knowledge synthesis, providing
an important link between data sources and decision support systems. Developments of
hybrid models take a pathway where some of the ecological processes defining the ecological
system under study are (i.e. realized niche) are modelled explicitly (process-based or
mechanistically), while others are kept in the correlative niche model. Hybrid approaches
derive from the interest to balance realism and flexibility in model building with limited
knowledge. However, this approach is not without problems. The way different models are
integrated into hybrid approaches often is a difficult issue. Gallien et al. (2010) indicate that
one of the current limitations of these hybrid approaches is the form and strength of the
relationship between habitat suitability and demographic parameters. Changes in habitat
suitability are normally integrated with population processes by limiting carrying capacity.
Furthermore, how ecological processes (e.g., growth, dispersal, and thermal tolerance)
respond to environmental changes is unclear, and is often assumed to be unimodal or linear.
Nonlinear functional response could make the model more complex. More importantly, most
functional responses used in the model are assumed, rather than confirmed from experiments.
Fortunately, when simple occupancy models are used, model integration becomes easier: cells
that become unsuitable force or induce extinction, whereas colonization is allowed in those
cells that become suitable only.
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Broadly speaking, mechanisms determining ecosystem dynamics can be related to the ecology
of species, species interactions and evolutionary processes (Lavergne et al. 2010). Any
biological process of interest should have an explicit link with the components formulated in
the model. However, this link does not need to be one-to-one (Lurgui et al 2014). The
implementation of these processes in the model may be carried out in a wide variety of ways
spanning a broad range of complexities from cellular automata (e.g. Iverson et al. 2004; Smolik
et al. 2010), metapopulation models (e.g. Wilson et al. 2009), structured metapopulation
models (e.g., Akçakaya 2000) to spatially-explicit population models (e.g., Cabral & Schurr
2010), individual-based models (Grimm & Railsback 2005) and reaction-diffusion models (e.g.,
Wikle 2003; Hui et al. 2011). Models with emergent dynamics may also include species
interactions (e.g., Albert et al. 2008), or abiotic processes included via feedbacks (e.g., wildfires
vs. vegetation growth; Grigulis et al. 2005). Emergent dynamics can be spatially auto
correlated or not. For example, a spread model for an invasive species will generate autocorrelated spatial patterns (e.g., Merow et al. 2011), whereas a module simulating local birth
and death may generate temporal dependency but may not generate auto correlated spatial
structures by itself.
4.4 Modelling biodiversity feedbacks and issues across
temporal and spatial scales.
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Both human and non-living environmental drivers influence biodiversity through a number of
processes. In turn, biodiversity exerts feedbacks on both systems (Fig. 4.6). Considering the
feedbacks is important because they are susceptible to cause non-linearity in the interaction
dynamics. This is especially important as non-linearity can potentially lead to whole-system
bifurcations (e.g. regime shift) when forcing variables such as atmospheric CO2 evolve beyond
observed levels. Changes in biodiversity interact with different drivers of biodiversity change
(i.e. climate change, disturbance regimes such as forest fires, invasive and pests, and
ecosystem processes) over different temporal and spatial scales. Changes in biodiversity and
range shifts (plant traits) can influence climate at global and regional scales. For instance,
General Circulation Models (GCMs) based simulations indicate that widespread replacement of
deep-rooted tropical trees by shallow-rooted pasture grasses would reduce evapotranspiration
and lead to a warmer, drier climate (Shukla et al. 1990). Similarly the replacement of snowcovered tundra by a dark conifer canopy at high latitudes may increase energy absorption
sufficiently to act as a powerful positive feedback to regional warming (Foley et al 1994).
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Figure 4.5 Schematic diagram of the interactions between biodiversity, the human system and the nonliving environment. The figure represents the feedbacks between biodiversity and the drivers to
biodiversity change and their interactions (Source: Chapin et al 2000).
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Besides, changes in biodiversity are known to induce changes in the microclimate. Such
changes in microclimate may further impact the biodiversity. For example, in a boreal forest
ecosystems, where soil temperatures have a strong influence on nutrient supply and
productivity, the presence of moss, which reduces heat flux into the soil, provides stability to
the permafrost leading to the low rates of nutrient cycling (Von Cleve et al 1991). As fire
frequency increases in response to high-latitude warming, moss biomass decline, the stability
of permafrost declines, nutrient supply increases, and the species compositions of forests are
further altered.
Chapin et al. (2010) outlines the possible interactions and feedbacks between biodiversity,
biodiversity change drivers and the interactions between these drivers and suggests that
anthropogenic activities (arrow 1 on Figure 4.5) cause environmental and ecological changes of
global significance (2). By a variety of mechanisms, these global changes impact biodiversity,
and changing biodiversity feeds to the susceptibility to species invasions (3, purple arrows).
Changes in biodiversity can have direct consequences for ecosystem services and as well as
human economic and social activities (4) In addition, changes in biodiversity can influence
ecosystem processes (5) and ecosystem services (6). These further feedbacks to alter
biodiversity (7, red arrow). Global changes may also directly affect ecosystem processes (8,
blue arrows). Chapin et al. (2010) further conclude that “depending on the circumstances, the
direct effects of global change may be either stronger or weaker than effects mediated by
changes in diversity”.
Changes in biodiversity influence annual rates of primary productivity (NPP) and
decomposition of organic matters in soils. Increased NPP production relates to increased
carbon sequestration from the atmosphere, and thereby provides negative feedback to
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climate warming. Similarly, increased decomposition of soil organic matters and litter relates
to a loss of carbon from the biosphere to the atmosphere, and thereby provides a positive
feedback to climate warming. Several studies using experimental species assemblages
conclude “annual rates of primary productivity and nutrient retention increase with increasing
plant species richness, but saturate at a rather low number of species” (Tillman et al 1996,
Hector et al 1999). Salonius (1981) concludes that microbial richness can lead to increased
decomposition of organic matter.
Further, particular species can have strong effects on ecosystem processes by directly
mediating energy and material fluxes or by altering abiotic conditions that regulate the rates of
these processes. Introduction of the deep-rooted salt cedar (Tamarix sp.) to the Mojave and
Sonoran Deserts of North America increased the water and soil solutes accessed by vegetation,
enhanced productivity, and increased surface litter and salts. This inhibited the regeneration of
many native species, leading to a general reduction in biodiversity.
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4.4.1 Tools for assessing feedbacks
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Feedbacks between drivers and biodiversity or ecosystem levels usually involve high level of
complexity in the models because changes in state variables at different levels (either
biological or biological and other) should be able to interact and cause emergent dynamics.
Changes in biodiversity for instance can impact disturbance regimes such as fire, which in turn
is strongly determined by climate (Pausas and Keely 2009) and fire-suppression efforts
(Brotons et al. 2013). As an illustration, several species of nutritious but flammable grasses
were introduced to the Hawaiian Islands to support cattle grazing. Some of these grasses
spread into protected woodlands, where they caused a 300-fold increase in the extent of fire.
Most of the woody plants, including some endangered species, are eliminated by fire, whereas
grasses rebound quickly (D’Antonio et al 1992). Similar increases in the ecological role of fire
resulting from grass invasions have been widely observed in the Americas, Australia, and
elsewhere in Oceania. Species diversity is also known to reduce the probability of outbreaks
by 'pest' species or invasives by diluting the availability of their hosts. This phenomenon
particularly decreases host-specific diseases (Burdon, 1993), plant-feeding nematodes
(Wasilewska, 1995) and consumption of preferred plant species (Bertness and Leonard, 1997).
Further, biodiversity characteristics can impact the ability of exotic species to invade
communities through either the influence of traits of resident species or some cumulative
effect of species richness. Early theoretical models and observations of invasions on islands
indicate that species-poor communities would be more vulnerable to invasions because they
offered more empty niches (Elton, 1958). Fire disturbance models at the landscape scale have
been used to integrate different processes and model feedbacks between vegetation and
disturbances regimes. The LANDIS for instance model simulates forest succession, disturbance
(including fire, wind, harvesting, insects), climate change, and seed dispersal across large
landscapes and allows tracking the spatial distribution of discrete tree and shrub species
(Scheller et al. 2007).
If feedbacks are to be incorporated across scales or very different domains of applications,
approaches explicitly integrating different models should be developed. One example of this
complexity is exemplified by the use of Integrated Assessment Models (IAMs, see Figure 4.7).
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In the IPCC Third Assessment Report (IPCC, 2001) integrated assessment was defined as “an
interdisciplinary process that combines, interprets, and communicates knowledge from diverse
scientific disciplines from the natural and social sciences to investigate and understand causal
relationships within and between complicated systems”. It is generally agreed that there are
two main principles to integrated assessment, i.e. integration over a range of relevant
disciplines; and the provision of information suitable for decision-making (Harremoes and
Turner, 2001). IAMs thus aim to describe the complex relations between environmental, social
and economic drivers that determine current and future state of the ecosystem and the effects
of climate change, in order to derive policy-relevant insights (Vuuren et al., 2009). One of the
essential characteristics of integrated assessment is the simultaneous consideration of the
multiple dimensions of environmental problems.
IAMs typically describe the cause-effect chain of climate change from economic activities and
emissions to changes in climate and related impacts on e.g. ecosystems, human health and
agriculture, including some of the feedbacks between these elements. In order to make their
construction and use tractable, many IAMs use relatively simple equations to capture relevant
phenomena. This simplification is most obvious for the climate system and carbon cycle,
which, in many IAMs, consists of only a few equations (Goodess et al. 2003). However, the
behaviour of these components can have a significant impact on IAM results and the quality of
policy advice, with the possibility of simplifications in the earth system projections leading to
imprecision (or even error) in projecting impacts and costs of mitigation.
Figure 4.6 General structure of an IAM.
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Over the last decade, IAMs have expanded their coverage in terms of land use and terrestrial
carbon cycle representation, non-CO2 gases and air pollutants and by looking into specific
impacts of climate change. Some IAMs have a stronger focus on economics, such as multisectoral computable general equilibrium models that are combined with climate modules and
models focussed on cost–benefit analysis. Whereas, other IAMs are more focussed on the
physical processes in both the natural system and the economy (integrated structural
models/biophysical impact models). Few examples of IAMs are: IMAGE, DICE, FUND and
MERGE. The basic structure of IMAGE 2.4 version is described below (see Figure 4.7). The key
drivers of change, population and the macro-economy, can be derived from various external
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and internal sources. For macro-economic drivers the exogenous source depends on the study
in which the model is applied.
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However, as IAMs aim to integrate different aspects of the environment, they run the risk of
becoming extremely complex. Thus, the developers of such models have to make decisions
about the focus of their study and how they wish to express the impacts they are attempting
to estimate, whether it is through the reporting of physical changes in emissions, shifts in landuse activity or mortality rates, or through cost-benefit analysis of damages resulting from
climate change (Goodess et al., 2003). The data requirements for these IAMs are also large and
not always feasible.
Figure 4.7 Framework of IMAGE 2.4. Source: Bouwman et al., 2006
4.5 Accounting for uncertainty
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Biodiversity and ecosystems are under stress due to drivers, and the stress is likely to increase
in the future due to drivers such as climate change, habitat modification, exploitation, and
pollution. Policy making on biodiversity and ecosystem functioning may have to take place
based on current knowledge. This must be done recognizing that uncertainty is associated with
all modelling, due to limitations of data, representation of processes, and resolution of the
ecosystem scale. However, policy makers have to take decisions even in the face of
uncertainty, to act on the drivers in order to conserve ecosystems and biodiversity.
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No discussion of modelling frameworks would be complete without consideration of
uncertainty (Leung et al 2012). Uncertainty in modelling of biodiversity and ecosystem
processes under changing climate and land-use dynamics arises from multiple factors. For
example, existing impact assessment studies including the biophysical and integrated
assessment model (IAM) based studies, generally tend to examine the ‘means’ of the impacts
probability distribution, neglecting the low-probability, high impact tails of the distribution
(Marten et al 2013; Ackerman 2010; Weitzman 2009).
Further, impact projections generally model linear climate change and inadequately account
for factors such as non-linearity and tipping elements in the climate system, and irreversibility
of the impacts – which are difficult to model (Whiteman et al. 2013). Impact studies generally
focus on single-sector or single region-based assessments. The potential interactions, among
sectors and regions, which can adversely impact the biodiversity and ecosystems, are not
adequately included in the quantitative estimates (Warren 2011). Additionally, impact
assessment models generally leave out the natural processes and feedbacks that are difficult
to model at current state of knowledge, even though these processes may cause large impacts.
For example, pest attack and fire dynamics in terrestrial and forest ecosystems are often not
included in the biophysical impact assessment processes.
Similarly the ambient policy and management practices and socio-economic stresses leading to
degradation of natural resources are also not included in majority of the sectorial impact
assessment models. Key human related issues such as armed conflicts, migrations and loss of
cultural heritage has a lot of potential to impact the natural ecosystems, however impact
assessment models do not include these stresses related to the human systems (Hope 2013;
Theisen 2013). IAMs based economic analyses of impacts are generally conservative as these
studies make optimistic assumptions about the scale and effectiveness of adaptation (Marten
et al 2013; Hope 2013). Fischling et al. (2007) conclude that the most advanced tool to assess
the impact of climate change on vegetation dynamics at global scale include DGVMs.
4.5.1 Sources of uncertainty
Link et al. (2012) and Leung et al. (2013) highlighted six major sources of uncertainty
confronting ecosystem modellers (Figure 4.8).
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Figure 4.8 A conceptual diagram of the flow of information and actions in a typical LMR management
system. Rectangles represent components of the system, solid arrows indicate flows of information and
actions between components, and ellipses represent major sources of uncertainty (Link et al. 2012)
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1) Natural variability
Natural variability or stochasticity includes biological differences among individuals within a
population, differences among populations within a community, changes in spatial
distributions through time, density-dependent or independent variation in a vital rate,
seasonal or interannual variability in realized environmental conditions, or shifts in
productivity regimes. Natural variability increases ecosystem model uncertainty by reducing
the precision of parameter estimates.
2) Observation error
Observation error is inevitable when studying organisms in either a single species or an
ecosystem context (e.g., Morris and Doak 2002; Ives et al. 2003 as cited in Link et al. 2012). The
environmental characteristics of a particular area (even those that we can measure fairly
accurately) are difficult to relate directly to the full experience of mobile organisms that move
into and out of that area. Thus, natural variability can actually exacerbate observation error.
Observation error adds uncertainty to ecosystem models through reduced precision, misspecified parameter distributions, and biased parameter estimates.
3) Structural complexity
The structural complexity of a model arises from many factors, such as the number of
parameters it includes; the number of ecosystem components and processes it simulates; the
temporal scale; the nonlinearities, log effects, thresholds, and cumulative effects incorporated
in those processes; and whether or not it includes features such as spatial dynamics or
stochasticity (Fulton et al., 2003 as cited in Link et al. 2012). Structurally complex ecosystem
models are gaining in use, in part due to improved computing capabilities and also to the
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based management. Thus, models have optimal levels of complexity (Walters 1986, Fulton et
al., 2003).
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Epistemic uncertainty contains parameter uncertainty, model (structural) uncertainty reflected
in the model structural complexity and data/observation error. Epistemic uncertainty reflects
our level of knowledge about a system, and can be reduced with additional information. We
build verbal (qualitative) or mathematical (quantitative) models to represent our
understanding of processes underlying a system. We use empirical data to parameterise the
model. However, given stochastic elements present, we never have perfect estimates of
underlying parameters and processes. Furthermore, given that models are our conceptual
abstractions of the real world, even the basic structure of the model is uncertain. In addition,
there will always be some uncertainty in our data sources, which may propagate through the
risk model.
4) Inadequate communication
Inadequate communication (or linguistic uncertainty) relates to the difficulty of effectively
conveying information between scientists, managers and stakeholders. Communication
problems range from the subtle to the obvious (Peterman, 2004 as cited in Link et al. 2012).
When communication is ineffective, information is lost, which can manifest itself as
uncertainty in many ways (Martin et al. 2012).
5) Unclear management objectives
There are numerous obstacles to the development of clear objectives. Many issues related to
resource use or conservation are poorly understood and are not amenable to clear objectives,
or involve vague constructs that are difficult to link to performance metrics (sensu Smith et al.
1999 as cited in Link et al. 2012)
6) Outcome uncertainty
Outcome uncertainty arises when realized in situ values deviate from expected values derived
from a model. It is also referred to as “implementation error” or “implementation uncertainty”
because it is commonly associated with differences between a management goal and the
implementation of the management plan (Rice and Richards 1996; Dichmont et al. 2006; Holt
and Peterman 2006; Dorner et al. 2009 as cited in Link et al. 2012). Outcome uncertainty may
also be connected to other forms of uncertainty described above. For example, unclear
management objectives or other forms of inadequate communication could lead stakeholders
to reject management plans because the stakeholders feel their best interests are being
overlooked (Watson-Wright et al. 2009 as cited in Link et al. 2012).
All of the ecosystem models are diverse in terms of scope and approach, but share the general
feature of a large number of parameters with complex interactions. These models are
necessarily built with imperfect information. Given these inevitable uncertainties, large and
complex ecosystem models must be evaluated through sensitivity analyses before their output
can be effectively applied to conservation problems (Hilborn and Mangel 1997; Saltelli et al
2000b; Regan et al. 2002; Clark 2003; Harwood and Stokes 2003; Pielke and Conant 2003; Tang
et al. 2006 as cited in McElhany et al. 2010). Uncertainty in climate scenarios arises from
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different greenhouse gas emission storylines and from differences between climate models
even if driven with the same greenhouse gas emission scenario (e.g., Buisson et al. 2010 as
cited in McElhany et al. 2010). This can be partly addressed by using climate change scenario
data from several emission scenarios but also by using results from multi‐model studies (i.e. an
ensemble of climate models). Process‐based models (PBMs) are important tools in forest and
ecological science and widely used to assess the impacts of climate change on forest
ecosystems (Landsberg 2003; Fontes et al. 2010 as cited in McElhany et al. 2010). Parameter
uncertainty has received less attention in climate change impact studies. Climate change
impact studies that do not integrate parameter uncertainty may over‐ or underestimate
climate change impacts on forest ecosystems.
Box. Uncertainty in DGVMs
Process based, dynamic models capture the transient response of vegetation to a changing
environment using explicit representation of key ecological processes such as establishment,
tree growth, competition, death, nutrient cycling (Shugart 1984, Botkin 1993). With the
adoption of DGVMs reliability of results has improved in relation to previous generations,
however validation of some of these models is an ongoing exercise (e.g., Prentice et al. 2007).
The large structural and parametric uncertainty in the DGVMs concerning the processes of
recruitment, competition and tree mortality means that existing DGVMs produce a wide
variety of predictions regarding the future strength and direction of the climate carbon cycle
feedback (Thornton et al., 2007; Sitch et al., 2008). For example, some models predict
catastrophic declines in the Amazon and boreal forests, while others predict relatively stable
ecosystem composition and carbon storage, even with the same future climate drivers (as
illustrated by Sitch et al., 2008). Model biases introduced by these uncertainties are not readily
estimated because limited observations exist to constrain demographic processes under
rapidly altering climates (Allen et al., 2009). For issues such as climate change effects on forest
growth, there are large uncertainties in both the magnitude of future climate change, and the
magnitude of various responses to a changed climate (Dixon and Wisniewski 1995).
Many of the DGVMs do not yet simulate forest fires dynamically as well as pest attacks and
land use change dynamics. Further, migration and seed dispersal constraints are not yet
incorporated in DGVMs. Many of the DGVMs are known to over-simulate grasslands (Bonan et
al. 2003) and at the same time there are suggestions that compared with other dynamic
vegetation models, some DGVMs tends to simulate a fairly strong CO2 fertilization effect
(Cramer et al. 2001; McGuire et al. 2001). Additionally uncertainty in the projections of
biodiversity and ecosystem processes arises from uncertainty in climate projections as well,
particularly in precipitation at downscaled regional levels (Chaturvedi et al 2012). Poor land
use projections (including afforestation, reforestation and forest regeneration) due to
anthropogenic influences further add to the uncertainty. However it is important that the
uncertainty is adequately represented and acknowledged in the impact assessment studies.
We suggest the following specific improvements for the future research to capture the full
range of uncertainty in the impact assessments: Use of multiple climate models and use of
multiple impact assessment models; Integrated modelling of the natural and production
system Developing Plant functional Types for more vegetation types and at species level and
for consideration of fauna.
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Similarly the results of complex models, such as lake ecosystem models, often suffer, however,
from limitations due to various sources of error and uncertainty such as the initial conditions,
input data, model structure, model parameters, validation data, etc. (Beck 1987 as cited in Gal
2012). Parameter uncertainty is a key issue when dealing with a complex model due to the
large number of parameters and the uncertainty as to their true values (Helton et al. 2006). In
order to increase the reliability of the model as a management tool it is important to estimate
the degree of uncertainty surrounding those predicted relationships. There are diverse
approaches for estimating and quantifying uncertainty of the various model components
affecting model outcome (Walker et al. 2003; Helton et al. 2006).
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Box. Uncertainty in species distributions models (SDMs).
Modelling exercises should aim at minimising and when possible explicitly accounting for
known sources of uncertainty and critical sources of uncertainty. Sources of uncertainty in
biodiversity modelling used in a decision making context are not equivalent and therefore, a
critical analysis should be performed in assessing which potential sources of uncertainty may
impact the outcomes of the modelling approach (see Chapter 5 on decision making). Recent
reviews on the role of uncertainty in biodiversity modelling approaches such as SDMs have
indentified a number of uncertainty sources of different types and suggested solutions and
good practices to deal with them (Rocchini et al. 2011). They identified significant sources of
uncertainty in the SDMs modeling building approach and explicitly mention possible solutions
that could be indentified as good practice. As an example uncertainty in input data cold be
related to uncertainty in coordinate information or positional error associated to presence
records that should be dealt with by improving screening of data previous to model building.
This work strongly suggests that the collection of these uncertainty sources and the collection
of possible solutions and good practices should be favoured in the future and greatly facilitate
the development of new applications.
4.6 Options for reducing uncertainty
Simeon et al. (XX) highlighted some considerations on reducing uncertainty in the structural
form of models of ecosystem dynamics.
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3.
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5.
Identify the purpose of the modelling exercise in terms of management objectives, and
the performance measures by which the attainment of these objectives will be assessed.
Identify the key uncertainties about the system. This should occur during the process of
assembling information and formulating models; it is necessary to identify and highlight
uncertainties rather than to make assumptions that constrain the models to a single
view of any important process.
Develop models or parameterisations that represent plausible limits to each key
uncertainty. Consider more than one basic model structure.
Always include models, assumptions, and parameterisations that are not on the bounds,
(and hence may be more plausible), and ensure that the choice of models, parameter
values, and assumptions is balanced given the purpose of the modelling.
Establish the full range of model behaviours by considering different combinations of
models and parameters. Sensitivity analysis is useful at this stage to determine the
importance of each source of uncertainty
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Consider the interaction between models and data. Specifically, do the models capture
the full range of potential conditions, or just the conditions represented by the data? If
the latter is the case, it may be advisable to consider adopting plausible future scenarios
that extend outside of the range of historic data, as may occur, for example, under
current predictions for climate change.
Ensure that each model is logically consistent. For example, assumptions or fixed values
for key parameters in an ecosystem model will also need to be made in models used to
derive prior density functions for the ecosystem model’s input parameters.
Weigh models by plausibility if information exists to do this. Ideally this weighting would
be on the basis of posterior probability, but a more subjective weighting by prior
probability might be necessary.
Run each model multiple times to incorporate the effects of parameter uncertainty and
natural variability.
Avoid averaging model results unless the distribution of results suggested by all models
is unimodal.
If it is possible to weight models, present the results in terms of the risk that each
management objective will not be met. If it is not possible to weight models, present the
results in terms of the trade-offs associated with each management action for each
alternative model.
Make sure the assumptions and limitations of the approach are presented along with
the results.
4.7 Ways forward in biodiversity and ecosystem modeling
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Modelling biodiversity and ecosystem response to direct drivers of change adequately for
predicting future outcomes is a challenge that can hardly be surmounted because of the
inherent complexity of real-world processes (Araújo et al. 2005). In the quest towards models
with enhanced predictability, biodiversity and ecosystem models will certainly become more
complex and include different components. One of the costs inherent to increasing model
complexity is the need for appropriate model parameters. Building complex models with
unrealistic parameters derived from lack of knowledge or data is often less preferable to
simple models with realistic parameters. Both model calibration and evaluation call for data,
and whereas the need of obtaining realistic models for a world with many species is on the
rise, little information on their spatial dynamics is currently available. An increase in the
number of processes explicitly formulated in process-based models is leading to a larger
number of parameters and unsurprisingly to less predictive capability and increase the
uncertainty associated with the results (Walters 1986). Therefore, increased model complexity
should be justified and match the scientific question at hand. In addition, increased model
complexity will challenge the ability of modellers to communicate what is being formulated
into the model and how this is being conceptually done.
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Increased complexity in biodiversity model applications can be due to: (1) increased number of
drivers acting in a model through input layers; or (2) increased number of processes explicitly
considered in the model. In correlative niche models many biological processes are collapsed
and implicitly included (Guisan & Thuiller 2005; Soberón 2007). The pathway of increasing the
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number of external inputs may lead to poor predictive performance of temporal
extrapolations from correlative niche models (Vallecillo et al. 2009), because there is no way to
specify or control the interaction between the processes implicitly included. In process-based
models biological processes are included explicitly (Kearney & Porter 2009). This means that an
increase of the number of external inputs can be handled appropriately if knowledge is
available on the functional responses that relate all inputs with species performance. However,
interactions between even a few processes can make such functions very complex (e.g.,
Rayfield et al. 2009). Therefore, the pathway of increasing the number of processes modelled
externally may be either limited by the knowledge available or condemned to poor
performance.
4.7.1 Handling increased complexity
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Matching model complexity with biological objectives at hand is a major challenge in future
development of biodiversity and ecosystem models. We describe here three general strategies
that should help limiting model complexity.
4.7.1.1 Formulating critical biological processes
20
Avoiding unnecessary increases in model complexity requires a careful assessment of the
biological processes that most directly affect species distributions at the spatial and temporal
scales of interest for each particular study (Guisan & Thuiller 2005). Although there is no
general recipe to select the relevant biological processes, processes related to species autoecology will always have a central role. Habitat selection and population dynamics in species
level models may be formulated with more or less detail, but are fundamentally important to
predict species distribution dynamics (Willis et al. 2009; Kunstler et al. 2011).
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Moreover, movement has been recently identified as one of the most important biological
processes because it allows the modelling of emergent spatial dynamics and constrains species
distributions (Soberón & Nakamura 2009). However, movement is a function not only of
individual species’ characteristics, but also the structure of the landscape (or seascape) over
which dispersal is occurring, including the presence of natural barriers or fragmentation of
habitats (Nathan & Muller-Landau 2000; Schurr et al. 2007).
At the community level, biotic interactions have also been identified as important but their
importance varies across species (generalist species are less dependent upon interactions with
other species than specialists) and scales (interactions are more likely to be determinant at
finer scales). Despite its importance, biotic interactions are likely to constitute one of the most
difficult challenges faced in biodiversity models. There are some examples that have explicitly
simulated the effects of competition in the distributions of individual species (e.g. Lischke et al.
2006; Albert et al. 2008), but complex sets of interactions among species are seldom
considered in modelling exercises (Araújo et al. 2011; Guisan & Rahbek 2011). Finally, plasticity
and evolutionary processes are rarely considered in the context of species distribution
dynamics but they may result critical for species whose short generation time allows them to
rapidly adapt to environmental changes (e.g., Kearney et al. 2009).
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4.7.1.2 Hierarchical modular modelling approaches
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Biological processes should be formulated explicitly and internally (i.e. process based models)
only when they are critical for the questions at hand. The remaining processes can be
modelled externally and formulated into the model by means of input spatial layers or
parameters modified by additional modelling frameworks (Smith et al. 2001). Such an
approach may facilitate the structure of species distribution models by allowing sub-models to
be plugged into one another (e.g., McRae et al. 2008). In this modular structure, the upper
levels provide external contextual information (and hence external dynamics) to the lower
ones. Hierarchical modular structures have the advantage of: (1) more easily integrate across
different spatial and temporal scales (e.g. to downscale the results of processes formulated at
higher levels) (e.g., Del Barrio et al. 2006); and (2) assess the levels of uncertainty added at
each stage (Larson et al. 2004, Chisholm & Wintle 2007). However, modularity may be limited
for those target species that modify their environment or interact with other biotic entities
(Midgley et al. 2010).
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4.7.1.3 Comparing models with different levels of complexity
20
Research is needed to compare the outputs of models with different degrees of complexity in
light of validation data appropriate for the process or driver under study (Roura-Pascual et al.
2010). Only in this case will it be possible to build a body of reference regarding the minimum
acceptable levels of complexity to analyze a given problem.
4.7.1.4 Model communication: making use of standard protocols
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In the quest for more reliable predictions, SDM applications are moving beyond simple
correlative models towards a wider range of process-based modeling strategies, thus
converging with developments in other modeling disciplines such as population and
metapopulation dynamics. This trend will likely complicate the interpretation and
communication of model structure and functionality in a similar way to the problem identified
more generally in other fields such as agent based modeling (Grimm et al. 2006, Grimm et al.
2010). One way to better communicate the origin of dynamics in SDMs would be the general
application of standard protocols for model communication such as the ODD (Overview,
Design concepts, and Detail, Grimm et al. 2006). The ODD protocol forces the modeler to be
explicit in the critical issues we have identified in our conceptual review. First, the Overview
section of the ODD protocol is a conceptual description of the model that includes (1) the
specific purpose of the model; (2) what are basic biological entities and state variables used in
model formulation; and (3) which are the processes explicitly included in the model. In line
with the ODD protocol, the conceptual description of a SDM should include the biological
question in hand (section 1 in Overview), a detailed description of target state variable and
drivers included (section 2 in Overview) and the biological processes of interest (section 3 in
Overview). The second main component in the ODD protocol tackles the design concepts
defining the model formulation. In this section emergence is especially important allowing the
identification of which system-level phenomena truly emerge from the explicitly formulated
processes (emergent dynamics) and which are merely imposed from the inputs (imposed
dynamics). When considering specific models from scientific applications, it will be important
to see whether it is possible to tease apart (i) what was explicitly included in the model vs.
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what was imported from other sources and how; and (ii) what was actually modelled in the
model vs. what was considered by the authors to be modelled, and what is the link.
Finally, there is a need for approaches that combine the rigor of the small-scale studies
precision with the breadth of broad-scale assessments (Naidoo and Ricketts 2006; Egoh et al.
2008; and Nelson et al. 2008 for some initial attempts). Spatially explicit values of biodiversity
across landscapes that might inform land-use and management decisions are still lacking.
Without quantitative assessments, and some incentives for landowners to preserve
biodiversity, this tends to be ignored by land-management decisions. Similarly, rights-based
fisheries have shown to have positive impacts on management of marine resources (Costello
et al. 2008). Without information on the impacts of land-use management practices on
biodiversity, it is impossible to design policies or payment programs that will provide the
desired ecosystem services.
4.7.2 Conclusions
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Modelling is the only option to policy makers to assess the implications of different drivers on
biodiversity and ecosystem functioning, especially in the future context. The best examples are
from climate change, where Global Circulation Models and Earth System Models help
understand the role of greenhouse gas emission driver on future climate. Similarly DGVMs
assist policy makers on the impact of projected climate change driver on terrestrial ecosystems
and biodiversity. Modelling complex systems is characterized by inherent limitations since all
processes and drivers and their linkage to biodiversity or ecosystem functions cannot be
quantified and included. Further no single model that exists today covers all potential biophysical and socio-economic drivers, their inter-relationships and feedbacks. But policy-making
requires robust model projections of impacts of drivers on biodiversity and ecosystem
functioning. All the limitations and multiplicity of models was presented in this chapter. In the
coming decades drivers such as climate change, land use and pollution will become more
important. Thus scientific community has to develop strategies to address the limitations of
current models and reduce uncertainty involved. Some potential examples of strategies
include the following:
• Formation of Model inter-comparison group, similar to CMIP - Coupled Model
Intercomparison Project Phase 5 (http://cmip-pcmdi.llnl.gov/cmip5/) or ISIMIP Inter-Sectoral Impact Model Intercomparison Project (https://www.pikpotsdam.de/research/climate-impacts-and-vulnerabilities/research/rd2-crosscutting-activities/isi-mip) projects, involving a large number of modelling groups
working on biodiversity and ecosystem modelling
• Development of Integrated models that can be applied at sea/landscape or ecosystem
level to assess the impact of drivers on biodiversity and ecosystem functioning. These
integrated models should consider both bio-physical and socio-economic drivers at
scales relevant to decision making.
• Apply multiple models, and get a range of outputs to policy makers, rather than giving
single model outputs, since there are multiple future pathways.
• Need to generate data for different ecosystems at multiple locations and multiple
scales by forming research networks.
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Initiate long term monitoring studies to assess the response of ecosystems to changing
drivers such as climate change, pollution and hydrological changes
(http://unesdoc.unesco.org/images/0009/000938/093876eo.pdf).
Develop protocols for modelling drivers impacting biodiversity and ecosystem
functions.
Encourage more collaboration between modelling groups and field ecologists.
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