Spatial Population Models for Animals - Environmental Modelling: Finding Simplicity in Complexity - page 212

13

Spatial Population Models
for Animals

George L.W. Perry 1 and Nick R. Bond 2
1 School of Geography, Geology and Environmental Sciences, University of Auckland, New
Zealand
2 Australian Rivers Institute, Griffith University, Australia

directly, often within an interdisciplinary setting (Benda
et al ., 2002) and to provide forecasts and scenarios of the
future of natural systems (Audrey et al ., 2009).
Despite the scope and complexity of the natural
systems that ecologists seek to understand, reduction-
ist approaches have been widely used to understand
cause-effect relationships, frequently based on simple
experiments conducted at small spatio-temporal scales,
often with few species and with just one or a few factors
allowed to vary at once (Eberhardt and Thomas, 1991).
Underpinning this experimental approach is the hope
that such experiments can then be scaled up (or gener-
alized) to provide a picture of what will happen in real,
much larger and more complex, systems (Beck, 1997).
This philosophy of experimentation developed under the
belief that manipulative experiments provide stronger
inference than most other types of evidence (Weiner,
1995; Beyers, 1998). Although challenging to implement,
carefully designed experiments combined with modern
statistical approaches, such as generalized linear mixed
models (GLMM - Bolker et al ., 2009) and hierarchical
statistical models (Cressie et al ., 2009), offer the potential
to gain considerable insight into the dynamics of eco-
logical systems. The experimental approach also offers
the allure of quicker, better replicated, more powerful,
and more tightly controlled outcomes than field-based
studies do. In adopting an experimental approach, the

13.1 The complexity: introduction

Ecology plays an increasingly important, sometimes cen-
tral, role in natural resource management, particularly
in understanding the relative role of natural and anthro-
pogenic factors in influencing the abundance and dis-
tribution of plants and animals (Hastings et al ., 2005a).
The problems entailed in addressing these problems are
diverse, but common among most are two factors that
present ecologists with a particularly strong challenge:
complexity and scale. The inherent complexity of ecosys-
tems has long been recognized by ecologists (Shugart,
1998; Liu et al ., 2007). Ecosystems consist of large num-
bers of species interacting with each other via processes
such as competition and predation. At the same time, the
biotic processes operating in an ecosystem are affected by
and can, in turn, via feedbacks, affect the physical envi-
ronment. Depending on the mobility and longevity of the
organisms involved, these interactions occur across mul-
tiple spatial and temporal scales (Wiens and Milne, 1989)
but played out in real landscapes, such as in forests and
oceans, these scales can be large. Likewise, management
decisions are often based on desired outcomes across large
spatial scales. Consequently, in areas of applied research
and management, ecologists are (quite reasonably) now
being asked to address some of these large-scale questions