Scientific Traditions in Ecosystem Ecology
Ecosystems are complex and may include hundreds or even thousands of
biotic and abiotic components (Loreau 2010). Studying or modelling all
aspects of ecosystem complexity is not possible or desirable. To
represent ecosystem variation, one needs a basis for simplification and
this simplification comes with trade-off (Geary et al 2020). In the
basic ecosystem sciences, the customary emphasis on ecological function
(Box 2), has come at the expense of detailed information on biotic
properties (Loreau 2010, Mokany et al 2016) and on the contributing
roles of abiotic complexity (Johnson and Martin 2016, Richter and
Billings 2015, Dor-Haim et al 2019, Hjort et al 2022). Many of these
traditions are long-standing and mirrored across general ecology (Loreau
2010). Loreau (2010) attributes them to a divergence of practice between
community and ecosystem ecologists. Species interactions and patterns
are largely studied by community ecologists. Ecosystem ecologists
instead emphasize interactions between species and their abiotic
environments, particularly those interactions that give rise to fluxes
of materials. These latter functions (Box 1) are widely considered the
fundamental foci for basic modelling and empirical inquiry at the
ecosystem level. While biota and biotic processes are considered
ecosystem components, they are normally relegated to the community
(Loreau 2010) and generally interpreted as components of biodiversity.
These enduring traditions of practice are also echoed across the
spectrum of basic and applied ecosystem ecology, and their recognition
within this broader scope of ecosystem science is not new (e.g., Rowe
1961, Blew 1996).
Blew (1996) outlined three major traditions of ecosystem science each
rooted in relatively distinct perceptions of the ecosystem concept and
its salient features (Box 2). The three traditions place differing
levels of emphasis on biotic, abiotic, or functional ecosystem features
(Table S1). While Blew’s (1996) summary has not been updated to include
modern theory and modelling approaches, some of which strive for
improved integration (e.g., Geary et al 2020), similar conventions in
ecosystem science exist today
(Baveye et al 2018, Zarnetske et
al 2019, Alahuhta et al 2020). Blew (1996) attributes such divergence of
scientific practice to controversies surrounding the ecosystem concept
itself, which Loreau (2020) shows have pervaded since its inception.
Many of the debates originate because the ecosystem concept is at once
both comprehensive and flexible (Pickett and Candenasso 2002, Currie
2011). The ecosystem concept’s inherent flexibility lends itself to
different approaches for modelling wholeness and integration among key
unit components (Pickett and Candenasso 2002, Gignoux et al. 2011,
Jørgensen 2016). However, part of the debate with past modelling efforts
have been discussions over which system-level properties (Table S1) best
exemplify wholeness, adequately represent complexity, and underpin
quantifications of variation (Currie 2011). We concur with Jørgensen
(2016) and suggest there are many departure points for simplifying the
ecosystem, as an object for modelling, to help address these
considerations, and to advance understanding of ecosystem patterns.
Furthermore, we highlight the importance of spatially explicit
approaches as a distinct strategy for bridging different analytical
traditions, and for producing predictive models needed for ecosystem
forecasts. These ecosystem modelling trade-offs will necessarily vary
according to purpose. And while research to distinguish ecosystem
modelling strategies and to assess their relative merits are on-going
(e.g., Geary et al 2020), our novel approach falls outside the scope of
recent efforts and related recommendations.
In the next section, we present a framework for ecosystem
conceptualization and spatial pattern modelling. The framework
integrates elements of the ecosystem science traditions outlined above
and presented in Box 2. It does not emphasize biotic, abiotic, or
functional features of ecosystem variation. Instead, our framework
focuses on the causes and consequences of these features’ concordance
(Box 1) in space and time. Additionally, we show how that spatial
concordance shapes patterns of ecosystem heterogeneity at local and
regional landscape extents.