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.