Discussion
Our integrated modelling approach helps fill a gap in ecosystem spatial modelling capacity. We extend Ferrier and Guisan’s (2006) well established ‘assemble and predict together ’ strategy for community-level distribution modelling to predict ecosystem spatial patterns. Here, we model joint biotic and abiotic responses as a function of environmental gradients. Thereafter, we apply spatial turnover among different combinations of modelled biotic and abiotic responses as a basis for identifying ecosystem patterns. This effort marks one of the first spatial ecosystem models to integrate of biotic and abiotic responses. The novel approach helps illuminate relationships among biotic and abiotic ecosystem properties, and their drivers. It also provides a basis for resolving the collective contributions biotic and abiotic model responses make to ecosystem spatial heterogeneity across landscapes. Our approach can be applied in other regions to provide conservation planners and decision makers with a tool to predict how and where ecosystems vary, and a means to help understand the origins of these patterns. The approach is well suited to regions where the density of survey sites is low relative to the grain of spatial turnover in the targeted ecological entity (e.g., community, ecosystem) (Ferrier et al. 2007). It is also ideal when the objective is to predict continuous spatial variation in ecosystem makeup and to apply those predictions for mapping emergent biodiversity patterns (Basquill and Leroux 2023).
The ecosystem is central to recent global conservation agreements, such the Kunming-Montreal Global Biodiversity Framework, and new scientific guidance is available to implement these and other ecosystem targets across, global, national and sub-national extents (Nicholson et al. 2024, Venegas-Li et al. 2024). A critical impediment is that few sub-global regions have adequate ecosystem mapping (Xiao et al. 2024), and statistical models required to establish those maps are rare (Geary et al. 2020). To address these challenges, previous terrestrial approaches have included modelling the distribution of pre-classified ecosystem types (e.g., Simensen et al. 2020, Naas et al. 2024), or proxies including pre-classified vegetation community types (e.g., Comer et al. 2020, Jiménez‐Alfaro et al. 2023) or remotely sensed land cover classes (e.g., Murray et al. 2022). Most examples have been conducted at coarser spatial grains and correspond with Ferrier and Guisan’s (2006) ‘assemble first, predict later ’ strategy for modelling biodiversity patterns. With this strategy, individual observations – species for example – are assembled (e.g., through numerical classification) into discrete units, such as community types, and those units are modelled in space. Following the ‘assemble and predict together ’ strategy, our alternative approach fills a gap for modelling ecosystem patterns at the landscape extent. In addition, we predict at a fine spatial grain. This grain matches the spatial resolution where ecosystem restoration, and much ecosystem-based management and conservation, is implemented (e.g., Mäkelä et al. 2012, Pressey et al. 2013, Aubin et al. 2024).
Different analytical approaches have distinct strengths, and their characteristic applications reflect varied modelling data and intents (Deschamps et al. 2023, Naas et al. 2024). Our adaptation of Ferrier and Guisan’s (2006) ‘assemble and predict together ’ strategy enabled us to parse the shared and independent components and determinants of ecosystem heterogeneity. More specifically, it allowed us to predict biotic, abiotic, and ecosystem responses to diverse environmental circumstances, which we operationalized in three separate models. Two of our models represent lower levels of ecological organization, which Levins (2011) contends have a controlling influence on ecosystem pattern and process. Predictions from lower ecological levels also allows ecosystem conservation practitioners to represent biotic and abiotic properties in model-informed conservation plans. Here, these distinctions may be necessary for managing or restoring specific groups of ecosystem components or services (e.g., wildlife habitat - Van der Biest et al. 2020; carbon sequestration and storage - Ameray et al. 2021; soil properties - Rader et al. 2022). Our modelling framework provides this functionality, enabling researchers to predict whole ecosystems and their parts, while identifying potential mechanisms.
Our foremost predictors of ecosystem dissimilarity were all vegetation-based, namely leaf area index, softwood basal area, and normalized difference vegetation index (Table 1). Vegetation predictors (e.g., canopy height, normalized vegetation difference index) similarly explained the largest proportion of variation in forest ecosystem distribution models of Norway (Naas et al. 2024). In distribution models of vegetation-based ecosystem proxies (e.g., Ponomarenko et al. 2019, Lee et al. 2021) spectral vegetation indices (e.g., normalized vegetation difference index) and remotely sensed vegetation cover are frequently the primary predictors. These mutual findings suggest the potential effectiveness of employing vegetation canopy features – which represent a fraction of total ecosystem composition and structure – to predict forest ecosystems.
The most influential predictors in our biotic and ecosystem models were identical. This might imply the predominant drivers of biotic properties mirror those shaping whole ecosystems – including constituent facets of abiotic diversity. An alternative interpretation is that the relative influence of abiotic predictors on ecosystem variation has been masked in our ecosystem formulation. Results of our independent biotic and abiotic response models lend relevant evidence. In the abiotic response model, terrain ruggedness and depth to water, both frequent topographic predictors in biodiversity and geodiversity models (Dilts et al 2023, Toivanen et al. 2024), were the top two predictors, accounting for 80% of total deviance explained (Figure 4). Neither of these predictors were important in the biotic or ecosystem response models. One explanation for these disparate findings may lie in our abiotic response model’s performance, which was the lowest of the three builds. This model’s lower relative performance may indicate new predictors of abiotic variation are warranted. Furthermore, including these same new predictors in an ecosystem response model could shift the ratio of meaningful predictors more evenly among biotic and abiotic predictors. Indeed, commonalities between the biotic and ecosystem response models, and the intermediate performance of the latter model, suggest these two formulations are more strongly favouring biotic properties and their respective predictors.
Generally, our models are exploratory and could benefit from empirical testing with out of sample data from different study areas or time frames (Tredennick et al. 2021). The absence of these data highlights a limitation of our adaptive approach (sensu Holling and Allen 2002). This approach is common in models where relationships between predictors and response variables are poorly understood (Tredennick et al. 2021, Planque et al. 2022), such as they are between ecosystem properties and their determinants (Soranno et al. 2019). Empirical testing could also lead to hypothesis generation and theory development, particularly for mechanisms of ecosystem assembly, which are inadequately substantiated (Levin 1998). Model outcomes could also be employed for making secondary predictions to explicitly link pattern and process (Gallagher et al. 2021). The framework we develop lends itself well to these objectives. It can be rapidly fit to large datasets, allowing for the development and testing of nested models for successive resolution of ecosystem patterns and drivers at various scales and levels of complexity.
One strategy for strengthening our ecosystem build is to draw more explicitly from geodiversity modelling and its potential for improving biodiversity predictions. Recognition of the interplay between biodiversity and geodiversity – the diversity of Earth’s abiotic features and processes, including climate – has risen sharply, partly in response to rapidly changing global conditions (Schrodt et al. 2024). Efforts to bridge these two disciplinary foci have emerged from both biodiversity (Vernham et al. 2023) and geodiversity (Alahuhta et al. 2024, Toivanen et al. 2024) research streams. The ecosystem is an apparent focal unit for synthesizing across disciplines, for geodiversity is integral to ecosystems (Richter and Billings 2015, Holling 1992, Ochoa-Hueso et al. 2021). Yet, few ecosystem models jointly incorporate facets of biotic and abiotic diversity as responses (Basquill and Leroux 2023). While we strived to adequately represent abiotic responses in our ecosystem model, the inherent coarse spatial grain of many geodiversity predictors – including geology and climate (e.g., Hjort and Luoto 2012, Read et al. 2020) – precluded their use in our study. We selected predictors to match our survey grain following best practices for biodiversity distribution modelling (Chauvier et al. 2022). Although omitting coarse-grain predictors in our models may seem like a modelling impediment, it raises an opportunity for exploring ecosystem scaling relationships.
Ecosystems occur at all spatial scales (Fritsch et al. 2020) and scales selected for modelling correspond to study objectives (Geary et al. 2020). The fine resolution (10 m) in our models reflects the spatial grain where many topographically controlled terrestrial ecosystem properties (e.g., vegetation – Moeslund et al. 2013; soil – Seibert et al. 2007; water – Detty and McGuire 2010; organic matter – Burton et al. 2011) are strongly expressed. This grain coincides with the fine-grained mapping needed for much natural resource management (Pressey et al. 2013, D’Urban Jackson et al. 2020, Senf 2022) and applied forecasting (Dobrowski 2011). However, ecosystem properties may also be driven by processes occurring at other scales (Holling 1992). For example, climatic processes affect biodiversity from macro to microscales (Coelho et al. 2023, Kemppinen et al. 2024). The paucity of microscale climate data has limited their availability for fine-grained studies (Kling et al. 2024) and resulted in possibly misleading biodiversity predictions (Slavich et al. 2014, Manzoor et al. 2018). Notwithstanding this data deficit, one solution for investigating scaling relationships is to systematically examine the influence of spatial grain on model outcomes (e.g., Guisan et al. 2007). Numerous methods are available for altering grain to advance understanding of scaled relationships across landscapes (Graham et al. 2019, Markham et al. 2023). Our modelling framework would lend itself well to this inquiry, providing a means to test how scaling is affected by grain and level of ecological organization (i.e., biotic, abiotic, and ecosystem levels). Another benefit of our framework is that it could be coupled with separate spatial models to predict assemblages of ecosystem properties.
Our GDM-based framework is a strategy for predicting shifting combinations or turnover of ecosystem properties, analogous to GDM models of community turnover. Working from this premise, we posit the comparable but distinct value of predicting spatially structured assemblages of ecosystem properties. This objective could also be operationalized by extending community-level modelling techniques, such as joint species distribution modelling (jSDM; Basquill and Leroux (2023). jSDM is a community modelling technique (Pollock et al. 2014) which can be applied to predict biotic composition (Franklin 2023). The two adaptations of community-level modelling techniques – GDM in the present study and jSDM – offer complementary approaches for predicting ecosystem patterns. Here, we suggest they could be coupled to predict spatially structured 1) assemblages of ecosystem properties (via jSDM) and 2) continuous shifts in those assemblages (via GDM). Similar pairings of GDM and jSDM were employed for predicting taxonomic and functional composition and turnover of European peat bogs (Robroek et al. 2017), and the assembly and turnover of meiofaunal communities across Denmark (Macher et al. (2024).