Ecosystem models are typically built to predict patterns of one or more ecosystem properties, and those properties are often biotic. While some ecosystem models incorporate either biotic and abiotic responses, biotic and abiotic variables are rarely applied jointly as responses in ecosystem models. Here we model continuous spatial turnover among 21 biotic and abiotic properties to explore forest ecosystem patterns across landscapes of Nova Scotia, Canada (55 000 km2) at high (10 x 10 m) resolution. To achieve this objective, we fit generalized dissimilarity models to field collected data on biotic and abiotic response variables and geographic and environmental gradients described by remotely sensed predictor variables. We develop three separate models targeting ecosystem, biotic, and abiotic responses to identify relationships among forest ecosystem properties, across levels of ecological organization. Our final ecosystem, abiotic, and biotic models explained 41.4, 29.03, and 50.9 percent of variance. Vegetation-based predictors were the most significant for our ecosystem and biotic response models, while topographic and hydrological predictors were foremost in our abiotic response model. We show how relationships among biotic and abiotic ecosystem properties collectively give rise to predicted patterns of forest ecosystem heterogeneity across Nova Scotia, with the strongest variations occurring along elevational and north-south gradients. Our emphasis on multiple ecosystem properties, and our simultaneous modelling of both biotic and abiotic responses, including ecosystem structural, compositional, and functional variables, differs from the approaches taken in most spatial ecosystem models. This study provides an analytical road map for scientists and conservation practitioners looking to predict continuous variation in ecosystem makeup and to apply those predictions for mapping emergent spatial ecosystem patterns. Such spatial models of ecosystem pattern are crucial for achieving national and sub-national commitments to global ecosystem conservation targets.

Predictive modelling is fundamental to ecology and essential for objective biodiversity assessment. However, while predictive biodiversity models are generally well-developed, models for predicting patterns within and among ecosystems have not been adequately operationalized. We contend the scarcity of such models marks a concerning gap in the scientific community’s ability to make ecosystem predictions across landscapes, and more broadly for supporting the conservation of biodiversity and ecosystem functions. We propose ecosystem spatial pattern models (ESPM) to fill this gap in modelling capacity. Under our approach to ESPM, spatial patterns of ecosystem properties are the basis for resolving ecosystem organization at local and landscape extents. Our integrative modelling framework differs from others in that it accords biotic and abiotic constituents equally, based on with their joint mechanistic influence on ecosystem dynamics. Development of ESPM is especially timely for ecosystem assessment is undergoing a contemporary groundswell, as scientists and conservation groups propose ambitious targets for ecosystem conservation and restoration.