Stoichiometric distribution models: ecological stoichiometry at the landscape extent

Abstract

Human activities are altering the fundamental geography of biogeochemicals. Yet we lack an understanding of how the spatial patterns in organismal stoichiometry affect biogeochemical processes and the tools to predict the impacts of global changes on biogeochemical processes. In this contribution we develop stoichiometric distribution models (StDMs), which allow us to map spatial structure in resource elemental composition across a landscape and evaluate spatial responses of consumers. We parameterise StDMs for a consumer-resource (moose-white birch) system and demonstrate that we can develop predictive models of resource stoichiometry across a landscape and that such models could improve our predictions of consumer space use. With results from our study system application, we argue that explicit consideration of the spatial patterns in organismal elemental composition may uncover emergent individual, population, community and ecosystem properties that are not revealed at the local extents routinely used in ecological stoichiometry. We discuss perspectives for further developments and application of StDMs to advance three emerging frameworks for spatial ecosystem ecology in an era of global change; meta-ecosystem theory, macroecological stoichiometry and remotely sensed biogeochemistry. Progress on these emerging frameworks will allow for the integration of ecological stoichiometry and individual space use and fitness.

Spatial estimation of soil carbon, nitrogen and phosphorus stoichiometry in complex terrains: a case study of Schrenk's spruce forest in the Tianshan Mountains

“…Similarly to our approach, the StDMs also fitted generalized linear regression models between elemental response variables and covariates. According to their results, StDMs will allow researchers to map element resources across geographic spaces and hold the promise of describing geographical patterns in organismal elemental traits at various spatial extents (Leroux et al, 2017). In this study, we found that the R 2 of the MLR models are high (Table 1), further supporting the reliability of MLR models in spatial estimation of elemental concentrations and stoichiometric ratios.…”

“…In contrast, our study not only confirmed the general variations in the concentrations of elements and their stochiometric ratios but also provided the spatial patterns or variations of the concentrations and stochiometric ratios based on the data derived from sampling sites. In fact, these spatial patterns are more useful for a better understanding of the regional biological and ecological processes corresponding to soil chemical characteristics (Prater et al, 2017;Leroux et al, 2017).…”

“…However, very few studies have investigated the spatial patterns of variability in nutrient stoichiometry and the implications of these patterns on ecosystem functioning. In their pioneering work, Leroux et al (2017) reported. These methods include geostatistics and spatial interpolation as reported in Liu et al (2013), in which the authors obtained a spatial distribution map of soil total N and P density across the Loess Plateau of China.…”

“…Recently, stoichiometric distribution models (StDMs), which imitate the species distribution model and aim to delineate the spatial pattern of elemental stoichiometry, were developed and used to map spatial structures in resource elemental composition and the response of consumers across a landscape (Leroux et al, 2017). Similarly to our approach, the StDMs also fitted generalized linear regression models between elemental response variables and covariates.…”

“…Like the limited references to spatial distribution 93 or variation in soil C, N and P, available information on spatial patterns of soil C:N:P 94 is also rare. Recently, a study that aimed to explore general soil C:N:P ratios and the 95 patterns in these ratios with regard to soil depth, developmental stages and climate 96 was conducted in China and revealed that while C:N ratios showed relatively small 97 variation among climatic zones, C:P and N:P showed high spatial heterogeneity and 98 large variation between different climatic zones (Tian et al, 2010a changing biogeochemistry on ecosystem function and services (Leroux et al, 2017 conditions (Yang et al, 2010;Sardans et al, 2016;Müller et al, 2017) (Wang et al, 2011), a continued spatial pattern of soil C, N, P and C:N:P 138 stoichiometric ratios could be expected. alpine-frostaction-barren zone, and permanent snow and ice Aegopodium podagraria (Wang et al, 2016).…”