A “toy” model of biogeochemical dynamics on climate gradients

Abstract

We used a simple “toy” model to aid in the evaluation of the controls of biogeochemical patterns along a climate gradient. The model includes simplified treatments of water balance (precipitation minus Potential Evapotranspiration), leaching, weathering of cation- and P-bearing minerals, N cycling and loss, biomass production, and biological N fixation. We use δ15N as a central integrator of biogeochemical processes, because δ15N integrates multiple pathways of N input, output, and transformation in ecosystems. The model simulated the location and magnitude of a peak in δ15N on a gradient on Kohala Volcano, Hawai‘i which peaked ~ + 14 ‰ in sites receiving ~ 3.5 cm/month average precipitation (− 1300 mm/year water balance); the model also captured a peak in total P in surface soil at intermediate levels of precipitation and water balance, and other biogeochemical features on the gradient. We then applied the model to understanding the patterns of and mechanisms underlying nutrient limitation to net primary production (NPP) and plant biomass on the gradient, testing for the existence and extent of N and P limitation by simulated additions of N and/or P in the model. Both a simulated symbiotic biological N fixer and a simulated non-fixer were limited by P supply across the gradient; the non-fixer was independently limited by N supply in wetter sites. By running the toy model with and without the influence of temperature, we demonstrated that water is the most important factor shaping biogeochemical patterns on this gradient.

Has nitrogen availability decreased over much of the land surface in the past century? A model-based analysis

“…We also used a version of the toy model to evaluate changes in δ 15 N because a decline in 15 N was used as evidence for declining N availability by Mason et al (2022a), and this was an area of disagreement between Mason et al (2022a,b) and Olff et al (2022). Publications by Vitousek et al (2021) and Burnett et al (2022) evaluated δ 15 N in soils and plants (respectively) across this broad precipitation gradient (285-3240 mm/year) in an unpolluted region on the Simulated effects of increasing temperature on the NPP of a non-N-fixing plant and on N availability (calculated as described in the legend of Fig. 2), calculated at a precipitation level of 20 cm/month using the program tcnewprodnofixcr with BNF turned off.…”

“…We used simple models developed by Vitousek et al (2021Vitousek et al ( , 2022 to evaluate the effects of increasing temperature, increasing atmospheric concentrations of CO 2 , temporal variability in precipitation, and ecosystem-level disturbance on N availability and on N limitation to NPP (net primary productivity), and changes in the atmospheric deposition of 15 N on δ 15 N pools within ecosystems. This class of models was described in detail in Vitousek et al (2021), and modifications were described in Vitousek et al (2022); here (since our focus is on changes in N availability), we reproduce and update the description of how the N cycle (including 15 N) and NPP are simulated.…”

“…In this paper, we apply simple models of N cycling that have been validated across a climate gradient on the Island of Hawaiʻi (Vitousek et al 2021) to reveal general trends of N availability change and to evaluate whether components of anthropogenic global change can cause widespread decreases in N availability. We use simple models to reveal a general trend of N availability change, because unlike changes in carbon cycle (which are often easily-observed structural changes such as biomass or C pool changes), changes in the N cycle are usually functional (such as changes in N concentrations in organisms, and the consequent changes in N fluxes) which are more challenging to observe and to analyze.…”

Modeling Hawaiian Agroecology: Depicting traditional adaptation to the world's most diverse environment

“…We follow previous modeling (Ladefoged et al, 2009) in applying a simple Rainfall-Elevation Index (REI) to estimate soil fertility as a function of substrate age, due to the lack of available soil fertility data or high resolution fertility models across the islands. This approach assumes that soil pedogenesis is largely a function of cumulative rainfall (rainfall x time), slightly augmented by temperature (Vitousek et al, 2021(Vitousek et al, , 2022. Previously defined categorical limits (Ladefoged et al, 2009) were fit in a biexponential model to define a continuous REI threshold as a function of Geologically young substrates (<4 ky) were excluded due to inadequate soil development (Ladefoged et al, 2009;.…”