Utilizing Ecosystem Function Multifunctionality to Assess Supporting Ecosystem Services of Three Land-Use Types in Northwestern Virginia

Supporting ecosystem services are vital in the maintenance of other ecosystem services, yet are often overlooked due to their less obvious and longer-term impacts on human society. They also do not have agreed upon metrics, making them difficult to quantify and apply to environmental decision-making. A suite of aboveground, belowground, and aboveground-belowground interface ecosystem properties, processes, and functions that inform the supporting services of soil formation, nutrient cycling, and primary productivity were measured in three land-use types under variable management in northwestern Virginia over the course of two growing seasons (2017 and 2018): an unmanaged early successional field, a semi-managed native prairie, and a heavily managed agricultural field. The ecosystem functions measured (net nitrogen (N) mineralization, soil respiration, primary productivity, decomposition, and N leaching) were compared across land-use types and then were applied to an ecosystem function (EF)-multifunctionality framework based on associations with carbon, nitrogen, and supporting services. During the 2017 and 2018 growing seasons, NNM did not differ among fields. Decomposition was greatest in the early successional field in both 2017 and 2018. Primary productivity was greatest in the agricultural field in 2017 when it was planted; in 2018 when the agricultural field was left fallow, primary productivity was greatest in the early successional field and native prairie in 2018. During 2018, mean soil respiration was greatest in the early successional field. The native prairie, with higher belowground root mass, had the lowest leaching potential index in the top 20 cm of soil. The sum of C and N multifunctionality differed in both years; the agricultural field had the greatest level of multifunctionality when planted but least when not planted. The native prairie and early successional field appear to be comparable in terms of their total C and N multifunctionality, though N multifunctionality was greatest in the early successional field in 2018. While EF-multifunctionality associated with supporting ES was greatest in the agricultural field in 2017 when it was planted, its mean multifunctionality over both years balanced out to near zero (near the overall mean multifunctionality across all fields), suggesting that the agricultural field provides a suite of supporting ES when planted, but when accounting for interannual variability associated with crop rotations, it does not appear to consistently provide those supporting ES. The EF-multifunctionality of the native prairie over both years was several standard deviations below the overall mean, suggesting that it provides supporting ES at a lower rate than the other land-use types. The early successional field demonstrated the highest mean EF-multifunctionality across both growing seasons, suggesting that it consistently provides a suite of supporting ES. Including the ecological cost of human management alters the provision of supporting services; the agricultural field and native prairie failed to demonstrate greater overall multifunctionality than the early successional field in order to compensate for their ecological cost of management, so they do not appear as valuable in terms of the net supporting services they provide. This study is the first of its kind to implement an EF-multifunctionality approach to inform supporting ES, while also accounting for disservices and cost of management. Assessing multifunctionality with z-scores raises the question of whether the importance and variation can be equated across all functions. Applying multifunctionality in this way unveiled some shortcomings of the framework: equating importance and variation across all functions as well as the undervaluation of conservative nutrient cycling. This work is, however, a step toward elucidating the difficult task of quantifying supporting services in order to inform sustainable land-cover decisions.