Grassland biodiversity can pay.

The biodiversity-ecosystem functioning (BEF) literature provides strong evidence of the biophysical basis for the potential profitability of greater diversity but does not address questions of optimal management. BEF studies typically focus on the ecosystem outputs produced by randomly assembled communities that only differ in their biodiversity levels, measured by indices such as species richness. Landholders, however, do not randomly select species to plant; they choose particular species that collectively maximize profits. As such, their interest is not in comparing the average performance of randomly assembled communities at each level of biodiversity but rather comparing the best-performing communities at each diversity level. Assessing the best-performing mixture requires detailed accounting of species' identities and relative abundances. It also requires accounting for the financial cost of individual species' seeds, and the economic value of changes in the quality, quantity, and variability of the species' collective output-something that existing multifunctionality indices fail to do. This study presents an assessment approach that integrates the relevant factors into a single, coherent framework. It uses ecological production functions to inform an economic model consistent with the utility-maximizing decisions of a potentially risk-averse private landowner. We demonstrate the salience and applicability of the framework using data from an experimental grassland to estimate production relationships for hay and carbon storage. For that case, our results suggest that even a risk-neutral, profit-maximizing landowner would favor a highly diverse mix of species, with optimal species richness falling between the low levels currently found in commercial grasslands and the high levels found in natural grasslands.

Nutrient enrichment, biodiversity loss, and consequent declines in ecosystem productivity.

Anthropogenic drivers of environmental change often have multiple effects, including changes in biodiversity, species composition, and ecosystem functioning. It remains unknown whether such shifts in biodiversity and species composition may, themselves, be major contributors to the total, long-term impacts of anthropogenic drivers on ecosystem functioning. Moreover, although numerous experiments have shown that random losses of species impact the functioning of ecosystems, human-caused losses of biodiversity are rarely random. Here we use results from long-term grassland field experiments to test for direct effects of chronic nutrient enrichment on ecosystem productivity, and for indirect effects of enrichment on productivity mediated by resultant species losses. We found that ecosystem productivity decreased through time most in plots that lost the most species. Chronic nitrogen addition also led to the nonrandom loss of initially dominant native perennial C4 grasses. This loss of dominant plant species was associated with twice as great a loss of productivity per lost species than occurred with random species loss in a nearby biodiversity experiment. Thus, although chronic nitrogen enrichment initially increased productivity, it also led to loss of plant species, including initially dominant species, which then caused substantial diminishing returns from nitrogen fertilization. In contrast, elevated CO2 did not decrease grassland plant diversity, and it consistently promoted productivity over time. Our results support the hypothesis that the long-term impacts of anthropogenic drivers of environmental change on ecosystem functioning can strongly depend on how such drivers gradually decrease biodiversity and restructure communities.

Low biodiversity state persists two decades after cessation of nutrient enrichment.

Although nutrient enrichment frequently decreases biodiversity, it remains unclear whether such biodiversity losses are readily reversible, or are critical transitions between alternative low- and high-diversity stable states that could be difficult to reverse. Our 30-year grassland experiment shows that plant diversity decreased well below control levels after 10 years of chronic high rates (95-270 kg N ha(-1) year(-1)) of nitrogen addition, and did not recover to control levels 20 years after nitrogen addition ceased. Furthermore, we found a hysteretic response of plant diversity to increases and subsequent decreases in soil nitrate concentrations. Our results suggest that chronic nutrient enrichment created an alternative low-diversity state that persisted despite decreases in soil nitrate after cessation of nitrogen addition, and despite supply of propagules from nearby high-diversity plots. Thus, the regime shifts between alternative stable states that have been reported for some nutrient-enriched aquatic ecosystems may also occur in grasslands.

Expanding the coverage and accuracy of parcel-level land value estimates.

Planning for cost-effective conservation requires reliable estimates of land costs, spatially-differentiated at high resolution. Nolte (2020) provides a county-by-county, parcel-level estimation approach that dramatically improves estimates of fair market value for undeveloped land across the contiguous Unites States. Much undeveloped land of conservation interest is under threat of conversion to agricultural use or is already agricultural. This paper demonstrates the value of accounting for additional variables that affect agricultural productivity and demand for undeveloped land, as well as the benefit of modeling at scales corresponding to regional agricultural markets. We find that countywide median home value, climatic variables, and several parcel-level soil type variables contribute substantially to predictive power. Enlarging the set of predictors and the geographical scale of modeling improves accuracy by approximately 15 percent and, relative to a more restricted modeling benchmark adapted from Nolte (2020), extends coverage into 376 counties occupying 1.35 million km2. To assess the practical benefits of our modeling approach, we simulate the protection of 30 percent of US lands via government purchasing, modeled after the Biden administration's "30x30" initiative. Using our proposed modeling strategy, the purchasing agency saves approximately $15 million per year, or 4 percent of the USDA's annual land easement budget.