Abstract: It is well recognized that natural land is of great importance, and measures of the value of natural lands are required when making data-driven policy decisions between land development and land preservation. One of the most important values of natural land areas is the recreational services provided. In this study, we apply the travel cost method to estimate the recreation use value provided by the natural land in New England. Specifically, this study calculates the total consumer surplus for hunting, fishing, and wildlife-watching in the New England region. We also investigate whether and how people from households of different race and surroundings have different recreational habits. Using data from the National Survey of Fishing, Hunting, & Wildlife-Associated Recreation, we found that New England natural lands provide a remarkable amount of recreation use value—$88 billion per year to U.S. citizens who partake in wildlife-related activities, accordingly to the travel cost method. Our estimates can serve as input for economic projection and policy analysis models and allow more equitable and appropriate data-driven policy decisions.

Abstract: As authors of Meiler et al. (2022), we welcome Zehr and Riemann's (2023) comment and discussion. We agree, of course, with the general statement that “quantification of gene copy numbers is valuable in marine microbial ecology” and wish to clarify that one of the purposes of Meiler et al. (2022) was to address the specific challenge of using a compilation of quantitative polymerase chain reaction (qPCR) nifH data to evaluate the skill of biogeochemical models. In that particular case, the data were most helpful in constraining the range of diazotrophs, but several sources of uncertainty limited more detailed quantitative evaluations. This was not intended to imply a lack of value or promise for such applications of qPCR data: we believe that testing and constraining biogeochemical and ecological models will be an important application of qPCR data, yet the quantitative interface between molecular data and biogeochemical models remains at its infancy.

In the following, we first provide a background perspective for the Meiler et al. (2022) study, pointing out why observations and simulations are rooted in different currencies. We then discuss in more detail some of the specific points raised by Zehr and Riemann (2023) and highlight why further efforts toward intercalibration of currencies used to measure and simulate marine microbial populations is particularly significant if we are to fully exploit the data in biogeochemical and climate modeling applications. We end by summarizing some potentially fruitful avenues for future effort stimulated by this dialog.

Authors' Summary: Phytoplankton contribute roughly half of the photosynthesis on earth and fuel fisheries around the globe. Yet, few direct measurements of phytoplankton concentration are available. Frequently, concentrations of phytoplankton are instead estimated using the optical properties of water. Backscattering is one of these optical properties, representing the light being scattered backwards. Previous studies have suggested that backscattering could be a good method to estimate phytoplankton concentration. However, other particles that are present in the ocean also contribute to backscattering. In this paper we examine how well backscattering can be used to estimate phytoplankton.

To address this question, we use data from drifting instruments that are spread across the ocean and a computer model that simulates phytoplankton and backscattering over the global oceans. We find that by using backscattering, phytoplankton can be overestimated/underestimated on average by ∼20%. This error differs between regions, and can be larger than 100% at high latitudes. Computer simulations allowed us to quantify spatial and temporal variability in backscattering signal composition, and thereby understand potential errors in inferring phytoplankton with backscattering, which could not have been done before due to the lack of phytoplankton data.