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Abstract The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly‐generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations. 

Arizona is home to many mosquito species, some of which are known vectors of infectious diseases that harm both humans and animals. Here, we provide an overview of the 56 mosquito species that have been identified in the State to date, but also discuss their known feeding preference and the diseases they can (potentially) transmit to humans and animals. This list is unlikely to be complete for several reasons: (i) Arizona’s mosquitoes are not systematically surveyed in many areas, (ii) surveillance efforts often target specific species of interest, and (iii) doubts have been raised by one or more scientists about the accuracy of some collection records, which has been noted in this article. There needs to be an integrated and multifaceted surveillance approach that involves entomologists and epidemiologists, but also social scientists, wildlife ecologists, ornithologists, representatives from the agricultural department, and irrigation and drainage districts. This will allow public health officials to (i) monitor changes in current mosquito species diversity and abundance, (ii) monitor the introduction of new or invasive species, (iii) identify locations or specific populations that are more at risk for mosquito-borne diseases, and (iv) effectively guide vector control. 

The State of Arizona in the south-western United States supports a high diversity of insects. Digitised occurrence records, especially from preserved specimens in natural history collections, are an important and growing resource to understand biodiversity and biogeography. Underlying bias in how insects are collected and what that means for interpreting patterns of insect diversity is largely untested. To explore the effects of insect collecting bias in Arizona, the State was regionalised into specific areas. First, the entire State was divided into broad biogeographic areas by ecoregion. Second, the 81 tallest mountain ranges were mapped on to the State. The distribution of digitised records across these areas were then examined. A case study of surveying the beetles (Insecta, Coleoptera) of the Sand Tank Mountains is presented. The Sand Tanks are a low-elevation range in the Lower Colorado River Basin subregion of the Sonoran Desert from which a single beetle record was published before this study. The number of occurrence records and collecting events are very unevenly distributed throughout Arizona and do not strongly correlate with the geographic size of areas. Species richness is estimated for regions in Arizona using rarefaction and extrapolation. Digitised records from the disproportionately highly collected areas in Arizona represent at best 70% the total insect diversity within them. We report a total of 141 species of Coleoptera from the Sand Tank Mountains, based on 914 digitised voucher specimens. These specimens add important new records for taxa that were previously unavailable in digitised data and highlight important biogeographic ranges. Possible underlying mechanisms causing bias are discussed and recommendations are made for future targeted collecting of under-sampled regions. Insect species diversity is apparently at best 70% documented for the State of Arizona with many thousands of species not yet recorded. The Chiricahua Mountains are the most densely sampled region of Arizona and likely contain at least 2,000 species not yet vouchered in online data. Preliminary estimates for species richness of Arizona are at least 21,000 and likely much higher. Limitations to analyses are discussed which highlight the strong need for more insect occurrence data. 

Parasitic plants often attack multiple host species with unique defenses, physiology, and ecology. Reproductive phenology and vectors of parasitic plant genes (pollinators and dispersers) can contribute to or erode reproductive isolation of populations infecting different host species. We asked whether desert mistletoe, Phoradendron californicum (Santalaceae tribe Visceae syn. Viscaceae), differs ecologically across its dominant leguminous hosts in ways affecting reproductive isolation. Parasite flowering phenology on one host species (velvet mesquite, Prosopis velutina) differed significantly from that on four others, and phenology was not predicted by host species phenology or host individual. Comparing mistletoe populations on mesquite and another common host species (catclaw acacia, Senegalia greggii) for which genetically distinct host races are known, we tested for differences in interactions with vectors by quantifying pollinator visitation, reward production, pollen receipt, and fruit consumption. Mistletoes on mesquite produced more pollinator rewards per flower (1.86 times the nectar and 1.92 times the pollen) and received ~ 2 more pollen grains per flower than those on acacia. Mistletoes on the two host species interacted with distinct but overlapping pollinator communities, and pollinator taxa differed in visitation according to host species. Yet, mistletoes of neither host showed uniformly greater reproductive success. Fruit set (0.70) did not differ by host, and the rates of fruit ripening and removal differed in contrasting ways. Altogether, we estimate strong but asymmetric pre-zygotic isolating barriers between mistletoes on the two hosts. These host-associated differences in reproduction have implications for interactions with mutualist vectors and population genetic structure. 

Abstract A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human‐centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long‐term costs. In this manuscript, we first review the current status of instrument‐based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound‐producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground‐based measurements for plant biodiversity, and laboratory‐based imaging for physical specimens and samples in the NEON biorepository. Through its data science‐literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human‐driven surveys. 

Abstract It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.