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Authorship of academic publications is central to scientists’ careers, but decisions about how to include and order authors on publications are often fraught with difficult ethical issues. To better understand scholars’ experiences with authorship, we developed a novel concept, authorship climate, which assesses perceptions of the procedural, informational, and distributive justice associated with authorship decisions. We conducted a representative survey of more than 3,000 doctoral students, postdoctoral researchers, and assistant professors from a stratified random sample of U.S. biology, economics, physics, and psychology departments. We found that individuals who tend to have more power on science teams perceived authorship climate to be more positive than those who tend to have less power. Alphabetical approaches for assigning authorship were associated with higher perceptions of procedural justice and informational justice but lower perceptions of distributive justice. Individuals with more marginalized identities also tended to perceive authorship climate more negatively than those with no marginalized identities. These results illustrate how the concept of authorship climate can facilitate enhanced understanding of early-career scholars’ authorship experiences, and they highlight potential steps that can be taken to promote more positive authorship experiences for scholars of all identities. 

Early research on the impact of COVID-19 on academic scientists suggests that disruptions to research, teaching, and daily work life are not experienced equally. However, this work has overwhelmingly focused on experiences of women and parents, with limited attention to the disproportionate impact on academic work by race, disability status, sexual identity, first-generation status, and academic career stage. Using a stratified random survey sample of early-career academics in four science disciplines ( N = 3,277), we investigated socio-demographic and career stage differences in the effect of the COVID-19 pandemic along seven work outcomes: changes in four work areas (research progress, workload, concern about career advancement, support from mentors) and work disruptions due to three COVID-19 related life challenges (physical health, mental health, and caretaking). Our analyses examined patterns across career stages as well as separately for doctoral students and for postdocs/assistant professors. Overall, our results indicate that scientists from marginalized (i.e., devalued) and minoritized (i.e., underrepresented) groups across early career stages reported more negative work outcomes as a result of COVID-19. However, there were notable patterns of differences depending on the socio-demographic identities examined. Those with a physical or mental disability were negatively impacted on all seven work outcomes. Women, primary caregivers, underrepresented racial minorities, sexual minorities, and first-generation scholars reported more negative experiences across several outcomes such as increased disruptions due to physical health symptoms and additional caretaking compared to more privileged counterparts. Doctoral students reported more work disruptions from life challenges than other early-career scholars, especially those related to health problems, while assistant professors reported more negative changes in areas such as decreased research progress and increased workload. These findings suggest that the COVID-19 pandemic has disproportionately harmed work outcomes for minoritized and marginalized early-career scholars. Institutional interventions are required to address these inequalities in an effort to retain diverse cohorts in academic science. 

Stream and lake fishes are important economic and recreational resources that respond to alterations in their surrounding watersheds and serve as indicators of ecological stressors on aquatic ecosystems. Research suggests that fish species diversity is largely influenced by surface water connectivity, or the lack thereof; however, few studies consider freshwater connections and their effect on both lake and stream fish communities across broad spatial extents. We used fish data from 559 lakes and 854 streams from the midwestern–northeastern United States to examine the role of surface water connectivity on fish species richness and community composition. We found that although lakes and streams share many species, connectivity had a positive effect on species richness across lakes and streams and helped explain species composition. Taking an integrated approach that includes both lake and stream fish communities and connectivity among freshwaters helps inform scientific understanding of what drives variation in fish species diversity at broad spatial scales and can help managers who are faced with planning for state-, regional-, or national-scale monitoring and restoration. 

The LAGOS-US RESERVOIR data module (hereafter, RESERVOIR) classifies all 137,465 lakes > 4 hectares in the conterminous U.S. into one of the following three categories using a machine-learning predictive model based on visual interpretation of lake outlines and a classification rule based on lake shape. Natural Lakes (NLs) are defined as lakes that are likely to be entirely or mostly naturally-formed and that do not have large, flow-altering structures on or near them; Reservoir Class A’s (RSVR_A) are defined as lakes that are likely to be either human-made or highly human-altered by the presence of a relatively large water control structure that appears to significantly change the flow of water; and Reservoir Class B’s (RSVR_Bs) are lakes that are likely to be entirely human-made based on isolation from rivers and a highly angular shape that is rarely, if ever, seen in natural lakes also often. We trained the machine learning models on 12,162 manually-classified lakes to assign probabilities of a lake being in 1 of 2 of the categories (NL or RSVR), then we further classified the RSVR classification into either A or B based on NHD Fcodes, isolation, and angularity. The data module includes a detailed User Guide, metadata tables, and a data table that includes information such as location, lake geometry, surface water connectivity class, and official name. Using our definition, our classification indicates that over 46 % of lakes > 4 ha in the conterminous U.S. are reservoir lakes. These data can be combined with other LAGOS-US data modules and U.S. national databases using unique lake identifiers to study both reservoir lakes and natural lakes at broad scales. 

Global declines in biodiversity have the potential to affect ecosystem function, and vice versa, in both terrestrial and aquatic ecological realms. While many studies have considered biodiversity-ecosystem function (BEF) relationships at local scales within single realms, there is a critical need for more studies examining BEF linkages among ecological realms, across scales, and across trophic levels. We present a framework linking abiotic attributes, productivity, and biodiversity across terrestrial and inland aquatic realms. We review examples of the major ways that BEF linkages form across realms–cross-system subsidies, ecosystem engineering, and hydrology. We then formulate testable hypotheses about the relative strength of these connections across spatial scales, realms, and trophic levels. While some studies have addressed these hypotheses individually, to holistically understand and predict the impact of biodiversity loss on ecosystem function, researchers need to move beyond local and simplified systems and explicitly investigate cross-realm and trophic interactions and large-scale patterns and processes. Recent advances in computational power, data synthesis, and geographic information science can facilitate studies spanning multiple ecological realms that will lead to a more comprehensive understanding of BEF connections.