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This article explores the concept of "lay summaries" in scientific research articles, which aim to make research findings more accessible to a wider audience. However, the article argues that these summaries often still contain complex language and are difficult for nonspecialists to understand. The article provides recommendations for authors, publishers, and editors on how to improve the accessibility of these summaries, focusing on reducing jargon and improving readability. The authors suggest using shorter sentences, simpler language, and active voice, and recommend that publishers and editors provide clearer instructions and examples. The goal is to increase the visibility and impact of research by making summaries more easily understood by a broader range of people. [Extracted from the article]

As scientific teams in academia have become increasingly large, interdisciplinary, and diverse, more attention has been paid to honorary authorship (i.e., giving authorship to those not making a significant contribution). Our study examined whether honorary authorship occurs because of the desire to include all or many team members. Interviews with project principal investigators (n = 6) and early-career project members (n = 6) from 6 interdisciplinary environmental science research teams revealed that principal investigators frequently employed inclusion-motivated honorary authorship but that this practice had some negative impacts on early-career team members with less power and status, thereby undermining true inclusion of those from underrepresented groups. We believe our findings are of import not only for environmental scientists, but also for scholars who are interested in issues of authorship decision-making regardless of disciplinary affiliation.

A variety of classification approaches are used to facilitate understanding, prediction, monitoring, and the management of lakes. However, broad‐scale applicability of current approaches is limited by either the need for in situ lake data, incompatibilities among approaches, or a lack of empirical testing of approaches based on ex situ data. We developed a new geographic classification approach for 476,697 lakes ≥ 1 ha in the conterminous U.S. based on lake archetypes representing end members along gradients of multiple geographic features. We identified seven lake archetypes with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. Individual lakes were assigned weights for each of the seven archetypes such that groups of lakes with similar combinations of archetype weights tended to cluster spatially (although not strictly contiguous) and to have similar limnological properties (e.g., concentrations of nutrients, chlorophyll a (Chl a), and dissolved organic carbon). Further, archetype lake classification improved commonly measured limnological relationships (e.g., between nutrients and Chl a) compared to a global model; a discrete archetype classification slightly outperformed an ecoregion classification; and considering lakes as continuous mixtures of archetypes in a more complex model further improved fit. Overall, archetype classification of US lakes as continuous mixtures of geographic features improved understanding and prediction of lake responses to limnological drivers and should help researchers and managers better characterize and forecast lake states and responses to environmental change. [ABSTRACT FROM AUTHOR]

The LAGOS‐US RESERVOIR data module classifies all 137,465 lakes ≥ 4 ha in the conterminous U.S. into three categories using a machine learning predictive model based on visual interpretation of lake outlines and a lake shape classification rule. Natural Lakes (NLs) are defined as naturally formed, lacking large, flow‐altering structures; Reservoir Class A's (RSVR_A) are defined as lakes likely human‐made or human‐altered by a large water control structure; and Reservoir Class B's (RSVR_Bs) are lakes likely human‐made but are not connected to streams and have a shape rare in NLs. We trained machine learning models on 12,162 manually classified lakes to predict assignment as an NL or RSVR, then further classified RSVRs based on NHD Fcodes, isolation, and angularity. Our classification indicates that > 46% of lakes ≥ 4 ha in the conterminous U.S. are reservoir lakes. These data can be easily combined with other LAGOS‐US modules and U.S. national databases for the broad‐scale study of reservoir lakes and NLs. [ABSTRACT FROM AUTHOR]

Student discussions are a common teaching approach in graduate and advanced undergraduate courses because of their benefits to student learning, and to future professional development for natural resources professionals. However, traditional student-led discussions often are ineffective at meeting course and learning objectives and suffer from many common pitfalls, such as dominance by a few vocal students. I present the "student facilitator approach" to student-led discussions that changes the roles of all students in the discussion classroom, particularly the leader, and provides students clear guidelines about preparing for and participating in classroom discussions. A key feature of the approach is the replacement of the student discussion leader (who has control over both discussion content and process) with a student facilitator (who has control over only the discussion process). Using this approach, I found that students responded very positively, that it tended to encourage student participation, and that it created an environment where students took more ownership of the discussion. (Contains 2 tables.)

Depth is a fundamental property of lakes, essential for understanding and quantifying a range of biological, chemical, and physical processes of individual lakes as well as the collective functioning of the millions of lakes across the globe. Despite this importance, lake depth is rarely available for large numbers of lakes across the broad scales of regions and continents. We describe a new open‐access dataset, LAGOS‐US DEPTH, which contains 17,675 maximum depths and 6,137 mean depths for lakes in the conterminous United States. These data represent one of the largest compilations of lake depths in the world and are connected to other data products through the LAGOS‐US research platform. Here, we describe characteristics of lake maximum depth across the conterminous United States and identify gaps in data coverage that we encourage other researchers to fill by linking their own depth datasets for lakes within the LAGOS‐US spatial footprint. Extending this open‐access effort to include other regions and countries across the globe to build a more comprehensive and representative database of lake depth would better position scientists to quantify and articulate the critical roles that lakes play globally in biogeochemical cycling, maintaining biodiversity, and in maintaining the many ecosystem services that lakes provide. [ABSTRACT FROM AUTHOR]

Macroscale studies of aquatic ecosystems are needed to address many contemporary broad‐scale problems related to global change, particularly to inform national‐scale environmental policies. In this data paper, we fill two important gaps in data availability for lakes at the scale of the conterminous U.S., the lack of: (1) high‐resolution geographic representations of lakes and their watersheds and (2) an open data platform to connect disparate U.S. national‐scale lake datasets. We describe the LAGOS‐US LOCUS v1.0 data module that includes a detailed User Guide, which is part of the LAGOS‐US extensible research‐ready open data platform. This platform can be used to study the 479,950 lakes and reservoirs larger than or equal to 1 ha across the entire conterminous U.S. at multiple scales of space and time. The LOCUS module includes spatially‐explicit information on lakes and their watersheds, including location information, physical characteristics, and identifiers of other important national‐scale datasets. [ABSTRACT FROM AUTHOR]

Peer‐review and subject‐matter editing is the backbone of scientific publishing. However, early‐career researchers (ECRs) are given few opportunities to participate in the editorial process beyond reviewing articles. Thus, a disconnect exists: science needs high‐quality editorial talent to conduct, oversee and improve the publishing process, yet we dedicate few resources to building editorial talent nor giving ECRs formal opportunities to influence publishing from within. ECRs can contribute to the publishing landscape in unique ways given their insight into new and rapidly developing publishing trends (e.g. open science). Here, we describe a two‐way fellowship model that gives ECRs a "seat" at the editorial table of a field‐leading journal. We describe both the necessary framework and benefits that can stem from editorial fellowships for ECRs, editors, journals, societies, and the ​broader scientific community. [ABSTRACT FROM AUTHOR]