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Climate-driven decreases in body size have been documented for a variety of taxa and proposed as a universal response to climate change. However, empirical support among taxa, including fishes, has been mixed, with some fishes growing larger at higher temperatures, and causal mechanisms for faster or slower growth under debate. We simulated effects of climate warming on bluegill (Lepomis macrochirus) growth and consumption and used linear regression and boosted regression trees (BRTs) to model length-at-age for bluegill from Michigan lakes from 1945 to 2019. Bioenergetics models showed bluegill growth and consumption both increase under climate warming. In contrast, linear regression revealed that bluegill ages 1–4 decreased (–0.20 to –0.55 mm/year) in mean length-at-age and that ages 5–8 increased or did not statistically change. BRTs demonstrated that growth had a unimodal relationship with surface water temperature and degree days, peaking at intermediate values. This mismatch between simulations and empirical data may be from increased recruitment leading to increased food limitation at higher temperatures. Future research should empirically test this hypothesis and assess the consequences for ecosystem functions and services. 

Decimeter scale robots in human environments are small relative to obstacles they encounter, making them prone to flipping over and needing to self-right. We present a multifaceted shell that by its geometry alone enables the hexapedal robot MediumANT to passively self-right without the need for additional sensory feedback.We designed the shell by specifying the cross-sectional geometry in the yz and xy planes such that the robot returns to an upright position by rolling around the longitudinal (x) axis, and then tweaked the design to reduce the number of faces. We then attached the shell to the robot by modifying some of its chassis structural plates to extend to and support the shell. We evaluated the effectiveness of the shell in two experimental scenarios: passive righting – balancing the robot on each face of the shell before releasing the robot – and an intentional fall – walking the robot off a ledge at various approach angles. As intended by our design, the robot recovered the upright orientation from all starting faces in the passive righting test and righted itself and continued walking in all falling trials. This work presents an example of using biologically inspired simplicity to solve what would otherwise be a technically challenging problem. 

This dataset contains a record of daily mean air temperature for each of the U.S. Great Lakes from January 1, 1897 to October 22, 2023. These temperatures were derived using the following method. Daily maximum and minimum air temperature data were obtained from the Global Historical Climatology Network-Daily (GHCNd, Menne, et al. 2012) and the Great Lakes Air Temperature/Degree Day Climatology, 1897-1983 (Assel et al. 1995). Daily air temperature was calculated by taking a simple average of daily maximum and minimum air temperature. Following Cohn et al. (2021), a total of 24 coastal locations along the Great Lakes were selected. These 24 locations had relatively consistent station data records since the 1890s. Each of the selected locations had multiple weather stations in their proximity covering the historical period from 1890s to 2023, representing the weather conditions around the location. For most of the locations, datasets from multiple stations in the proximity of each location were combined to create a continuous data record from the 1890s to 2023. When doing so, data consistency was verified by comparing the data during the period when station datasets overlap. This procedure resulted in almost continuous timeseries, except for a few locations that still had temporal gaps of one to several days. Any temporal data gap less than 10 days in the combined timeseries were filled based on the linear interpolation. This resulted in completely continuous timeseries for all the locations. Average daily air temperature was calculated from by simply making an average of timeseries data from corresponding locations around each lake. This resulted in daily air temperature records for all five Great Lakes (Lake Superior, Lake Huron, Lake Michigan, Lake Erie, and Lake Ontario). 

We conducted a macroscale study of 2,210 shallow lakes (mean depth ≤ 3m or a maximum depth ≤ 5m) in the Upper Midwestern and Northeastern U.S. We asked: What are the patterns and drivers of shallow lake total phosphorus (TP), chlorophyll a (CHLa), and TP–CHLa relationships at the macroscale, how do these differ from those for 4,360 non-shallow lakes, and do results differ by hydrologic connectivity class? To answer this question, we assembled the LAGOS-NE Shallow Lakes dataset described herein, a dataset derived from existing LAGOS-NE, LAGOS-DEPTH, and LAGOS-CLIMATE datasets. Response data variables were the median of available summer (e.g., 15 June to 15 September) values of total phosphorus (TP) and chlorophyll a (CHLa). Predictor variables were assembled at two spatial scales for incorporation into hierarchical models. At the local or lake-specific scale (including the individual lake, its inter-lake watershed [iws] or corresponding HU12 watershed), variables included those representing land use/cover, hydrology, climate, morphometry, and acid deposition. At the regional scale (e.g., HU4 watershed), variables included a smaller set of predictor variables for hydrology and land use/cover. The dataset also includes the unique identifier assigned by LAGOS-NE(lagoslakeid); the latitude and longitude of the study lakes; their maximum and mean depths along with a depth classification of Shallow or non-Shallow; connectivity class (i.e., whether a lake was classified as connected (with inlets and outlets) or unconnected (lacking inlets); and the zone id for the HU4 to which each lake belongs. Along with the database, we provide the R scripts for the hierarchical models predicting TP or CHLa (TPorCHL_predictive_model.R), and the TP—CHLa relationship (TP_CHL_CSI_Model.R) for depth and connectivity subsets of the study lakes. 

The LAGOS-US LIMNO data package is one of the core data modules of LAGOS-US, an extensible research-ready platform designed to study the 479,950 lakes and reservoirs larger than or equal to 1 ha in the conterminous US (48 states plus the District of Columbia). The LIMNO module contains in situ observations of 47 parameters of lake physics, chemistry, and biology (hereafter referred to as chemistry) from lake surface samples (defined as observations taken from the epilimnion of a lake) obtained from the Water Quality Portal, the National Lakes Assessment (2007, 2012, 2017), and NEON programs. LIMNO provides 3,511,020 observations across all parameters collected between 1975 and 2021 from 20,329 lakes; the number of observations per lake ranged from 1 to 20,605 with a median of 32. The database design that supports the LAGOS-US research platform was created based on several important design features: lakes are the fundamental unit of consideration, all lakes in the spatial extent above the minimum size must be represented, and most information is connected to individual lakes. The design is modular, interoperable (the modules can be used with each other, as well as other comprehensive lake data products such as the USGS NHD), and extensible (future database modules can be developed and used in the LAGOS-US research platform by others). Users are encouraged to use the other two core data modules that are part of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds) and GEO (characteristics defining geospatial and temporal ecological setting quantified at multiple spatial divisions) that are each found in their own data packages. 

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.