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An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015

This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the surface and subsurface runoff. The file name has four parts: the variable collection, the used parameterization, the time scale, and the suffix. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).

An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° ET over the conterminous United States, 1980–2015

This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration). The file name has four parts: the variable collection, the used parameterization, the time scale, and the suffix. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).

An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015

This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time scale, and the suffix. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).

FIG. 3 in First occurrences of Palaeogale von Meyer, 1846 in the Pacific Northwest, United States

FIG. 3. — Dentaries of Palaeogale dorothiae MacDonald, 1963: A, lingual view (JODA 6177); B, occlusal view (JODA 6177); C, buccal view (JODA 6177); D, lingual view (JODA 5893); E, occlusal view (JODA 5893); F, buccal view (JODA 5893). Scale bars: 1 cm.

FIG. 2 in First occurrences of Palaeogale von Meyer, 1846 in the Pacific Northwest, United States

FIG. 2. — Skull of Palaeogale sp. (JODA 13221): A, dorsal view; B, ventral view; C, left lateral view; D, right lateral view. Scale bars: 1 cm.

FIG. 1 in First occurrences of Palaeogale von Meyer, 1846 in the Pacific Northwest, United States

FIG. 1. — Map of distributions of North American Palaeogale von Meyer, 1846 specimens both published and from museum records. Localities are colour coded by North American Land Mammal Age. Black shaded area represents Oregon. Distribution data downloaded from Paleobiology Database (https://paleobiodb. org/) and iDigBio (https://www.idigbio.org/) on 23 April 2020. The Texas occurrence is reported in Albright (1996).

FIG. 4 in First occurrences of Palaeogale von Meyer, 1846 in the Pacific Northwest, United States

FIG. 4. — Bivariate plot of anterior-posterior length (APL) and transverse width (TW) of Palaeogale carnassials: A, upper fourth premolar (P4); B, lower first molar (m1). Points are colour coded by species and labelled with their specimen numbers. All measurements are in mm and can be found in Tables 1 and 2.

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Flows of invasive ants worldwide to the United States

International trade and human movements have accidentally transported thousands of species worldwide at an unprecedented scale. The resulting biological invasions are among the greatest drivers of species extinctions and can cause enormous economic losses. Understanding how globalization affects the accidental transport of species is urgent to prevent new invasions. However, global trade networks have had mixed success so far in explaining intercontinental species movements. Here, we show that commonly used proxies of global trade flows such as general imports and agricultural imports differed greatly from flows of alien ants from their donor regions to the United States. The analysis of 97 individual commodity flows revealed instead that plants and fruit imports, which are a small subset of all agricultural commodities, were associated with invasion flows. All 95 other commodities differed from flows of alien ants, including most "agricultural" commodities which had extremely heterogenous geographic origins. This highlights the need to know precisely which commodities serve as introduction pathways for a particular taxonomic group in order to explain invasion flows and identify likely source regions of future invasions in a world of changing trade relationships.

Figures 31–36 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 31–36. Dirrhagofarsus unicolor (Hisamatsu). 31) Elytral apices, lateral view. 32) Male, dorsal habitus. 33) Head, frontal view. 34) Pronotum, lateral view. 35) Female, dorsal habitus. 36) Aedeagus, dorsal view. (Scale: 31–36 = 1.0 mm.)

Figures 11–14. Dirrhagofarsus species, frontal carina. 11 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 11–14. Dirrhagofarsus species, frontal carina. 11) Dirrhagofarsus modestus (Fleutiaux). 12) Dirrhagofarsus unicolor (Hisamatsu). 13) Dirrhagofarsus ernae Otto, Muona and McClarin. 14) Dirrhagofarsus brevis Otto, new species.

Figures 27–30 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 27–30. Dirrhagofarsus modestus (Fleutiaux) habitat, Chequamegon-Nicolet National Forest, along Diamond Roof Road, Oconto County WI, U.S.A., 23-VI-2021. 27) Rotten stump. 28) Female beetle on stump. 29) Female beetle on nearby fallen log. 30) Nearby fallen log. Images taken by author with Samsung Galaxy S10e smartphone.

Figures 21–26 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 21–26. Dirrhagofarsus modestus (Fleutiaux). 21) Elytral apices, lateral view. 22) Male, dorsal habitus. 23) Head, frontal view. 24) Pronotum, lateral view. 25) Female, dorsal habitus. 26) Aedeagus, dorsal view. (Scale: 21–26 = 1.0 mm.)

Figures 6–10 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 6–10. Dirrhagofarsus lewisi (Fleutiaux). 6) Dorsal habitus. 7) Head, frontal view. 8) Pronotum, lateral view. 9) Elytral apices, lateral view. 10) Aedeagus, dorsal view. (Scale: 6–10 = 1.0 mm.)

Figures 1–5 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 1–5. Dirrhagofarsus ernae Otto, Muona and McClarin. 1) Dorsal habitus. 2) Head, frontal view. 3) Pronotum, lateral view. 4) Elytral apices, lateral view. 5) Aedeagus, dorsal view. (Scale: 5 = 0.5 mm; 1–4 = 1.0 mm.)

Figures 15–20. Dirrhagofarsus brevis Otto new species. 15 in A new species and new records for two other exotic species of Dirrhagofarsus Fleutiaux, 1935 (Coleoptera: Eucnemidae: Melasinae: Dirhagini) in the United States

Figures 15–20. Dirrhagofarsus brevis Otto new species. 15) Male holotype, elytral apices, lateral view. 16) Male holotype, dorsal habitus. 17) Male holotype, head, frontal view. 18) Male holotype, pronotum, lateral view. 19) Female allotype, dorsal habitus. 20) Male paratype, aedeagus, dorsal view. (Scale: 15–20 = 1.0 mm.)

opencc-by-4.0Jun 2022View details →

Supporting Data for "Decarbonization pathways for the residential sector in the United States"

This repository contains input and processed data for the analysis presented in "Decarbonization pathways for the residential sector in the United States" by Berrill et al. (2022), in Nature Climate Change.  Description of the files can be found in the 'Data_Descriptors.docx' file. Please direct any enquiries relating to this dataset to peter.berrill@aya.yale.edu

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Data from: Controlled drainage and subirrigation suitability in the United States: A meta-analysis of crop yield and soil moisture effects

Controlled drainage and subirrigation (CDSI) is an important water management strategy in many regions, but the conditions under which CDSI is most likely to increase crop yield and soil moisture are not fully understood. A meta-analysis, consisting of 154 pairwise observations from replicated and randomized trials in 30 peer-reviewed primary research articles on CDSI (6 controlled drainage, 24 CDSI, analyzed together due to data scarcity), was conducted to study the responses of yield and soil moisture to CDSI, and investigate how crop type, soil texture, and cumulative growing season precipitation (PGS) influence these responses. Based on the yield response to these moderating factors, we used a fuzzy-logic approach to map potentially suitable locations for CDSI in the conterminous United States. On average, CDSI increased yield by 8.0% (95% CI = 1.8–14.7%) compared with conventional free drainage. The yield response to CDSI did not differ among crops. However, a greater yield response to CDSI was observed in medium-textured soils (19.4% increase; 95% CI = 12.4–27.0%) than in coarse- or fine-textured soils. The positive effect of CDSI on yield increased with decreasing PGS in coarse- and medium-textured soils. There was no clear effect of CDSI on soil moisture, nor did any moderators influence this relationship, though this may be attributed to the scarcity of studies on CDSI reporting soil moisture. The fuzzy-logic-based approach revealed that while potentially suitable areas are mostly concentrated in the well studied U. S. Midwest, these areas also exist in other regions where CDSI may warrant further study.

opencc-zeroJul 2022View details →

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Differential equity in access to public and private coastal infrastructure in the Southeastern United States

Despite the ubiquity of coastal infrastructure, it is unclear what factors drive its placement, particularly for water access infrastructure (WAI) that facilitates entry to coastal ecosystems such as docks, piers, and boat landings. The placement of WAI has both ecological and social dimensions, and certain segments of coastal populations may have differential access to water. In this study, we employed an environmental justice framework to assess how public and private WAI in South Carolina, USA is distributed with respect to race and income. Using publicly available data from state agencies and the US Census Bureau, we mapped the distribution of these structures across the 301 km of the South Carolina coast. Using spatially explicit analyses with high resolution, we found that census block groups with lower income contain more public WAI, but racial composition has no effect. On the other hand, private docks showed the opposite trends, as the abundance of docks is significantly, positively correlated with census block groups that have greater percentages of White residents, while income has no effect. Under a "need-based" model of equity, we argue that WAI are not equitably distributed in South Carolina and constitute an environmental justice issue. We contend that the racially unequal distribution of docks is likely a consequence of the legacy of Black land loss, especially of waterfront property, throughout the coastal Southeast over the past half-century. Knowledge of racially inequitable distribution of WAI can guide public policy to rectify this imbalance and support advocacy organizations working to promote public water access. Our work also points to the importance of considering race in ecological research, as the spatial distribution of coastal infrastructure both directly affects ecosystems through the structures themselves and regulates which groups access water and what activities they can engage in at those sites.

opencc-zeroJul 2022View details →

opencc-by-4.0Aug 2022View details →

An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015 (time-merged version)

This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the surface and subsurface runoff. The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format. The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97). The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).