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opencc-by-4.0Sep 2021View details →

FIGURE 3 in Taxonomic and functional turnover of Amazonian stream fish assemblages is determined by deforestation history and environmental variables at multiple scales

FIGURE 3 | Standardized effect sizes for each taxonomic and functional turnover metric (mean and 95% confidence intervals). RC = Raup-Crick; MPD = mean pairwise distance; MNTD = mean nearest taxon distance; ref = streams with forested watersheds; new = streams with recently deforested watersheds; old = streams with historically deforested watersheds.

Fig. 8 in Nematode morphometry and biomass patterns in relation to community characteristics and environmental variables in the Mekong Delta, Vietnam

Fig. 8. Regression model functions between nematode length and feeding types/biomass along the Mekong estuary ECC nematode length L and percentage of feeding types 1B and 2B (a); nematode densities of individual biomass and total biomass (b).

Fig. 7. Regression functions between the length and ratio L in Nematode morphometry and biomass patterns in relation to community characteristics and environmental variables in the Mekong Delta, Vietnam

Fig. 7. Regression functions between the length and ratio L/W with other characters (maturity index, feeding types) of nematodes at all stations. length L and genera richness S, maturity index MI (a); length L and percentage % of feeding type 1A and 2B (b); nematode width W with percentage of feeding type 2A (c); ratio L/W with the percentage of feeding type 2B (d).

Fig. 4 in Nematode morphometry and biomass patterns in relation to community characteristics and environmental variables in the Mekong Delta, Vietnam

Fig. 4. Nematode length (L) and width (W) at all mouth stations and along the Co Chien river estuary.

Fig. 3 in Nematode morphometry and biomass patterns in relation to community characteristics and environmental variables in the Mekong Delta, Vietnam

Fig. 3. Nitrite and nitrate concentrations (mean±SD; raw data multiplied by 10) and ammonium concentrations across a vertical sediment profile at the mouth of the Mekong delta (a), and along the Co Chien estuary (b).

Fig. 2 in Nematode morphometry and biomass patterns in relation to community characteristics and environmental variables in the Mekong Delta, Vietnam

Fig. 2. Chloroplastic Pigment Equivalents (mean CPE±SD; μg L−1) and chlorophyll a (mean±SD; μg L−1) at the mouth stations (a) and along the Co Chien estuary (b).

Fig. 1 in Nematode morphometry and biomass patterns in relation to community characteristics and environmental variables in the Mekong Delta, Vietnam

Fig. 1. Locations of sampling stations in the Co Chien estuary (ECC1 through ECC4) and at the mouth of Mekong Delta (from north to south, mouth stations ECT, ECD, EBL, EHL, ECH, EDA and ETD) in Vietnam.

opencc-by-4.0Jun 2011View details →

Fig. 3 in Temporal distribution of ichthyoplankton in the Ivinhema River (Mato Grosso do Sul State/ Brazil): Influence of environmental variables

Fig. 3. Average fish larva abundance of the ten most important taxa captured in the Ivinhema River (Mato Grosso do Sul State, Brazil) during the period between April 2005 and March 2006.

opencc-by-4.0Jun 2011View details →

Fig. 4 in Temporal distribution of ichthyoplankton in the Ivinhema River (Mato Grosso do Sul State/ Brazil): Influence of environmental variables

Fig. 4. Average values and standard error of the environmental variables obtained at the Ivinhema River (Mato Grosso do Sul State, Brazil) during the period between April 2005 and March 2006. A. Water outflow and rainfall; B. Dissolved oxygen and water temperature; C. Electrical conductivity and pH; and D. Total zooplankton.

opencc-by-4.0Jun 2011View details →

Fig. 2 in Temporal distribution of ichthyoplankton in the Ivinhema River (Mato Grosso do Sul State/ Brazil): Influence of environmental variables

Fig. 2. Fish egg (A) and larva (B) abundance in the Ivinhema River (Mato Grosso do Sul State, Brazil) for the period between April 2005 and March 2006 (circles = average value; bars = standard error).

opencc-by-4.0Mar 2009View details →

Fig. 1 in Influence of environmental variables and anthropogenic perturbations on stream fish assemblages, Upper Paraná River, Central Brazil

Fig. 1. Locations of the sampled sites (dots) in the streams of the Ouvidor River, Goiás State, Brazil. Squares indicate the main cities.

opencc-by-4.0Jan 2023View details →

Monsoon low-pressure system (LPS) tracks in ERA5 over India (1979-2019) with added environmental variables

Derived from the LPS v3.0 dataset (https://doi.org/10.5281/zenodo.7568990). Filtered to monsoon LPSs (majority of track lifetime between June and September), with genesis over the Bay of Bengal and making landfall over India. Temporal resolution also reduced from hourly to six-hourly. This dataset accompanies the paper "Using interpretable gradient-boosted decision-tree ensembles to uncover novel dynamical relationships governing monsoon low-pressure systems" (DOI to follow). Aside from the core variables described in the main LPS dataset (linked above), this version includes a large number of environmental variables, listed below. All are computed from ERA5 unless otherwise stated, "mean" means that the variable is computed as an average within 400 km of the LPS centre, "mcz" means that the variable is computed as an average in the box [75-85°E, 18.5-27°N]. mean_u200: 200 hPa zonal wind (m s-1) mean_u850: 850 hPa zonal wind (m s-1) mean_skt: surface temperature (K) mean_land_frac: fraction of area within 400 km that is over land mcz_tcwv: mean total column water vapour over monsoon trough (kg m-2) vortex_depth: mean_vort_500 x mean_vort_700/mean_vort_8502 over_land: flag for LPS centre (Boolean) dvo850_dt: rate of change of mean_vort_850 (10-5 s-1 day-1)  acc_land_time: accumulated time where over_land = True (hours) total_land_time: final value of acc_land_time} for a given LPS (hours) qshear_850: meridional shear of 850 hPa specific humidity over India (m3 m-3 (°)-1) ushear_850: meridional shear of 850 hPa zonal wind over India (m s-1 (°)-1) mean_cape: CAPE (J kg-1) mcz_cape: mean CAPE over the monsoon trough (J kg-1) mean_dthetae_dp_900_750: d(theta_e)/dp between 900 and 750 hPa, a measurement of atmospheric stability (K hPa-1) mean_dthetae_dp_750_500: d(theta_e)/dp between 750 and 500 hPa, a measurement of atmospheric stability (K hPa-1) mean_land_skt: land surface temperature (K; NaN over ocean) mean_sst: sea surface temperature (K; NaN over land) mean_swvl1: soil moisture in the top layer (m3 m-3; <7 cm; NaN over ocean) mean_swvl2: soil moisture in the second layer (m3 m-3; 7-28 cm; NaN over ocean) mean_swvl1_grad: mean absolute horizontal gradient of mean_swvl1 (m3 m-4) mean_swvl2_grad: mean absolute horizontal gradient of mean_swvl2 (m3 m-4) olr_90: 90th percentile of negative OLR (i.e. ~90th percentile of cloud top height) (W m-2) olr_75: 75th percentile of negative OLR (W m-2) olr_50: 50th percentile of negative OLR (W m-2) qshear_850_background: qshear\_850 averaged over the previous ten days (m3 m-3 (°)-1) ushear_850_background: ushear\_850 averaged over the previous ten days (m3 s-1 (°)-1) mean_q_850: 850 hPa specific humidity (m3 m-3) orography_height: elevation of land surface under LPS centre (m) peak_vorticity: largest value of mean_vort_850} attained by a given LPS (10-5 s-1) reached_peak: False if peak\_vorticity has not been reached yet, else True mean_prcp_400: mean precipitation rate within 400 km of the LPS centre over the next six hours (kg m-2 s-1) mean_prcp_800: mean precipitation rate within 800 km of the LPS centre over the next six hours (kg m-2 s-1) max_prcp_400: maximum precipitation rate within 400 km of the LPS centre over the next six hours (kg m-2 s-1) max_prcp_800: maximum precipitation rate within 800 km of the LPS centre over the next six hours (kg m-2 s-1) mean_vimfd_400: vertically integrated moisture flux convergence (kg m-2 s-1) mean_v200: 200 hPa meridional wind speed (m s-1) mean_v500: 500 hPa meridional wind speed (m s-1) mean_v850: 850 hPa meridional wind speed (m s-1) mean_u500: 500 hPa zonal wind speed (m s-1) zonal_speed: zonal (x) component of LPS propagation velocity (m s-1) merid_speed: meridional (y) component of LPS propagation velocity (m s-1) mean_prcp_imerg: as mean_prcp_400 but computed using IMERG data, rather than ERA5 (kg m-2 hr-1)   qshear_850, ushear_850 and their backgrounds are averaged over 5° longitude either side of the LPS centre, with the gradient computed between 10°N and 27°N, reflecting the moisture and zonal wind gradients across the monsoon region.

opencc-by-3.0-usMar 2023View details →

Code Appendix: Do Seed Dispersal Strategies Reflect Adaptation to Environmental Variability? Functional Ecology, 2023

This code appendix contains all of the R code and data for the manuscript: Van Den Elzen, C. L., N. Sigman, and N. C. Emery. 2023. Do Seed Dispersal Strategies Reflect Adaptation to Environmental Variability?. Functional Ecology (Manuscript ID: FE-2022-00850). Abstract:  1. Dispersal is one of the primary mechanisms by which organisms adapt to spatial and temporal variation in the environment. Theory predicts that increasing spatiotemporal variation drives selection for offspring dispersal away from their natal habitat and one another. However, due to inherent difficulties in measuring dispersal in plant systems, there are few empirical tests of the extent to which this hypothesis can explain variation in seed dispersal strategies. 2. In this study, we characterized and compared the dispersal patterns of three closely related plant species that segregate across gradients in spatiotemporal variation in seasonal wetlands. 3. We tracked individual seeds as they dispersed in their natural habitats to measure seed dispersal distance (the distance traveled from the maternal plant) and inter-seed spread (distances between dispersed seeds), and to identify the plant traits causing within-species variation in seed dispersal. We also evaluated the seed traits causing within-species variation in seed flight distance and terminal velocity in a wind tunnel and a drop tube, respectively. 4. We found that average seed dispersal distance was lowest in the species that occupies the most spatiotemporally variable habitat, contradicting our predictions; however, inter-seed spread was lowest in the species from the least variable habitat, which aligned with our expectations. 5. The maternal plant and seed traits explaining intraspecific variation in seed dispersal varied among species as well as the method used to measure dispersal potential. Two traits had non-intuitive effects on dispersal, including pappus size, which reduced seed flight distance in two of the focal taxa. 6. Overall, our results indicate that the differences we detected in seed dispersal among three closely related plant taxa can be only partially explained by current patterns of environmental variability in their respective habitats, and that the traits driving within species variation in seed dispersal can evolve rapidly and change with the environmental context in which they are measured.

opencc-by-4.0May 2023View details →

Files associated with Christopher Holder and Anand Gnanadesikan, How well do Earth System Models capture apparent relationships between phytoplankton biomass and environmental variables? [Version 1]

1. process_cmip_rf.m is a matlab script that reads a single file, generates a random forest using the parameters in the associated paper and computes permutation importance and sensitivities. Note- in order to get process_cmip_rf.m to work as written you must have the Statistics and Machine Learning toolbox installed on Matlab and download the table_modis.asc file below.  Files 2-16 are tabular filew containing all datapoints used in Random Forest analysis for the NCAR CESM2 model. Columns are               1. Index of point, enabling a mapping back to the model grid if the resolution is known.               2. Longitude               3. Latitude               4. Month               5. Iron in mol/m3.               6. Mixed layer in m.               7. Ammonia in mol/m3               8. Nitrate in mol/m3.               9. Phytoplankton carbon in mol/m3.               10. Phosphate in mol/m3.               11. Shortwave radiation (net solar radiation at ocean surface in W/m2).               12. Silicate in mol/m3.               13. Salinity in PSU               14. Temperature in C.               15. Upwelling velocity in m/s. If variable is not included in the dataset, the column will be filled with zeros. 2.table_cesm2.asc:  Data created from Danabasoglu, G., 2019, NCAR CESM model output prepared for CMIP6 CMIP esm-pi-control <a href="http://doi.org/10.22033/ESGF/CMIP6.7579">http://doi.org/10.22033/ESGF/CMIP6.7579</a>. Grid is 360x180x12 3.table_cems2_fv2.asc: Data created from Danabasoglu, G., 2019,  NCAR CESM-FV2 model output prepared for CMIP6 CMIP pi-control  <a href="http://doi.org/10.22033/ESGF/CMIP6.11301">http://doi.org/10.22033/ESGF/CMIP6.11301</a>. Grid is 360x180x12 4. table_cesm2_waccm.asc: Data created from Danabasoglu, G., 2019, NCAR CESM2-WACCM model output prepared for CMIP6 CMIP piControl <a href="http://doi.org/10.22033/ESGF/CMIP6.10094">http://doi.org/10.22033/ESGF/CMIP6.10094</a>. Grid is 360x180x12 5. table_cesm2_waccm_fv2.asc: Data created from Danabasoglu, G., 2019, NCAR CESM-WACCM-FV2 model output prepared for CMIP CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.11302">http://doi.org/10.22033/ESGF/CMIP6.11302</a>. Grid is 360x180x12 6. table_gfdl_cm4.asc: Data created from Guo, Huan; John, Jasmin G; Blanton, Chris et al,2018, NOAA-GFDL GFDL-CM4 model output piControl,   <a href="http://doi.org/10.22033/ESGF/CMIP6.8666">http://doi.org/10.22033/ESGF/CMIP6.8666</a>. Grid is 360x180x12 7.table_gfdl_esm4.asc Data created from Krasting, John P.; John, Jasmin G; Blanton, Chris et al., 2018, NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8669">http://doi.org/10.22033/ESGF/CMIP6.8669</a>. Grid 360x180x12 8. table_ipsl_cm5a2_inca.asc: Data created from Boucher, Olivier; Denvil, Sébastien; Levavasseur, Guillaume et al.: 2021, IPSL IPSL-CM5A2-INCA model output prepared for CMIP6 CMIP piControl  <a href="http://doi.org/10.22033/ESGF/CMIP6.13683">http://doi.org/10.22033/ESGF/CMIP6.13683</a>. Grid is 182x149x12 9. table_ipsl_cm6a_lr.asc: Data created from Boucher, Olivier; Denvil, Sébastien; Levavasseur, Guillaume et al., 2018:, IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.5251">http://doi.org/10.22033/ESGF/CMIP6.5251</a>. Grid is 362x332x12. 10. table_mpi_esm1-2-ham.asc: Neubauer, David; Ferrachat, Sylvaine; Siegenthaler-Le Drian, Colombe et al., 2019: HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.5037">http://doi.org/10.22033/ESGF/CMIP6.5037</a>. Grid is 256x220x12. 11. table_mpi_esm1-2-hr.asc:  Data created from Jungclaus, Johann; Bittner, Matthias; Wieners, Karl-Hermann et al., 2019: MPI-M MPI-ESM1.2-HR model output prepared for CMIP6 CMIP piControl <a href="http://doi.org/10.22033/ESGF/CMIP6.6674">http://doi.org/10.22033/ESGF/CMIP6.6674</a>. Grid is 802x404x12. 12. table_mpi_esm1-2-lr.asc: Data created from Wieners, Karl-Hermann; Giorgetta, Marco; Jungclaus, Johann et al. 2019:MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP piControl  <a href="http://doi.org/10.22033/ESGF/CMIP6.6675">http://doi.org/10.22033/ESGF/CMIP6.6675</a>. Grid is 256x220x12. 13. table_noresm2-lm.asc: Seland, Øyvind; Bentsen, Mats; Oliviè, Dirk Jan Leo et al.,2019 NCC NorESM2-LM model output prepared for CMIP6 CMIP piControl, <a href="http://doi.org/10.22033/ESGF/CMIP6.8217">http://doi.org/10.22033/ESGF/CMIP6.8217</a>. Grid is 360x385x12 14. table_noresm2-mm.asc: Data created from Bentsen, Mats; Oliviè, Dirk Jan Leo; Seland, Øyvind et al.,2019 : NCC NorESM2-MM model output prepared for CMIP6 CMIP piControl,  <a href="http://doi.org/10.22033/ESGF/CMIP6.8221">http://doi.org/10.22033/ESGF/CMIP6.8221</a>. Grid is 360x385x12. 15-16. table_kostadinov.asc, table_modis.asc Data is a merger of observational products and model output Observational climatologies for temperature, salinity, mixed layer depth, silicate, phosphate, and nitrate were downloaded from the World Ocean Atlas (WOA) 2018 (Garcia et al., 2019; Locarnini et al., 2019; Zweng et al., 2019). MODIS-POC was downloaded from oceancolor.nasa.gov. Kostadinov POC is taken from <a href="https://doi.pangaea.de/10.1594/PANGAEA.859005">https://doi.org/10.1594/PANGAEA.859005</a> Grid is 360x180x12.

opencc-by-4.0May 2023View details →

Plant Community Formation and Species Distribution Patterns in relation to environmental variables in Endiras Natural Forest, Fogera District, South Gondar Zone, Ethiopia

We need to deposit the data for the manuscript entitled plant community formation and species distribution patterns in relation to environmental variables in Endiras forest, Fogera District, South Gondar Zone, Ethiopia so as to be cited easily

opencc-by-4.0Jul 2023View details →

Supporting Information from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)

Supporting Information (Open Data) from The Next Frontier of Environmental Unknowns: Substances of Unknown or Variable Composition, Complex Reaction Products, or Biological Materials (UVCBs)

opencc-zeroSep 2023View details →

Data from: Environmental variables influence patterns of mammal co-occurrence following introduced predator control

Co-occurring species often overlap in resource use and can interact in complex ways. However, shifts in environmental conditions or resource availability can lead to changes in patterns of species co-occurrence, which may be exacerbated by global escalation of human disturbances to ecosystems, including conservation directed alterations. We investigated the relative abundance and co-occurrence of two naturally sympatric mammal species following two forms of environmental disturbance: wildfire and introduced predator control. Using 14 years of abundance data from repeat surveys at long-term monitoring sites in south-eastern Australia, we examined the association between a marsupial, the common brushtail possum Trichosurus vulpecula, and a co-occurring native rodent, the bush rat Rattus fuscipes. We asked: Is the increase in abundance of common brushtail possums following control of an introduced predator associated with a decline in abundance of the bush rats? Using Bayesian regression models, we tested hypotheses that the abundance of each species would vary with changes in environmental and disturbance variables, and that the negative association between bush rats and common brushtail possums was stronger than the association between bush rats and disturbance. Our analyses revealed that bush rat abundance varied greatly in relation to environmental and disturbance variables, whereas common brushtail possums showed relatively limited variation in response to the same variables. There was a negative association between common brushtail possums and bush rats, but this association was weaker than the initial decline and subsequent recovery of bush rats in response to wildfires. Using co-occurrence analysis, we can readily infer negative relationships in abundance between co-occurring species, but to understand the impacts of such associations, and plan appropriate conservation measures, we require more information on interactions between the species and environmental variables. Co-occurrence can be a powerful and novel method to diagnose threats to communities and understand changes in ecosystem dynamics.

publicNov 2022View details →

Saw Kill river (NY, USA) metagenomics and environmental variables

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publicNov 2023View details →

Odontocete detections and corresponding values of environmental variables in the Hawaiian Archipelago

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