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Figure 1. Most parsimonious phylogeny among 12 in Monophyly and taxonomy of the Neotropical seasonal killifish genus Leptolebias (Teleostei: Aplocheiloidei: Rivulidae), with the description of a new genus

Figure 1. Most parsimonious phylogeny among 12 species of the Rivulidae (tree length, L = 118; consistency index, CI = 0.82; retention index, RI = 0.85). Numbers above branches are bootstrap values.

Figure 2 in Monophyly and taxonomy of the Neotropical seasonal killifish genus Leptolebias (Teleostei: Aplocheiloidei: Rivulidae), with the description of a new genus

Figure 2. Frontal squamation and neuromast pattern in Leptolebias aureoguttatus. Abbreviations: A–H, frontal scales A–H; AIS, anterior infraorbital series; AN, anterior nostril; ARN, anterior rostral neuromast; ASN, anterior supraorbital neuromast; PAS, parietal series; PBS, preorbital series; PN, posterior nostril; PRN, posterior rostral neuromast; PSS, posterior supraorbital series. Scale bar: 1 mm.

Figure 5 in Monophyly and taxonomy of the Neotropical seasonal killifish genus Leptolebias (Teleostei: Aplocheiloidei: Rivulidae), with the description of a new genus

Figure 5. Geographical distribution of Leptolebias in Rio de Janeiro state, south-eastern Brazil: 1, L. marmoratus, L. splendens, and L. opalescens; 2, L. marmoratus and L. opalescens; 3, L. splendens; 4, L. citrinipinnis.

Figure 10 in Monophyly and taxonomy of the Neotropical seasonal killifish genus Leptolebias (Teleostei: Aplocheiloidei: Rivulidae), with the description of a new genus

Figure 10. Leptolebias itanhaensis, male, not preserved; Brazil, Estado de São Paulo, Itanhaém (photo by G. C. Brazil).

opencc-zeroNov 2023View details →

Body size modulates the extent of seasonal diet switching by large mammalian herbivores in Yellowstone National Park

<div> Large mammalian herbivores vary their diets markedly with changes in resource availability yet the ways that seasonal changes in individual foraging behaviors scale up to reconfigure complex trophic networks are poorly understood. Two years of dietary DNA data enabled us to quantify fine-grained dietary variation within and among populations of five large herbivore species at Yellowstone National Park, revealing remarkably strong and significant correlations between body size and five key indicators of diet seasonality (R2 = 0.71–0.80). Data from GPS collars implicated seasonal changes in each species' movement- and habitat-use patterns as potential determinants of foraging constraints and specializations that give rise to the strong allometry in diet composition. Bison and elk showed relatively muted seasonal changes compared to smaller species that exhibited stronger switches. Whereas the taxonomic breadth of individual diets contracted for all species in winter, larger species generally consumed a greater functional diversity of plants and thus maintained more unique dietary niches under resource limitations. </div>

opencc-by-4.0Sep 2023View details →

Data provided in manuscript Mid-Holocene rainfall seasonality and ENSO dynamics over the southwestern Pacific

Here we provide datasets of trace elements (LA-ICP-MS), carbon and oxygen stable isotopes, and greyscale values extracted from stalagmite C132 from Niue Island, covering the mid-Holocene (6.4 to 5.4 ka BP). The dataset includes the speleothem 230Th dates, and layer counting.

opencc-by-4.0Nov 2023View details →

Supplement A. Wolf et al: 'Western Caucasus regional hydroclimate controlled by cold-season temperature variability since the Last Glacial Maximum'

This repository contains all proxy data presented in A. Wolf et al, "Western Caucasus regional hydroclimate controlled by cold-season temperature variability since the Last Glacial Maximum". The data can be used to replicate figures and analyses presented in the main text. Additionally, data can be accessed in the supplement material and in the data availability statement.

opencc-by-4.0Jan 2023View details →

Figure 3 in Seasonal incidence of Raoiella indica Hirst (Acari: Tenuipalpidae) on different varieties of date palm in Kachchh region of Western India

Figure 3. Pattern of distribution of red palm mite, Raoiella indica in different directions on three different varieties (pooled).

opencc-zeroDec 2023View details →

Marmot capture history data and growing season length data

Seasonal environmental conditions shape the behavior and life history of virtually all organisms. Climate change is modifying these seasonal environmental conditions, which threatens to disrupt population dynamics. It is conceivable that climatic changes may be beneficial in one season but result in detrimental conditions in another because life-history strategies vary between these time periods. We analyzed the temporal trends in seasonal survival of yellow-bellied marmots (Marmota flaviventer) and explored the environmental drivers using a 40-y dataset from the Colorado Rocky Mountains (USA). Trends in survival revealed divergent seasonal patterns, which were similar across age-classes. Marmot survival declined during winter but generally increased during summer. Interestingly, different environmental factors appeared to drive survival trends across age-classes. Winter survival was largely driven by conditions during the preceding summer and the effect of continued climate change was likely to be mainly negative, whereas the likely outcome of continued climate change on summer survival was generally positive. This study illustrates that seasonal demographic responses need disentangling to accurately forecast the impacts of climate change on animal population dynamics. We were able to impute body mass for each individual twice during each year following their first capture using a similar approach to Ozgul et al. (2010) (for more details on the modeling procedure see SI Appendix within the main paper). Body mass measurements were log-transformed.

opencc-zeroDec 2023View details →

Patterns in bird and pollinator occupancy and richness in a mosaic of urban office parks across scales and seasons

Urbanization is a leading cause of global biodiversity loss, yet cities can provide resources required by many species throughout the year. In recognition of this, cities around the world are adopting strategies to increase biodiversity. These efforts would benefit from a robust understanding of how natural and enhanced features in urbanized areas influence various taxa. We explored seasonal and spatial patterns in occupancy and taxonomic richness of birds and pollinators among office parks in Santa Clara County, California, USA, where natural features and commercial landscaping have generated variation in conditions across scales. We surveyed birds and insect pollinators, estimated multi-species occupancy and species richness, and found that spatial scale, season, and urban sensitivity were all important for understanding how communities occupied sites. Features at the landscape- and local-scale (i.e., distance to streams or baylands and tree canopy, shrub, or impervious cover, respectively) were the strongest predictors of avian occupancy in all seasons. The pollinator richness index was influenced by local tree canopy and impervious cover in spring, and distance to baylands in early and late summer. We predicted relative contributions of different spatial scales to annual bird species richness by assigning values to simulated sites representing "good" and "poor" quality, based on influential covariates returned by models. Shifting from poor to good quality conditions locally increased annual avian richness by up to 6.8 species with no predicted effect of the quality of the neighborhood. Conversely, sites of poor local- and neighborhood-scale quality in good quality landscapes were predicted to harbor 11.5 more species than sites of good local- and neighborhood-scale quality in poor quality landscapes. Finally, more urban sensitive bird species were gained at good quality sites relative to urban tolerant species, suggesting that urban natural features at the local- and landscape-scales disproportionately benefited them.

opencc-zeroJan 2024View details →

Early-season biomass and weather enable robust cereal rye cover crop biomass predictions

Farmers need accurate estimates of winter cover crop biomass to make informed decisions on termination timing or to estimate potential release of nitrogen from cover crop residues to subsequent cash crops. Utilizing data from an extensive experiment across 11 states from 2016 to 2020, this study explores the most reliable predictors for determining cereal rye cover crop biomass at the time of termination. Our findings demonstrate a strong relationship between early-season and late-season cover crop biomass. Employing a random forest model, we predicted late-season cereal rye biomass with a margin of error of approximately 1,000 kg ha-1 based on early-season biomass, growing degree days, cereal rye planting and termination dates, photosynthetically active radiation, precipitation, and site coordinates as predictors. Our results suggest that similar modeling approaches could be combined with remotely sensed early-season biomass estimations to improve the accuracy of predicting winter cover crop biomass at termination for decision support tools.

opencc-zeroJan 2024View details →

R scripts, input and output data for: Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs

An important part of infectious disease management is predicting factors that influence disease outbreaks, such as R, the number of secondary infections arising from an infected individual. Estimating R is particularly challenging for environmentally transmitted pathogens given time lags between cases and subsequent infections. Here, we calculated R for Bacillus anthracis infections arising from anthrax carcass sites in Etosha National Park, Namibia. Combining host behavioural data, pathogen concentrations, and simulation models, we show that R is spatially and temporally variable, driven by spore concentrations at death, host visitation rates and early preference for foraging at infectious sites. While spores were detected up to a decade after death, most secondary infections occurred within two years. Transmission simulations under scenarios combining site infectiousness and host exposure risk under different environmental conditions led to dramatically different outbreak dynamics, from pathogen extinction (R<1) to explosive outbreaks (R>10). These transmission heterogeneities may explain variation in anthrax outbreak dynamics observed globally, and more generally, the critical importance of environmental variation underlying host-pathogens interactions. Notably, our approach allowed us to estimate the lethal dose of a highly virulent pathogen non-invasively from observational studies and epidemiological data, useful when experiments on wildlife are undesirable or impractical.

opencc-zeroFeb 2024View details →

Data from: Seasonal diet partition among top predators of a small island, Iriomotejima island in the Ryukyu Archipelago, Japan

In general, small islands lack predators because species at higher trophic levels often cannot survive. However, two predators—the Iriomote cat Prionailurus bengalensis iriomotensis, and the Crested Serpent Eagle Spilornis cheela perplexus—live on Iriomotejima Island in the Ryukyu Archipelago, which covers an area of approximately 284 square kilometers. To understand how these two top predators coexist on such a small island with limited resources, we focused on their seasonal feeding habits which are considered crucial for survival in such an island ecosystem. To compare the diets of the Iriomote cat and Crested Serpent Eagle, we used DNA metabarcoding analysis of their fecal samples. In the summer, we identified 16 prey items from Iriomote cat fecal samples, and 15 Crested Serpent Eagle fecal samples. In the winter, we identified 37 and 14 prey items, respectively. Using a non-metric multidimensional scaling (NMDS) and a permutational multivariate analysis of variance (PERMANOVA), our study reveals significant differences in the diet composition at the order level between the predators during both seasons. Furthermore, although some prey items at the species-to-order level overlapped between the two predators, the frequency of occurrence of most prey items differed between them in both seasons. These results suggest that this difference in diets was one of the reasons why the Iriomote cat and the Crested Serpent Eagle coexisted on such a small island.

opencc-zeroMar 2024View details →

Data from: Seasonal bee communities vary in their responses to resources at local and landscape scales: Implication for land managers

Context: There is great interest in land management practices for pollinators; however, a quantitative comparison of landscape and local effects on bee communities is necessary to determine if adding small habitat patches can increase bee abundance or species richness. The value of increasing floral abundance at a site is undoubtedly influenced by the phenology and magnitude of floral resources in the landscape, but due to the complexity of measuring landscape-scale resources, these factors have been understudied. Objectives: To address this knowledge gap, we quantified the relative importance of local versus landscape scale resources for bee communities, identified the most important metrics of local and landscape quality, and evaluated how these relationships vary with season. Methods: We studied season-specific relationships between local and landscape quality and wild-bee communities at 33 sites in the Finger Lakes region of New York, USA. We paired site surveys of wild bees, plants, and soil characteristics with a multi-dimensional assessment of landscape composition, configuration, insecticide toxic load, and a spatio-temporal evaluation of floral resources at local and landscape scales. Results: We found that the most relevant spatial scale and landscape factor varied by season. Early-season bee communities responded primarily to landscape resources, including the presence of flowering trees and wetland habitats. In contrast, mid to late-season bee communities were more influenced by local conditions, though bee diversity was negatively impacted when sites were embedded in highly agricultural landscapes. Soil composition had complex impacts on bee communities, and likely reflects effects on plant community flowering. Conclusions: Early-season bees can be supported by adding flowering trees and wetlands, while mid to late-season bees can be supported by local addition of summer and fall flowering plants. Sites embedded in landscapes with a greater proportion of natural areas will host a greater bee species diversity.

opencc-by-4.0Nov 2013View details →

Figs 2, 3 in Seasonal variations in ixodid tick populations on a commercial game farm in the Limpopo Province, South Africa

Figs 2, 3. Numbers of Rhipicephalus (Boophilus) decoloratus collected in wetter and drier months (2), and in warmer and cooler months (3).

opencc-by-4.0Dec 2023View details →

Fig. 2 in Seasonal Variation (Winter Vs. Summer) Crustacean Fauna Of The Oualidia Lagoon, Morocco

Fig. 2. Changes in the composition and structure of the crustacean assemblage between winter and summer: A — abundance (ind./m2); B — species richness; C —diversity of Shannon (H') and (D) evenness (J'). Mean ± standard deviation.

opencc-by-4.0Apr 2024View details →

Argos Platform Transmitter Terminal data for 3 Southern Giant Petrel (Macronectes giganteus) during breeding season in Fildes Peninsula.

Argos Platform Transmitter Terminal raw data for 3 Southern Giant Petrel (Macronectes giganteus) during breeding season in Fildes Peninsula, more specifically in the Islet known as Diomedea or Albatross Islet. All location classes are included. Locations are in EPSG 4326 (WGS 84). All animals had an active nest during data collection.   Data generates with funding from INACH Programa Areas Marinhas Protegidas (24 04 052) and Agencia Nacional de Investigación y Desarrollo, Instituto Milénio Base ICN2021_002

opencc-by-4.0Apr 2024View details →

NASA/GMAO Subseasonal to Seasonal Version 3.0 SYNOBS Contribution

This dataset is a subset of the output of the NASA Global Modelling and Assimilation Office (GMAO) Sub-seasonal-To-Seasonal (S2S) Version 3 coupled ocean/atmosphere forecasting system [see Hackert et al., 2023 for full details].  This system, consists of the Goddard Earth Observing System (GEOS) general circulation atmosphere model which is coupled to the GFDL MOM5-based general circulation ocean model.  The S2S-3 data assimilation is weakly coupled assimilation meaning both the ocean and atmosphere have complete data assimilation systems that are coupled through the model.   Both the ocean and atmosphere also have comprehensive observation data sets as well as diagnostic codes. Many of the features of this latest version of the model are duplicated from the previous version [Molod et al., 2020].   However, S2S-3 has several improvements upon the previous version and these are highlighted below.    •   Ocean Model: Although the S2S-3 continues to use the  GFDL Modular Ocean Model-5 (MOM5), the resolution is improved to global 0.25°x0.25° in the horizontal and 50 levels.  ([Griffies et al., 2005], [Griffies, 2012]). The ocean component has nominal 10 m resolution in the upper 100 m, with expanding thicknesses down to ~5600 m, and employs the non-local K-profile parameterization of [Large et al., 1994] and a parameterization of tidal mixing.  Horizontal mixing uses the isoneutral method of [Gent and McWilliams, 1990].  The horizontal viscosity uses the anisotropic scheme of [Large et al., 2001] for better representation of equatorial currents, upwelling and mixing.   •    Atmosphere Model: The atmospheric model is now the “Icarus generation” Goddard Earth Observing System (GEOS) atmospheric general circulation model with 72 layers and approximately 0.5° resolution ([Rienecker et al., 2008], [Molod et al., 2015]).  The atmosphere is replayed [Orbe et al., 2017] (i.e., similar to nudging) to an atmospheric model known as the GEOS-IT (for Goddard Earth Observing System, Instument Team) using the technique of “Dual Ocean”.  •    Dual Ocean: The S2S-3 weakly coupled data assimilation system includes a new feature that is called "Dual Ocean". The term Dual Ocean refers to the use of both a "Data Ocean" component that reads the observation-based surface temperature and sea ice fraction that the atmospheric assimilation system's model used, and a "real ocean" component, or MOM5. In the first component of the Dual Ocean scheme the atmospheric model component "sees" the observed sea surface temperature (SST) and sea ice (SICE) rather than the predicted values from MOM5 and CICE4 (i.e., the ice model). As the S2S-3 coupled assimilation "replays" to a pre-computed atmospheric assimilation, it is critical for the computation of the turbulent surface fluxes to preserve the near-surface gradients. •    Atmosphere Ocean Interface Layer: Since the ocean model’s vertical grid is 10m thick at the top layer and up to 100m depth, its representation of SST diurnal cycle is inadequate.  The Atmospheric Ocean Interface Layer (AOIL) in GEOS implements a prognostic model for skin SST which builds the ocean model top level temperature, and it does that in a way that preserves the heat flux budget [Akella and Suarez, 2018].   The control S2S-3 system routinely assimilates a wide range of global ocean in situ and satellite observations. Here we briefly list all assimilated observational data sets. In situ temperature and salinity are provided by 1) tropical moorings from Tropical Atmosphere Ocean/Triangle Trans Ocean Buoy Network (TAO/TRITRON - [McPhaden et al., 2010]), Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA - [McPhaden et al., 2009]), and the PIlot Research moored Array in the Tropical Atlantic (PIRATA - [Servain et al., 1998]) for the Pacific, Indian, and Atlantic Oceans, respectively.  All moorings maintain an array of surface meteorological observations and subsurface thermistor chains, while many moorings include salinity measurements. 2) The Argo float array, which provides profiles of temperature and salinity to 2 km depth every few degrees on average, every 10 days ([Roemmich et al., 2009]).   These in situ measurements are supplemented by a smaller amount of shipborne quality-controlled profile temperature and salinity observations from Conductivity/Temperature/Depth (CTD) profilers and temperature profiles from expendable bathythermographs (XBT) [Good et al., 2013].   Along-track (Level 2) sea level (SL) data are obtained from the Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO, <a href="https://www.aviso.altimetry.fr/data/products/sea-surface-height-products/global/along-track-sea-level-heights.html">https://www.aviso.altimetry.fr/data/products/sea-surface-height-products/global/along-track-sea-level-heights.html</a>) ), combined with gravity data from the Gravity and Ocean Explorer (GOCE - [Johannessen et al., 2003]) and the Gravity Recovery and Climate Experiment (GRACE - [Tapley et al., 2004]), and assimilated as absolute dynamic topography (ADT).  Over the period of our reanalysis experiments, we include all available satellite sea level data that were available. In addition to all in situ profiles of temperature and salinity and altimetry data, the production GEOS-S2S-3 system routinely assimilates all available along-track satellite SSS from Aquarius ([NASA_Aquarius_Project, 2017])  for 2014 – June, 2015, the Soil Moisture Active Passive (SMAP) [Fore et al., 2016] for April 2015-present, and Soil Moisture/Ocean Salinity (SMOS) [Boutin et al., 2018] for the entire period of this study from June 2014 to the end of 2015.     The GMAO ocean reanalysis system  assimilates the ocean observation sets using a technique similar to the Local Ensemble Transform Kalman Filter (LETKF) implementation of [Penny et al., 2013].  Our implementation of the LETKF is applied on a 5-day assimilation cycle with twenty fixed ensemble members from a free-running coupled experiment (with similar model setup as S2S-3 but without any assimilation) which has realistic ENSO characteristics. The advantage of this ensemble Kalman Filter ocean data assimilation system (ODAS) over a less expensive deterministic filter such as the 3-dimensional variational (3DVar) data assimilation approach is that it allows the error covariances to evolve with the seasonal cycle and the phase of ENSO more accurately since the twenty ensembles are recentered around the current model ocean state. We localize these error covariances to eliminate spurious correlations between distant grid points and inflate the error covariances to prevent the ensemble members from becoming too similar [Houtekamer and Zhang, 2016].   For profile data, we only localize in the horizontal, with a decorrelation length-scale that is proportional to the Rossby deformation radius [Chelton et al., 1998].     The quality of the initial conditions also depends on our specification of observation error (the sum of the intrinsic instrument error and the error due to physical processes such as internal waves that are unresolved in our system  [Janjić et al., 2018]).  Within the data assimilation code, profile data are assigned observational error depending on the depth gradient of the observation. For the S2S-3 ODAS code, vertical temperature and salinity gradients are scaled by a factor of 10 to give the final profile observation error. In this way, the highest observation errors are assigned at depths where the thermocline and hence the greatest uncertainty resides. Vertical localization is turned off for profile data. This has the benefit of calculating the analysis only once (as opposed to 40 times for 40 levels) and unique vertical localization profiles for each observation type are no longer required.  This technique has the additional benefit of allowing assimilation of vertical profiles and satellite altimetry data within a single ODAS process. While the assimilation of most profile observations is quite straightforward, assimilating sea level as absolute dynamic topography (ADT) is a little more complicated. ADT’s main impact on the ocean state is through its covariance with temperature and salinity (and thus the depth of the pycnocline). Because of the sheer volume of the data, ADT observations are thinned prior to assimilation (see <a href="https://www.aviso.altimetry.fr/data/products/sea-surface-height-products/global/along-track-sea-level-heights.html">https://www.aviso.altimetry.fr/data/products/sea-surface-height-products/global/along-track-sea-level-heights.html</a> for resolution details).  A Gaussian weighted mean is calculated for the central point of +/-10 along-track observations using a decorrelation scale of 1000 km.  This mean is then output for assimilation. The observation error we assign to ADT is estimated via the variability of the data within the Gaussian length scale and increases from < 2 cm at the equator to a maximum of 10 cm at high latitudes to reflect the decreasing error covariance between ADT and density. Finally, the observed mean sea level trend is added back after assimilation to prevent the sea level observations from affecting the time-mean barotropic circulation. For SSS assimilation, SMOS, Aquarius, and SMAP have effective resolutions of 50 km, 100 km, and 40 km, respectively. However, the observation error is treated differently than for ADT. For SSS assimilation, we simply use the error provided by the various product teams.

opencc-by-4.0May 2023View details →

F I G U R E 2 Fitted logistic curves with 95 in Circadian and seasonal flight activity differences between the sexes of the biocontrol agent Eadya daenerys (Hymenoptera: Braconidae) and the impact of host size on adult emergence

F I G U R E 2 Fitted logistic curves with 95% confidence intervals for the effect of Paropsisterna agricola beetle prepupal weight (mg) for three post-beetle prepupal outcomes (dead beetle prepupa, beetle or E. daenerys wasp).