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opencc-by-4.0Jan 2022View details →

Terrestrial paleoclimate reconstruction of the UK Neogene (?Langhian to Piacenzian) comparing CREST, CRACLE and the Co-existence Approach

Abstract  The first detailed reconstruction of the terrestrial paleoclimate development of the UK Neogene (?Langhian to Piacenzian) is presented. The paleoclimate data are derived from the paleobotanical record using two probability-based reconstruction techniques CREST (Climate REconstruction SofTware) (Chevalier et al. 2014) and CRACLE (Climate Reconstruction Analysis using Coexistence Likelihood Estimation) (Harbert & Nixon 2015) that use Bayesian and likelihood estimation probability respectively. The results of these reconstructions are presented alongside reconstructions using the widely-applied Co-existence Approach (CA) (Utescher et al. 2014) for comparison. While all three techniques use the climate requirements of their Nearest Living Relatives as the basis of their reconstruction, they use different database observations. CREST and CRACLE use the GBIF (Global Biodiverstiy Information Facility) (GBIF, 2021) as well as WorldClim inputs for the 19 bioclimate variables used by BIOCLIM (<a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>). Meanwhile, the CA uses the Palaeoflora database, meaning the input for the three models is different. The reconstructions for the UK Neogene palaeoclimate come from 4 localities (12 samples total) spanning the Middle Miocene (Langhian) to Pliocene (Piacenzian): Trwyn y Parc, Anglesey (Middle Miocene), Brassington Formation, Derbyshire (Serravallian-Tortonian), Coralline Crag Formation (latest Zanclean-earliest Piacenzian) and Red Crag Formation (Piacenzian-Gelasian) of southeast England. We present CREST and CRACLE reconstructions of Mean Annual Temperature (MAT), Mean Temperature of Warmest Quarter (MTWQ), Mean Temperature of Coldest Quarter (MTCQ), Mean Annual Precipitation (MAP) and precipitation seasonality (CoV ×100). The CA does not reconstruct MTWQ, MTCQ or precipitation seasonality. Instead, the CA reconstructs Warmest Month Mean Temperature (WMMT) and Coldest Month Mean Temperature (CMMT). The proportion of rainfall falling in the wettest months of the year (RMPwet(%)) was used as a proxy for precipitation seasonality following the methodology of Jacques et al. (2011) and Utescher et al. (2015). The CREST R-code output provides 0.5 and 0.95 (2-σ) uncertainties as well as an optimum and mean for each variable. The CRACLE R-code output provides both parametric and non-parametric joint likelihoods (P-CRACLE and N-CRACLE) with 0.95 (2-σ) uncertainties and a mean that is based on P-CRACLE. The CA generates a minimum and maximum likelihood which together comprise the coexistence interval. The Neogene climate reconstruction of the UK shows a cooling trend from the Langhian to the Pliocene-Pleistocene boundary. CREST and CRACLE produce trends and values consistent with Co-existence Approach data with 0.95 uncertainties overlapping with the CA coexistence interval. File Descriptions  Table S1 displays the complete reconstruction for the UK Neogene using CREST, CRACLE and the Co-existence Approach. Table S2 displays detailed site information including: modern and paleo latitude and longitude, dating technique, modern climatology and fossil assemblage diversity (number of fossil taxa versus number of NLRs used for climate reconstruction). Modern climatology has been included to serve as a comparison to the reconstructed Neogene climate. This data has been extracted from WorldClim 2.1 (Fick & Hijmans, 2017). Data Set S1 contains the list of fossil spore and pollen taxa per site and associated Nearest Living Relatives (NLRs), where identifiable, used as the input for CREST, CRACLE and the Co-existence Approach. Relic taxa are included and highlighted in red. Data Set S2 is included to show the effect relic taxa have on paleoclimate reconstructions. The relic taxa are removed following the protocol of Utescher et al. (2014) whereby known relic taxa are removed from analyses to avoid biased reconstructions. Relic taxa removed from analyses include Cathaya, Cryptomeria, Pinus sylvestris and Sciadopitys when present. Data Set S3 is included to show the effects of removing family-level identifications in CRACLE reconstructions. Removing families is shown to generate a less informative reconstruction. Including both genera- and family-level classifications of NLR (Nearest Living Relative) is recommended, however we suggest identifying NLRs (Nearest Living Relatives) to genera-level wherever possible.

opencc-by-4.0Jan 2022View details →

A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation

This repository contains the datasets used in the research article "A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation". The repository contains the following files: 1) Input - contains all the processed input used for training the deep learning models and the datasets used for creating the figures in the article. 2) Output - contains the final deep learning models and the outputs (evaporation and transpiration stress factor) outputs from the hybrid model developed in the study. Formats: All scripts are in the programming language Python. The datasets are in HDF5 and NetCDF file formats. The codes related to the research article and deep learning model are available in the following repository: https://github.com/akashkoppa/StressNet

Fig. 12 in Morphometry Of The Digestive Gland Of Terrestrial Mollusks Cornu Aspersum (Gastropoda, Helicidae)

Fig. 12. Digestive ducts of Cornu aspersum with different amounts of brown granules (Weigert's resorcinfuchsin): ducts with high (1) and low (2) brown granule content.

Fig. 9 in Morphometry Of The Digestive Gland Of Terrestrial Mollusks Cornu Aspersum (Gastropoda, Helicidae)

Fig. 9. Parallel rows of straight muscle bundles (arrows) in the parenchyma of digestive gland of Cornu aspersum (aldehyde-fuchsin after Gabe-Dyban).

Fig. 6 in Morphometry Of The Digestive Gland Of Terrestrial Mollusks Cornu Aspersum (Gastropoda, Helicidae)

Fig. 6. Structure of the digestive gland capsule of Cornu aspersum with two muscle bundle layers (Heidenhain's azan): internal muscle bundle layer (1), outer muscle bundle layer (2), collagen fibers layer (3), collagen fibers in muscle layer (4), epithelial layer (5), epithelial microciliae (6).

Fig. 4 in Morphometry Of The Digestive Gland Of Terrestrial Mollusks Cornu Aspersum (Gastropoda, Helicidae)

Fig. 4. Collagen fibers between digestive ducts of the digestive gland of Cornu aspersum (Heidenhain's azan): digestive duct (1), collagen fibers (2).

Fig. 2 in Morphometry Of The Digestive Gland Of Terrestrial Mollusks Cornu Aspersum (Gastropoda, Helicidae)

Fig. 2. Structure of the digestive gland of Cornu aspersum snail (haematoxylin-eosin): hemocoelical ducts (1), digestive ducts (2), stomach duct (3).

Fig. 5 in Morphometry Of The Digestive Gland Of Terrestrial Mollusks Cornu Aspersum (Gastropoda, Helicidae)

Fig. 5. Collagen fibers in the parenchyma of the digestive gland of Cornu aspersum (Heidenhain's azan): digestive duct (1), stomach duct (2), collagen fibers (3), supporting cells of haemocelical duct (4).

Fig. 11 in Morphometry Of The Digestive Gland Of Terrestrial Mollusks Cornu Aspersum (Gastropoda, Helicidae)

Fig. 11. Topography of vacuoles and brown granules in the digestive gland of Cornu aspersum (Potassium ferricyanide after Schmorl): Calcium cell (1), digestive cells (2), large brown granules (3), formation of large granules by small granules joining together (4).

Fig. 3 in Morphometry Of The Digestive Gland Of Terrestrial Mollusks Cornu Aspersum (Gastropoda, Helicidae)

Fig. 3. Structure of ducts of the digestive gland of Cornu aspersum (stained by haematoxylin-eosin): digestive cells (1), Calcium cells (2), vacuoles without brown granules (3), vacuoles with brown granules (4), duct lumen (5), inter-duct connective tissue (6), cells of inter-duct connective tissue (7).

Fig. 10 in Morphometry Of The Digestive Gland Of Terrestrial Mollusks Cornu Aspersum (Gastropoda, Helicidae)

Fig. 10. Vacuoles and granules in digestive ducts of Cornu aspersum (PAS reaction): Calcium cell (1), digestive cell (2), large brown granules (3), small granules joining into large granules (4).

Fig. 8 in Morphometry Of The Digestive Gland Of Terrestrial Mollusks Cornu Aspersum (Gastropoda, Helicidae)

Fig. 8. Muscle bundles in parenchyma of the digestive gland of Cornu aspersum (aldehyde-fuchsin after Gabe-Dyban): straight muscle bundles (1), horseshoe-shaped muscle bundles (2).

opencc-by-4.0Apr 2022View details →

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).

dryad40/100

Data from: Divergent physiological acclimation responses to warming between two co-occurring salamander species and implications for terrestrial survival

Small differences in physiological responses are known to influence demographic rates such as survival. We tested for differences in the physiological acclimation responses of two closely-related salamander species that often co-occur, Ambystoma maculatum and A. opacum. Specifically, we measured changes in critical thermal maxima (CTmax), standard metabolic rates (SMRs), and respiratory surface area water loss (RSAWL) following exposure to three temperature treatments under laboratory conditions. While the magnitude of RSAWL and CTmax acclimation responses to warming did not differ between the study species, SMR was maintained across acclimation temperatures among A. maculatum, but declined among A. opacum acclimated to warmer temperatures. Metabolic compensation may facilitate maintained A. maculatum activity levels during warm periods following the relatively cool spring breeding season. In contrast, metabolic suppression may allow A. opacum to conserve energy when exposed to surface conditions during fall breeding and nest guarding. We simulated how these different SMR responses would likely alter post-metamorphic survival in our study species using previously collected data representing six weeks under relatively warm seminatural conditions. Our simulation indicated that, following warming and under identical study conditions, metabolic compensation may allow juvenile A. maculatum to maintain survival likelihoods, whereas metabolic depression may cause juvenile A. opacum to experience increased survivorship. These findings underscore that comparable physiological responses among ecologically similar, sympatric species cannot be assumed. Further, results of this study suggest that metabolic responses may play an important role in amphibian species persistence as temperatures increase due to habitat modification and climate change.

opencc-zeroApr 2022View details →

Fig. 6 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change

Fig. 6. Result of the analysis of Binomial tests (CliMond 2090 (2081–2100)): A — T. graeca; B — T. hermanni.

Fig. 3 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change

Fig. 3. Niche clustering (Geographic space, CliMond 1975 (1970–2000)) from: A — T. graeca (1. T. g. ibera, 2. T. nikolskii, 3. T. g. anamurensis, 4. T. g. floweri, 5. T. g. antakyensis, 6. T. g. pallasi, 7. T. g. armenica, 8. T. g. perses, buxtoni, 9. T. g. terrestris); B — T. hermanni (1. T. h. hermanni, 2. T. h. hervegovinensis, 3. T. h. boettgeri), red circles showing the approximate ranges of subspecies according to "Turtles…, 2017" World" (2017).

Fig. 2 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change

Fig. 2. The "Ecological envelope" — relationship bio01 "Annual mean temperature", °C & bio12 "Annual precipitation", mm (DivaGis): A — T. graeca; B — T. hermanni.

Fig. 5 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change

Fig. 5. Potential (probabilistic) model of T. hermanni world expansion built in the Maxent program based on the CliMond: A — 1975 (1970–2000); B — 2090 (2081–2100)) climatic data and GBIF data (2021). Areas of the highest habitat suitability (> 0.3–0.5) are colored in red and areas of the lowest (<0.2) — in blue (SAGA GIS).

Fig. 4 in Gis Modelling Of The Distribution Of Terrestrial Tortoise Species: Testudo Graeca And Testudo Hermanni (Testudines, Testudinidae) Of Eastern Europe In The Context Of Climate Change

Fig. 4. Potential (probabilistic) model of T. graeca expansion built in the Maxent program based on the CliMond: A — 1975 (1970–2000); B — 2090 (2081–2100)) climatic data and GBIF data (2021 a). Areas of the highest habitat suitability (> 0.3–0.5) are colored in red and areas of the lowest (<0.2) — in blue (SAGA GIS).