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3,136 results for “Terrestrialization”
Living on the edge: Predicting invertebrate richness and rarity in disturbance-prone aquatic–terrestrial ecosystems
<p>1. Temporal fluctuations in water levels cause the spatial extent of wet and dry habitats to vary in aquatic–terrestrial riverine ecosystems, complicating their biomonitoring. As such, biomonitoring efforts may fail to characterise the species that inhabit such habitats, hampering assessments of their biodiversity and implementation of evidence-informed management strategies.</p> <p>2. Relationships between the dynamic characteristics of aquatic-terrestrial habitats and their communities are well known. Thus, habitat characteristics may enable estimation of faunal assemblage characteristics such as taxonomic richness, regardless of in-channel conditions.</p> <p>3. We investigated whether indicators summarising habitat survey data can predict two metrics representing terrestrial invertebrate assemblages (e.g. taxa richness) in two aquatic–terrestrial habitats: exposed riverine sediments and dry temporary streams. We also compared the performance of unimetric and multimetric habitat indicators in making predictions.</p> <p>4. In exposed riverine sediments, >88% of predictions were correlated with observed taxa richness and an index of conservation status. Values predicted by exposed riverine sediment samples were correlated with those observed in temporary stream channels with comparable riparian (i.e. largely agricultural) land use, but not those observed in channels with contrasting (i.e. more urban) land use.</p> <p>5. Unimetric habitat indicators performed similarly to more complex multimetric indicators, with each explaining ≤6% of the variability in taxa richness and the index of conservation status. The different spatial scales at which invertebrates respond to habitat conditions and at which indicators record habitat conditions, and a more comprehensive training dataset that incorporates a full range of habitat conditions (i.e. land use), may improve future predictions.</p> <p>6. We demonstrate that invertebrate assemblage characteristics can be predicted regardless of in-channel conditions. Agreement between exposed riverine sediment predictions and temporary stream observations suggests that these predictions are transferable among a range of aquatic–terrestrial habitat types, and could thus be widely applied to aid conservation of riverine biodiversity in dynamic aquatic–terrestrial ecosystems.</p>
Data for: Geographic isolation reduces genetic diversity of a wide-ranging terrestrial vertebrate, Canis lupus
<p>Genetic diversity is theorized to decrease in populations closer to a species' range edge, where habitat may be suboptimal. However, generalist species capable of long-range dispersal may maintain sufficient gene flow to counteract this, though the presence of significant barriers to dispersal (e.g., large water bodies, human-dominated landscapes) may still lead to, and exacerbate, the edge effect. We used microsatellite data for 2,426 gray wolves (<em>Canis</em> <em>lupus</em>) from 24 sub-populations (groups) to model how allelic richness and expected heterozygosity varied with two measures of range edge (mainland-island position, latitude, and distance from range center) across >7.3 million km<sup>2</sup> of northern North America. We found that allelic richness and expected heterozygosity of island groups was measurably less than that of mainland groups and that these differences increased with the island's distance to the species' range center in the study area. Our results demonstrate how multiple axes of geographic isolation (distance from range center and island habitation) can act synergistically to erode the genetic diversity of wide-ranging terrestrial vertebrate populations despite the counteracting influence of long-range dispersal ability. These findings emphasize how geographic isolation is a potential threat to the genetic diversity and viability of terrestrial vertebrate populations even among species capable of long-range dispersal. </p> <p>This dataset is comprised of four separate sub-datasets, sourced from Carmichael et al. (2007), Musiani et al. (2007), McNay (2006), and a manuscript in progress (referred to as "MacNulty" in the data files, to be first published in Frevol et al. 2023). The raw data are comprised of sample IDs, latitude and longitude points indicating where the sample was collected or recorded, and microsatellite genetic information. To aid in re-use, the raw genetic data has also been formatted and presented for use with common population genetics software (Genepop, MICROCHECKER, Genetix, FSTAT). The dataset also includes spatial data files of the sub-populations described in the study, allelic richness and expected heterozygosity data derived from the raw and spatial data, and the R script used to create the models. </p> <ul> <li>Carmichael LE, Krizan J, Nagy JA, et al (2007) Historical and ecological determinants of genetic structure in arctic canids. Molecular Ecology 16:3466–3483. https://doi.org/10.1111/j.1365-294X.2007.03381.x</li> <li>McNay ME (2006) Preliminary results of parentage analysis using microsatellite markers from an exploited wolf population in central Alaska. Alaska Department of Fish and Game, Division of Wildlife Conservation, Juneau, Alaska</li> <li>Musiani M, Leonard JA, Cluff HD, et al (2007) Differentiation of tundra/taiga and boreal coniferous forest wolves: genetics, coat colour and association with migratory caribou. Molecular Ecology 16:4149–4170. https://doi.org/10.1111/j.1365-294X.2007.03458.x</li> </ul>
Datasets from: Local- and landscape-scale drivers of terrestrial herbaceous plant diversity along a tropical rainfall gradient in Western Ghats, India
<p>This data set contains information on local- and landscape-level terrestrial herbaceous plant diversity and critical abiotic factors along a 36-km East-West transect in Mudumalai Tiger Reserve, Tamil Nadu India. Understory angiosperms with no above-ground wood (secondary cambial growth) were considered as herbaceous plants.</p>
What is a mammalian omnivore? Insights into terrestrial mammalian diet diversity, body mass, and evolution
<p>Mammalian omnivores are a broad and diverse group of mammals that are often lumped together in ecological studies. As a result, there are open questions about what macroevolutionary and macroecological information can be gained when omnivorous dietary differences are investigated in ecological and phylogenetic comparative studies. In this study, we investigate the frequency at which vertebrate prey, invertebrate prey, fibrous plant material, and non-fibrous plant material co-occur in the diets of omnivorous species. We quantify the body size distributions and phylogenetic signal of terrestrial mammals that consume different omnivorous diets and using multistate reversible jump MCMC, we assess the transition rates between mammalian diet strategies on the mammalian phylogenetic tree. We find that omnivores that consume all four food types are rare and most omnivorous mammals consume only invertebrate prey and non-fibrous plants. We also find that omnivores that only consume invertebrate prey are on average smaller than omnivores that incorporate vertebrate prey as many are from within Rodentia. Our transition rate models show that there are high transition rates from invertivorous omnivory to herbivory, and from vertebrate predation to prey mixing and ultimately invertivory. Our results suggest that prey type is an important aspect of omnivore macroevolution and macroecology, as it is correlated with body mass, evolutionary history, and diet-related evolutionary transition rates.</p>
Data from: Phylogenetic structure of the extinction and biotic factors explaining differential survival of terrestrial notosuchians at the Cretaceous-Palaeogene crisis
<p><span>Although the clade Crocodylomorpha is represented by few extant species (Crocodylia), it has a rich fossil record. Hundreds of species adapted to terrestrial, semi-aquatic and marine environments, have existed over more than 200 million years. Numerous studies have attempted to characterize the factors driving the diversification and extinction events of Crocodylomorpha, resulting in ambiguous and even contradictory conclusions, which points to the need for phylogenetically and temporally smaller-scaled studies. Here, we focus on differential survival at the Cretaceous-Palaeogene crisis of Notosuchia, a diverse clade of mostly terrestrial Crocodylomorpha </span><span>that</span><span> achieved great diversity during the Cretaceous. More precisely, we tested the effect of body size and palaeotemperatures on notosuchian survival probability during the K-Pg crisis as well as the effect of diet on the evolution of their body size. We find that Notosuchia showed an evolutionary trend towards larger body sizes through time, associated with a shift from an omnivorous to a carnivorous diet. This may explain why sebecids were the only notosuchians to survive the K-Pg crisis. We also corroborate conclusions of previous studies that detected a Lagerstätten effect occurring in the Adamantina Formation (Upper Cretaceous, Brazil, Bauru Group). This work confirms the value of more finely-scaled macroevolutionary studies for understanding the history of a rich and complex group </span><span>such as</span><span> Crocodylomorpha.</span></p>
Supplementary Information for "Mapping potential conflicts between global agriculture and terrestrial conservation"
<p>This archive contains supplementary figures from mapping potential conflicts between global agriculture and terrestrial conservation in 2010 and 2070. Please see the captions of these figures below.</p> <p><strong>Supplementary Figure 3.</strong> Distribution of regional land use by agricultural commodity and conservation priority (CP) index intervals (48 x 3 sub-figures).<br> <strong>Supplementary Figure 4.</strong> Distribution of regional land use (left) and global land use (right) of 48 agricultural commodities (48 x 3 sub-figures). The x-axis is the land use as a proportion of the total global production area.<br> <strong>Supplementary Figure 5.</strong> Comparison of the distribution of regional land use for 48 agricultural commodities between 2010 and 2070 scenarios (48 x 7 x 2 sub-figures).<br> <strong>Supplementary Figure 6.</strong> Comparison of land use and conservation conflict between national and global levels. The y-axis refers to the land use as a proportion of the total global production area. There are 48 agricultural commodities for 197 countries (197 x 48 x 3 sub-figures).<br> <strong>Supplementary Figure 7.</strong> Comparison of the land use distribution of top producers for 48 agricultural commodities between 2010 and 2070 scenarios (48 x 10 x 2 sub-figures).<br> <strong>Supplementary Figure 8.</strong> Spatial distribution maps of production areas for 48 agricultural commodities (48 x 3 maps).<br> <strong>Supplementary Figure 9.</strong> Conflict between conservation priority sites and (non-)domestic land use for 42 agricultural commodities associated with consumption in 197 countries (197 x 42 x 3 sub-figures).<br> <strong>Supplementary Figure 10.</strong> Land use maps of 42 agricultural commodities linked to consumption in 197 countries (197 x 42 x 3 maps).<br> <strong>Supplementary Figure 11.</strong> Conservation priority index and export rate of selected agricultural commodity for primary production cells (land use of each agricultural commodity > 10% of cell area). <strong>a</strong>) Current status in 2010. <strong>b</strong>) Shifts of conservation priority in 2070 (RCP 8.5). Triangles point-up and point-down to indicate increased and decreased CP, respectively. Density plots on top and right show cell densities corresponding to export rate and CP index, respectively.<br> <strong>Supplementary Figure 12.</strong> Maps of global conservation priority index in 2010 and 2070.<br> <strong>Supplementary Figure 13. a</strong>, Performance curves for prioritization per scenario 2010, 2070-RCP2.6, and 2070-RCP8.5). Curves show the average fraction of species' range covered in each scenario weighted by each species weight, divided by the total weight of species (y-axis), by a given fraction of the landscape (x-axis). <strong>b</strong>, Histograms and boxplots of all CP map pixels. Each jittered point in boxplots represents a map pixel.</p>
Fig. 28 in Terrestrial mollusc species richness and diversity in Omo Forest Reserve, Ogun State, Nigeria
Fig. 28. Dendrogram of Bray-Curtis similarity between plots in sites I and II.
Fig. 2 in Terrestrial mollusc species richness and diversity in Omo Forest Reserve, Ogun State, Nigeria
Fig. 2. Rank abundance curve for terrestrial snails from Omo Forest Reserve in Ogun State, Nigeria.
Embracing fine-root system complexity in terrestrial ecosystem modelling
<p>Model outputs for manuscript " <strong>Embracing fine-root system complexity in terrestrial ecosystem modelling</strong>".</p>
A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data
<p> </p> <p>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data.</p> <p>Introduction</p> <p>This dataset is the Planet Labs PBC (VanderSat B.V.) contribution to the ESA 4DMED hydrology project (<a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org/</a>). It includes Soil Moisture, Land Surface Temperature and Vegetation Optical Depth for the 4DMED spatial domain and time period (2015-2021) at 1km pixel size. If you use the data please include the following reference:</p> <blockquote> <p>Jaap Schellekens, Tessa Kramer, Michel van Klink, Robin van der Schalie, Yoann Malbeteau, Arjan Geers, Richard de Jeu. (2022) <em>A 1km experimental dataset for the Mediterranean terrestrial region of Soil Moisture, Land Surface Temperature and Vegetation Optical Depth from passive microwave data</em>. DOI: 10.5281/zenodo.7684993. Planet Labs PBC/VanderSat B.V., ESA Contract No. 4000136272/21/I-EF</p> </blockquote> <p> </p> <p><em>Figure 1: Average L-Band Soil moisture for 2020 over the 4dmed spatial domain</em></p> <p>Variables and files</p> <p>The dataset consists of the following files and products for the 4DMED domain. Detailed information about the products can also be found at <a href="https://docs.vandersat.com/data_products/soil_water_content/specification.html">docs.vandersat.com</a>:</p> <ul> <li><strong><code>planet-teff-4dmed-V4.0.zip</code></strong> - LST (TEFF) ascending (daytime) and descending (nighttime) <ul> <li><code>TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime (13:30 solar time) at 1 km</li> </ul> </li> <li><code>TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature nighttime (01:30 solar time) at 1 km</li> </ul> </li> </ul> </li> <li><strong><code>planet-teff-qf-4dmed-V4.0.zip</code></strong> - LST (TEFF) quality flags <ul> <li><code>QF-TEFF-AMSR2-ASC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> <li><code>QF-TEFF-AMSR2-DESC_V4.0_1000</code> <ul> <li>Land surface temperature daytime quality flag. <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">docs.vandersat.com flags</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">docs.vandersat.com python example</a></li> </ul> </li> </ul> </li> <li><strong><code>planet-vod-4dmed-V4.1.zip</code></strong> - vegetation optical depth C and X band (interpolated from C3S passive soil moisture) <ul> <li><code>VOD_AMSR2_C1_DESC_V41_1000</code> <ul> <li>C1 band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> <li><code>VOD_AMSR2_X_DESC_V41_1000</code> <ul> <li>X band Vegetation Optical Depth (nighttime, 01:30 solar time) at 1km (interpolated from 25 km)</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-4dmed-V4.0.zip</code></strong> - All soil moisture products (C1, X and L-band) <ul> <li><code>SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-qf-4dmed-V4.0.zip</code></strong> - Soil moisture quality maps see <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html</a> and <a href="https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python">https://docs.vandersat.com/data_products/soil_water_content/data_flags.html#decoding-a-flag-file-using-python</a> <ul> <li><code>QF-SM-AMSR2-C1-DESC_V4.0_1000</code> <ul> <li>C1 band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-AMSR2-X-DESC_V4.0_1000</code> <ul> <li>X band soil moisture (nighttime, 01:30 solar time) at 1km</li> </ul> </li> <li><code>QF-SM-SMAP-L-DESC_V4.0_1000</code> <ul> <li>L band soil moisture quality flags (06:00 solar time) at 1km</li> </ul> </li> </ul> </li> <li><strong><code>planet-sm-cor-4dmed-V4.0.zip</code></strong> - Yearly correlation maps of soil moisture derived from the difference microwave bands. To be used as an extra quality indicator (for example undetected RFI) or for uncertainty estimation <ul> <li><code>SM-CORR-C1-X-DESC_V4.0_1000</code> - yearly C1 vs X band pearson's correlation maps</li> <li><code>SM-CORR-L-C1-DESC_V4.0_1000</code> - yearly L vs C1 band pearson's correlation maps</li> <li><code>SM-CORR-L-X-DESC_V4.0_1000</code> - yearly L vs X band pearson's correlation maps</li> </ul> </li> <li><strong><code>planet-aux-flags-4dmed-V4.0</code></strong> - Extra flags for frozen soil and bare soil. Determined at 0.25 degree and interpolated to the 4dmed grid <ul> <li><code>QF-SNOWFROZEN-AMSR2-ASC_1000::RD</code> - Frozen soil determined from dayttime data</li> <li><code>QF-SNOWFROZEN-AMSR2-DESC_1000::RD</code> - Frozen soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-DESC_1000::RD</code> - Bare soil determined from nighttime data</li> <li><code>QF-BARESOIL-AMSR2-ASC_1000::RD</code> - Bare soil determined from daytime data</li> </ul> </li> </ul> <p>All files are archived into one zip file per product group. Each individual netcdf file in the zip file consists of one observation for the whole domain. If you need you can combine the files into one file using the cdo software <a href="https://code.mpimet.mpg.de/projects/cdo">https://code.mpimet.mpg.de/projects/cdo</a> (e.g. <code>cdo -f nc4c mergetime *.nc outfile.nc</code>).</p> <p>License</p> <p>The data for 4DMED is released under the Creative Commons license: CC BY-NC-SA 4.0 (<a href="https://creativecommons.org/licenses/by-nc-sa/4.0/">https://creativecommons.org/licenses/by-nc-sa/4.0/</a>)</p> <ul> <li>Contains modified Copernicus Sentinel data 2015-2021</li> <li>Contains modified JAXA GCOM-W1/AMSR2 data 2015-2021</li> <li>Contains modified SMAP L1B Radiometer data: Piepmeier, J. R., P. Mohammed, J. Peng, E. J. Kim, G. De Amici, J. Chaubell, and C. Ruf. 2020. SMAP L1B Radiometer Half-Orbit Time-Ordered Brightness Temperatures, Version 4,5. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: <a href="https://doi.org/10.5067/ZHHBN1KQLI20">https://doi.org/10.5067/ZHHBN1KQLI20</a></li> </ul> <p>Contact</p> <p>Jaap Schellekens: <a href="mailto:jaap@planet.com">jaap@planet.com</a></p> <p>Versions</p> <ul> <li>1.0 Initial creation</li> <li>1.1 Adjusted 4DMED Mask. Data itself unchanged but more LST (TEFF) measurements added</li> <li>1.2 Removed VOD and replaced by 25km C3S VOD interpolated to 1km (V4.1)</li> </ul> <p>Further information</p> <p>More information on the data and the flags can be found at <a href="https://docs.vandersat.com/">https://docs.vandersat.com</a> and <a href="https://www.4dmed-hydrology.org/">https://www.4dmed-hydrology.org</a></p> <p>Background publications</p> <p>R.A.M. De Jeu, A.H.A. De Nijs, M.H.W. Van Klink (2016) <em>Method and system for improving the resolution of sensor data</em>, US10643098B2,EP3469516B1, WO2017216186A1</p> <p>De Jeu, R. A., Holmes, T. R., Parinussa, R. M., & Owe, M. (2014). <em>A spatially coherent global soil moisture product with improved temporal resolution</em>. Journal of hydrology, 516, 284-296.</p> <p>Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I. and Forkel, M., 2020. <em>The global long-term microwave vegetation optical depth climate archive (VODCA)</em>. Earth System Science Data, 12(1), pp.177-196.</p> <p>Schmidt, L., Forkel, M., Zotta, R.-M., Scherrer, S., Dorigo, W. A., Kuhn-Régnier, A., van der Schalie, R., and Yebra, M.: <em>Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties, Biogeosciences Discuss.</em> [preprint], <a href="https://doi.org/10.5194/bg-2022-85">https://doi.org/10.5194/bg-2022-85</a>, in review, 2022</p> <p>Van der Schalie, R., de Jeu, R.A.M., Kerr, Y.H., Wigneron, J.P., Rodríguez-Fernández, N.J., Al- Yaari, A., Parinussa, R.M., Mecklenburg, S. and Drusch, M. (2017), <em>The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E</em>, Remote Sensing of Environment, 189, pp.180-193.</p> <p>van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T., Dorigo, W., 2020. Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sensing 12, 3439. <a href="https://doi.org/10.3390/rs12203439">https://doi.org/10.3390/rs12203439</a></p>
Data for: The Neonicotinoid Imidacloprid Impairs Sucrose Solution Consumption, Learning and Locomotor Activity Levels In Bumblebees (Bombus Terrestris)
<p># README</p> <p>The following files are for creating the figures from the paper: </p> <p>## `plot_flowervisits_nectar.ipynb`</p> <p>Jupyter notebook that creates the figures concerning flower visits, nectar consumption and the proportion of empty honeypots.</p> <p>## `plot_activity.py`</p> <p>Python script that takes trajectory fragments from video analysis and computes the locomotor activity level through making histograms of bumblebee speeds. Makes two figures that are equivalent to the figure on locomotor activity in the paper.</p> <p>## `statistical analysis.py`</p> <p>R markdown notebook that performs all the hypothesis testing for the paper.</p> <p>## Data</p> <p>These files contain the data, and are located in the folder called `data`. </p> <p>`activity/activityproportions.csv` contains the computed locomotor activity level for easy plotting.</p> <p>`boldata/boldata.csv` contains data about the nectar bag weight before and after experiment and the counted number of empty and full honeypots. Used by `plot_flowervisits_nectar.ipynb`</p> <p>`flower_data/flowerData.csv` contains the computed number of visits to blue and yellow flowers per hive for easy plotting.</p> <p>`humlevideo_production/*/traj*_trajectories*.csv` contains constructed trajectories from all experiments seen from both cameras. These are being used by the script `plot_activity`.</p> <p>`humlevideo_production/*/traj*.json` contains data about the occurence of bees on flowers in each frame in each experiment.</p> <p>`landinger_csv` contains data about landings, that have been extracted from the `humlevideo_production/*/traj*.json` files. Used by `plot_flowervisits_nectar.ipynb`.</p> <p> </p>
Microhabitat humidity rather than food availability drives thermo-hydroregulation responses to drought in a terrestrial lizard
<p>The content of this archive includes raw data and metadata for the paper "Microhabitat humidity rather than food availability drives thermo-hydroregulation responses to drought in a terrestrial lizard" by Bodineau et al. In this experimental study, we investigated how microclimate conditions and food availability influence thermo-hydroregulation strategies and body condition of lizards (Zootoca vivipara) during a simulated drought. We found that water restriction and food deprivation caused physiological alterations such as muscle catabolism and mobilization of caudal energy reserves. Lizards developed behavioural strategy to counteract water and food deprivation like decreased thermoregulation effort, higher shelter use and increased eye closure behaviours. Hydric quality of the shelter rather than food availability was the main modulator of trade-offs between thermoregulatory and hydroregulatory activities and an important buffer against the deleterious effects of water restriction. These original findings challenge the assumptions of mechanistic models of ectotherms, which are currently calibrated on the thermal biology of ectotherms. They demonstrate the urgent need to take into account the dual effect of warming and drought events, behavioural interactions between thermoregulation and hydroregulation and the buffering role of microclimatic conditions.</p>
Directly dating Plio-Pleistocene climate change in the terrestrial record
<p class="MsoNormal">This is the Supporting Information for Dröllner et al. "Directly dating Plio-Pleistocene climate change in the terrestrial record" published in Geophysical Research Letters. The data comes from a ferruginous induration in the arid landscapes of the Nullarbor Plain in southern Australia (30° 54' 33.84'' S, 132° 13' 5.88'' E). The data provides constraints on the timing of Plio-Pleistocene aridification in the continental realm and supports the use of ferruginous indurations as targets to obtain absolute ages on landscape evolution. The dataset includes chemical-mineralogical results, which suggest that the formation of ferruginous indurations was linked with a decline of the groundwater table. (U-Th)/He geochronology of goethite from ferruginous indurations provides age constraints that link the textural and chemical-mineralogical observations to a rapid climatic shift from humid Late Pliocene to arid Early Pleistocene conditions. Specifically, this Supporting Information includes a pdf file (available through Zenodo; <a href="https://doi.org/10.5281/zenodo.7739575" title="https://doi.org/10.5281/zenodo.7739575">https://doi.org/10.5281/zenodo.7739575</a>) with detailed analytical methods for X-ray powder diffraction (XRD, Text S1), energy-dispersive spectroscopy (Text S2), Raman spectroscopy (Text S3), and (U-Th)/He geochronology (Text S4). This pdf file also includes Supporting Figures S1 (sample material, and fragments used for dating) and S2 (XRD spectra), as well as Supporting Tables S1 (XRD bulk mineralogy), S2 ((U-Th)/He data), S3 (XRD instrument parameters), and the captions for Table S4 (Raman data) and Table S5 (XRD data). The latter two datasets are provided as separate files available through Dryad (this data).</p>
Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM) simulations focused on Eight Mile Lake, Alaska and Imnavait Creek, Alaska [2000-2015]
<p>This set of files store model simulations using the biosphere model Dynamic Vegetation Model Dynamic Organic Soil Terrestrial Ecosystem Model (DVM-DOS-TEM), developed to simulate biophysical and biogeochemical interactions between the soil, vegetation and atmosphere. To improve predictions of net carbon releases from thawing permafrost, we tested the sensitivity of a suite of model parameters. We analyzed the responses of ecosystem carbon balances to permafrost thaw by running site-level simulations at two long-term tundra ecological monitoring sites in Alaska: Eight Mile Lake (EML) and Imnavait Creek watershed (IMN). These sites are characterized by similar tussock tundra vegetation but differing soil drainage conditions and climate, IMN consists of well-drained soils, and EML has historically well-drained soils, however permafrost thaw has altered drainage conditions to wetter soils. Simulations were conducted at a 1km resolution, over a 1,000 km2 area (10x10 km square) centered on two long term ecological research sites in Alaska: Eight Mile Lake located in Interior Alaska (63.8900° N, 149.2535° W), and Imnavait creek watershed located on the northern foothills of the Brooks range (68°37′ N, 149°18′ W).</p> <p>Historical simulations are spanning the 2000 to 2015, and forced using climate simulations from the Climate Research Unit, time series 4.0. We ran 1,000 site level simulations for each model variable. The variables that are produced are gross primary productivity (GPP, in gC.m-2.m-1), net ecosystem exchange (NEE, gC.m-2.m-1), ecosystem respiration (RECO, gC/m2/m-1), active layer thickness (ALT, m), soil temperature (TLAYER,°C) at 5, 10, 40 cm depths, soil moisture (LWCLAYER, m-3/m-3) at 5, 10 cm depths, and snow depth (SNOWDEPTH, m), evapotransipiration(EET, mm/m2/time), potential evapotransipiration (PET, mm/m2/time), leaf area index (LAI, m2/m2), organic layer thickness (OLT, m). The data are stored as compiled csv files, with time as the index, and each model sample output stored in the columns. In addition, there is a postprocessing python script to demonstrate the step and workflow used to generate the individual csv files post processed from the raw model outputs stored as netcdfs.</p>
Input data and model implementation from: How do terrestrial wildlife communities respond to small-scale Acacia plantations embedded in harvested tropical forest?
<p class="MsoNormal"><span>To offset the declining timber supply from shifting towards more sustainable forestry practices, industrial tree plantations are expanding in tropical production forests. The conversion of natural forest to tree plantation is generally associated with loss of biodiversity and shifts toward more generalist and disturbance tolerant communities; but effects of mixed-landuse landscapes integrating natural and plantation forest remain little understood. Using camera traps, we surveyed the medium-to-large bodied terrestrial wildlife community across two mixed-land-use forest management areas in Sarawak, Malaysia Borneo which include areas dedicated for logging of natural forest and adjacent planted <em>Acacia</em> forests. We analysed data from a 25-wildlife species community using a Bayesian community occupancy model to assess species richness and species-specific occurrence responses to <em>Acacia</em> plantations at a broad scale, and to remote-sensed local habitat conditions within the different forest land-use types. All species were estimated to occur in both land-use types, but species-level percent area occupied and predicted average local species richness were slightly higher in the natural forest management areas compared to licensed planted forest. Similarly, occupancy-based species diversity profiles and defaunation indices for both a full community and only threatened and endemic species suggested the diversity and occurrence were slightly higher in the natural forest management areas. At the local scale, forest quality was the most prominent predictor of species occurrence. These associations with forest quality varied among species but were predominantly positive. Our results highlight the ability of a mixed-land-use landscape with small-scale<em> Acacia</em> plantations embedded in natural forest to retain terrestrial wildlife communities while providing an alternate source of timber. Nonetheless, there was a tendency towards reduced biodiversity in planted forests, which would likely be more pronounced in plantations that are larger or embedded in a less natural matrix.</span></p>
Database for terrestrial water system research
<p><strong>Database for terrestrial water system research</strong></p> <p>This database was developed in a study of “<strong>Mohanna Zarei & Georgia Destouni, Research gaps and priorities for tressterial water and Earth System connections from catchment to Global scale, Earth's Future, Wiley, 2024. (https://doi.org/10.1029/2023EF003792)</strong>). It includes data from a scoping literature review, based on searches performed with the Web of Science (WoS) search engine across all available databases. The search was performed on April 22nd 2022, encompassing the fields of title, abstract, authors’ keywords and WoS generated keywords. See <strong>Mohanna Zarei & Georgia Destouni, Research gaps and priorities for tressterial water and Earth System connections from catchment to Global scale, Earth's Future, Wiley, 2024. (https://doi.org/10.1029/2023EF003792)</strong> for further search method and other study descriptions.</p> <p> </p> <p>The database is provided in an Excel (xsls) file, comprising and organised in four sheets as follows:</p> <p>1. Description of the data and project;</p> <p>2. Water flow papers;</p> <p>3. Water storage papers;</p> <p>4. Water quality papers.</p> <p> </p> <p>Column A in each data sheet (2-4) lists the published papers by the end of 2021 that address both ground- and surface water at scales of whole catchment or larger, up to global, for the aspects of water flow (Sheet 2: Water flow papers), water storage (Sheet 3: Water storage papers) or water quality (Sheet 4: Water quality papers), as returned from the automatic WOS search, along with their WOS index (column B) and our article number ID (column C). The other columns in each sheet list manually extracted data regarding the: i) study method (columns D-J), ii) study scale (columns K-N), and iii) study region (columns O-U).</p> <p> </p> <p><strong>Reference</strong></p> <p><strong>Mohanna Zarei & Georgia Destouni, Research gaps and priorities for tressterial water and Earth System connections from catchment to Global scale, Earth's Future, Wiley, 2024. (https://doi.org/10.1029/2023EF003792)</strong></p>
Datasets of eponyms for South American plants and Mozambican terrestrial vertebrates
<p>Two datasets containing information on the Eponyms of South American plants and Mozambique's terrestrial vertebrates.</p>
Dataset for terrestrial climate and vegetation change in the western Tasmanian region from the late Eocene to late Oligocene
<p>Datasets accompanying Terrestrial climate and vegetation change in the western Tasmanian region from the late Eocene to late Oligocene by Amoo et al.</p> <p>Supplementary table S1: Raw palynomorph assemblage data, total counts, ODP Site 1168 </p> <p>Supplementary table S2: Sporomorph-based climate estimates including MAT, WMMT, CMMT and MAP </p> <p>Supplementary table S3: Sporomorph diversity indices</p> <p>Supplementary table S4: Nearest living relatives, botanical affinity, and climate range of individual taxa.</p>
Figs 53–55 in Terrestrial molluscs of Cabo Delgado and adjacent inland areas of north-eastern Mozambique
Figs 53–55. Trochozonites (Crenatinanina) crenulata, 8.2×12.8 mm, subadult, three views, stn 9.
Datasets: Urbanisation generates multiple trait syndromes for terrestrial animal taxa worldwide
<p>This repository contains the datasets used in the main article:</p> <ul> <li>There is one Excel file per taxonomic group (amphibians, bats, bees, birds, ground beetles, and reptiles).</li> <li>Each file consists of three Excel spreadsheets: "Species" = matrix of species by sites; "Sites" = ID and coordinates of sites + urban and forest land cover variables; "Traits" = matrix of species by traits.</li> <li>Each spreadsheet contains the raw data used for the analyses in the article. For more information on how to handle the data, see the "Method" section.</li> </ul>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.