Find research datasets worth reusing

Range-wide habitat use of the Harpy Eagle indicates four major tropical forest gaps in the Key Biodiversity Area network

Quantifying habitat use is important for understanding how animals meet their requirements for survival and provides information for conservation planning. Currently, assessments of range-wide habitat use that delimit species distributions are incomplete for many taxa. The Harpy Eagle (Harpia harpyja) is a raptor of conservation concern, widely distributed across Neotropical lowland forests, that currently faces threats from habitat loss and fragmentation. Here, we use penalized logistic regression to identify species-habitat associations and predict habitat suitability based on a new International Union for the Conservation of Nature range metric, termed Area of Habitat. From the species-habitat model, we performed a gap analysis to identify areas of high habitat suitability in regions with limited coverage in the Key Biodiversity Area (KBA) network. Range-wide habitat use indicated that Harpy Eagles prefer areas of 70-75% evergreen forest cover, low elevation, and high vegetation species richness. Conversely, Harpy Eagles avoid areas of >10% cultivated landcover and mosaic forest, and topographically complex areas. Our species-habitat model identified a large continuous area of potential habitat across the pan-Amazonia region, and a habitat corridor from the Chocó-Darién ecoregion of Colombia running north along the Caribbean coast of Central America. Little habitat was predicted across the Atlantic Forest biome, which is now severely degraded. The current KBA network covered 18% of medium to high Harpy Eagle habitat exceeding a target biodiversity area representation of 10%, based on species range size. Four major areas of high suitability habitat lacking coverage in the KBA network were identified in north and west Colombia, western Guyana, and north-west Brazil. We recommend these multiple gaps of habitat as new KBAs for strengthening the current KBA network. Modelled area of habitat estimates as described here are a useful tool for large-scale conservation planning and can be readily applied to many taxa.

Climate trends and behavior of a model Amazonian terrestrial insectivore, Black-faced Antthrush, indicate adjustment to hot and dry conditions

Rainforest loss threatens terrestrial insectivorous birds throughout the world's tropics. Recent evidence suggests these birds are declining in undisturbed Amazonian rainforest, possibly due to climate change. Here, we first asked whether Amazonian terrestrial insectivorous birds were exposed to increasingly extreme ambient conditions using 38 years of climate data. We found long-term trends in temperature and precipitation at our study site, especially in the dry season, which was ~1.3 °C hotter and 21% drier in 2019 than in 1981. Second, to test whether birds actively avoided hot and dry conditions, we used field sensors to identify periodic intervals of ambient extremes and prospective microclimate refugia within undisturbed rainforest from 2017–2019. Simultaneously, we examined how tagged Black-faced Antthrushes (Formicarius analis) used this space. We collected >1.3 million field measurements quantifying ambient conditions in the forest understory, including along elevation gradients. For 11 birds, we obtained GPS data to test whether birds adjusted their cover usage using variation in GPS fix success (n = 2,724) as a proxy and elevation using successful locations (n = 640) across seasonal and daily cycles. For four additional birds, we collected >180,000 light and temperature readings to assess exposure. Field measurements in the modern landscape revealed that temperature was higher in the dry season and highest on plateaus. Thus, low-lying areas were relatively buffered, providing microclimate refugia during hot afternoons in the dry season. At those times, birds apparently entered cover and shifted downslope. Because climate change intensifies the hot, dry conditions that antthrushes seemingly avoid, our results are consistent with the hypothesis that climate change decreases habitat quality for this species. If other terrestrial insectivores are similarly sensitive, climate-induced changes to otherwise intact rainforest may be related to their recent declines.

Sowerby's beaked whale biosonar and movement strategy indicate deep-sea foraging niche differentiation in mesoplodont whales

Closely related species are expected to diverge in foraging strategy, reflecting the evolutionary drive to optimize foraging performance. The most speciose cetacean genus, Mesoplodon, comprises beaked whales with little diversity in external morphology or diet, and overlapping distributions. Moreover, the few studied species of beaked whales (Ziphiidae) show very similar foraging styles with slow, energy-conserving movement during long, deep foraging dives. This raises the question of what factors drive their speciation. Using data from animal-attached tags and aerial imagery, we tested the hypothesis that two similar-sized mesoplodonts, Sowerby's(Mesoplodon bidens) and Blainville's (Mesoplodon densirostris) beaked whales, exploit a similar low-energy niche. We show that, compared with the low-energy strategist Blainville's beaked whale, AQ6 Sowerby's beaked whale lives in the fast lane. While targeting a similar mesopelagic/bathypelagic foraging zone, they consistently swim and hunt faster, perform shorter deep dives, and echolocate at a faster rate and with higher frequency clicks. Further, extensive nearsurface travel between deep dives challenges the interpretation of beaked whale shallow inter-foraging dives as a management strategy for decompression sickness. The distinctively higher frequency echolocation clicks do not hold apparent foraging benefits. Instead, we argue that a high-speed foraging style influences dive duration and echolocation behaviour, enabling access to a distinct prey population. Our results demonstrate that beaked whales exploit a broader diversity of deep-sea foraging and energetic niches than hitherto suspected. The marked deviation of Sowerby's beaked whales from the typical ziphiid foraging strategy has potential implications for their response to anthropogenic sounds, which appears to be strongly behaviourally driven in other ziphiids.

Evaluating the use of hair as a non-invasive indicator of trace mineral status in woodland caribou (Rangifer tarandus caribou)

Trace mineral imbalances can have significant effects on animal health, reproductive success, and survival. Monitoring their status in wildlife populations is, therefore, important for management and conservation. Typically, livers and kidneys are sampled to measure mineral status, but biopsies and lethal-sampling are not always possible, particularly for Species at Risk. We aimed to: 1) determine baseline mineral levels in Northern Mountain caribou (Rangifer tarandus caribou; Gmelin, 1788) in northwestern British Columbia, Canada, and 2) determine if hair can be used as an effective indicator of caribou mineral status by evaluating associations between hair and organ mineral concentrations. Hair, liver, and kidney samples from adult male caribou (nHair= 31; nLiver, nKidney= 43) were collected by guide-outfitters in 2016-2018 hunting seasons. Trace minerals and heavy metals were quantified using inductively-coupled plasma mass spectrometry, and organ and hair concentrations of same individuals were compared. Some organ mineral concentrations differed from other caribou populations, though no clinical deficiency or toxicity symptoms were reported in our population. Significant correlations were found between liver and hair selenium (rho=0.66, p<0.05), kidney and hair cobalt (rho=0.51, p<0.05), and liver and hair molybdenum (rho=0.37, p<0.10). These findings suggest that hair trace mineral assessment may be used as a non-invasive and easily-accessible way to monitor caribou selenium, cobalt, and molybdenum status, and may be a valuable tool to help assess overall caribou health.

opencc-by-4.0Apr 2022View details →

Thunderstorm activity over the Qinghai–Tibet Plateau indicated by the combined data of the FY-2E geostationary satellite and WWLLN

This dataset is for the article titled "Thunderstorm activity over the Qinghai–Tibet Plateau indicated by the combined data of the FY-2E geostationary satellite and WWLLN" which is being submitted to the Remote Sensing for review.  Abstract: Thunderstorm activity over the Qinghai–Tibet Plateau (QTP) has important climatic effects and disaster impacts. Using the thunderstorm feature dataset (TFD) established based on the black body temperature (TBB) and cloud classification (CLC) products of the Fengyun-2E (FY-2E) geostationary satellite, as well as the lightning data of the World Wide Lightning Location Network (WWLLN), the temporal and spatial distributions and some cloud properties of the thunderstorms over the QTP were analyzed. Approximately 93.9% and 82.7% of thunderstorms over the QTP occur from May to September and from 12 to 21 o'clock local time, and the corresponding peaks are in August and at 14:00, respectively. There are three centers featuring frequent thunderstorms in the southeast, south-central, and southwest regions of the QTP. The average thunderstorm cloud area (the region with TBB ≤ −32℃) is 1.8 × 104 km2. Approximately 32.9% of thunderstorms have strong convective cells (SCCs) composed of areas with TBB ≤ −52℃.The average number and area ratio of SCCs are 3.6 and 25.4%, respectively, and their spatial distribution is given. The average cloud area and the number and area ratio of SCCs of extreme-lightning thunderstorms (thunderstorms with the top 10% of lightning numbers) are approximately 30.0, 3.9, and 1.5 times those of normal thunderstorms. The spatial distribution of the thunderstorm activity is quite different from that of lightning activity given by the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) over the northeastern and southwestern QTP, which may mean that the convection intensity, cloud structure, and charge structure of the thunderstorms over the QTP are different between different regions and seasons.

opencc-zeroJun 2022View details →

Monitoring genetic diversity with new indicators applied to an alpine freshwater top predator

Genetic diversity is the basis for population adaptation and long-term survival, yet rarely considered in biodiversity monitoring. One key issue is the need for useful and straightforward indicators of genetic diversity. We monitored genetic diversity over 40 years (1970-2010) in metapopulations of brown trout (Salmo trutta) inhabiting 27 small mountain lakes representing 10 lake systems in central Sweden using >1200 fish per time point. We tested six newly proposed indicators; three were designed for broad, international use in the UN Convention on Biological Diversity (CBD and are currently applied in several countries. The other three were recently elaborated for national use by a Swedish science-management effort and applied for the first time here. The Swedish indicators use molecular genetic data to monitor genetic diversity within and between populations (indicators ΔH and ΔFST, respectively) and assess the effective population size (Ne-indicator). We identified 29 genetically distinct populations, all retained over time. Twelve of the 27 lakes harbored more than one population indicating that brown trout biodiversity hidden as cryptic, sympatric populations are more common than recognized. The Ne indicator showed values below the threshold (Ne≤500) in 20 populations with five showing Ne<100. Statistically significant genetic diversity reductions occurred in several populations. Metapopulation structure appears to buffer against diversity loss; applying the indicators to metapopulations suggest mostly acceptable rates of change in all but one system. The CBD indicators agreed with the Swedish ones but provided less detail. All these indicators are appropriate for managers to initiate monitoring of genetic biodiversity.

opencc-by-4.0Dec 2021View details →

Supp. Info. for Further analysis of metagenomic datasets containing GD and GX pangolin CoVs indicates widespread contamination, undermining pangolin host attribution

Supplemanty Information for Further analysis of metagenomic datasets containing GD and GX pangolin CoVs indicates widespread contamination, undermining pangolin host attribution Files: Supp_Info_1_PRJNA641544_DG14_DG18.xlsx Supp_Info_3_PRJNA606875_SRR11093270_reads_blast_nt_seq5_hsps1_PCT80_E0.05_hsps.txt Supp_Info_4_PRJNA573298_Analysis.xlsx

openncgl-uk-2.0Jul 2022View details →

Mean sea-level pressure and related indices from control and tropical relaxation winter seasonal hindcasts using the Met Office GloSea5 system.

© Crown Copyright, Met Office The accompanying data is made available under the terms of the Non-Commercial Government Licence (http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/). These data are the results of ensemble modelling simulations using the Met Office GloSea5 numerical seasonal prediction system. Files in (zipped) netcdf format contain winter seasonal-mean mean sea-level pressure (MSLP) fields for each member of specified ensemble and hindcast winter (December-February from 1993-94 to 2015-16). The files are named according to the specification of relaxation towards observational reanalysis in each. HCAST has no relaxation, ALL has tropical (approximately 22.5S to 22.5N) relaxation at all atmospheric heights, TROP is similar to ALL but with relaxation limited to below 18km and STRAT is similar to ALL but with relaxation limited to above 18km. The relaxation is designed to constrain the atmospheric state to be close to that which was observed within the domain over which it is applied. Details of the technique can be found in Maidens et al., 2021* and references therein. An additional text file is provided to give summary statistics of key MSLP-based winter indices.   * Maidens, A., Knight, J. R., &  Scaife, A. A. (2021). Tropical and stratospheric influences on winter atmospheric circulation patterns in the North Atlantic sector. Environmental Research Letters, 16, 024035. https://doi.org/10.1088/1748-9326/abd8aa

opencc-by-4.0Jan 2022View details →

Annual indices of heat and humidity, U.S. Army installations, 1990-2018

R data files for annual indices of heat of 25 Continental U.S. (CONUS) U.S. Army installations from 1990-2018 in list and long formats. Annual indices were derived from hourly meteorological estimates from the North American Land Data Assimilation System 2 (NLDAS-2) forcing dataset served as the primary source of weather and atmospheric data. We selected NLDAS grid cells containing the centroid of each installation based on shapefiles from the Department of Defense (DoD) Military Installations, Ranges, and Training Areas (MIRTA) Dataset. We calculated relative humidity from specific humidity, temperature, and atmospheric pressure; heat index (HI) from temperature and relative humidity based on a US National Weather Service algorithm; and outdoor WBGT from air temperature, relative humidity, solar irradiance, barometric pressure, and wind speed using the method of Liljegren et al.

opencc-zeroJul 2022View details →

Multidecadal, continent-level analysis indicates agricultural practices impact wheat aphid loads more than climate change

Temperature has a large influence on insect abundances, thus under climate change, identifying major drivers affecting pest insect populations is critical to world food security and agricultural ecosystem health. Here, we conducted a meta-analysis with data obtained from 120 studies across China and Europe from 1970 to 2017 to reveal how climate and agricultural practices affect populations of wheat aphids. Here we showed that aphid loads on wheat had distinct patterns between these two regions, with a significant increase in China but a decrease in Europe over this time period. Although temperature increased over this period in both regions, we found no evidence showing climate warming affected aphid loads. Rather, differences in pesticide use, fertilization, land use, and natural enemies between China and Europe may be key factors accounting for differences in aphid pest populations. These long-term data suggest that agricultural practices impact wheat aphid loads more than climate warming.

AgsSAT Multiannual (2017-2021) Sentinel-2 Water Indices Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Water Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Other Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Vegetation Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites

AgsSAT Multiannual (2017-2021) Sentinel-2 Built-Up Indices Composites The 2,815 images available for the state of Aguascalientes, Mexico for the years 2017 to 2021 were processed using the Open Data Cube (ODC) platform [Lewis et al. (2017), Gavin et al. (2018), <a href="https://www.opendatacube.org/">https://www.opendatacube.org/</a>]. These images correspond to multiple coverages of the region of interest. The images were then used to generate cloud-free annual composites by applying geometric median (geomedian) algorithm, as defined in [Roberts et al. (2017)].   Geomedian algorithm produces a pixel-level summary for every pixel, in this case this means that each summary corresponds to a 10m x 10m region in the territory and its observations throughout a calendar year.  All these summary pixels form a 12-band (coastal aerosol, blue, green, red, vegetation red edge 5, vegetation red edge 6, vegetation red edge 7, near-infrared, narrow nir, water vapor, swir1 and swir2) composite of the state of Aguascalientes.   Another product called GeoMad was generated, which calculates the robust dispersion statistic called MAD, as defined in [Roberts, D., Dunn, B., & Mueller, N. (2018)]. In the resulting image composite, each of the three-pixel bands represents the variation over three distances: Spectral Distance (smad), Euclidean Distance (emad) and the Bray-Curtis Distance (bcmad).   More bands were generated to represent different environmental conditions during the study years (2017-2021), these conditions can be captured by analyzing various combinations of bands, these combinations are also called spectral indices, which allow detecting vegetation, presence of water, urbanization, etc., Finally, 28 indices divided into 4 categories were calculated:  Vegetation Indices  (Atmospherically Resistant Vegetation Index, Kaufman 1972)  (Enhanced Vegetation Index, Huete 2002):  (Modified Soil Adjusted Vegetation Index, Qi Et Al. 1994)  (Normalized Difference Chlorophyll Index, Mishra & Mishra, 2012)  (Normalised Difference Moisture Index, Gao 1996)  (Normalized Difference Vegetation Index, Rouse 1973)  (Optimized Soil Adjusted Vegetation Index, Rondeaux. 1996)  (Simple Ratio Vegetation Index Jordan, C.F.1 969)  (Soil Adjusted Vegetation Index, Huete 1988)  (Visible Atmospherically Resistant Index, Gittleson 2002)     Built-up Indexes  (Band Ration For Built-up Area, Waqar 2012)  (Built-up Area Extraction Index, Bouzekri 2015)  (Built-up Index, He Et Al. 2010)  (Index-based Built-up Index, Xu 2008)  (New Built-up Index, Jieli Et Al. 2010)  (Normalized Difference Built-up Index, Zha 2003)  (Normalized Built-up Area Index, Waqar 2012)  (Urban Index, Kawamura 1996)     Water Indices  (Modified Normalized Difference Water Index, Xu 1996)  (Normalized Difference Water Index, Mcfeeters 1996)  (Water Index, Fisher 2016)     Other Indices  (Bare Soil Index, Rikimaru Et Al. 2002)  (Bare Soil Index, Wanhui 2004)  (Burn Area Index, Martin 1998)  (Clay Minerals Ratio, Drury 1987)  (Ferrous Minerals Ratio, Segal 1982)  (Iron Oxide Ratio, Segal 1982)  (Normalized Burn Ratio, Lopez Garcia 1991)  (Normalised Difference Snow Index, Hall 1995).

opencc-by-4.0Aug 2022View details →

VCF file created by Synteny and Rearrangement Indicator software

VCF file created by Synteny and Rearrangement Indicator software for the rat reference genome mRatBN7.2 and the SHRSP genome assembly

opencc-by-4.0Aug 2022View details →

Indicators of the regional rehabilitation systems development in Russia 2018-2020

Indicators of the regional rehabilitation systems development in Russia 2018-2020

opencc-zeroAug 2022View details →

Crickets as indicators of ecological succession in tropical

Cricket (Ensifera, Grylloidea) are not commonly used as ecological indicators in contrary to other Orthoptera (e.g. grasshoppers and katydids). However, they are sensitive to environmental changes and abundant in tropical regions. To evaluate if crickets are relevant bioindicators of tropical ecosystems, we investigated cricket assemblages along a tropical ecological gradient. We collected crickets during both day and night in southern New Caledonia for three stages of an ecological succession: open shrubland, preforest and forest. Simultaneously, we measured several environmental variables, such as temperature and relative humidity, at each sampling site. Cricket species assemblages showed a clear response to ecological succession. The highest and lowest species richness and abundances of individuals were respectively found in forest and shrubland, with species specialized in each ecological stage revealing the conservation value of each of these stages. Similar results were found when considering only the part of cricket communities with ability to acoustically communicate. This work is part of a larger research program about Neocaledonian crickets and contributes to support the use of acoustic approaches to monitor tropical environments. In conclusion, these findings highlight the potential value of crickets as an environmental indicator in tropical ecosystems. The results also contribute to the discussion of the intrinsic conservational value of shrublands in New Caledonia and similar ecotypes.

opencc-by-4.0Sep 2022View details →

Dataset of Solar Wind Shocks and GICy-indices for Solar Cycles 23 and 24

Dataset of solar wind shocks and GICy-indices for solar cycles 23 and 24 (CFA-SC23 and CFA-SC24) obtained using the Harvard Centre for Astrophysics (CFA) solar wind shock database and geomagnetic field data from the Australian region. Columns 1-5 show the year, month, day, hour (UT), minute, of the solar wind shock; Columns 6-9 show the ground magnetic field change dB (nT) and GIC-index values for the x- and y-components respectively; Columns 10-12 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind IMF Bz (nT); Columns 13-15 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind IMF Bt (nT); Columns 16-18 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind Vx (km/s); Columns 19-21 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind Vt (km/s); Columns 22-24 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind density (cm-3); Columns 25-27 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for calculated solar wind dynamic pressure Pswx (nPa); Columns 28-30 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for calculated solar wind dynamic pressure Pswt (nPa); Columns 31-36 provide the station abbreviation, geographic latitude and longitude, geomagnetic latitude and longitude, and declination; Columns 37-40 are observing satellite, shock normal angle, shock Mach number, shock type (all events in the table are Fast-Forward shocks); and Column 41 provides the calculated decimal time (hours), ∆t, of the geomagnetic station from local noon for each event.

opencc-by-4.0Sep 2022View details →

Dataset of Solar Wind Shocks and GICy-indices for Solar Cycle 23

Dataset of solar wind shocks and GICy-indices for solar cycle 23 obtained using ACE satellite solar wind data and geomagnetic field measurements from the Australian region. Columns 1 and 2 show the date and decimal time (hours UT) of the solar wind shock. Columns 3-17 show the pre-shock (minimum values), the post-shock (maximum values), and the calculated step change for solar wind velocity (km/s), density (cm-3), temperature (°K), magnetic field strength (nT), and dynamic pressure (nPa) respectively. Columns 18-24 show the geomagnetic field observing station, geographic longitude and latitude, geomagnetic longitude and latitude, decimal time (hours UT), and GICy-index values associated with the shock parameters of columns 1-17.