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180 results for “20 years”
20-Year Synthesis of Soil Respiration Data at Harvard Forest 1991-2008
All data on soil carbon flux (“soil respiration”) collected using chamber-based methods at Harvard Forest from a range of observational and experimental plots were collated, their units were harmonized, and geographic (locations) and environmental characteristics (soil series, drainage class, soil temperature, soil moisture, vegetation type, etc.) were identified for each observation. This yielded a dataset with 106,192 observations of soil respiration taken between 1991 and 2008. These data provide a unique resource for exploring spatial and temporal patterns in soil respiration in a range of common New England forest types. For the Giasson, et al. (2013) publication, we also used 24 site-years of eddy covariance measurements from two Harvard Forest sites (EMS and Hemlock towers) to examine the relationship between soil and ecosystem respiration. Here, we present all derived/synthetic datasets associated with the manuscript. M.-A. Giasson, A. M. Ellison, R. D. Bowden, P. M. Crill, E. A. Davidson, J. E. Drake, S. D. Frey, J. L. Hadley, M. Lavine, J. M. Melillo, J. W. Munger, K. J. Nadelhoffer, L. Nicoll, S. V. Ollinger, K. E. Savage, P. A. Steudler, J. Tang, R. K. Varner, S. C. Wofsy, D. R. Foster, and A. C. Finzi 2013. Soil respiration in a northeastern US temperate forest: a 22-year synthesis. Ecosphere 4:art140. http://dx.doi.org/10.1890/ES13.00183.1
20-Year Root Mass in Chronic Nitrogen Amendment Experiment at Harvard Forest 2008
Forests typically respond to nitrogen additions with increased productivity, hence a long-held paradigm was that chronic additions of anthropogenically-derived atmospheric deposition would have positive ecosystem effects. However, twenty years of work at the Harvard Forest Chronic N Deposition plots, and in other forest ecosystems as well, has shown that that this simplistic view is incomplete. For example, enhanced ammonium uptake increases soil acidity, leading to mobilization of aluminum, and loss of nutrient cations (e.g. Mg2+, Ca2+, and K+ ) all of which influence root mass, turnover, and activity. To address long-term impacts of N additions on forest root mass, we removed O-horizon (forest floor) samples from the Chronic N hardwood and red pine stands to quantify the total mass of roots. We found that long term N additions had contrasting results in the two forests. In the hardwood plots, total root mass (less than 2mm) increased from 0.167 + 0.026 (S.E.) kg m-2 in the control plot to 0.434 + 0.170 kg m-2 in the high N plots. In contrast, in the red pine stand, roots declined from 0.074 + 0.011 (S.E.) kg m-2 in the control plot to 0.031 + 0.016 kg m-2 in the high N plots. These data are in agreement with data for aboveground productivity at the Chronic N plots, which show stimulated growth in the hardwoods and severe growth declines and enhanced mortality in the pine stand.
Spatial and temporal differences in shrimp numbers (1 year, 20 pools)
We added woody debris to stream pools in three streams in an experiment designed to increase cover for freshwater shrimp. We trapped four species of freshwater shrimp during 4 months following wood additions. Stream pool morphology was estimated using maximum depth, surface area, and volume. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Nutrient, phytoplankton and zooplankton data, 20 year surface biomass from Darwin
<p>Nutrient, phytoplankton and zooplankton data to accompany Sonnewald et al. : Elucidating Ecological Complexity: Unsupervised Learning determines global marine eco-provinces.</p> <p>Note: We discard the areas (gridcells) with biomass <10-4</p> <p> </p>
A 20-year (1998-2017) global sea surface dimethyl sulfide gridded dataset with daily resolution
<p>This dataset contains (1) the matched and binned data used for constructing an artificial neural network (ANN) ensemble model to simulate the sea surface concentration of dimethyl sulfide (DMS); (2) the simulated global daily sea surface concentrations of DMS ranging from 1998 to 2017 by ANN model and the calculated total transfer velocities (Kt) and sea-to-air fluxes. The input variables of this ANN ensemble model include chlorophyll <em>a</em>, sea surface temperature (SST), mixed layer depth (MLD), nitrate, phosphate, silicate, dissolved oxygen (DO), downward short-wave radiation (DSWF), and sea surface salinity (SSS). The spatial resolution of the simulated dataset is 1°×1°. The units of DMS concentration, Kt, and flux are nmol L–1, m d–1, and μmol S m–2 d–1, respectively.</p> <p><strong>Update Note</strong></p> <ol> <li>In Version 4.0 and earlier versions, the sea ice cover data (from the OISST dataset) used to calculate Kt and DMS flux contained certain time periods with completely missing values, which were incorrectly replaced with zeros. This led to a significant overestimation of Kt and DMS flux in polar regions where sea ice coverage exists. The missing data periods include: November 27–28, 2011; January 7–9, 2016; April 18 to June 30, 2016; and January 7 to February 28, 2017. This issue was resolved beginning with Version 5.0 through the use of updated sea ice data.</li> <li>Compared to Version 5.0, the current version introduces a correction to a bug in the DMS concentration simulation. When input data were missing—primarily in polar regions—the DMS concentration should have been flagged as missing. However, it was previously assigned a value of 1.6031 nM in Version 5.0. In the current version, these values are now replaced with -999 to indicate missing data.</li> </ol>
Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (CMG)
<p>Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). </p> <p>The 500m global surface blue-sky daily albedo climatology dataset is available at .... After reprojection and aggregation, the global Climate Modeling Grid (CMG) albedo climatology datasets at 0.05° and 0.5° are available here. All of the published datasets include historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached in the CMG files. The International Geosphere-Biosphere Programme (IGBP) and PFT classification results of MCD12Q1 since 2001 were reprojected and aggregated to 0.05° and 0.5° by find mode in each aggregation group. In order to check the heterogeneity of the land cover climatology, the percentage of the dominant type in each aggregation group was also calculated.</p>
From 20,000 years ago to near present climate classification of North America
<p>For North America and most of Central America during 20, 14, 13, 11, 10, 7, 5, and 1 thousand years ago (ka) and recent years, I applied a Köppen-Trewartha classification system. I slightly modified the system by keeping the thermal classes for the dry classes, subdividing boreal and polar classes based on an ecological threshold of the number of months at 5 °C, and condensing the number of rare subclasses.</p> <p>Climate data were from:</p> <p>Lorenz, DJ, Nieto-Lugilde, D, Blois, JL, Fitzpatrick, MC and Williams, JW. 2016a. Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100 AD. <em>Scientific Data</em>, 3, 1-19.</p> <p>Lorenz, DJ, Nieto-Lugilde, D, Blois, JL, Fitzpatrick, MC and Williams, JW. 2016b. Data from: Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD. Dryad Dataset. Available at https://doi.org/10.5061/dryad.1597g [last accessed 01 December 2020].</p>
Real-time ZTD and gradients from 20 IGS stations; year 2019
<p>This dataset contains multi-GNSS real-time products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties estimated every 1 minute over the entire year 2019 for 20 IGS stations: ALGO, BIK0, CAS1, CHPG, CPVG, FAA1, JPLM, KERG, KZN2, LHAZZ, LMMF, NKLG, NNOR, PNGM, POVE, REYK, RGDG, ULAD, WROC, YEL2.</p> <p>Two subsets are available that correspond to two processing strategies, i.e. common and advanced. A detailed description, validation and comparison of both strategies can be found in: https://link.springer.com/article/10.1007/s10291-020-01014-w</p> <p>Data are stored in standard Matlab MAT files. Each file contains a single table array (Matlab format) with the complete set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using in-build Matlab function "writetable.m".</p>
Total CO2 absorption (ton) 20 years in Valladolid City
<p>Urban vegetation plays a vital role in offsetting atmospheric CO<sub>2</sub> by acting as a sink and storing carbon. Carbon sinks can be at the city or urban regional level, with urban green areas having local importance as carbon sinks. However, urban parks can also function as carbon sources due to their management and use producing more CO<sub>2</sub> emissions than their carbon sequestration capacity.</p>
Total CO2 absorption (ton) 20 years - Valladolid City
<p>Urban vegetation plays a vital role in offsetting atmospheric CO<sub>2</sub> by acting as a sink and storing carbon. Carbon sinks can be at the city or urban regional level, with urban green areas having local importance as carbon sinks. However, urban parks can also function as carbon sources due to their management and use producing more CO<sub>2</sub> emissions than their carbon sequestration capacity.</p>
Dataset of biodiversity of the Seine nursery over 20 years in a highly disturbed environment
<p>Estuaries are crucial ecosystems where human activities deeply affect numerous ecological functions. The Seine estuary, located on the French coast of the eastern English Channel, is a very dynamic environment where this conflict between the two contrasting backgrounds exists. The Seine watershed is highly disturbed by human activities due to significant industrial development and high population density. The estuary is a historic fishing ground for brown shrimp (<em>Crangon crangon</em>) and various species of flatfish, among which sole (<em>Solea solea</em>) and plaice (<em>Pleutronectes platessa</em>). However, it is a nursery area for fish and plays a crucial role in the life cycle of many demersal and benthic fish and invertebrates.</p> <p>Here we present a survey dataset of biodiversity in the nursery of the Seine estuary and eastern bay of Seine collected using a beam trawl throughout three periods from 1995 to 2019. IFREMER (the French Institute for the Exploitation of the Sea) implemented scientific cruises on coastal nursery grounds aimed at describing the fish population and give an insight into the ecosystem functioning in these areas. The NOURSEINE survey presented here came to existence in this context. The surveys happen at the start of autumn to maximize the catchability of juvenile fish. The beam trawl targets mainly benthic and demersal species over a more than 600 square kilometers study area. The dataset includes abundance and densities of 161 species for 634 hauls performed at around 40 stations each year. These data can be used by fishery scientists and ecologists motivated by earlier stage life of commercial species or by the impact of human disturbances, such as harbor developments, on estuarine communities. They can help in understanding how the nursery functions may change through time and potential human disturbances.</p> <p> </p> <p>Dataset.csv: The data represents the density for the different species encounters in the trawl stations across the 14 years where the NOURSEINE campaign took place. The table contains 22435 rows and 22 columns. Each row corresponds to the density of a species or individuals of the same size in a given haul, and this separation comes from the sorting operation. After each haul, the content of the trawl is emptied on deck, and a total or partial sorting is carried out depending on the volume and homogeneity of the capture. All species, both fish and benthic, are sorted, identified, counted and weighted. Fishes of commercial value and all others flatfish are measured. Fish’s otoliths are collected on the main commercial fish species (sole, plaice, flounder, dab, pouting, large whiting and European bass) and their age group determined later on in the laboratory. In 1999, the sampling was incomplete for technical reasons, and only commercial invertebrates were sampled (King scallop and lobster).</p> <p>Sorting the capture can be separated into three different steps (See pdf figure attached):</p> <p> 1. Total capture weighting: when the hauls are emptied on the deck, the whole capture is distributed in several baskets/box in order to weight it.</p> <p> 2. Fish and large taxa sorting: All fish and large taxa of invertebrates easily identified (edible crab, common spider crab, large cephalopods) are sorted, identified, numbered, measured (for fish) and weighted (total weight per taxa). Depending on the size of the capture, a subsample might be necessary, and the operation is only performed on it. In case visual identification is too difficult (for instance due to a large mud proportion), the capture may be washed using a 5mm sieve. The weight ratio between the total capture and the subsample form a “division” variable that allows the calculation of the density. Another subsampling may be needed if a taxon has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density.</p> <p> 3. Benthic fauna sorting: What is left from the second step is weighted before the sorting operation. All taxa constituting benthic fauna are sorted, identified, numbered and weighted (total weight per taxa). Some taxa may be measured (whelk, scallop). Just like step 2, according to the quantity of benthic fauna, a subsample might be necessary before sorting. All observations are manually recorded on fieldwork paper book before being checked and registered on the NOURSEINE database.</p> <p>All observations are manually recorded on fieldwork paper book before being checked and registered on the NOURSEINE database.</p> <p> </p> <p> </p> <p>Description of the columns found in the dataset</p> <ul> <li><strong>Year</strong></li> </ul> <p>The year when the sampling event took place. Ranges between 1995 and 2019.</p> <ul> <li><strong>Month</strong></li> </ul> <p>The month when the sampling event took place. Either 08 (August) or 09 (September).</p> <ul> <li><strong>Day</strong></li> </ul> <p>The day of the month when the sampling event took place. Ranges from 01 to 30.</p> <ul> <li><strong>Boat_Engine_kw</strong></li> </ul> <p>Engine power reflecting the boat used for sampling, given in kilowatt. Ranges between 81 and 552.</p> <ul> <li><strong>Gear_Code</strong></li> </ul> <p>Code describing the fishing gear used during the sampling event. “BT2” corresponds to beam trawl 2 meters and “BT3” is beam trawl 3 meters.</p> <ul> <li><strong>Haul_duration</strong></li> </ul> <p>The number of minutes of the haul operation. For 1250 rows, NA values indicate that this information was not available.</p> <ul> <li><strong>Station_Code</strong></li> </ul> <p>A unique number identifying the sampling event. Ranges between 1 and 1299.</p> <ul> <li><strong>Starting_Longitude_decimal</strong></li> </ul> <p>The longitudinal coordinate of the starting point of the fishing operation. Given in decimal in the WGS 84 system.</p> <ul> <li><strong>Ending_Longitude_decimal</strong></li> </ul> <p>The longitudinal coordinate of the ending point of the fishing operation. Given in decimal in the WGS 84 system.</p> <ul> <li><strong>Starting_Latitude_decimal</strong></li> </ul> <p>The latitudinal coordinate of the starting point of the fishing operation. Given in decimal in the WGS 84 system.</p> <ul> <li><strong>Ending_Latitude_decimal</strong></li> </ul> <p>The latitudinal coordinate of the ending point of the fishing operation. Given in decimal in the WGS 84 system.</p> <ul> <li><strong>Sector</strong></li> </ul> <p>The sector of the sampling area where the sampling event took place. The study area is divided in 12 sectors roughly based on bathymetry and distance to the mouth of the estuary and identified by letters from “A” to “M”.</p> <ul> <li><strong>Starting_Depth</strong></li> </ul> <p>The depth registered when the sampling event started. Given in meters, ranges from 2 to 26. For 13 rows, NA values indicate that this information was not available.</p> <ul> <li><strong>Ending_Depth</strong></li> </ul> <p>The depth registered when the sampling event ended. Given in meters, ranges from 2 to 26. For 313 rows, NA values indicate that this information was not available.</p> <ul> <li><strong>Trawled_Distance_m</strong></li> </ul> <p>The distance covered between the starting position and the ending position. Given in meters, ranges from 332 to 3128.</p> <ul> <li><strong>Trawled_Surface_m2</strong></li> </ul> <p>The surface trawled between the starting position and the ending position. Given in square meters, ranges from 963 to 9070.</p> <ul> <li><strong>Scientific_Name</strong></li> </ul> <p>Scientific name of the individuals identified in the haul. All names have been checked on WORMS (last accessed: 11/12/2019).</p> <ul> <li><strong>Number_Measured</strong></li> </ul> <p>The number of individuals measured during the sampling event. Ranges between 1 and 340.</p> <ul> <li><strong>Size</strong></li> </ul> <p>Size recorded during the measure of the individuals’ size. Given in centimeters for all species, except benthic fauna where millimeters are used. Ranges between 1 and 185.</p> <ul> <li><strong>Age_Group</strong></li> </ul> <p>Code giving the age category of a particular individual belongs to. “G0” means the individuals are less than 1 year old and born the year the sampling event took place. “G1” means the individuals are 1 year old and born the year before the sampling event took place. “G2+” means they are 2 years old</p> <ul> <li><strong>Number_in_Haul</strong></li> </ul> <p>The number of individuals counted or estimated in the entire haul (if individuals have not been numbered). Depending on the size of the capture, a subsample might be necessary. Another subsampling may be needed if a species has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density.</p> <ul> <li><strong>Weight_in_Haul</strong></li> </ul> <p>The weight of individuals counted or estimated in the entire haul (if individuals have not been numbered). Given in gram. Depending on the size of the capture, a subsample might be necessary. The weight ratio between the total catch and the subsample form a “division” variable that allows the calculation of the density. Another subsampling may be needed if a species has a high abundance. In that case, for practical reasons, only a subsample of the individuals are numbered and measured. The weight ratio between the total abundance and the subsample form a “coefficient” variable that is also used in the calculation of the density. For 7 rows, NA values indicated that this information was not available.</p> <ul> <li><strong>Subsample</strong></li> </ul> <p>Depending on the size of the capture, a subsample might be performed to estimate abundance and weight. This column indicates “Yes” if the capture has been subsample to identified the taxa and “No” if the taxa as identified on the whole catch.</p> <ul> <li><strong>Division</strong></li> </ul> <p>A number used to calculate the density from the “Number_in_Haul” column. It represents the weight ratio between the total capture and the subsample. It ranges between 1 and 512. When it is equal to one, the species was sorted in the whole capture.</p> <ul> <li><strong>Species_Density</strong></li> </ul> <p>The density calculated in individuals per square meters. Species densities are calculated based on the trawled surface but also taking into account if the haul has been partially sorted or not. The formula to calculate the density of individual per surface unit is:</p> <p><em>Density = (Number_in_Haul * Division) / Trawled_Surface_m2</em></p> <p>where <em>Division </em>is a factor used to elevate the abundance if the whole haul was not sorted.</p>
Climate and vegetation change in a coastal marsh: two snapshots of groundwater dynamics and tidal flooding at Piermont Marsh, NY spanning 20 years
<p>Groundwater hydrology plays an important role in coastal marsh biogeochemical function, in part because groundwater dynamics drive the zonation of macrophyte community distribution. Changes that occur over time, such as sea level rise and shifts in habitat structure are likely altering groundwater dynamics and eco-hydrological zonation. We examined tidal flooding and marsh water table dynamics in 1999 and 2019 and mapped shifts in plant distributions over time, at Piermont Marsh, a brackish tidal marsh located along the Hudson River Estuary near New York City. We found evidence that the marsh surface was flooded more frequently in 2019 than in 1999, and that tides were propagating further into the marsh in 2019, although marsh surface elevation gains were largely matching that of sea level rise. The changes in groundwater hydrology that we observed are likely due to the high tide rising at a rate that is greater than that of mean sea level. In addition, we reported on changes in plant cover by <em>P. australis</em>, which has displaced native marsh vegetation at Piermont Marsh. Although <em>P. australis</em> has increased in cover, wrack deposition and plant die off associated Superstorm Sandy allowed for native vegetation to rebound in part of our focus area. These results suggest that climate change and plant community composition may interact to shape ecohydrologic zonation. Considering these results, we recommend that habitat models consider tidal range expansion and groundwater hydrology as metrics when predicting the impact of sea level rise on marsh resilience.</p>
Figures 20–26 in Revisiting Potter Cove, King George Island, Antarctica, 12 years later: new observations of marine benthic diatoms
Figures 20–26: Marine benthic diatoms in Potter Cove, King George Island, Antarctica, summer 2015. (20, 21) Actinocyclus actinochilus. (22) Trigonium arcticum. (23) Triceratium favus. (24–26) Odontella litigiosa.
Occurrences and R code for: Dynamic distribution modeling of the Swamp Tigertail dragonfly Synthemis eustalacta (Odonata: Anisoptera: Synthemistidae) over a 20-year bushfire regime
<p>Intensity and severity of bushfires in Australia have increased over the past few decades due to climate change, threatening habitat loss for numerous species. Although the impact of bushfires on vertebrates is well-documented, the corresponding effects on insect taxa are rarely examined, although they are responsible for key ecosystem functions and services. Understanding the effects of bushfire seasons on insect distributions could elucidate long-term impacts and patterns of ecosystem recovery. Here, we investigated the effects of recent bushfires, land-cover change, and climatic variables on the distribution of a common and endemic dragonfly, the swamp tigertail (<em>Synthemis</em> <em>eustalacta</em> (Burmeister, 1839)), which inhabits forests that have recently undergone severe burning. We used a temporally dynamic species distribution modeling approach that incorporated 20 years of community-science data on dragonfly occurrence and predictors based on fire, land cover, and climate to make yearly predictions of suitability. We also compared this to an approach that combines multiple temporally static models that use annual data. We found that for both approaches, fire-specific variables had negligible importance for the models, while percent of tree and non-vegetative cover were the most important. We also found that the dynamic model outperformed the static ones when evaluated with cross-validation. Model predictions indicated temporal variation in area and spatial arrangement of suitable habitat but no patterns of habitat expansion, contraction, or shifting. These results highlight not only the efficacy of dynamic modeling to capture spatiotemporal variables, such as vegetation cover for an endemic insect species, but also provide a novel approach to mapping species distributions with sparse locality records.</p>
PESMEX20: 20 years of payments for ecosystem services in Mexico
<p><strong>Introduction</strong></p> <p>As some of the world’s largest, longest lasting and most researched initiatives that reward individual and communal landowners for conserving forests and associated ecosystem services, Mexico´s Payments for Ecosystem Services (PES) programmes provide a significant opportunity to examine questions of how, where, and by whom PES scholarship has been produced. The PESMEX20 database is the most up-to-date and comprehensive compendium of peer-reviewed publications for PES in a single country. It is a systematic analysis of the relevant peer-reviewed literature in English and Spanish through 2022 (N=140). Studies represent a breadth of themes, disciplinary and methodological approaches, and spatial scales.</p> <p><strong>Research questions</strong></p> <p>We address two specific research questions: </p> <ol> <li> <p>How has the body of PES research evolved over time in terms of methodological, temporal, and geographic coverage?</p> </li> <li> <p>What have been the main research priorities and reported results so far and how do these compare between in-country (i.e. based in Mexican institutions) and foreign scholars?</p> </li> </ol> <p><strong>Search strategy and inclusion criteria</strong></p> <p>Our approach to article selection followed PRISMA established norms (Moher et al., 2009). We employed advanced searches for publications in several internationally recognised databases as suggested by previous PES reviews (Calvet-Mir et al., 2015; Perevochtchikova et al., 2021; Schomers & Matzdorf, 2013; Kolinjivadi et al., 2023) and other related literature reviews. Specifically, we conducted searches in three internationally recognised databases: i) Scopus (international database of peer-reviewed academic publications worldwide); ii) Scielo (international database of peer-reviewed academic publications, mainly from Latin America, Ibero-America, and South Africa); and iii) Redalyc (international database of peer-reviewed academic publications from Latin America, the Caribbean, Spain, and Portugal).</p> <p>Additionally, given our specific interest in PES literature based in Mexico, we also conducted online searches of articles published in nationally-recognised scientific journals based in Mexico. Our analysis focused on peer-reviewed journals with indexation by Mexico’s Science and Technology Journal Classification System (CRMCYT) as of 2023. From a list of 109 CRMCYT indexed journals from seven thematic areas, we narrowed our search to 14 journals based on title and thematic relevance. Of these 14 journals, 11 were already included in Scopus, Scielo, or Redalyc. We therefore conducted additional searches in the 3 remaining Mexican journals: Estudios Sociológicos de El Colegio de México; Política y Gobierno; Papeles de Población.</p> <p>The systematic review process first consisted of online searches of publications with keywords related to PES programmes in Mexico in English, Spanish, and Portuguese. The search elements were sought specifically in the title, abstract and keywords for Scopus and Scielo, and in the full text of the publications for Redalyc and the three individual Mexican journals. The syntax used included a wide array of combinations of the following terms: </p> <p>Mexico/Mexican AND payment/programme/scheme + ecosystem/environmental/ hydrological/biodiversity/carbon service. </p> <p>In total, 1357 records were obtained and downloaded in a single Excel spreadsheet containing records until 2022. Duplicate records (N=152) were identified and excluded, with the final publication database including a total of 1205 publications. The following step consisted of identifying the set of records related to PES programmes in Mexico from the publication database, and thus relevant for analysis. We coded each record according to two dimensions:</p> <ul> <li> <p>PES focus: publication related to actual or hypothetical PES programmes and/or providing insights related to their application. Actual PES programmes included the federal PES programmes and other public and private programmes addressed by the studies.</p> </li> <li> <p>Mexico focus: publication provides insights and/or data collected in Mexico. This includes comparative studies based in Mexico and other geographic regions.</p> </li> </ul> <p>For each dimension, the coding process consisted of two main steps: 1) search for keywords in the title related to PES or Mexico; 2) reading the abstract to confirm PES or Mexico focus. Whenever the publication’s classification was unclear after steps 1) and 2), the body of the publication was further scanned. This codification was developed by the first author but was validated by the team of co-authors to ensure its accuracy. Once all records were coded, only those publications that combined a focus on PES and Mexico were selected for our analysis.</p> <p>In total, 140 publications met this requirement and formed the empirical basis for our analysis (Included in Supplementary Materials). These 140 publications included 137 peer-reviewed journal articles and three book chapters.</p> <p><strong>Coding strategy</strong></p> <p>The publication database explores a series of variables that cover four main components: basic features, thematic coverage, methodological coverage, and reported effects. Each entry was coded twice independently in separate spreadsheets by two co-authors who have experience with PES programmes and research in Mexico. Subsequently, the spreadsheets were merged and each result was double checked for consistency and, in cases where discrepancies were found, the coders jointly deliberated to reach a final result.</p> <p>The basic features component identifies the lead authors and country and institutional affiliation, publication outlet, publication year, and number of citations (citation number retrieved from Google Scholar on 8 November 2023). </p> <p>The thematic coverage component identifies the set of themes addressed by the publication. Our assessment framework situates PES research within four main stages of PES design and implementation: Assessment & planning, Implementation & evaluation, and Improvement. Each of these stages involves, in turn, a series of theme-specific categories. The framework was developed based on our existing knowledge acquired from long-term research of PES programmes and is composed of a series of well-established themes in PES literature. These themes are not mutually exclusive (publications in our database addressed on average 2.15 themes).</p> <p>The methodological coverage component characterises the programme(s) studied (type, geographic location) and the study's design (if and where primary data was collected, the type of methods employed and the temporal scale of analysis). We also captured the degree of critique towards PES based on the overall tone and language of the study’s abstract and conclusions.</p> <p>The reported effects component addresses the 56 studies in our database that provided empirical evidence related to PES environmental and/or social impacts or outcomes and excluded reviews as well as conceptual, experimental and hypothetical studies. Specifically, we examined what scholars have concluded about the social and ecological effects of Mexican PES programmes in terms of key indicators used, whether the study assesses outcomes (measurable indicators of programme effects) versus impact (through counterfactual analysis), and the authors’ conclusions on the direction (positive, negative or neutral) and magnitude of effects. Classification as positive or negative was based on our interpretation of the authors' overall assessment of environmental and social outcomes (separately). If reported outcomes were predominantly positive, we classified the effects as positive, and vice versa for negative outcomes. Where findings did not allow an assessment of outcome direction, or in instances where there was a balance of positive and negative outcomes, we designated these as neutral.</p> <p><strong>Variable list</strong></p> <p>The description of variables and their corresponding values can be found in the second sheet of the Excel spreadsheet.</p> <p><strong>Acknowledgements</strong></p> <p>We thank Daniela Oropeza Calderón and Lucero Hidalgo García for excellent research assistance with the publication database in the early stages of this manuscript. We also acknowledge the helpful input from Vijay Kolinjivadi, Gert van Hecken and Pierre Merlet for the design of the coding strategy.</p>
Dataset: US Treasury 20 Year Bond ETF (UTWY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares 20+ Year Treasury Bond ETF (TLT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 100 Schizophrenia Patients to 48 Patients with Major Depression -Figure 4. Histogram age distributions of the 100 Schizophrenia patients illustrating clusters of patients at 20, 30, 40, and 50 years old.
<p>The figure below is a histogram distribution of the ages of one hundred Schizophrenia<br> patients in this study. There appears to be a cyclical peak every ten-years cycles at 20, 30, 40, and<br> 50-year-old patients. This may suggest a recent finding that CD8 T cells, which play a pivotal role<br> in mediating long-term immunity to Toxoplasma, are down-regulated in schizophrenia patients<br> (Bhadra et al., 2013). Based on the aforementioned statements, a vaccine for Toxoplasmosis may be<br> difficult to achieve, but the treatment and screening possibilities in patients with psychosis are<br> available today.</p>
Text-fig. 5. Reconstruction of aeduellids from the Bohemian Massif. 1 – Spinarichthys dispersus (FRITSCH, 1895) (after Štamberg 1986). 2 – Neslovicella elongata ŠTAMBERG, 2010 (after Štamberg 2010). 3 – Neslovicella rzehaki ŠTAMBERG, 2007 (after Štamberg 2007). 4 – Bourbonnella hirsuta ŠTAMBERG, 2007 (after Štamberg, 2007). Scale bar represents 20 mm. in Knowledge Of The Carboniferous And Permian Actinopterygian Fishes Of The Bohemian Massif - 100 Years After Antonín Frič
Text-fig. 5. Reconstruction of aeduellids from the Bohemian Massif. 1 – Spinarichthys dispersus (FRITSCH, 1895) (after Štamberg 1986). 2 – Neslovicella elongata ŠTAMBERG, 2010 (after Štamberg 2010). 3 – Neslovicella rzehaki ŠTAMBERG, 2007 (after Štamberg 2007). 4 – Bourbonnella hirsuta ŠTAMBERG, 2007 (after Štamberg, 2007). Scale bar represents 20 mm.
Text-fig. 2. Reconstruction of some Westphalian and Stephanian actinopterygians. Figs 1 – 5 after Štamberg (1991), Fig. 6 after Westoll (1944). Scale bars represent 20 mm. 1 – Acrolepis gigas (FRIČ, 1877), 2 – "Elonichthys" krejcii (FRITSCH, 1895), figured after the original holotype of Acrolepis sphaerosideritarum FRITSCH, 1895, 3 – "Elonichthys" krejcii (FRITSCH, 1895), figured after the original holotype of Acrolepis krejcii FRITSCH, 1895, 4 – Sphaerolepis kounoviensis FRIČ, 1877, 5 – Sceletophorus biserialis FRITSCH, 1894, 6 – Pyritocephalus sculptus (FRIČ, 1875). in Knowledge Of The Carboniferous And Permian Actinopterygian Fishes Of The Bohemian Massif - 100 Years After Antonín Frič
Text-fig. 2. Reconstruction of some Westphalian and Stephanian actinopterygians. Figs 1 – 5 after Štamberg (1991), Fig. 6 after Westoll (1944). Scale bars represent 20 mm. 1 – Acrolepis gigas (FRIČ, 1877), 2 – "Elonichthys" krejcii (FRITSCH, 1895), figured after the original holotype of Acrolepis sphaerosideritarum FRITSCH, 1895, 3 – "Elonichthys" krejcii (FRITSCH, 1895), figured after the original holotype of Acrolepis krejcii FRITSCH, 1895, 4 – Sphaerolepis kounoviensis FRIČ, 1877, 5 – Sceletophorus biserialis FRITSCH, 1894, 6 – Pyritocephalus sculptus (FRIČ, 1875).
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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.