Recognized as one of the world's most vital natural and cultural resources, the Amazon faces a wide variety of threats from natural resource and infrastructure development. Within this context, rigorous scientific study of the region's complex social-ecological system is critical to inform and direct decision-making toward more sustainable environmental and social outcomes. Given the Amazon's tightly linked social and ecological components and the scope of potential development impacts, effective study of this system requires an easily accessible resource that provides a broad and reliable data baseline. This paper brings together multiple datasets from diverse disciplines (including human health, socio-economics, environment, hydrology, and energy) to provide investigators with a variety of baseline data to explore the multiple long-term effects of infrastructure development in the Brazilian Amazon.

The Geoecology database is a compilation of environmental data for the period 1941 to 1981. The Geoecology database contains selected data on terrain and soils, water resources, forestry, vegetation, agriculture, land use, wildlife, air quality, climate, natural areas, and endangered species. Data on selected human population characteristics are also included to complement the environmental files. Data represent the conterminous United States at the county level. These historical data are provided as a source of 1970s baseline environmental conditions for the United States.

The long-term goal of the SOFIA program is to provide a central archive of data and products for all Department of Interior products related to the south Florida Ecosystem restoration program.

 The primary objectives of this project were to 1) provide a central location for the archive of all products and data collected as part of the USGS PES program and related work for the restoration of the South Florida Ecosystem; and 2) provide a means for customers to obtain the archived information. The database was been developed using PostgreSQL, an open source relational database management system. PostgreSQL was selected because it is freely available, widely used, actively maintained and supported, and runs on all modern UNIX-like and Windows based computer systems. As projects were completed or data sets were made available to the public, the data was to be loaded into the database. 

 The South Florida restoration effort requires multidisciplinary information relating to present and historical conditions for use in responsible decision-making. The South Florida Information Access (SOFIA) database is the cornerstone of information management for the South Florida place-based science program.

 Responsible resource management requires a firm scientific basis for decision making. The SOFIA database will provide hydrologic, cartographic, geologic, and biologic data and metadata to aid in the decision process. Ecosystem restoration and sound resource management will benefit south Florida by allowing a balance of uses and interests. The data are available to Federal, State, and local agencies through a web interface and can be retrieved from the SOFIA database. The database has been redesigned to provide storage and access to a greater variety of data types. A new web-based interface will be developed to provide access to the database. The interaction between the database, the other SOFIA products, and other available web-based databases is being evaluated. The database will be updated to provide greater interoperability between the database, other SOFIA products and other web-based databases.

The vulnerable marine ecosystem (VME) concept emerged from discussions at the United Nations General Assembly (UNGA) and gained momentum after UNGA Resolution 61/105. VMEs constitute areas that may be vulnerable to impacts from fishing activities.

The vulnerable marine ecosystems database was developed in collaboration with the regional bodies with mandates to manage deep-sea fisheries in areas beyond national jurisdiction (ABNJ). It is a compilation of information on management measures taken to reduce current or potential impact on areas where VMEs are known or likely to occur.

The database is designed to facilitate the work of scientists and managers working on these fisheries and also to promote transparency and accessibility of work that has been done in relation to VMEs to the general public.

The content of the database is linked to the data providers (e.g. RFMOs and other multi-lateral bodies) and users have access the primary source of the information through direct links.

This database was developed specifically in response to a request from the UN General Assembly (61/105, paragraph 90) to create a database of information on vulnerable marine ecosystems (VMEs) in ABNJ. It has been developed within the FAO Deep-sea Fisheries Programme to promote the use of the International Guidelines for the Management of Deep-Sea Fisheries in the High Seas that provides guidance to States and Regional Fisheries Management Organizations or Arrangements (RFMO/As) to ensure the long-term conservation and sustainable use of marine living resources in the deep seas. This includes preventing Significant Adverse Impacts on vulnerable marine ecosystems.

For more information please visit - https://www.fao.org/in-action/vulnerable-marine-ecosystems/en/

Our dataset collects comprehensive life-history, ecological, and biogeographic data on global snakes.

InvaCost is the most up-to-date, comprehensive, standardized and robust data compilation and description of economic cost estimates associated with invasive species worldwide1. InvaCost has been constructed to provide a contemporary and freely available repository of monetary impacts that can be relevant for both research and evidence-based policy making. The ongoing work made by the InvaCost consortium2,3,4 leads to constantly improving the structure and content of the database (see sections below). The list of actual contributors to this data resource now largely exceeds the list of authors listed in this page. All details regarding the previous versions of InvaCost can be found by switching from one version to another using the “version” button above. IMPORTANT UPDATES: 1. All information, files, outcomes, updates and resources related to the InvaCost project are now available on a new website: http://invacost.fr/2. The names of the following columns have been changed between the previous and the current version: ‘Raw_cost_estimate_local_currency’ is now named ‘Raw_cost_estimate_original_currency’; ‘Min_Raw_cost_estimate_local_currency’ is now named ‘Min_Raw_cost_estimate_original_currency’; ‘Max_Raw_cost_estimate_local_currency’ is now named ‘Max_Raw_cost_estimate_original_currency’; ‘Cost_estimate_per_year_local_currency’ is now named ‘Cost_estimate_per_year_original_currency’3. The Frequently Asked Questions (FAQ) about the database and how to (1) understand it, (2) analyse it and (3) add new data are available at: https://farewe.github.io/invacost_FAQ/. There are over 60 questions (and responses), so there’s probably yours.4. Accordingly with the continuous development and updates of the database, a ‘living figure’ is now available online to display the evolving relative contributions of different taxonomic groups and regions to the overall cost estimates as the database is updated: https://borisleroy.com/invacost/invacost_livingfigure.html5. We have now added a new column called ‘InvaCost_ID’, which is now used to identify each cost entry in the current and future public versions of the database. As this new column only affects the identification of the cost entries and not their categorisation, this is not considered as a change of the structure of the whole database. Therefore, the first level of the version numbering remains ‘4’ (see VERSION NUMBERING section).

CONTENT: This page contains four files: (1) 'InvaCost_database_v4.1' which contains 13,553 cost entries depicted by 66 descriptive columns; (2) ‘Descriptors 4.1’ provides full definition and details about the descriptive columns used in the database; (3) ‘Update_Invacost_4.1’ has details about the all the changes made between previous and current versions of InvaCost; (4) ‘InvaCost_template_4.1’ (downloadable file) provides an easier way of entering data in the spreadsheet, standardizing all the terms used on it as much as possible to avoid mistakes and saving time at post-refining stages (this file should be used by any external contributor to propose new cost data).

METHODOLOGY: All the methodological details and tools used to build and populate this database are available in Diagne et al. 20201 and Angulo et al. 20215. Note that several papers used different approaches to investigate and analyse the database, and they are all available on our website http://invacost.fr/.

VERSION NUMBERING: InvaCost is regularly updated with contributions from both authors and future users in order to improve it both quantitatively (by new cost information) and qualitatively (if errors are identified). Any reader or user can propose to update InvaCost by filling the ‘InvaCost_updates_template’ file with new entries or corrections, and sending it to our email address (updates@invacost.fr). Each updated public version of InvaCost is stored in this figShare repository, with a unique version number. For this purpose, we consider the original version of InvaCost publicly released in September 2020 as ‘InvaCost_1.0’. The further updated versions are named using the subsequent numbering (e.g., ‘InvaCost_2.0’, InvaCost_2.1’) and all information on changes made are provided in a dedicated file called ‘Updates-InvaCost’ (named using the same numbering, e.g., ‘Updates-InvaCost_2.0’, ‘Updates-InvaCost_2.1’). We consider changing the first level of this numbering (e.g. ‘InvaCost_3.x’ ‘InvaCost_4.x’) only when the structure of the database changes. Every user wanting to have the most up-to-date version of the database should refer to the latest released version.

RECOMMENDATIONS: Every user should read the ‘Usage notes’ section of Diagne et al. 20201 before considering the database for analysis purposes or specific interpretation. InvaCost compiles cost data published in the literature, but does not aim to provide a ready-to-use dataset for specific analyses. While the cost data are described in a homogenized way in InvaCost, the intrinsic disparity, complexity, and heterogeneity of the cost data require specific data processing depending on the user objectives (see our FAQ). However, we provide necessary information and caveats about recorded costs, and we have now an open-source software designed to query and analyse this database6.

CAUTION: InvaCost is currently being analysed by a network of international collaborators in the frame of the InvaCost project2,3,4 (see https://invacost.fr/en/outcomes/). Interested users may contact the InvaCost team if they wish to learn more about or contribute to these current efforts. Users are in no way prevented from performing their own independent analyses and collaboration with this network is not required. Nonetheless, users and contributors are encouraged to contact the InvaCost team before using the database, as the information contained may not be directly implementable for specific analyses.

RELATED LINKS AND PUBLICATIONS:

1 Diagne, C., Leroy, B., Gozlan, R.E. et al. InvaCost, a public database of the economic costs of biological invasions worldwide. Sci Data 7, 277 (2020). https://doi.org/10.1038/s41597-020-00586-z

2 Diagne C, Catford JA, Essl F, Nuñez MA, Courchamp F (2020) What are the economic costs of biological invasions? A complex topic requiring international and interdisciplinary expertise. NeoBiota 63: 25–37. https://doi.org/10.3897/neobiota.63.55260

3 Researchgate page: https://www.researchgate.net/project/InvaCost-assessing-the-economic-costs-of-biological-invasions

4 InvaCost workshop: https://www.biodiversitydynamics.fr/invacost-workshop/

5 Angulo E, Diagne C, Ballesteros-Mejia L. et al. (2021) Non-English languages enrich scientific knowledge: the example of economic costs of biological invasions. Science of the Total Environment 775:144441. https://doi.org/10.1016/j.scitotenv.2020.144441

6Leroy B, Kramer A M, Vaissière A-C, Courchamp F and Diagne C (2020) Analysing global economic costs of invasive alien species with the invacost R package. BioRXiv. doi: https://doi.org/10.1101/2020.12.10.419432

Global Species Abundance and Diversity

Ecological Insights for the Anthropocene

By [source]

About this dataset

BioTIME is an invaluable open source biodiversity database, brought to life by an international research collective. Comprised of species abundance and diversity data from different ecological sites around the world, BioTIME provides a comprehensive global perspective on species richness in the Anthropocene. This extensive dataset can help us understand and comprehend trends and insights about the history of global biodiversity for many years to come.

From current to past records, this dataset offers detailed information about species composition, abundance levels and diversity throughout time. Through such analysis, researchers can better recognize the intricate connections between global ecosystems over time - providing insight into changes in climate and habitats due to human activity or natural causes. With its global scope and unparalleled depth of data points, this dataset sets itself apart as a unique resource for future ecological studies - available free to all!

Look through each column provided: DAY, MONTH ,YEAR ,SAMPLE_DESC ,PLOT ,LATITUDE ,LONGITUDE ,sum.allrawdata.ABUNDANCE ,sum.allrawdata.BIOMASS GENUS ,SPECIESGENUS_SPECIES REALMCLIMATE GENERAL_TREATMENT TREATMENT TREAT_COMMENTS TREAT_DATEHABITATPROTECTED_AREA BIOME_MAP TAXA ORGANISMSTITLE AB_BIOHAS_PLOTDATA_POINTSSTART_YEAREND _YEARCENT _LATCENT _LONGNUMBER _OF . SPECIESSNUMBER _OF . SAMPLESNUMBER _

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How to use the dataset

First, it is important to understand the columns included in this dataset: DAY, MONTH, YEAR, SAMPLE_DESC (description of sample), PLOT (where sample was taken), LATITUDE & LONGITUDE (coordinates), sum.allrawdata.ABUNDANCE & sum.allrawdata.BIOMASS(total abundance/biomass of species observed in samples), GENUS & SPECIES (genus/species observed in samples). REALM (the geographic realm where samples were taken from) CLIMATE(climate type for study area), GENERAL_TREAT & TREATMENT (general/specific treatments applied to study area) TREAT_COMMENTS(additional comments on the treatment) HABITAT(habitat type from study area) PROTECTED_AREA whether or not it is a protected area BIOME_MAP biome map TAXA taxonomic group ORGANISMS organisms studied TITLE title description AB_BIO abundance or biomass HAS_PLOT whether or not the study has a plot DATA POINTS number of data points START_YEAR start year END_YEAR end year CENT-LAT central latitude CENT-LONG central longitude NUMBER OF SPECIES number of species studied NUMBER OF SAMPLES number of samples taken NUMBER LAT LONG number latitude and longitude GRAIN SIZE TEXT grain size text GRAIN SQ KM grain size kilometers AREA SQ KM area square kilometers CONTACT 1 primary contact CONTACT 2 secondary contact CONT 1 MAIL primary contacts email address CONT 2 MAIL secondary contacts email address LICENSE license associated with studies WEB LINK web link DATA SOURCE source of data METHODS methods used SUMMARY METHODS summary methods COMMENTS additional comments DATE STUDY ADDED date added to database ABUNDANCE TYPE type abundance data COLLECTED BIOMASS TYPE type biomass collected SAMPLE DESC NAME name sample description

The second step towards understanding this dataset is exploring how each column can be utilized within your research project; depending on your research topic the usage will vary according to what information you may be needing or searching for within

Research Ideas

• Investigating historical patterns of species distribution – By leveraging the temporal data in this dataset, researchers can observe changes in species abundance and diversity over a given period of time and compare it to environmental factors. This could shed light on current distributions of species as well as inform conservation efforts by providing information about formerly healthy ecosystems or unsustainable management practices.
• Determining the impact of human actions on biodiversity – Through analysis of BioTIME data, land development and subsequent changes to habitat loss may be identified, allowing researchers to understand the impact human action has had upon a species population size or geographic range over time.
• Analysing climate change effects on biodiversity – By examining changes in abundance, diversity and geographic range across different study sites captured over several years within this dataset, researchers may detect correlations between climatic conditions such as temperature increases and precipitation levels with certain species diversity acr...

TreeGhatsData is a compilation of lists of tree taxa found in Western Ghats, South India:

taxa for which the word "tree" appears in habit description in the book Flowering plants of the Western Ghats edited by the Tropical Botanic Garden Research Institute (TBGRI), including planted or cultivated taxa (Nayar, Beegam, and Sibi. 2014);

tree taxa described after 2014 in journal articles;

taxon names used in forest surveys published by the French Institute of Pondicherry (IFP), in journal articles from 2000, and in the Atlas of endemics of the Western Ghats (Ramesh and Pascal 1997);

taxon names reported with "tree" habit in Indian Biodiversity Portal (http://indiabiodiversity.org/).

For each plant name, TreeGhatsData includes the following taxonomic information: family, genus epithet, species epithet, infrataxon rank, infrataxon epithet, authority. Both the family name used in TBGRI book and the corresponding family name according to Angiosperm Phylogeny Group system III (APGIII; Bremer et al. 2009) are provided.

TreeGhatsData includes the taxonomic status, the reference name and the authority according to TBGRI flora, along with taxonomic status from The Plant List version 1.1 (http://www.theplantlist.org/). From these two sources, a taxonomic status is suggested for each taxon name, with corresponding reference names and authorities.

TreeGhatsData also includes ecological and biogeographic information from TBGRI and completed by the botanists of French Institute of Pondicherry (IFP).

Because most vegetation surveys do not provide taxon names at infraspecific level, TreeGhatsData includes both the infraspecific taxa mentioned in Western Ghats and the corresponding specific binomial names.

TreeGhatsData is provided as a CSV file with comma separator.

Related references

Bremer, B., Bremer, K., Chase, M. W., Fay, M. F., Reveal, J. L., Soltis, D. E., Soltis, P. S., Stevens, P. F., Anderberg, A. A., Moore, M. J., Olmstead, R. G., Rudall, P. J., Sytsma, K. J., Tank, D. C., Wurdack, K., Xiang, J. Q. Y. & Zmarzty, S. (2009) An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Botanical Journal of the Linnean Society, 161, 105-121.

Nayar, T., Rasiya Beegam, A. & Sibi, M. (2014) Flowering plants of the Western Ghats, India, Volume 1 Dicots; Volume 2 Monocots. Jawaharlal Nehru Tropical Botanic Garden and Research Institute.

Ramesh, B. & Pascal, J.-P. (1997) Atlas of endemics of the Western Ghats (India): distribution of tree species in the evergreen and semi-evergreen forests. French Institute of Pondicherry, Pondicherry, India.

This database contains literature citations and associated abstracts pertaining to the ecology and hydrology of terminal lakes in the Great Basin region of the western United States. This is not meant to be an exhaustive list, nor did we perform a systematic meta-analysis; rather, literature records were included based on topical relevance.

The Environmental Protection Agency’s (EPA) Smart Location Database provides data on the relationship between land use and transportation efficiency. The Smart Location Database (SLD) summarizes several demographic, employment, and land use variables for Census block groups. PolicyMap made an excerpt from this larger body of work available on its platform.

Frequency of transit service provides a general metric of the quality of public transit options in an area. EPA calculated transit frequency through an analysis of General Transit Feed Specification (GTFS) data between 4:00 and 7:00 PM on a weekday. Then, for each block group, EPA identified transit routes with service that stops within 0.4 km (0.25 miles). Finally, EPA summed aggregate service frequency by block group. Values for this metric are expressed as service frequency per hour of service. GTFS is a transit data reporting standard that allows public transit agencies to publish data in a standard format. EPA also calculated frequency of transit service per square mile by dividing frequency of transit service per hour by total land acreage then converting to units per square mile. Where the total land acreage was zero, total block group acreage was used as the denominator. To create the indicators on job or workforce accessibility by auto travel, the EPA joined an origin-destination matrix to employment and demographic data from the 2010 Census. Although the transit accessibility indicators were analyzed the same way as the auto accessibility, it was analyzed for evening peak travel period only, as this is normally the period of relatively intense levels of transit service.

Bio-ORACLE is a set of GIS rasters providing geophysical, biotic and environmental data for surface and benthic marine realms. The data are available for global-scale applications at a spatial resolution of 5 arcmin (approximately 9.2 km at the equator).

Linking biodiversity occurrence data to the physical and biotic environment provides a framework to formulate hypotheses about the ecological processes governing spatial and temporal patterns in biodiversity, which can be useful for marine ecosystem management and conservation.

Bio-ORACLE offers a user-friendly solution to accomplish this task by providing 18 global geophysical, biotic and climate layers at a common spatial resolution (5 arcmin) and a uniform landmask.

The data available in Bio-ORACLE are documented in two peer reviewed articles that you should cite: Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281. Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography.

The current database updates and extends the database on arthropods inhabiting European arable crops established in 2012 (Meissle et al. 2012). The data was collected to support environmental risk assessment of genetically modified (GM) crops in the European Union and provides a detailed overview of the arthropod fauna in arable crops across Europe.The data was obtained from systematic literature searches conducted to identify publications on small grain cereals and to identify additional publications on the crops covered by the previous database (maize, beet, potato, oilseed rape, rice, cotton, soy). The final database contains information on more than 4000 arthropod species, > 27700 records, and > 2000 references.

The database consists of three tables containing information on species (taxonomy, ecological function, feeding guild, habitat), abundances (crop, collection method, location, sampling duration, collected species), and references (authors, year, title, source). Taxonomy was verified with European and global taxonomic catalogues and taxonomic experts. Ecological information, in particular feeding guilds of adults and juveniles, was double checked with appropriate literature, and provided in detail. References for taxonomic and ecological information were included for each species record

For maize, beet, potato, oilseed rape, rice, and soybean, 258 additional studies were found and entered into the database, resulting in 2774 additional records. For those crops, the updated database contains 16610 records of 3264 species. Most of the records are available for maize (6648), followed by beet, potato, and oilseed rape (ca. 3000 records each). Relatively few records are available for rice (601), soy (231), and cotton (184). Overall, small grain cereals in Europe were reported to harbour more than 2000 arthropod species. Most information is available for wheat (7626 records and 1664 species), followed by barley (2308 records and 893 species). Rye, oats, and triticale are represented by 453, 369, and 273 records and 269, 187, and 171 species, respectively. Only few records are available for buckwheat and sorghum, and no records for millet and canary seed. Overall, small grain cereals in Europe are reported to harbour more than 2000 arthropod species. For the other crops, the updated database contains more than 3200 species. Most of the species recorded in small grain cereals are predators (63% of the abundance records), followed by herbivores (21%), decomposers (8%), parasitoids (7%), and pollinators (1%).

The database contains reports from 37 countries in Europe and was extracted from references with a publication date ranging from 1925-2014.

This dataset was created for the following publication: Cheruvelil, K.S., S. Yuan, K.E. Webster, P.-N. Tan, J.-F. Lapierre, S.M. Collins, C.E. Fergus, C.E. Scott, E.N. Henry, P.A. Soranno, C.T. Filstrup, T. Wagner. Under review. Creating multi-themed ecological regions for macrosystems ecology: Testing a flexible, repeatable, and accessible clustering method. Submitted to Ecology and Evolution July 2016. This dataset includes lake total phosphorus (TP) and Secchi data from summer, epilimnetic water samples, as well as 52 geographic variables at the HU-12 scale; it is a subset of the larger LAGOS database (Lake multi-scaled geospatial and temporal database, described in Soranno et al. 2015). LAGOS compiles multiple, individual lake water chemistry datasets into an integrated database. We accessed LAGOSLIMNO version 1.054.1 for lake water chemistry data and LAGOSGEO version 1.03 for geographic data. In the LAGOSLIMNO database, lake water chemistry data were collected from individual state agency sampling and volunteer programs designed to monitor lake water quality. Water chemistry analyses follow standard lab methods. In the LAGOSGEO database geographic data were collected from national scale geographic information systems (GIS) data layers. The dataset is a subset of the following integrated databases: LAGOSLIMNO v.1.054.1 and LAGOSGEO v.1.03. For full documentation of these databases, please see the publication below: Soranno, P.A., E.G. Bissell, K.S. Cheruvelil, S.T. Christel, S.M. Collins, C.E. Fergus, C.T. Filstrup, J.F. Lapierre, N.R. Lottig, S.K. Oliver, C.E. Scott, N.J. Smith, S. Stopyak, S. Yuan, M.T. Bremigan, J.A. Downing, C. Gries, E.N. Henry, N.K. Skaff, E.H. Stanley, C.A. Stow, P.-N. Tan, T. Wagner, K.E. Webster. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science and data reuse. GigaScience 4:28 doi:10.1186/s13742-015-0067-4 .

To address the need for a centralized root trait database, we compiled the Fine-Root Ecology Database (FRED) from published and unpublished data sources. We have continued to add to the FRED database since the release of FRED 2.0 in 2018, and a new version of FRED is now available. FRED 3.0 has more than 150,000 observations of more than 330 root traits, with data collected from more than 1400 data sources. FRED 3.0 has 45% more root trait observations than FRED 2.0, particularly in the categories of root anatomy, morphology, and microbial associations; ancillary data on associated site, vegetation, edaphic, and climatic conditions from across the globe have also increased concurrently. FRED is focused on fine roots (traditionally defined as roots less than 2 mm in diameter), as coarse roots are studied using different methodology, often at very different scales, and have different traits and trait interpretations. However, FRED accepts data collected from roots of all sizes, and already contains several observations of coarse roots. Data collection will continue for the foreseeable future.

Decisions demand data, and poor quality data can lead to wrong, inaccurate, or late decisions. The way in which data is collected, stored and shared will reverberate in its quality and accuracy, consequently, reflecting on the ability to understand the aspects they represent. The private sector acting in the environmental area demands objectivity and assertiveness, and that is why it is essential to treat the data that subsidize conservation and restoration actions with exceptional care. To assess the state of biodiversity and environmental impacts, extensive field surveys are often required; for this, independent service providers are hired, who are specialized in obtaining a variety of types of information. Consequently, different collection methods are applied, and almost always methodological and formatting inconsistencies can be found in the resulting data. For the subsequent integration of this data into databases, it will be necessary to extract, adjust and standardize them, generating an entirely new demand, consuming time, human effort and financial resources. In addition, this demand also increases the risk of misinterpretation, typing and digitization errors, which can compromise quality, or even lead to loss of information. The standardization of data used in the survey, inventories, storage and sharing processes is a strategic solution to increase efficiency, reduce costs and risks of information degradation and loss. Furthermore, it brings a number of other benefits, such as the transformation of the analogic field recording system (field notebooks) to an entirely digital format, with the integration of cameras, tablets, dataloggers, and other widely available technologies. When it comes to preparing a recommendation for the standardization of data in a comprehensive and inclusive way, we mapped the biodiversity data frequently used by researchers from the Biodiversity and Ecosystem Services group at The Instituto Tecnológico Vale. Through this mapping, we seek to understand the types of data that already exist, how they have been used, stored and shared in databases, but also their convergence and peculiarities. With the participation of researchers, we seek to develop and validate a preliminary system of terms and metadata, including recommendations for best practices, aiming to improve the use of environmental and biodiversity data. The mapping showed a series of correspondences regarding the types of data used by the BES-ITV group, especially in the data applied in studies of Conservation and Restoration, Landscape Ecology, Genomics and Radio Frequency Identification. But also a great diversity of research topics (Total=29), focusing on six large biological groups, aspects that demonstrate the high multidisciplinary and wide coverage of environmental, ecological, genetic and biodiversity data used by the group. Based on these results, a system of terms and metadata is being developed, as well as the idealization of a modular system for the automatic generation of field digital spreadsheets, in order to simplify data collection through exclusively digital means.

A list of all stakeholders (trade associations, companies, NGOs, consultants, law firms and individuals) that have requested to be kept updated on the environmental regulations for which BIS has the lead Government responsibility.

The dataset contains a current inventory of vascular plant species and their attributes present in the flora of Britain and Ireland. The species list is based on the most recent key to the flora of Britain and Ireland, with taxon names linked to unique Kew taxon identifiers and the World Checklist of Vascular Plants, and includes both native and non-native species. Attribute data stem from a variety of sources to give an overview of the current state of the vascular flora. Attributes include functional traits, distribution and ecologically relevant data (e.g. genome size, chromosome numbers, spatial distribution, growth form, hybridization metrics and native/non-native status). The data include previously unpublished genome size measurements, chromosome counts and CSR life strategy assessments. The database aims to provide an up-to-date starting point for flora-wide analyses. Full details about this dataset can be found at https://doi.org/10.5285/9f097d82-7560-4ed2-af13-604a9110cf6d

The Habitat Use Database (HUD) was specifically designed to address the need for habitat-use analyses in support of groundfish EFH, HAPCs, and fishing and nonfishing impacts components of the 2005 EFH EIS. HUD functionality and accessibility, and the ecological information upon which the HUD is based, will be improved in order for this database to fully support fisheries and ecosystem science and management. Upgrades to and applications of the HUD will be facilitated through a series of prioritized phases: • Fully integrate the data entry, quality control, and reporting capabilities from the original HUD Access database with a web-based and programmatic interface. Improve software for HUD to accommodate the most current habitat maps and habitat classification codes. This will be achieved by NMFS in consultation with HUD architects at Oregon State University. • Review and update the biological and ecological information in the HUD. • Develop and apply improved models that will be used to create updated habitat suitability maps for all west coast groundfish species using the updated HUD and Pacific coast seafloor habitat maps. • Integrate habitat suitability models with the online groundfish EFH data catalog (http://efh-catalog.coas.oregonstate.edu/overview/). 2005 habitat-use analysis supporting groundfish EFH.

Ecological theories often encompass multiple levels of biological organization, such as genes, individuals, populations, and communities. Despite substantial progress toward ecological theory spanning multiple levels, ecological data rarely are connected in this way. This is unfortunate because different types of ecological data often emerge from the same underlying processes and, therefore, are naturally connected among levels. Here, we describe an approach to integrate data collected at multiple levels (e.g., individuals, populations) in a single statistical analysis. The resulting integrated models make full use of existing data and might strengthen links between statistical ecology and ecological models and theories that span multiple levels of organization. Integrated models are increasingly feasible due to recent advances in computational statistics, which allow fast calculations of multiple likelihoods that depend on complex mechanistic models. We discuss recently developed integrated models and outline a simple application using data on freshwater fishes in south-eastern Australia. Available data on freshwater fishes include population survey data, mark-recapture data, and individual growth trajectories. We use these data to estimate age-specific survival and reproduction from size-structured data, accounting for imperfect detection of individuals. Given that such parameter estimates would be infeasible without an integrated model, we argue that integrated models will strengthen ecological theory by connecting theoretical and mathematical models directly to empirical data. Although integrated models remain conceptually and computationally challenging, integrating ecological data among levels is likely to be an important step toward unifying ecology among levels.