Biodiverskripsi seeks to increase the accessibility of local biodiversity research in Indonesia by collating ecological monitoring data inherent in student theses in a sustainable platform to ease its use in a larger scope of research. We expect this initiative to fill the knowledge gap regarding Indonesian ecological data, to assist more advanced and impactful researches such as designing a conservation area or modelling the effect of climate change on biodiversity, and further improve scientific practices in Indonesian universities.

This dataset is part of a long-term study at the Sevilleta LTER measuring net primary production (NPP) across four distinct ecosystems: creosote-dominant shrubland (Site C, est. winter 1999), black grama-dominant grassland (Site G, est. winter 1999), blue grama-dominant grassland (Site B, est. winter 2002), and pinon-juniper woodland (Site P, est. winter 2003). Net primary production is a fundamental ecological variable that quantifies rates of carbon consumption and fixation. Estimates of NPP are important in understanding energy flow at a community level as well as spatial and temporal responses to a range of ecological processes. Above-ground net primary production is the change in plant biomass, represented by stems, flowers, fruit and and foliage, over time and incoporates growth as well as loss to death and decomposition. To measure this change the vegetation variables in this dataset, including species composition and the cover and height of individuals, are sampled twice yearly (spring and fall) at permanent 1m x 1m plots within each site. A third sampling at Site C is performed in the winter. The data from these plots is used to build regressions correlating biomass and volume via weights of select harvested species obtained in SEV157, "Net Primary Productivity (NPP) Weight Data." This biomass data is included in SEV182, "Seasonal Biomass and Seasonal and Annual NPP for Core Research Sites." This dataset is designated as NA-US-011 in the Global Index of Vegetation-Plot Databases (GIVD). To aid tracking of the use of databases in this index, please also reference this number when citing this data. The GIVD report for SEV129 can be found in: Biodiversity and Ecology 4 - Vegetation Databases for the 21st Century (2012) by J. Dengler et al.

In order to support the Southeast Asian biodiversity research Institute Chinese Academy of Sciences to carry out botanical research, the scientific and IT work team of Kunming Institute of Botany has constructed a data set and developed an information service platform of plant biodiversity in Myanmar. The data set has systematically filtered and integrated most of published biodiversity data and information scattered in different platforms in the world. Including plant checklist, chorography, specimen records and literature, the data set contains about 15 thousand of plant species in Myanmar, and 450 thousand data records totally. The integrated scientific data and service will support for the botanical research in this area.

The data includes a compilation of group memberships for experts appointed to the work of the Intergovernmental Panel on Climate Change (IPCC), the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), the Future Earth Programme and the World Climate Research Programme (WCRP). The data was collected as part of the Government's analysis, assessment and research activities (VN TEAS) project on the state and potential of Finnish science diplomacy. The data includes scientists and experts formally nominated and participating in the assessment processes of the Intergovernmental Panel on Climate Change (IPCC) and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) that took place between 2016 and 2021. In addition, the dataset includes information on the scientists appointed in 2020 to two international scientific organisations closely associated with the IPCC and IPBES, Future Earth and the World Climate Research Programme (WCRP). The data collected is presented as an undirected, bivariate network analysis matrix. For each respondent, it is indicated whether the respondent belongs to the mentioned forums. For IPCC and IPBES, national contact points and secretariat representatives are excluded, and for Future Earth and WCRP, secretariat representatives and numerous occasional volunteers from the networks are excluded. For Future Earth and WCRP, only researchers whose details were available on the organisations' websites and who participated in forums that support the evaluation of research data or evaluation work in writing (excluding forums that are only networking and do not evaluate or produce data) have been included. In the observation matrix, each individual has been marked only once, i.e. any duplications have been removed. The membership matrix can be used to analyse the relationships between the experts appointed to write and peer review IPCC and IPBES reports, and hence also between the different review processes or forums of the organisations (membership of forums is indicated in the data with values 1 or 0, i.e. either a relationship exists or does not exist). There are 101 forums listed in the data. The data can be analysed using various network analysis tools, e.g. unidimensionally for relationships within the IPCC, such as which and which types of organisations are key background organisations for IPCC experts, or bidimensionally for relationships between all four groups of actors. The networks created by the organisations can also be compared with each other. From the data, basic information (such as frequencies of occurrence) can be calculated for the countries or groups of countries most represented in the networks. The background variables included the respondent's expert status, nationality, organisational membership and presumed gender. For those researchers who have membership of more than one forum, the role that is the most responsible of all the roles of the person is noted.

Diversity of biological, geological, and cultural record types held in Arctos collections.

Although species are being lost at alarming rates, previous research has provided conflicting results on the extent and even direction of global biodiversity change at the local scale. Here, we assessed the ability to detect global biodiversity trends using local species richness and how it is affected by the number of monitoring sites, sampling interval (i.e., time between original survey and re-survey of the site), measurement error (error of the measurement of the local species richness), spatial grain of monitoring (a proxy for the taxa mobility), and spatial sampling biases (i.e., site-selection biases). We use PREDICTS model-based estimates as a proxy for the real-world distribution of biodiversity and randomly selected monitoring sites to calculate local species richness trends. We found that while a monitoring network with hundreds of sites could detect global change in species richness within a 30-year period, the number of sites for detecting trends doubled for a decade, increased 10-fold within three years, and yearly trends were undetectable. Measurement errors had a non-linear effect on statistical power, with a 1% error reducing statistical power by a slight margin and a 5% error drastically reducing the power to reliably detect any trend. The ability to detect global change in local species richness was also related to spatial grain, making it harder to detect trends for sites sampled at smaller plot sizes. Spatial sampling biases not only reduced the ability to detect negative global biodiversity trends but sometimes yielded positive trends. We conclude that detecting accurate global biodiversity trends using local richness may simply be unfeasible with current approaches. We suggest that monitoring a representative network of sites implemented at the national level, combined with models accounting for errors and biases, can help improve our understanding of global biodiversity change.

A major goal of community ecology is understanding the processes responsible for generating biodiversity patterns along spatial and environmental gradients. In stream ecosystems, system specific conceptual frameworks have dominated research describing biodiversity change along longitudinal gradients of river networks. However, support for these conceptual frameworks has been mixed, mainly applicable to specific stream ecosystems and biomes, and these frameworks have placed less emphasis on general mechanisms driving biodiversity patterns. Rethinking biodiversity patterns and processes in stream ecosystems with a focus on the overarching mechanisms common across ecosystems will provide a more holistic understanding of why biodiversity patterns vary along river networks. In this study, we apply the Theory of Ecological Communities (TEC) conceptual framework to stream ecosystems to focus explicitly on the core ecological processes structuring communities: dispersal, speciation, niche selection, and ecological drift. Using a unique case study from high elevation networks of connected lakes and streams, we sampled stream invertebrate communities in the Sierra Nevada, CA to test established stream ecology frameworks and compared them to the TEC framework. Local diversity increased and β-diversity decreased moving downstream from the headwaters, consistent with the river continuum concept and the small but mighty framework of mountain stream biodiversity. Local diversity was also structured by distance below upstream lakes, where diversity increased with distance below upstream lakes, in support of the serial discontinuity concept. Despite some support for the biodiversity patterns predicted from the stream ecology frameworks, no single framework was fully supported, suggesting “context dependence”. By framing our results under the TEC, we found species diversity was structured by niche selection, where local diversity was highest in environmentally favorable sites. Local diversity was also highest in sites with small community sizes, countering predicted effects of ecological drift. Moreover, higher β-diversity in the headwaters was influenced by dispersal and niche selection, where environmentally harsh and spatially isolated sites exhibit higher community variation. Taken together our results suggest that combining system specific ecological frameworks with the TEC provides a powerful approach for inferring the mechanisms driving biodiversity patterns and provides a path toward generalization of biodiversity research across ecosystems. Methods Study Area The study area was located in the Sierra Nevada Mountains of eastern California (USA) and encompasses portions of Inyo National Forest and Sequoia-Kings Canyon National Park. Over the ice-free seasons (June-September), we sampled five distinct lake-stream networks, where each network was within a spatially distinct catchment and were treated as independent replicate systems (Fig. 3). The Kern (n=24) and Bubbs (n=26) networks were sampled in 2011, the Evolution (n=21) and Cascades (n=11) networks in 2018, and Rock Creek (n=36) in 2019. For each lake-stream network, streams were sampled throughout the network along a spatial gradient from headwaters downstream as well as along a spatial gradient downstream from lakes. Because the spatial distances of the river networks and the distance separating lakes naturally vary among networks as well as backcountry sampling constraints, the number of sites sampled along the distance from headwaters gradient varied (n=11 to n=36) and the downstream lake gradient varied (n=1 to n=9). This field system and the data collected naturally provide spatial gradients relevant to test stream ecology theories. In addition, this data is ideal for testing TEC processes because of the naturally varying gradients of community size, connectivity, and environmental heterogeneity present in our sampling design. Field Methods At each sampling location, we established transects in riffle sections of streams. At five equally spaced points along transects we measured stream depth and current velocity at mid-depth using a portable flow meter (Marsh-McBirney Flow Mate 2000). We then calculated stream discharge as the sum of the product of average depth x current velocity x width/5 over all transect points (Gordon et al. 2010; Herbst et al. 2018). A calibrated YSI multiparameter device was placed above transects to measure temperature, dissolved oxygen, conductivity, and pH. Benthic chlorophyll data was collected by scrubbing the entire surface area of three randomly selected cobble sized rocks (64-255 mm) of benthic algae (periphyton) with a toothbrush for 60 seconds (Herbst and Cooper 2010). Chlorophyll measurements were taken using a handheld fluorometer (Turner Designs Aquafluor), which measures raw fluorescence units. Florescent measurements were calibrated to chlorophyll concentration using a known concentration of Rhodamine. We standardized chlorophyll measurements by accounting for both the surface area of rocks and volume of water used to remove algae. Eight to twelve macroinvertebrate samples at each site were collected using a D-frame kick net (250 mm mesh, 30cm opening, 0.09m2 sample area) in riffle habitats, depending on the density of macroinvertebrate samples collected. We took samples by placing the net on the streambed, then turning and brushing all substrate by hand in the sampling area (30cm x 30cm) immediately above the net, with dislodged invertebrates being carried by currents into the net. All macroinvertebrate samples were preserved in 75% ethanol within 48 hours of sampling. Samples were sorted, identified, and counted in the laboratory. Taxa were identified to the finest taxonomic level possible, usually to genus or species for insects (excluding Chironomidae) and order or class for non-insects (Merritt, Cummins, and Berg 2019). The replicate samples taken at each site were pooled together and divided by the number of replicates and the area sampled to determine the density of invertebrate communities. Spatial Data Stream distance measurements were taken using the R package “riverdist”, which utilizes data from the USGS National Hydrological Dataset Flowline in order to determine pairwise distances from sampling sites along the river network (Tyers 2020). We determined distance below upstream lakes, with the closest upstream lake location being the outlet of the lake determined by the USGS Watershed Boundary Dataset. For sites where multiple upstream lakes were draining into streams, we defined the upstream lake as the closest upstream lake to sites that was also along the mainstem of the flowline. We determined distance from headwaters as the streamwise distance from sites to the uppermost portion (headwaters) of the mainstem of streams, where the headwaters of streams was determined by the endpoint (beginning) of the flowline in the USGS NHD Flowline Dataset (U.S. Geological Survey 2016). In cases where multiple headwater stream reaches corresponded to downstream sites, we defined the headwaters as the particular reach that accounted for the most discharge which was determined using USGS Flowline Dataset. Upstream lake area and perimeter measurements were determined using the USGS Watershed Boundary Dataset. Land-cover proportions were computed using the 2016 USGS National Land Cover Database (Jin et al. 2019).

The project, "Integrating Livelihoods and Multiple Biodiversity Values in Landscape Mosaics (or the Landscape Mosaics Project in short)", was the first project of the CIFOR-ICRAF Biodiversity Platform. The project conducted research on socio-economic, governance and biophysical characteristics and dynamics of the five study landscapes and the interactions between these factors. It also investigated the potential for reward mechanisms for environmental services. The project aimed to inform and facilitate negotiation processes on natural resource use rights allocation between communities and district level and other key stakeholders in order to enable them to manage landscape mosaics more sustainably. The project worked in the following study sites: Tanzania: East Usambara Mountains, Tanga Region; South West Cameroon: Takamanda-Mone Technical Operation Unit; Sumatra, Indonesia: Bungo District, Jambi Province; Northern Laos: Vieng Kham District, Luang Prabang Province; and Eastern Madagascar: Manompana corridor, Soanierana-Ivongo District. Within these countries, a landscape was selected that reflected a gradient from a densely forested protected area to land covers fragmented by agricultural uses. From these landscapes, three representative territories (villages) were selected in which the support to negotiations and empirical research took place. The project was funded by the Swiss Agency for Development and Cooperation and supported by other donors such as the European Commission, the Governments of Finland, the Netherlands and Australia. The Biodiversity Platform was launched in 2006 as a joint initiative of CIFOR and the World Agroforestry Centre. The Platform was launched in recognition of the role that multifunctional landscape mosaics have in preserving biodiversity conservation, both within and outside of protected areas. Tree cover in multifunctional landscape mosaics preserves important habitats and can play a crucial role in maintaining connectivity between large reserves, which has been demonstrated to be essential for the survival of many species. The occupation and use of these landscapes by many peoples, however, require that any conservation efforts in these mosaics consider the social dimensions of the use and conservation of biodiversity, in addition to their biophysical dimensions and dynamics.

This dataset examines the relationship between land use changes and biodiversity trends over time, covering the years 2005, 2010, 2021, and 2024. The primary source of raw data is the UN Data for Environment: Land Estimates, last updated in November 2024. It helps researchers, policymakers, and environmentalists analyze how human activities impact ecosystems and biodiversity conservation efforts.

Research Hypothesis: We hypothesize that increased agricultural land use and deforestation correlate with a decline in biodiversity protection. Specifically:

Higher arable land percentages lead to lower forest cover and fewer protected biodiversity sites. Countries with stronger conservation policies will show higher biodiversity protection values despite land use changes. Regional differences will depend on policy interventions, climate conditions, and economic development.

Key Findings: Deforestation is increasing in several regions due to agricultural expansion. Biodiversity protection efforts are uneven, with some countries improving and others stagnating. Some nations successfully balance agriculture and conservation, while others struggle with sustainability.

Dataset Structure The dataset has two key components:

🌍 Land Use Data (final_filtered_countries_cleaned.csv) Country & Year – Tracks changes over time Arable Land % – Land used for crops Forests % – Land covered by forests Crops % – Land for permanent crops Use – Helps monitor deforestation and land degradation trends 🦋 Biodiversity Data (bio_countries_cleaned.csv) Country & Year – Tracks biodiversity indicators Series – Description of the indicator measured Value – % of key biodiversity sites under protection Use – Helps track conservation and biodiversity protection How to Interpret the Data High arable land percentages & declining forest cover → Rapid agricultural expansion. Increasing protected biodiversity sites → Effective conservation policies. Year-to-year comparisons → Identify trends and policy effectiveness. Cross-country comparisons → Understand sustainable land-use practices. Applications & Importance ✔ Tracking Environmental Changes – Helps monitor deforestation and biodiversity loss. ✔ Policy Decision-Making – Supports sustainable land-use planning. ✔ Sustainable Development Goals (SDG 15) – Provides insights into forest and biodiversity conservation. ✔ Scientific Research & Climate Studies – Helps study climate change impacts. ✔ Educational Use – Real-world data for students and educators.

This dataset is a valuable resource for understanding land use, conservation policies, and biodiversity protection worldwide.

Dataset last updated: 8th January 2025

This dataset provides indicative areas of biodiversity hotspots in Greater London, identified by research and data analysis using methods derived from the Greater London Authority’s (GLA) “Planning for Biodiversity?” report (2016).

The dataset has been created by Greenspace Information for Greater London CIC (GiGL). GiGL mobilises, curates and shares data that underpin our knowledge of London’s natural environment. We provide impartial evidence to enable informed discussion and decision-making in policy and practice. The dataset is based on GiGL partnership data which are continuously updated.

The underlying data for the dataset may have been subject to changes since the current version was modelled. Subsequent versions will provide updated information from the GiGL database annually. The dataset is a coarse-resolution presentation of high-resolution data. To access data at their original resolution, please contact GiGL or visit www.gigl.org.uk for more information.

Research for this dataset has been assisted by London and South East England Local Records Centres (LaSER) and the London Boroughs Biodiversity Forum (LBBF), and is based on advice provided by the Open Data Institute (ODI).

Description

To meet Policy G6 D of The London Plan (2021), the capital’s spatial development strategy, " Development proposals should manage impacts on biodiversity and aim to secure net biodiversity gain. This should be informed by the best available ecological information and addressed from the start of the development process".

The Biodiversity Hotspots for Planning (BHP) dataset provides developers, homeowners and LPAs an indication of areas, where data are available, that have potential impacts on biodiversity and are likely to be relevant to local planning decisions by applying biodiversity criteria developed by GiGL, based on the original “Planning for Biodiversity?” research. ‘Hotspot’ areas indicate a detected presence of sensitive biodiversity that could potentially be affected by development. Original records can be accessed from GiGL to assist the decision-making process.

N.B. 1: Areas without these biodiversity indicator records may still have undetected biodiversity so should also be considered for biodiversity potential on a case-by-case basis.

N.B. 2: The dataset is purely indicative and an ecological data search report must still be commissioned as evidence for planning applications. See here for help on this.

Specification

The GIS file shows London as 100m hexagon tiles. Each tile is scored for the known presence of protected species, sites and habitats impact areas based on the impact buffer size as specified in the criteria table below, giving a cumulative score range of 0 to 3. Tiles are considered a hotspot where impact areas overlap the tile by more than 10%.

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Tiles with a score of 0 indicate that there are currently no known protected species, sites or habitats impact areas present in that area based on the criteria table, which excludes some protected species. Tiles with a score of 3 indicate the presence of impact areas for all three categories. Intermediate scores indicate the presence of impact areas for one or more of the categories without specifying which are present. The scores can be used in a thematic map to colour the tiles and visually indicate areas with greater presence of impact areas. A sample thematic map is provided.

The dataset will be updated annually using the latest protected species, sites and habitats data available to GiGL at time of creation. Please give GiGL appropriate credit when using, adapting or sharing the dataset following the guidance below:

In-text citation: GiGL, [dataset creation date]

Reference: "Biodiversity Hotspots for Planning" Greenspace Information for Greater London CIC, [dataset creation date]

Where data is used in maps: Map displays GiGL data [dataset creation date] </blockq

Global biodiversity loss is arguably the biggest problem facing humanity. Climate change, changes in land and sea use and other factors are synergistically eroding biodiversity to an unprecedented speed and extent, with cascading impacts on humanity and our livelihoods. Scientific advice on safeguarding biodiversity depends on all available information to understand past and current developments, and predict future responses of Earth’s ecosystems. This challenge requires integrative research across space, time, methods, and taxa, and integration of these data into a new generation of biodiversity models. Such research is currently thwarted because biodiversity data are stored in different formats and databases, and the largest sources of biodiversity data are still contained in physical repositories that are not fully accessible: collections of geological and biological specimens. To overcome this shortfall, natural history collections must be developed into specimen-based, integrated, and digitally accessible research platforms. We propose that a new conceptual framework, Collectomics, is required to underpin this vision; this aegis embraces the entirety of collection-based research, although we focus here on how it enables and fuels the research necessary to effectively confront the Anthropocene biodiversity crisis. Current technological developments provide an unprecedented opportunity to unleash the full potential of collections by fully integrating the myriad data dimensions from collection objects (manuscript submitted to PNAS on 6th April 2021, full manuscript available for download).

Authors: eMonocot Cypripedioideae Data type: darwin core archive Darwin Core Archive created from the eMonocot Cypripedioidea Scratchpad on 06/01/2014. File: oo_5522.zip

The project, "Integrating Livelihoods and Multiple Biodiversity Values in Landscape Mosaics (or the Landscape Mosaics Project in short)", was the first project of the CIFOR-ICRAF Biodiversity Platform. The project conducted research on socio-economic, governance and biophysical characteristics and dynamics of the five study landscapes and the interactions between these factors. It also investigated the potential for reward mechanisms for environmental services. The project aimed to inform and facilitate negotiation processes on natural resource use rights allocation between communities and district level and other key stakeholders in order to enable them to manage landscape mosaics more sustainably. The project worked in the following study sites: Tanzania: East Usambara Mountains, Tanga Region; South West Cameroon: Takamanda-Mone Technical Operation Unit; Sumatra, Indonesia: Bungo District, Jambi Province; Northern Laos: Vieng Kham District, Luang Prabang Province; and Eastern Madagascar: Manompana corridor, Soanierana-Ivongo District. Within these countries, a landscape was selected that reflected a gradient from a densely forested protected area to land covers fragmented by agricultural uses. From these landscapes, three representative territories (villages) were selected in which the support to negotiations and empirical research took place. The project was funded by the Swiss Agency for Development and Cooperation and supported by other donors such as the European Commission, the Governments of Finland, the Netherlands and Australia. The Biodiversity Platform was launched in 2006 as a joint initiative of CIFOR and the World Agroforestry Centre. The Platform was launched in recognition of the role that multifunctional landscape mosaics have in preserving biodiversity conservation, both within and outside of protected areas. Tree cover in multifunctional landscape mosaics preserves important habitats and can play a crucial role in maintaining connectivity between large reserves, which has been demonstrated to be essential for the survival of many species. The occupation and use of these landscapes by many peoples, however, require that any conservation efforts in these mosaics consider the social dimensions of the use and conservation of biodiversity, in addition to their biophysical dimensions and dynamics.

GEO BON with its scientific partners introduces a new generation of global indicators integrating biodiversity observations, remote sensing data, and models for assessing progress towards the CBD Strategic Plan 2011-2020 and Aichi Targets 5, 11, 12, 14, 15 and 19. A GEO BON (the Group on Earth Observations Biodiversity Observation Network) consortium involving researchers and organizations around the world has developed a novel set of global indicators to address important gaps in our understanding of biodiversity change across scales, from national to global. These indicators are embedded in open online analysis platforms following GEO data sharing principles and have the long-term commitment of established research institutions. The new set of indicators is characterized by the rigorous use of open access large global datasets, state of the art remote-sensing based information, model-based integration of multiple data sources and types, including in situ (ground based) observations, and online infrastructure enabling inexpensive and dynamic updates, with full transparency. This has become possible through direct collaboration with technical and research support partners such as Google and NASA, the development of a dedicated infrastructure such as Map of Life, and the engagement of the larger GEO BON community

The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas, updated on a monthly basis, and is one of the key global biodiversity data sets being widely used by scientists, businesses, governments, International secretariats and others to inform planning, policy decisions and management. The WDPA is a joint project between UN Environment and the International Union for Conservation of Nature (IUCN). The compilation and management of the WDPA is carried out by UN Environment World Conservation Monitoring Centre (UNEP-WCMC), in collaboration with governments, non-governmental organisations, academia and industry. There are monthly updates of the data which are made available online through the Protected Planet website where the data is both viewable and downloadable. Data and information on the world's protected areas compiled in the WDPA are used for reporting to the Convention on Biological Diversity on progress towards reaching the Aichi Biodiversity Targets (particularly Target 11), to the UN to track progress towards the 2030 Sustainable Development Goals, to some of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) core indicators, and other international assessments and reports including the Global Biodiversity Outlook, as well as for the publication of the United Nations List of Protected Areas. Every two years, UNEP-WCMC releases the Protected Planet Report on the status of the world's protected areas and recommendations on how to meet international goals and targets. Many platforms are incorporating the WDPA to provide integrated information to diverse users, including businesses and governments, in a range of sectors including mining, oil and gas, and finance. For example, the WDPA is included in the Integrated Biodiversity Assessment Tool, an innovative decision support tool that gives users easy access to up-to-date information that allows them to identify biodiversity risks and opportunities within a project boundary. The reach of the WDPA is further enhanced in services developed by other parties, such as the Global Forest Watch and the Digital Observatory for Protected Areas, which provide decision makers with access to monitoring and alert systems that allow whole landscapes to be managed better. Together, these applications of the WDPA demonstrate the growing value and significance of the Protected Planet initiative.

Biodiversity surveys are becoming increasingly popular. However, standard analysis techniques for these data have not yet been developed. This paper explores the use of multivariate ordination techniques for assessing species-habitat relationships using biodiversity data. The research was conducted in Glacier National Park, Montana, and birds and butterflies were chosen as the taxonomic groups of study. Biodiversity assessment sites were established through a range of habitats and monitored from 1987 through 1989. Presence/absence sampling over the total number of sampling sites was used to classify species commonness and rarity. Approximately 86% of the historically recorded butterflies and 70% of the historically recorded bird species have been observed in the 3 yr of sampling. During the 3 yr of this study there was a striking continuity of species richness per site. There was also a striking overlap between the sites that support high species diversity and sites that support rare species. Principal components analysis and cluster analysis worked well in discerning species-habitat relationships. Elevation, structural diversity of the site, and moisture were the major factors explaining species distributions. A chi-square analysis also provided some insights into species-habitat relationships, showing birds were more habitat specific than butterflies. Habitat diversity analyses demonstrated a positive but non-significant correlation between remotely sense spectral-class diversity of a site and species richness for both birds and butterflies. Aspect, slope and elevation diversity had a negative or negligible relationship with species richness.

This is a dataset containing extensive information on agroforestry systems (AFSs) and reference vegetation areas where the diversity of taxonomic groups or growth forms was measured. AFSs and their reference areas are located in tropical ecoregions across de globe, and data were gathered through a systematic review including 92 papers and 294 data sampling sites. We considered each taxonomic group or growth form studied in a sampling site (AFS or reference vegetation area) as an observation, and extracted the following information to the file “Main_data.csv†: location, ecoregion, climate, soil, size, method of biodiversity sampling, and number of species, individuals, and/or biodiversity index found in each AFS and reference area; origin (whether the AFS originated from areas with alternative land use, such as agriculture, pastures, or degraded areas, or in areas with natural vegetation that are converted into productive systems), type (simple or biodiverse), age, crop type, management ..., For our systematic review on biodiversity in agroforestry systems (AFSs) implemented in tropical ecoregions, we searched for papers using the following keywords present in the title: field 1- agroforest*; field 2- diversity OR richness OR abundance; field 3- tropical. We conducted our search in the platforms Periódicos CAPES, Web of Science, Scielo, and Google Scholar. We did not restrict the oldest publication date and we finished our search in July 2023. For a paper to be included in the systematic review, it necessarily had to compare the abundance, richness, and/or diversity index of some taxonomic group or growth form between AFSs and reference vegetation areas located in tropical ecoregions, anywhere in the world. We considered each taxonomic group or growth form studied in an AFS or reference area as an observation and extracted the information provided in Main_data.csv., # Data from: Biodiversity in agroforestry systems implemented in tropical ecoregions: A systematic review

Dataset DOI: 10.5061/dryad.dr7sqvb8v

Description of the data and file structure

This dataset contains the data used in the article:

Ortolan, E.; Maciel, E.A. & Martins, V.F. Biodiversity in agroforestry systems implemented in tropical ecoregions: a systematic review. Journal of Environmental Management.

Authors:

Ezequiel Ortolan

Programa de Pós-Graduação em Agricultura e Ambiente, Universidade Federal de São Carlos (UFSCar), Araras, SP, Brazil.

E-mail: ziqueortolan@yahoo.com.br

Everton A. Maciel

Chair of Plant Ecology, University of Bayreuth, Universitätsstr. 30, 95447 Bayreuth, Germany.

Departamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil.

E-mail: Everton.Maciel@uni-bayreuth.de

Valéria Forni Mar...,

This repository contains files of spatial environmental data layers which were used as predictors of nesting habitat suitability for six biodiversity indicator bird species in Finland: (i) three hawk species, the European honey buzzard (Pernis apivorus), the northern goshawk (Accipiter gentilis) and the common buzzard (Buteo buteo), and (ii) three woodpecker species, the white-backed woodpecker (Dendrocopos leucotos), the lesser spotted woodpecker (Dryobates minor) and the Eurasian three-toed woodpecker (Picoides tridactylus). These six bird species have been shown to provide useful indicators of different conservation and biodiversity values of boreal forests, such as occurrences of red listed polypores, and indicative of forest characteristics related to old-growth forests such as representative occurrences of dead wood. The modelling spesifically targetted the nest sites of the bird species where the role of critical suitable environmental conditions is elevated. The data of nesting sites of the bird species are not open access data due to its sensitivity, but can be requested for research purposes by sending a query to the head of the Zoology unit at the Finnish Museum of Natural History. However, the Maxent results of the nesting habitat suitability for bird species across the whole of Finland are available in Zenodo (https://doi.org/10.5281/zenodo.4779108). Please also note that the environmental data is split - due to its large size - into two repository locations, this one containing the 1 km buffer variables and the two climate variables, and the other one containing the local site variables and 500 m buffer variables. Taken together, 40 different environmental data layers were developed and their information sampled for a 96 x 96 m resolution lattice system covering the whole Finland. These 40 data layers were organised for the modeling into the following groups of predictor variables: (1) Data on forest structure and other forest stand characteristics (96 x 96 m cell) (8 variables), (2) Data on land cover at the forest stand (96 x 96 m cell) (8 variables), (3) Data on forest characteristics in the 500 m landscape buffer area (3 variables), (4) Data on forest characteristics in the 1 km landscape buffer area (3 variables), (5) Data on land cover in the 500 m landscape buffer area, (6) Data on land cover in the 500 m landscape buffer area, and (7) Climate data (2 variables). These data were used to examine what are the key determinants of the nesting site suitability of the six indicator bird species and to developed predictive maps across the whole Finland for the locations of most optimal nesting forest areas. The nesting habitat suitability modelling was done using the MaxEnt model. The forest structure and habitat quality predictor variables were developed based on national forest data gathered from three sources: (i) Finnish Forest Center (FFC), (ii) Metsähallitus Parks & Wildlife (MPW), and (iii) the multi-source national forest inventory carried out by the Natural Resources Institute Finland (LUKE). The land cover variables were measured using the CORINE Land Cover 2018 system, from the database produced, maintained and distributed by Syke. The two climate variables were initially for the SUMI project by the Finnish Meteorological Institute and further applied in this modelling study. The environmental variables, the six indicator bird species and the processses, steps and choices included in the MaxEnt modelling are described in full detail in the following publication: Virkkala, Raimo, Leikola, Niko, Kujala, Heini, Kivinen, Sonja, Hurskainen, Pekka, Kuusela, Saija, Valkama, Jari and Heikkinen, Risto K.: Developing fine-grained nationwide predictions of valuable forests using biodiversity indicator bird species, Ecological Applications, in press. The details of the environmental data are also described in the read_me.doc file uploaded into this repository.

This record describes the sediment collection and derived grainsize and composition data from two Marine National Facility charter voyages conducted under the Benthic Characterisation Project (3.1) of the Great Australian Bight Research Program (GABRP): SS2013_C02 and IN2016_C02. The GABRP aims to describe the key elements of the GAB marine ecosystem. This understanding of the structure and function of the ecosystem will be used to inform future integrated and sustainable ocean management and assessment/mitigation of potential future impacts. An overarching objective of the voyages was to contribute to developing models of ecosystem-level structure and function for the GAB. Sediments were collected primarily using the Integrated Coring Platform (ICP) and supplemented with the Smith-MacIntyre grab. The Integrated Coring Platform ( ICP) combines a number of technologies to maximise sampling in a single deployment. The ICP is built around a 6 barrel corer (KC, Denmark) and together with its central electronics module integrates cameras (cable, seafloor and corer views), CTD (SBE37IDO), altimeter, 120KHz scientific echo-sounders, Niskin bottles and hydrocarbon sensor suite. Sensor data is delivered in real time to the surface via fibre optic deployment cable. The Smith-MacIntyre grab is a comparatively simple tool collecting sediments. This metadata record describes the sediment collection using the grab and ICP taken on 5 transects in the central and eastern GAB at 6 depth strata (200m, 400m, 1000m, 1500m, 2000m and 3000m).

Policies requiring biodiversity no net loss or net gain as an outcome of environmental planning have become more prominent worldwide, catalysing interest in biodiversity offsetting as a mechanism to compensate for development impacts on nature. Offsets rely on credible and evidence-based methods to quantify biodiversity losses and gains. Following the introduction of the United Kingdom’s Environment Act in November 2021, all new developments requiring planning permission in England are expected to demonstrate a 10% biodiversity net gain from 2024, calculated using the statutory biodiversity metric framework (Defra, 2023). The metric is used to calculate both baseline and proposed post-development biodiversity units, and is set to play an increasingly prominent role in nature conservation nationwide. The metric has so far received limited scientific scrutiny. This dataset comprises a database of statutory biodiversity metric unit values for terrestrial habitat samples across England. For..., Study sites We studied 24 sites across the Environmental Change Network (ECN), Long Term Monitoring Network (LTMN) and Ecological Continuity Trust (ECT). Biodiversity units were calculated following field visits by the authors, whilst species data (response variables) were derived from long-term ecological change monitoring datasets collected by the sites and mostly held in the public domain (Table S1). We used all seven ECN sites in England. We selected a complementary 13 LTMN sites to give good geographic and habitat representation across England. We included four datasets from sites supported by the ECT where 2 x 2m vascular plant quadrat data were available for reuse. The 24 sites included samples from all terrestrial broad habitats (sensu Defra 2023) in England, except urban and individual trees: grassland (8), wetland (6), woodland and forest (5), sparsely vegetated land (2), cropland (2), heathland and shrub (1). Non-terrestrial broad habitats (rivers and lakes, marine inlets and..., Microsoft Excel/Open Office, # A database of Defra statutory biodiversity metric unit values for terrestrial habitat samples across England, with plant, butterfly and bird species data

Policies requiring biodiversity no net loss or net gain as an outcome of environmental planning have become more prominent worldwide, catalysing interest in biodiversity offsetting as a mechanism to compensate for development impacts on nature. Offsets rely on credible and evidence-based methods to quantify biodiversity losses and gains. Following the introduction of the United Kingdom’s Environment Act in November 2021, all new developments requiring planning permission in England are expected to demonstrate a 10% biodiversity net gain from 2024, calculated using the statutory biodiversity metric framework (Defra, 2023). The metric is used to calculate both baseline and proposed post-development biodiversity units, and is set to play an increasingly prominent role in nature conservation nationwide. The metric has so far recei...